From 7fcf111f8fa36626c832b70f0cb1d2fc956f6ded Mon Sep 17 00:00:00 2001
From: Neta Zmora <31280975+nzmora@users.noreply.github.com>
Date: Wed, 13 Feb 2019 10:23:52 +0200
Subject: [PATCH] Automatic RL compression + Greedy compression (#151)
Merging the 'amc' branch with 'master'.
This updates the automated compression code in 'master', and adds a greedy filter-pruning algorithm.
---
apputils/execution_env.py | 16 +-
distiller/pruning/__init__.py | 1 +
distiller/pruning/greedy_filter_pruning.py | 308 ++
examples/automated_deep_compression/ADC.py | 1065 +++--
.../automated_deep_compression/__init__.py | 2 +
.../adc_controlled_envs.py | 72 -
.../adc_random_env.py | 66 +
.../adc_sensitivity_analysis.ipynb | 414 --
.../amc-results.ipynb | 805 +++-
.../automated_deep_compression/automl_args.py | 36 +
.../comparing_model_spaces.ipynb | 1290 ------
.../automated_deep_compression/ddpg/README | 36 +
.../ddpg/__init__.py | 0
.../automated_deep_compression/ddpg/core.py | 45 +
.../automated_deep_compression/ddpg/ddpg.py | 327 ++
.../automated_deep_compression/fine_tune.yaml | 15 +
.../ppo-amc-results.ipynb | 550 +++
.../presets/ADC_ClippedPPO.py | 73 +
.../presets/ADC_DDPG.py | 45 +-
.../presets/ADC_PPO.py | 67 +
.../accuracy-guaranteed_clipped-ppo.amc.csv | 3153 +++++++++++++++
.../macs_constrained_clipped-ppo.amc.csv | 3550 +++++++++++++++++
.../AMC-Plain20___2018.12.29-033839/amc.csv | 461 +++
...rl_graph.main_level.main_level.agent_0.csv | 401 ++
.../events.out.tfevents.1546047525.cvds-18 | Bin 0 -> 927229 bytes
.../compress_classifier.py | 42 +-
examples/classifier_compression/parser.py | 6 +-
examples/greedy_pruning/greedy_pruning.ipynb | 269 ++
.../sample_logs/2019.01.08-185015/greedy.csv | 94 +
.../sample_logs/2019.01.09-010040/greedy.csv | 84 +
.../amc_accuracy_guaranteed_clipped_ppo.png | Bin 0 -> 286389 bytes
.../amc_flops_constrained_clipped_ppo.png | Bin 0 -> 317435 bytes
jupyter/compression_insights.ipynb | 124 +-
33 files changed, 11067 insertions(+), 2350 deletions(-)
create mode 100755 distiller/pruning/greedy_filter_pruning.py
create mode 100755 examples/automated_deep_compression/__init__.py
delete mode 100755 examples/automated_deep_compression/adc_controlled_envs.py
create mode 100755 examples/automated_deep_compression/adc_random_env.py
delete mode 100644 examples/automated_deep_compression/adc_sensitivity_analysis.ipynb
create mode 100755 examples/automated_deep_compression/automl_args.py
delete mode 100644 examples/automated_deep_compression/comparing_model_spaces.ipynb
create mode 100644 examples/automated_deep_compression/ddpg/README
create mode 100644 examples/automated_deep_compression/ddpg/__init__.py
create mode 100755 examples/automated_deep_compression/ddpg/core.py
create mode 100755 examples/automated_deep_compression/ddpg/ddpg.py
create mode 100755 examples/automated_deep_compression/fine_tune.yaml
create mode 100644 examples/automated_deep_compression/ppo-amc-results.ipynb
create mode 100755 examples/automated_deep_compression/presets/ADC_ClippedPPO.py
create mode 100644 examples/automated_deep_compression/presets/ADC_PPO.py
create mode 100644 examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
create mode 100644 examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
create mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
create mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
create mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/events.out.tfevents.1546047525.cvds-18
create mode 100644 examples/greedy_pruning/greedy_pruning.ipynb
create mode 100644 examples/greedy_pruning/sample_logs/2019.01.08-185015/greedy.csv
create mode 100644 examples/greedy_pruning/sample_logs/2019.01.09-010040/greedy.csv
create mode 100755 imgs/wiki/amc_accuracy_guaranteed_clipped_ppo.png
create mode 100755 imgs/wiki/amc_flops_constrained_clipped_ppo.png
diff --git a/apputils/execution_env.py b/apputils/execution_env.py
index 75c994b..1350650 100755
--- a/apputils/execution_env.py
+++ b/apputils/execution_env.py
@@ -88,8 +88,8 @@ def config_pylogger(log_cfg_file, experiment_name, output_dir='logs'):
"""Configure the Python logger.
For each execution of the application, we'd like to create a unique log directory.
- By default this library is named using the date and time of day, to that directories
- can be sorted by recency. You can also name yor experiments and prefix the log
+ By default this directory is named using the date and time of day, so that directories
+ can be sorted by recency. You can also name your experiments and prefix the log
directory with this name. This can be useful when accessing experiment data from
TensorBoard, for example.
"""
@@ -105,4 +105,16 @@ def config_pylogger(log_cfg_file, experiment_name, output_dir='logs'):
msglogger.logdir = logdir
msglogger.log_filename = log_filename
msglogger.info('Log file for this run: ' + os.path.realpath(log_filename))
+
+ # Create a symbollic link to the last log file created (for easier access)
+ try:
+ os.unlink("latest_log_file")
+ except FileNotFoundError:
+ pass
+ try:
+ os.unlink("latest_log_dir")
+ except FileNotFoundError:
+ pass
+ os.symlink(logdir, "latest_log_dir")
+ os.symlink(log_filename, "latest_log_file")
return msglogger
diff --git a/distiller/pruning/__init__.py b/distiller/pruning/__init__.py
index a26ecd5..6fea7d9 100755
--- a/distiller/pruning/__init__.py
+++ b/distiller/pruning/__init__.py
@@ -31,6 +31,7 @@ from .ranked_structures_pruner import L1RankedStructureParameterPruner, L2Ranked
ActivationAPoZRankedFilterPruner, \
RandomRankedFilterPruner, GradientRankedFilterPruner
from .baidu_rnn_pruner import BaiduRNNPruner
+from .greedy_filter_pruning import greedy_pruner
del magnitude_pruner
del automated_gradual_pruner
diff --git a/distiller/pruning/greedy_filter_pruning.py b/distiller/pruning/greedy_filter_pruning.py
new file mode 100755
index 0000000..96bda9a
--- /dev/null
+++ b/distiller/pruning/greedy_filter_pruning.py
@@ -0,0 +1,308 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""This package contains the implementation of a greedy algorithm that is similar to NetAdapt and CAR.
+
+We score improvement by means of the computation load (MACs/FLOPs) and do not use direct metrics because this is
+an open-source package that runs on many different hardware platforms.
+
+TODO: this code requires refactoring as is makes assumptions about applicative layers (e.g. the names of certain
+ members of the application `args` variable) and this is both tight-coupling and reverse-dependency.
+
+References:
+[1] Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning
+ Reza Abbasi-Asl, Bin Yu https://arxiv.org/abs/1705.07356
+
+[2] NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
+ Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam
+ https://arxiv.org/abs/1804.03230, ECCV 2018
+"""
+
+from copy import deepcopy
+import numpy as np
+import logging
+import torch
+import torch.multiprocessing as mp
+from multiprocessing import Process, Queue
+import multiprocessing
+import csv
+import os
+import distiller
+from apputils import SummaryGraph, save_checkpoint
+from distiller.data_loggers import PythonLogger
+from distiller import normalize_module_name
+
+__all__ = ['add_greedy_pruner_args', 'greedy_pruner']
+msglogger = logging.getLogger()
+
+
+def add_greedy_pruner_args(argparser, arch_choices=None, enable_pretrained=False):
+ """
+ Helper function to make it easier to add command-line arguments for Greedy Prunign
+ to any application.
+
+ Arguments:
+ argparser (argparse.ArgumentParser): Existing parser to which to add the arguments
+ """
+ argparser.add_argument('--greedy', dest='greedy', action='store_true', help='greedy filter pruning')
+ group = argparser.add_argument_group('Greedy Pruning')
+ group.add_argument('--greedy-ft-epochs', type=int, default=1,
+ help='number of epochs to fine-tune each discovered network')
+ group.add_argument('--greedy-target-density', type=float,
+ help='target density of the network we are seeking')
+ group.add_argument('--greedy-pruning-step', type=float, default=0.10,
+ help='size of each pruning step (as a fraction in [0..1])')
+ group.add_argument('--greedy-finetuning-policy', choices=["constant", "linear-grow"], default="constant",
+ help='policy used for determining how long to fine-tune')
+
+
+def prune_tensor(param, param_name, fraction_to_prune, zeros_mask_dict):
+ """Prune filters from a parameter tensor.
+
+ Returns the filter-sparsity of the tensor.
+ """
+ # Create a filter-ranking pruner
+ pruner = distiller.pruning.L1RankedStructureParameterPruner(name=None,
+ group_type="Filters",
+ desired_sparsity=fraction_to_prune,
+ weights=param_name)
+ pruner.set_param_mask(param, param_name, zeros_mask_dict, meta=None)
+ # Use the mask to prune
+ zeros_mask_dict[param_name].apply_mask(param)
+ return distiller.sparsity_3D(param)
+
+
+def prune_finetune_test(iteration, model_cpy, pruning_step, test_fn, train_fn,
+ app_args, param_name, effective_train_size):
+ pylogger = PythonLogger(msglogger)
+ zeros_mask_dict = distiller.create_model_masks_dict(model_cpy)
+ param = model_cpy.state_dict()[param_name]
+ if 0 == prune_tensor(param, param_name, pruning_step, zeros_mask_dict):
+ return (-1, -1, -1, zeros_mask_dict) # Did not prune anything
+
+ if train_fn is not None:
+ # Fine-tune
+ optimizer = torch.optim.SGD(model_cpy.parameters(), lr=app_args.lr,
+ momentum=app_args.momentum, weight_decay=app_args.weight_decay)
+ app_args.effective_train_size = effective_train_size
+ train_fn(model=model_cpy, compression_scheduler=create_scheduler(model_cpy, zeros_mask_dict),
+ optimizer=optimizer, epoch=iteration, loggers=[pylogger])
+
+ # Physically remove filters
+ dataset = app_args.dataset
+ arch = app_args.arch
+ distiller.remove_filters(model_cpy, zeros_mask_dict, arch, dataset, optimizer=None)
+
+ # Test and record the performance of the pruned model
+ prec1, prec5, loss = test_fn(model=model_cpy, loggers=None)
+ return (prec1, prec5, loss, zeros_mask_dict)
+
+
+def find_most_robust_layer(iteration, model, pruning_step, test_fn, train_fn,
+ app_args, tensors_to_prune=None, effective_train_size=1):
+ """Find the layer that is most robust to pruning 'pruning_step' filters.
+
+ For each layer: prune 'step' percent of the filters, fine-tune, and measure top1 accuracy
+ """
+ if tensors_to_prune is None:
+ tensors_to_prune = [param_name for param_name, _ in model.named_parameters()]
+ # best_layer format: (prec1, prec5, param_name, pruned_model, zeros_mask_dict)
+ best_layer = (-np.inf, -np.inf, None, None, None, np.inf)
+ for param_name in tensors_to_prune:
+ if model.state_dict()[param_name].dim() != 4:
+ continue
+ # Make a copy of the model, because when we prune the parameter tensor the model's weights are altered
+ torch.cuda.empty_cache()
+ model_cpy = deepcopy(model)
+ (prec1, prec5, loss, zeros_mask_dict) = prune_finetune_test(iteration, model_cpy, pruning_step, test_fn,
+ train_fn, app_args, param_name,
+ effective_train_size)
+ if (prec1, prec5, -loss) > (best_layer[0], best_layer[1], -best_layer[-1]):
+ best_layer = (prec1, prec5, param_name, model_cpy, zeros_mask_dict, loss)
+ del model_cpy
+ del zeros_mask_dict
+ return best_layer[:-1]
+
+
+def find_most_robust_layer_mp(iteration, model, pruning_step, test_fn, train_fn,
+ app_args, tensors_to_prune=None, effective_train_size=1):
+ """Multiprocessing version of find_most_robust_layer
+ """
+ if tensors_to_prune is None:
+ tensors_to_prune = [param_name for param_name, _ in model.named_parameters()]
+ # best_layer format: (prec1, prec5, param_name, pruned_model, zeros_mask_dict)
+ best_layer = (-np.inf, -np.inf, None, None, None, np.inf)
+ pqueue = Queue()
+ processes = []
+ for param_name in tensors_to_prune:
+ if model.state_dict()[param_name].dim() != 4:
+ continue
+ # Make a copy of the model, because when we prune the parameter tensor the model's weights are altered
+ model_cpy = deepcopy(model)
+ model_cpy.share_memory()
+ (prec1, prec5, loss, zeros_mask_dict) = prune_finetune_test(iteration, model_cpy, pruning_step,
+ test_fn, train_fn, app_args,
+ param_name, effective_train_size)
+ param = model_cpy.state_dict()[param_name]
+ p = mp.Process(target=prune_finetune_test, args=(pqueue, model_cpy, pruning_step, test_fn,
+ train_fn, app_args, param_name, param))
+ processes.append(p)
+ p.start()
+
+ for p in processes:
+ p.join()
+ (prec1, prec5, loss, zeros_mask_dict) = pqueue.get()
+ if (prec1, prec5, -loss) > (best_layer[0], best_layer[1], -best_layer[-1]):
+ best_layer = (prec1, prec5, param_name, model_cpy, zeros_mask_dict, loss)
+ return best_layer[:-1]
+
+
+def get_model_compute_budget(model, dataset, layers_to_prune=None):
+ """Return the compute budget of the Convolution layers in an image-classifier.
+ """
+ dummy_input = distiller.get_dummy_input(dataset)
+ g = SummaryGraph(model, dummy_input)
+ total_macs = 0
+ for name, m in model.named_modules():
+ if isinstance(m, torch.nn.Conv2d):
+ # Use the SummaryGraph to obtain some other details of the models
+ conv_op = g.find_op(normalize_module_name(name))
+ assert conv_op is not None
+ total_macs += conv_op['attrs']['MACs']
+ del g
+ return total_macs
+
+
+def get_param_densities(model, compressed_model, tensors_to_prune):
+ if tensors_to_prune is None:
+ tensors_to_prune = [param_name for param_name, _ in model.named_parameters()]
+
+ densities = []
+ for param_name in tensors_to_prune:
+ if model.state_dict()[param_name].dim() != 4:
+ continue
+ param = model.state_dict()[param_name]
+ compressed_param = compressed_model.state_dict()[param_name]
+ densities.append(torch.numel(compressed_param) / torch.numel(param))
+ return densities
+
+
+def create_scheduler(model, zeros_mask_dict):
+ scheduler = distiller.CompressionScheduler(model)
+ masks = {param_name: masker.mask for param_name, masker in zeros_mask_dict.items()}
+ scheduler.load_state_dict(state={'masks_dict': masks})
+ return scheduler
+
+
+def create_network_record_file():
+ """Create the CSV file and write the column names"""
+ fields = ['iteration', 'top1', 'param_name', 'normalized_macs', 'total_macs', 'densities']
+ with open(os.path.join(msglogger.logdir, 'greedy.csv'), 'w') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
+
+
+def record_network_details(fields):
+ """Write the details of one network to a CSV file and create a checkpoint file"""
+ with open(os.path.join(msglogger.logdir, 'greedy.csv'), 'a') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
+
+
+resnet50_params = ["module.layer1.0.conv1.weight", "module.layer1.0.conv2.weight",
+ "module.layer1.1.conv1.weight", "module.layer1.1.conv2.weight",
+ "module.layer1.2.conv1.weight", "module.layer1.2.conv2.weight",
+ "module.layer2.0.conv1.weight", "module.layer2.0.conv2.weight",
+ "module.layer2.1.conv1.weight", "module.layer2.1.conv2.weight",
+ "module.layer2.2.conv1.weight", "module.layer2.2.conv2.weight",
+ "module.layer2.3.conv1.weight", "module.layer2.3.conv2.weight",
+ "module.layer3.0.conv1.weight", "module.layer3.0.conv2.weight",
+ "module.layer3.1.conv1.weight", "module.layer3.1.conv2.weight",
+ "module.layer3.2.conv1.weight", "module.layer3.2.conv2.weight",
+ "module.layer3.3.conv1.weight", "module.layer3.3.conv2.weight",
+ "module.layer3.4.conv1.weight", "module.layer3.4.conv2.weight",
+ "module.layer3.5.conv1.weight", "module.layer3.5.conv2.weight",
+ "module.layer4.0.conv1.weight", "module.layer4.0.conv2.weight",
+ "module.layer4.1.conv1.weight", "module.layer4.1.conv2.weight",
+ "module.layer4.2.conv1.weight", "module.layer4.2.conv2.weight"]
+
+resnet20_params = ["module.layer1.0.conv1.weight", "module.layer2.0.conv1.weight", "module.layer3.0.conv1.weight",
+ "module.layer1.1.conv1.weight", "module.layer2.1.conv1.weight", "module.layer3.1.conv1.weight",
+ "module.layer1.2.conv1.weight", "module.layer2.2.conv1.weight", "module.layer3.2.conv1.weight"]
+
+resnet56_params = [ "module.layer1.0.conv1.weight", "module.layer2.0.conv1.weight", "module.layer3.0.conv1.weight",
+ "module.layer1.1.conv1.weight", "module.layer2.1.conv1.weight", "module.layer3.1.conv1.weight",
+ "module.layer1.2.conv1.weight", "module.layer2.2.conv1.weight", "module.layer3.2.conv1.weight",
+ "module.layer1.3.conv1.weight", "module.layer2.3.conv1.weight", "module.layer3.3.conv1.weight",
+ "module.layer1.4.conv1.weight", "module.layer2.4.conv1.weight", "module.layer3.4.conv1.weight",
+ "module.layer1.5.conv1.weight", "module.layer2.5.conv1.weight", "module.layer3.5.conv1.weight",
+ "module.layer1.6.conv1.weight", "module.layer2.6.conv1.weight", "module.layer3.6.conv1.weight",
+ "module.layer1.7.conv1.weight", "module.layer2.7.conv1.weight", "module.layer3.7.conv1.weight",
+ "module.layer1.8.conv1.weight", "module.layer2.8.conv1.weight", "module.layer3.8.conv1.weight"]
+
+
+def greedy_pruner(pruned_model, app_args, fraction_to_prune, pruning_step, test_fn, train_fn):
+ dataset = app_args.dataset
+ arch = app_args.arch
+ create_network_record_file()
+
+ # Temporary ugly hack!
+ resnet_layers = None
+ resnet_params = None
+ if arch == "resnet20_cifar":
+ resnet_params = resnet20_params
+ elif arch == "resnet56_cifar":
+ resnet_params = resnet56_params
+ elif arch == "resnet50":
+ resnet_params = resnet50_params
+ if resnet_params is not None:
+ resnet_layers = [param[:-len(".weight")] for param in resnet_params]
+
+ total_macs = dense_total_macs = get_model_compute_budget(pruned_model, dataset, resnet_layers)
+ iteration = 0
+ model = pruned_model
+
+ while total_macs > fraction_to_prune * dense_total_macs:
+ iteration += 1
+ if app_args.greedy_finetuning_policy == "constant":
+ effective_train_size = app_args.effective_train_size
+ elif app_args.greedy_finetuning_policy == "linear-grow":
+ effective_train_size = 1 - (total_macs / dense_total_macs)
+
+ prec1, prec5, param_name, pruned_model, zeros_mask_dict = find_most_robust_layer(iteration, pruned_model,
+ pruning_step,
+ test_fn, train_fn,
+ app_args, resnet_params,
+ effective_train_size)
+ total_macs = get_model_compute_budget(pruned_model, dataset, resnet_layers)
+ densities = get_param_densities(model, pruned_model, resnet_params)
+ compute_density = total_macs/dense_total_macs
+ results = (iteration, prec1, param_name, compute_density, total_macs, densities)
+ record_network_details(results)
+ scheduler = create_scheduler(pruned_model, zeros_mask_dict)
+ save_checkpoint(0, arch, pruned_model, optimizer=None, best_top1=prec1, scheduler=scheduler,
+ name="greedy__{}__{:.1f}__{:.1f}".format(str(iteration).zfill(3), compute_density*100, prec1),
+ dir=msglogger.logdir)
+ del scheduler
+ del zeros_mask_dict
+ msglogger.info("Iteration {}: top1-{:.2f} {} compute-{:.2f}".format(*results[0:4]))
+
+ assert iteration > 0
+ prec1, prec5, loss = test_fn(model=pruned_model)
+ print(prec1, prec5, loss)
+ scheduler = create_scheduler(pruned_model, zeros_mask_dict)
+ save_checkpoint(0, arch, pruned_model, optimizer=None, best_top1=prec1, scheduler=scheduler,
+ name='_'.join(("greedy", str(fraction_to_prune))),
+ dir=msglogger.logdir)
diff --git a/examples/automated_deep_compression/ADC.py b/examples/automated_deep_compression/ADC.py
index e8ded4d..a047811 100755
--- a/examples/automated_deep_compression/ADC.py
+++ b/examples/automated_deep_compression/ADC.py
@@ -15,8 +15,11 @@
#
"""To execute this code:
-$ time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --adc --resume=checkpoint.plain20_cifar.pth.tar --name="AMC-plain20" --lr=0.1
+$ time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-target-density=0.5 -p=50
+
+Coach installation:
+===================
After creating the virtual environment and installing Distiller's Python package dependencies, go ahead and
setup Coach per: https://github.com/NervanaSystems/coach#installation.
@@ -26,11 +29,37 @@ when you install Coach.
*NOTE: you may need to update TensorFlow to the expected version:
$ pip3 install tensorflow==1.9.0
-Finally, if you are running Coach in a development environment, you need to tell the Python runtime where to find the Coach code:
+Finally, if you are running Coach in a development environment, you need to tell the Python runtime where to find
+the Coach code:
$ export PYTHONPATH=<path-to-coach-code>
+
+Spinningup installation:
+========================
+
+Spinup require that we use exactly Python 3.6 so if you are not using this Python version see the instructions here:
+ http://ubuntuhandbook.org/index.php/2017/07/install-python-3-6-1-in-ubuntu-16-04-lts/
+ $ sudo update-alternatives --config python3
+
+For Python 3.6 you may also need to install a new virtual-env:
+ $ sudo apt-get install python3.6-venv
+
+Then create and activate your venv, and populate it with the Distiller packages:
+ $ python3 -m venv distiller_env_python3.6
+ $ source distiller_env_python3.6/bin/activate
+ $ pip3 install -r requirements.txt
+
+You want to install Spinup into this venv. First clone Spinup and then install it into your venv:
+ $ cd <spinningup-repo>
+ $ sudo apt-get install python3.6-dev
+ $ pip3 install -e .
+
+
+https://spinningup.openai.com/en/latest/user/installation.html?highlight=license
+
"""
import math
+import os
import copy
import logging
import numpy as np
@@ -43,25 +72,65 @@ from apputils import SummaryGraph
from collections import OrderedDict, namedtuple
from types import SimpleNamespace
from distiller import normalize_module_name
-from rl_coach import logger
-from rl_coach.base_parameters import TaskParameters
+from examples.automated_deep_compression.adc_random_env import random_agent
-# When we import the graph_manager from the ADC_DDPG preset, we implicitly instruct
-# Coach to create and use our CNNEnvironment environment.
-# So Distiller calls Coach, which creates the environment, trains the agent, and ends.
-from examples.automated_deep_compression.presets.ADC_DDPG import graph_manager, agent_params
-# Coach imports
-from rl_coach.schedules import ConstantSchedule, PieceWiseSchedule, ExponentialSchedule
-from rl_coach.core_types import EnvironmentSteps
+# Choose which RL library to use: Coach from Intel AI Lab, or Spinup from OpenAI
+#RLLIB = "spinup"
+RLLIB = "coach"
msglogger = logging.getLogger()
-Observation = namedtuple('Observation', ['t', 'n', 'c', 'h', 'w', 'stride', 'k', 'MACs', 'reduced', 'rest', 'prev_a'])
-
+Observation = namedtuple('Observation', ['n', 'c', 'h', 'w', 'stride', 'k', 'MACs', 'reduced', 'rest', 'prev_a'])
+LayerDesc = namedtuple('LayerDesc', ['t', 'n', 'c', 'h', 'w', 'stride', 'k', 'MACs', 'reduced', 'rest'])
+LayerDescLen = len(LayerDesc._fields)
ALMOST_ONE = 0.9999
-USE_COACH = True
-PERFORM_THINNING = False
-NUM_TRAINING_EPOCHS = 1
+
+
+def is_using_continuous_action_space(agent):
+ return agent in ("DDPG", "ClippedPPO-continuous", "Random-policy")
+
+
+if RLLIB == "spinup":
+ import tensorflow as tf
+ from spinup.algos.ddpg import core
+ from .ddpg import ddpg
+
+ def ddpg_spinup(env1, env2):
+ from spinup.utils.run_utils import setup_logger_kwargs
+ exp_name = "Test"
+ seed = 0
+ # The number and size of the Actor-Critic MLP hidden layers
+ layers, hid = 2, 300
+ logger_kwargs = setup_logger_kwargs(exp_name) # , seed)
+
+ ddpg.ddpg(env=env1, test_env=env2, actor_critic=core.mlp_actor_critic,
+ ac_kwargs=dict(hidden_sizes=[hid]*layers, output_activation=tf.sigmoid),
+ gamma=1, # discount rate
+ seed=seed,
+ epochs=400,
+ replay_size=2000,
+ batch_size=64,
+ start_steps=env1.amc_cfg.num_heatup_epochs,
+ steps_per_epoch=800 * env1.num_layers(), # every 50 episodes perform 10 episodes of testing
+ act_noise=0.5,
+ pi_lr=1e-4,
+ q_lr=1e-3,
+ logger_kwargs=logger_kwargs)
+
+
+if RLLIB == "coach":
+ from rl_coach.base_parameters import TaskParameters
+ from rl_coach.core_types import EnvironmentSteps
+ from rl_coach.schedules import ConstantSchedule, PieceWiseSchedule, ExponentialSchedule
+
+
+def log_amc_config(amc_cfg):
+ try:
+ msglogger.info('AMC configuration:')
+ for k, v in amc_cfg.items():
+ msglogger.info("\t{} : {}".format(k, v))
+ except TypeError as e:
+ pass
def count_conv_layer(model):
@@ -73,24 +142,59 @@ def count_conv_layer(model):
return conv_cnt
-def do_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
- np.random.seed()
+def mac_constrained_experimental_reward_fn(env, top1, top5, vloss, total_macs):
+ """A more intuitive reward for constraining the compute and optimizing the
+ accuracy under this constraint.
+ """
+ macs_normalized = total_macs/env.dense_model_macs
+ reward = top1/100
+ if macs_normalized > (env.amc_cfg.target_density+0.002):
+ reward = -3 - macs_normalized
+ else:
+ reward += 1
+ return reward
+
+
+def harmonic_mean_reward_fn(env, top1, top5, vloss, total_macs):
+ """This reward is based on the idea of weighted harmonic mean
+
+ Balance compute and accuracy provided a beta value that weighs the two components.
+ See: https://en.wikipedia.org/wiki/F1_score
+ """
+ beta = 1
+ #beta = 0.75 # How much to favor accuracy
+ macs_normalized = total_macs/env.dense_model_macs
+ reward = (1 + beta**2) * top1/100 * macs_normalized / (beta**2 * macs_normalized + top1/100)
+ return reward
+
+
+def amc_reward_fn(env, top1, top5, vloss, total_macs):
+ """This reward punishes the agent when it produces networks that don't comply with the MACs resource-constraint,
+ (the negative reward is in proportion to the network density). Otherwise, the reward is the Top1 accuracy.
+ """
+ if not env.is_macs_constraint_achieved(total_macs):
+ current_density = total_macs / env.dense_model_macs
+ reward = env.amc_cfg.target_density - current_density
+ else:
+ reward = top1/100
+ return reward
+
- if USE_COACH:
- return coach_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
- return random_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn)
+experimental_reward_fn = harmonic_mean_reward_fn
-def coach_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
- # task_parameters = TaskParameters(framework_type="tensorflow",
- # experiment_path="./experiments/test")
- # extra_params = {'save_checkpoint_secs': None,
- # 'render': True}
- # task_parameters.__dict__.update(extra_params)
- task_parameters = TaskParameters(experiment_path=logger.get_experiment_path('adc'))
+def do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
+ dataset = args.dataset
+ arch = args.arch
+ perform_thinning = True # args.amc_thinning
+ num_ft_epochs = args.amc_ft_epochs
+ action_range = args.amc_action_range
+ np.random.seed()
conv_cnt = count_conv_layer(model)
- # Create a dictionary of parameters that Coach will handover to CNNEnvironment
+ msglogger.info("Executing AMC: RL agent - %s RL library - %s", args.amc_agent_algo, RLLIB)
+
+ # Create a dictionary of parameters that Coach will handover to DistillerWrapperEnvironment
# Once it creates it.
services = distiller.utils.MutableNamedTuple({
'validate_fn': validate_fn,
@@ -101,43 +205,241 @@ def coach_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint
'dataset': dataset,
'arch': arch,
'optimizer_data': optimizer_data})
- if True:
- amc_cfg = distiller.utils.MutableNamedTuple({
- #'action_range': (0.20, 0.95),
- 'action_range': (0.20, 0.80),
- 'onehot_encoding': False,
- 'normalize_obs': True,
- 'desired_reduction': None,
- 'reward_fn': lambda top1, top5, vloss, total_macs: -1 * (1-top1/100) * math.log(total_macs),
- 'conv_cnt': conv_cnt,
- 'max_reward': -1000})
+
+ amc_cfg = distiller.utils.MutableNamedTuple({
+ 'protocol': args.amc_protocol,
+ 'agent_algo': args.amc_agent_algo,
+ 'compute_reward_every_step': args.amc_reward_every_step,
+ 'perform_thinning': perform_thinning,
+ 'num_ft_epochs': num_ft_epochs,
+ 'action_range': action_range,
+ 'conv_cnt': conv_cnt})
+
+ #net_wrapper = NetworkWrapper(model, app_args, services)
+ #return sample_networks(net_wrapper, services)
+
+ if args.amc_protocol == "accuracy-guaranteed":
+ amc_cfg.target_density = None
+ amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: -(1-top1/100) * math.log(total_macs)
+ amc_cfg.action_constrain_fn = None
+ elif args.amc_protocol == "mac-constrained":
+ amc_cfg.target_density = args.amc_target_density
+ amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: top1/100 #(90.5 - top1) / 10
+ amc_cfg.action_constrain_fn = DistillerWrapperEnvironment.get_action
+ elif args.amc_protocol == "mac-constrained-experimental":
+ amc_cfg.target_density = args.amc_target_density
+ amc_cfg.reward_fn = experimental_reward_fn
+ amc_cfg.action_constrain_fn = None
else:
- amc_cfg = distiller.utils.MutableNamedTuple({
- 'action_range': (0.10, 0.95),
- 'onehot_encoding': False,
- 'normalize_obs': True,
- 'desired_reduction': 1.5e8,
- 'reward_fn': lambda top1, top5, vloss, total_macs: top1/100,
- #'reward_fn': lambda top1, total_macs: min(top1/100, 0.75),
- 'conv_cnt': conv_cnt,
- 'max_reward': -1000})
-
- # These parameters are passed to the Distiller environment
- graph_manager.env_params.additional_simulator_parameters = {'model': model,
- 'app_args': app_args,
- 'amc_cfg': amc_cfg,
- 'services': services}
- exploration_noise = 0.5
- exploitation_decay = 0.996
+ raise ValueError("{} is not supported currently".format(args.amc_protocol))
+
steps_per_episode = conv_cnt
- agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
- (ConstantSchedule(exploration_noise), EnvironmentSteps(100*steps_per_episode)),
- (ExponentialSchedule(exploration_noise, 0, exploitation_decay), EnvironmentSteps(300*steps_per_episode))])
- graph_manager.create_graph(task_parameters)
- graph_manager.improve()
+ amc_cfg.heatup_noise = 0.5
+ amc_cfg.initial_training_noise = 0.5
+ amc_cfg.training_noise_decay = 0.996 # 0.998
+ amc_cfg.num_heatup_epochs = args.amc_heatup_epochs
+ amc_cfg.num_training_epochs = args.amc_training_epochs
+ training_noise_duration = amc_cfg.num_training_epochs * steps_per_episode
+ heatup_duration = amc_cfg.num_heatup_epochs * steps_per_episode
+
+ if amc_cfg.agent_algo == "Random-policy":
+ return random_agent(DistillerWrapperEnvironment(model, app_args, amc_cfg, services))
+
+ if RLLIB == "spinup":
+ msglogger.info("AMC: Using spinup")
+ env1 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
+ env2 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
+ ddpg_spinup(env1, env2)
+ else:
+ msglogger.info("AMC: Using coach")
+
+ # When we import the graph_manager from the ADC_DDPG preset, we implicitly instruct
+ # Coach to create and use our DistillerWrapperEnvironment environment.
+ # So Distiller calls Coach, which creates the environment, trains the agent, and ends.
+ if args.amc_agent_algo == "DDPG":
+ from examples.automated_deep_compression.presets.ADC_DDPG import graph_manager, agent_params
+ agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
+ (ConstantSchedule(amc_cfg.heatup_noise), EnvironmentSteps(heatup_duration)),
+ (ExponentialSchedule(amc_cfg.initial_training_noise, 0, amc_cfg.training_noise_decay),
+ EnvironmentSteps(training_noise_duration))])
+ # agent_params.exploration.noise_percentage_schedule = ConstantSchedule(0)
+ elif "ClippedPPO" in args.amc_agent_algo:
+ from examples.automated_deep_compression.presets.ADC_ClippedPPO import graph_manager, agent_params
+
+ # These parameters are passed to the Distiller environment
+ graph_manager.env_params.additional_simulator_parameters = {'model': model,
+ 'app_args': app_args,
+ 'amc_cfg': amc_cfg,
+ 'services': services}
+
+ coach_logs_dir = os.path.join(msglogger.logdir, 'coach')
+ os.mkdir(coach_logs_dir)
+ task_parameters = TaskParameters(experiment_path=coach_logs_dir)
+ graph_manager.create_graph(task_parameters)
+ graph_manager.improve()
+
+
+# This is a temporary hack!
+resnet50_params = ["module.layer1.0.conv1.weight", "module.layer1.0.conv2.weight",
+ "module.layer1.1.conv1.weight", "module.layer1.1.conv2.weight",
+ "module.layer1.2.conv1.weight", "module.layer1.2.conv2.weight",
+ "module.layer2.0.conv1.weight", "module.layer2.0.conv2.weight",
+ "module.layer2.1.conv1.weight", "module.layer2.1.conv2.weight",
+ "module.layer2.2.conv1.weight", "module.layer2.2.conv2.weight",
+ "module.layer2.3.conv1.weight", "module.layer2.3.conv2.weight",
+ "module.layer3.0.conv1.weight", "module.layer3.0.conv2.weight",
+ "module.layer3.1.conv1.weight", "module.layer3.1.conv2.weight",
+ "module.layer3.2.conv1.weight", "module.layer3.2.conv2.weight",
+ "module.layer3.3.conv1.weight", "module.layer3.3.conv2.weight",
+ "module.layer3.4.conv1.weight", "module.layer3.4.conv2.weight",
+ "module.layer3.5.conv1.weight", "module.layer3.5.conv2.weight",
+ "module.layer4.0.conv1.weight", "module.layer4.0.conv2.weight",
+ "module.layer4.1.conv1.weight", "module.layer4.1.conv2.weight",
+ "module.layer4.2.conv1.weight", "module.layer4.2.conv2.weight"]
+
+resnet20_params = ["module.layer1.0.conv1.weight", "module.layer2.0.conv1.weight", "module.layer3.0.conv1.weight",
+ "module.layer1.1.conv1.weight", "module.layer2.1.conv1.weight", "module.layer3.1.conv1.weight",
+ "module.layer1.2.conv1.weight", "module.layer2.2.conv1.weight", "module.layer3.2.conv1.weight"]
+
+resnet56_params = [ "module.layer1.0.conv1.weight", "module.layer2.0.conv1.weight", "module.layer3.0.conv1.weight",
+ "module.layer1.1.conv1.weight", "module.layer2.1.conv1.weight", "module.layer3.1.conv1.weight",
+ "module.layer1.2.conv1.weight", "module.layer2.2.conv1.weight", "module.layer3.2.conv1.weight",
+ "module.layer1.3.conv1.weight", "module.layer2.3.conv1.weight", "module.layer3.3.conv1.weight",
+ "module.layer1.4.conv1.weight", "module.layer2.4.conv1.weight", "module.layer3.4.conv1.weight",
+ "module.layer1.5.conv1.weight", "module.layer2.5.conv1.weight", "module.layer3.5.conv1.weight",
+ "module.layer1.6.conv1.weight", "module.layer2.6.conv1.weight", "module.layer3.6.conv1.weight",
+ "module.layer1.7.conv1.weight", "module.layer2.7.conv1.weight", "module.layer3.7.conv1.weight",
+ "module.layer1.8.conv1.weight", "module.layer2.8.conv1.weight", "module.layer3.8.conv1.weight"]
+
+resnet50_layers = [param[:-len(".weight")] for param in resnet50_params]
+resnet20_layers = [param[:-len(".weight")] for param in resnet20_params]
+resnet56_layers = [param[:-len(".weight")] for param in resnet56_params]
+
+
+class NetworkWrapper(object):
+ def __init__(self, model, app_args, services):
+ self.app_args = app_args
+ self.services = services
+ self.conv_layers, _, _ = self.collect_conv_details(model)
+ self.reset(model)
+
+ def get_model_resources_requirements(self, model=None):
+ if model is None:
+ model = self.model
+ _, total_macs, total_nnz = collect_conv_details(model, self.app_args.dataset, True)
+ return total_macs, total_nnz
+
+ @property
+ def arch(self):
+ return self.app_args.arch
+
+ def collect_conv_details(self, model):
+ # Temporary ugly hack!
+ resnet_layers = None
+ if self.app_args.arch == "resnet20_cifar":
+ resnet_layers = resnet20_layers
+ elif self.app_args.arch == "resnet56_cifar":
+ resnet_layers = resnet56_layers
+ elif self.app_args.arch == "resnet50":
+ resnet_layers = resnet50_layers
+ return collect_conv_details(model, self.app_args.dataset, True, resnet_layers)
+
+ def num_layers(self):
+ return len(self.conv_layers)
+
+ def get_layer(self, idx):
+ try:
+ return self.conv_layers[idx]
+ except KeyError:
+ return None
+
+ def get_layer_macs(self, layer):
+ """Return the number of MACs required to compute <layer>'s Convolution"""
+ if layer is None:
+ return 0
+ conv_module = distiller.model_find_module(self.model, layer.name)
+ # MACs = volume(OFM) * (#IFM * K^2)
+ dense_macs = (conv_module.out_channels * layer.ofm_h * layer.ofm_w) * (conv_module.in_channels * layer.k**2)
+ return dense_macs
+
+ def reset(self, model):
+ self.model = model
+ self.zeros_mask_dict = distiller.create_model_masks_dict(self.model)
+
+ def create_scheduler(self):
+ scheduler = distiller.CompressionScheduler(self.model)
+ masks = {param_name: masker.mask for param_name, masker in self.zeros_mask_dict.items()}
+ scheduler.load_state_dict(state={'masks_dict': masks})
+ return scheduler
+
+ def remove_structures(self, layer_id, fraction_to_prune, prune_what="channels"):
+ """Physically remove channels and corresponding filters from the model
+
+ Returns the compute-sparsity of the layer with index 'layer_id'
+ """
+ if layer_id not in range(self.num_layers()):
+ raise ValueError("idx=%d is not in correct range (0-%d)" % (layer_id, self.num_layers()))
+ if fraction_to_prune < 0:
+ raise ValueError("fraction_to_prune=%f is illegal" % (fraction_to_prune))
+ if fraction_to_prune == 0:
+ return 0
+ if fraction_to_prune == 1.0:
+ # For now, prevent the removal of entire layers
+ fraction_to_prune = ALMOST_ONE
-class CNNEnvironment(gym.Env):
+ layer = self.conv_layers[layer_id]
+ macs_before = self.get_layer_macs(layer)
+ conv_pname = layer.name + ".weight"
+ conv_p = distiller.model_find_param(self.model, conv_pname)
+
+ msglogger.info("ADC: removing %.1f%% %s from %s" % (fraction_to_prune*100, prune_what, conv_pname))
+
+ if prune_what == "channels":
+ calculate_sparsity = distiller.sparsity_ch
+ remove_structures = distiller.remove_channels
+ group_type = "Channels"
+ elif prune_what == "filters":
+ calculate_sparsity = distiller.sparsity_3D
+ group_type = "Filters"
+ remove_structures = distiller.remove_filters
+ else:
+ raise ValueError("unsupported structure {}".format(prune_what))
+ # Create a channel-ranking pruner
+ pruner = distiller.pruning.L1RankedStructureParameterPruner("adc_pruner", group_type,
+ fraction_to_prune, conv_pname)
+ pruner.set_param_mask(conv_p, conv_pname, self.zeros_mask_dict, meta=None)
+ del pruner
+
+ if (self.zeros_mask_dict[conv_pname].mask is None or
+ calculate_sparsity(self.zeros_mask_dict[conv_pname].mask) == 0):
+ msglogger.info("remove_structures: aborting because there are no channels to prune")
+ return 0
+
+ # Use the mask to prune
+ self.zeros_mask_dict[conv_pname].apply_mask(conv_p)
+ remove_structures(self.model, self.zeros_mask_dict, self.app_args.arch, self.app_args.dataset, optimizer=None)
+ conv_p = distiller.model_find_param(self.model, conv_pname)
+ return 1 - (self.get_layer_macs(layer) / macs_before)
+
+ def validate(self):
+ top1, top5, vloss = self.services.validate_fn(model=self.model)
+ return top1, top5, vloss
+
+ def train(self, num_epochs, episode=0):
+ # Train for zero or more epochs
+ opt_cfg = self.app_args.optimizer_data
+ optimizer = torch.optim.SGD(self.model.parameters(), lr=opt_cfg['lr'],
+ momentum=opt_cfg['momentum'], weight_decay=opt_cfg['weight_decay'])
+ compression_scheduler = self.create_scheduler()
+ for _ in range(num_epochs):
+ # Fine-tune the model
+ self.services.train_fn(model=self.model, compression_scheduler=compression_scheduler,
+ optimizer=optimizer, epoch=episode)
+ del compression_scheduler
+
+
+class DistillerWrapperEnvironment(gym.Env):
metadata = {'render.modes': ['human']}
def __init__(self, model, app_args, amc_cfg, services):
@@ -147,67 +449,52 @@ class CNNEnvironment(gym.Env):
self.app_args = app_args
self.amc_cfg = amc_cfg
self.services = services
- self.conv_layers, self.dense_model_macs, self.dense_model_size = collect_conv_details(model, self.app_args.dataset)
+ self.net_wrapper = NetworkWrapper(model, app_args, services)
+ self.dense_model_macs, self.dense_model_size = self.net_wrapper.get_model_resources_requirements(model)
self.reset(init_only=True)
- msglogger.info("Model %s has %d Convolution layers", self.app_args.arch, len(self.conv_layers))
+ msglogger.info("Model %s has %d Convolution layers", self.app_args.arch, self.net_wrapper.num_layers())
msglogger.info("\tTotal MACs: %s" % distiller.pretty_int(self.dense_model_macs))
- msglogger.info("Configuration:\n\tonehot_encoding={}\n\tnormalize_obs={}".format(self.amc_cfg.onehot_encoding,
- self.amc_cfg.normalize_obs))
- self.debug_stats = {'episode': 0}
+ log_amc_config(amc_cfg)
+
+ self.episode = 0
+ self.best_reward = -1000
self.action_low = amc_cfg.action_range[0]
self.action_high = amc_cfg.action_range[1]
# Gym spaces documentation: https://gym.openai.com/docs/
- self.action_space = spaces.Box(self.action_low, self.action_high, shape=(1,))
- self.action_space.default_action = self.action_low
-
+ if is_using_continuous_action_space(self.amc_cfg.agent_algo):
+ self.action_space = spaces.Box(self.action_low, self.action_high, shape=(1,))
+ self.action_space.default_action = self.action_low
+ else:
+ self.action_space = spaces.Discrete(10)
self.STATE_EMBEDDING_LEN = len(Observation._fields)
- if self.amc_cfg.onehot_encoding:
- self.STATE_EMBEDDING_LEN += (self.amc_cfg.conv_cnt - 1)
- self.observation_space = spaces.Box(0, float("inf"), shape=(self.STATE_EMBEDDING_LEN,))
- fields = ['top1', 'reward', 'total_macs', 'normalize_macs', 'total_nnz']
- with open(r'amc.csv', 'w') as f:
- writer = csv.writer(f)
- writer.writerow(fields)
+ #self.observation_space = spaces.Box(0, float("inf"), shape=(self.STATE_EMBEDDING_LEN+self.num_layers(),))
+ self.observation_space = spaces.Box(0, float("inf"), shape=(self.STATE_EMBEDDING_LEN+1,))
+ #self.observation_space = spaces.Box(0, float("inf"), shape=(LayerDescLen * self.num_layers(), ))
+ self.create_network_record_file()
def reset(self, init_only=False):
"""Reset the environment.
This is invoked by the Agent.
"""
msglogger.info("Resetting the environment (init_only={})".format(init_only))
- self.current_layer_id = 0 #-1
+ self.current_layer_id = 0
self.prev_action = 0
self.model = copy.deepcopy(self.orig_model)
- self.zeros_mask_dict = distiller.create_model_masks_dict(self.model)
- self._remaining_macs = self.dense_model_macs
+ self.net_wrapper.reset(self.model)
self._removed_macs = 0
+ self.action_history = []
+ self.agent_action_history = []
if init_only:
return
-
- layer_macs = self.get_layer_macs(self.current_layer())
- initial_observation = self._get_obs(layer_macs)
+ initial_observation = self.get_obs()
return initial_observation
- def num_layers(self):
- return len(self.conv_layers)
-
def current_layer(self):
- return self.get_layer(self.current_layer_id)
-
- def get_layer(self, idx):
- try:
- return self.conv_layers[idx]
- except KeyError:
- return None
+ return self.net_wrapper.get_layer(self.current_layer_id)
def episode_is_done(self):
- return self.current_layer_id == self.num_layers()
-
- def remaining_macs(self):
- """Return the amount of MACs remaining in the model's unprocessed Convolution layers.
- This is normalized to the range 0..1
- """
- return self._remaining_macs / self.dense_model_macs
+ return self.current_layer_id == self.net_wrapper.num_layers()
def removed_macs(self):
"""Return the amount of MACs removed so far.
@@ -221,293 +508,272 @@ class CNNEnvironment(gym.Env):
"""
if self.current_layer_id == 0:
msglogger.info("+" + "-" * 50 + "+")
- msglogger.info("Starting a new episode")
+ msglogger.info("Starting a new episode %d", self.episode)
msglogger.info("+" + "-" * 50 + "+")
- msglogger.info("Environment: current_layer_id=%d" % self.current_layer_id)
+ msglogger.info("Render Environment: current_layer_id=%d" % self.current_layer_id)
distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
- def get_action(self, a):
+ def get_action(self, pruning_action):
+ """Compute a resource-constrained action"""
reduced = self._removed_macs
- rest = self._remaining_macs
-
- #duty = self.desired_reduction - (1.2*reduced + rest)
- duty = self.amc_cfg.desired_reduction - (reduced + rest)
- flops = self.get_layer_macs(self.current_layer())
- msglogger.info("action ********** a={} duty={} desired_reduction={} reduced={} rest={} flops={}".
- format(a, duty, self.amc_cfg.desired_reduction, reduced, rest, flops))
-
- if duty > 0:
- #duty = 0.9*desired_reduction - (reduced + rest)
- duty = self.amc_cfg.desired_reduction - (reduced + rest)
- msglogger.info("action ********** duty/flops={}".format(duty / flops))
- msglogger.info("action ********** 1 - duty/flops={}".format(1 - duty / flops))
- #a = max(1-self.action_low, min(a, 1 - duty/flops))
-
- ##
- ## Consider using max=0 for R = error * macs
- ## using max= self.action_low for FLOP-limited? Add noise so it doesn't get stuck in one place?
- ##
- #a = max(self.action_low, min(a, 1 - duty/flops))
- a = max(0.05, min(a, 1 - duty/flops))
- return a
-
- def save_record(self, top1, reward, total_macs, normalized_macs, total_nnz):
- fields = [top1, reward, total_macs, normalized_macs, total_nnz]
- with open(r'amc.csv', 'a') as f:
- writer = csv.writer(f)
- writer.writerow(fields)
-
- def create_scheduler(self):
- scheduler = distiller.CompressionScheduler(self.model)
- masks = {param_name: masker.mask for param_name, masker in self.zeros_mask_dict.items()}
- scheduler.load_state_dict(state={'masks_dict': masks})
- return scheduler
-
- def save_checkpoint(self, is_best=False):
- # Save the learned-model checkpoint
- scheduler = self.create_scheduler()
- episode = self.debug_stats['episode']
- episode = str(episode).zfill(3)
- if is_best:
- name = "BEST_adc_episode_{}".format(episode)
- else:
- name = "adc_episode_{}".format(episode)
-
- self.services.save_checkpoint_fn(epoch=self.debug_stats['episode'], model=self.model,
- scheduler=scheduler, name=name)
-
- def step(self, action):
+ rest = self.rest_macs_raw() * self.action_high
+ target_reduction = (1 - self.amc_cfg.target_density) * self.dense_model_macs
+
+ duty = target_reduction - (reduced + rest)
+ flops = self.net_wrapper.get_layer_macs(self.current_layer())
+ assert flops > 0
+ pruning_action_final = min(self.action_high, max(pruning_action, duty/flops))
+ if pruning_action_final != pruning_action:
+ msglogger.info("action ********** pruning_action={}==>pruning_action_final={:.2f}: reduced={:.2f} rest={:.2f} target={:.2f} duty={:.2f} flops={:.2f}".
+ format(pruning_action, pruning_action_final, reduced/self.dense_model_macs,
+ rest/self.dense_model_macs, 1-self.amc_cfg.target_density,
+ duty/self.dense_model_macs,
+ flops/self.dense_model_macs))
+ return pruning_action_final
+
+ def step(self, pruning_action):
"""Take a step, given an action.
- The action represents the desired density.
+ The action represents the desired sparsity.
This function is invoked by the Agent.
"""
- msglogger.info("env.step - current_layer_id={} action={}".format(self.current_layer_id, action))
- assert action == 0 or (action >= self.action_low-0.001 and action <= self.action_high+0.001)
- if self.amc_cfg.desired_reduction is not None:
- action = self.get_action(action)
- msglogger.info("action ********** (leave) {}".format(action))
- action = 1 - action
- layer_macs = self.get_layer_macs(self.current_layer())
- if action > 0 and self.current_layer_id >= 0: # skip first convolution
- actual_action = self.__remove_structures(self.current_layer_id, action, prune_what="filters")
+ msglogger.info("env.step - current_layer_id={} episode={}".format(self.current_layer_id, self.episode))
+ msglogger.info("\tAgent pruning_action={}".format(pruning_action))
+
+ if is_using_continuous_action_space(self.amc_cfg.agent_algo):
+ pruning_action = np.clip(pruning_action[0], self.action_low, self.action_high)
else:
- actual_action = 0
+ # Divide the action space into 10 discrete levels (0%, 10%, 20%,....90% sparsity)
+ pruning_action = pruning_action / 10
+ msglogger.info("\tAgent clipped pruning_action={}".format(pruning_action))
+ self.agent_action_history.append(pruning_action)
+ if self.amc_cfg.action_constrain_fn is not None:
+ pruning_action = self.amc_cfg.action_constrain_fn(self, pruning_action=pruning_action)
+ msglogger.info("Constrained pruning_action={}".format(pruning_action))
+
+ total_macs_before, _ = self.net_wrapper.get_model_resources_requirements(self.model)
+ layer_macs = self.net_wrapper.get_layer_macs(self.current_layer())
+ msglogger.info("\tlayer_macs={:.2f}".format(layer_macs / self.dense_model_macs))
+ msglogger.info("\tremoved_macs={:.2f}".format(self.removed_macs()))
+ msglogger.info("\trest_macs={:.2f}".format(self.rest_macs()))
+
+ if pruning_action > 0:
+ pruning_action = self.net_wrapper.remove_structures(self.current_layer_id,
+ fraction_to_prune=pruning_action,
+ prune_what="filters")
+ else:
+ pruning_action = 0
- layer_macs_after_action = self.get_layer_macs(self.current_layer())
+ self.action_history.append(pruning_action)
+ total_macs_after, _ = self.net_wrapper.get_model_resources_requirements(self.model)
+ layer_macs_after_action = self.net_wrapper.get_layer_macs(self.current_layer())
# Update the various counters after taking the step
self.current_layer_id += 1
- #next_layer_macs = self.get_layer_macs(self.current_layer())
- self._removed_macs += (layer_macs - layer_macs_after_action)
- self._remaining_macs -= layer_macs # next_layer_macs
- #self.prev_action = 1 - actual_action
+ self._removed_macs += (total_macs_before - total_macs_after)
+
+ msglogger.info("actual_action={}".format(pruning_action))
+ msglogger.info("layer_macs={} layer_macs_after_action={} removed now={}".format(layer_macs,
+ layer_macs_after_action,
+ (layer_macs - layer_macs_after_action)))
+ msglogger.info("self._removed_macs={}".format(self._removed_macs))
+ assert math.isclose(layer_macs_after_action / layer_macs, 1 - pruning_action)
stats = ('Peformance/Validation/',
- OrderedDict([('requested_action', action),
- ('actual_action', 1-actual_action)]))
- distiller.log_training_progress(stats, None, self.debug_stats['episode'], steps_completed=self.current_layer_id,
- total_steps=self.amc_cfg.conv_cnt,
- log_freq=1, loggers=[self.tflogger])
+ OrderedDict([('requested_action', pruning_action)]))
+
+ distiller.log_training_progress(stats, None,
+ self.episode, steps_completed=self.current_layer_id,
+ total_steps=self.amc_cfg.conv_cnt, log_freq=1, loggers=[self.tflogger])
if self.episode_is_done():
+ msglogger.info("Episode is ending")
observation = self.get_final_obs()
reward, top1, total_macs, total_nnz = self.compute_reward()
- self.debug_stats['episode'] += 1
- normalized_macs = total_macs/self.dense_model_macs * 100
- self.save_record(top1, reward, total_macs, normalized_macs, total_nnz)
- if reward > self.amc_cfg.max_reward:
- self.amc_cfg.max_reward = reward
- self.save_checkpoint(is_best=True)
- msglogger.info("Best reward={} episode={} top1={}".format(reward, self.debug_stats['episode'], top1))
- else:
- self.save_checkpoint(is_best=False)
+ normalized_macs = total_macs / self.dense_model_macs * 100
+ normalized_nnz = total_nnz / self.dense_model_size * 100
+ self.record_network_details(top1, reward, total_macs, normalized_macs,
+ normalized_nnz, self.action_history, self.agent_action_history)
+ self.episode += 1
else:
- observation = self._get_obs(layer_macs)
+ observation = self.get_obs()
reward = 0
+ if self.amc_cfg.compute_reward_every_step:
+ reward, top1, total_macs, total_nnz = self.compute_reward(False)
- self.prev_action = 1 - action
- msglogger.info("###################### self.prev_action={}".format(self.prev_action))
+ self.prev_action = pruning_action
info = {}
return observation, reward, self.episode_is_done(), info
- def _get_obs4(self, current_layer_macs, current_layer, conv_module):
- """Produce a state embedding (i.e. an observation)"""
+ def one_hot(self, n, r):
+ """Produce a one-hot representation of the layer id"""
+ #return [1 if i == n else 0 for i in range(r)]
+ return [n]
- if self.amc_cfg.normalize_obs:
- obs = np.array([current_layer.t,
- conv_module.out_channels / 512,
- conv_module.in_channels / 512,
- current_layer.ifm_h / 32,
- current_layer.ifm_w / 32,
- current_layer.stride[0] / 2,
- current_layer.k / 3,
- current_layer_macs / self.dense_model_macs,
- self.removed_macs(), self.remaining_macs(), self.prev_action])
- else:
- obs = np.array([current_layer.t,
- conv_module.out_channels, conv_module.in_channels,
- current_layer.ifm_h, current_layer.ifm_w, current_layer.stride[0], current_layer.k,
- current_layer_macs/self.dense_model_macs,
- self.removed_macs(), self.remaining_macs(), self.prev_action])
-
- if self.amc_cfg.onehot_encoding:
- id = np.zeros(self.amc_cfg.conv_cnt)
- id[current_layer.t] = 1
- obs = np.concatenate([id, obs[1:]])
- msglogger.info("obs={}".format(obs))
- else:
- msglogger.info("obs={}".format(Observation._make(obs)))
-
- assert len(obs) == self.STATE_EMBEDDING_LEN
- print("macs(%)={}\nremoved macs={}\nremaining macs={}".format(current_layer_macs / self.dense_model_macs,
- self.removed_macs(),
- self.remaining_macs()))
- #assert (current_layer_macs / self.dense_model_macs + self.removed_macs() + self.remaining_macs()) <= 1
- assert (self.removed_macs() + self.remaining_macs()) <= 1
- return obs
+ def get_obs(self):
+ """Produce a state embedding (i.e. an observation)"""
- def _get_obs(self, current_layer_macs):
+ current_layer_macs = self.net_wrapper.get_layer_macs(self.current_layer())
+ current_layer_macs_pct = current_layer_macs/self.dense_model_macs
current_layer = self.current_layer()
conv_module = distiller.model_find_module(self.model, current_layer.name)
- return self._get_obs4(current_layer_macs, current_layer, conv_module)
+ obs = [#current_layer.t,
+ conv_module.out_channels,
+ conv_module.in_channels,
+ current_layer.ifm_h,
+ current_layer.ifm_w,
+ current_layer.stride[0],
+ current_layer.k,
+ current_layer_macs_pct*100,
+ self.removed_macs()*100,
+ self.rest_macs()*100,
+ self.prev_action*100]
+ onehot_id = self.one_hot(self.current_layer_id, self.net_wrapper.num_layers())
+ msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
+ obs = np.array(onehot_id + obs)
+ assert (self.removed_macs() + current_layer_macs_pct + self.rest_macs()) <= 1
+ return obs
def get_final_obs(self):
"""Return the final state embedding (observation)
The final state is reached after we traverse all of the Convolution layers.
"""
- obs = np.array([-1, 0, 0,
- 0, 0, 0, 0,
- 0, self.removed_macs(), 0, 1 - self.prev_action])
-
- if self.amc_cfg.onehot_encoding:
- id = np.zeros(self.amc_cfg.conv_cnt)
- obs = np.concatenate([id, obs[1:]])
-
- assert len(obs) == self.STATE_EMBEDDING_LEN
+ obs = [#-1,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ self.removed_macs()*100,
+ self.rest_macs()*100,
+ self.prev_action*100]
+ onehot_id = self.one_hot(self.net_wrapper.num_layers(), self.net_wrapper.num_layers())
+ msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
+ obs = np.array(onehot_id + obs)
return obs
- def get_layer_macs(self, layer):
- """Return the number of MACs required to compute <layer>'s Convolution"""
- if layer is None:
- return 0
-
- conv_module = distiller.model_find_module(self.model, layer.name)
- # MACs = volume(OFM) * (#IFM * K^2)
- dense_macs = (conv_module.out_channels * layer.ofm_h * layer.ofm_w) * (conv_module.in_channels * layer.k**2)
- if PERFORM_THINNING:
- return dense_macs
-
- # If we didn't physically remove structures, we need to use the structural sparsity to compute MACs
- conv_pname = layer.name + ".weight"
- conv_p = distiller.model_find_param(self.model, conv_pname)
- # return dense_macs * distiller.density_ch(conv_p) # Channel pruning
- return dense_macs * distiller.density_3D(conv_p) # Filter pruning
-
- def __remove_structures(self, idx, fraction_to_prune, prune_what="channels"):
- """Physically remove channels and corresponding filters from the model"""
- if idx not in range(self.num_layers()):
- raise ValueError("idx=%d is not in correct range (0-%d)" % (idx, self.num_layers()))
- if fraction_to_prune < 0:
- raise ValueError("fraction_to_prune=%f is illegal" % (fraction_to_prune))
-
- if fraction_to_prune == 0:
- return 0
- if fraction_to_prune == 1.0:
- # For now, prevent the removal of entire layers
- fraction_to_prune = ALMOST_ONE
-
- layer = self.conv_layers[idx]
- conv_pname = layer.name + ".weight"
- conv_p = distiller.model_find_param(self.model, conv_pname)
-
- msglogger.info("ADC: removing %.1f%% %s from %s" % (fraction_to_prune*100, prune_what, conv_pname))
-
- if prune_what == "channels":
- calculate_sparsity = distiller.sparsity_ch
- remove_structures = distiller.remove_channels
- group_type = "Channels"
- elif prune_what == "filters":
- calculate_sparsity = distiller.sparsity_3D
- group_type = "Filters"
- remove_structures = distiller.remove_filters
- else:
- raise ValueError("unsupported structure {}".format(prune_what))
- # Create a channel-ranking pruner
- pruner = distiller.pruning.L1RankedStructureParameterPruner("adc_pruner", group_type,
- fraction_to_prune, conv_pname)
- pruner.set_param_mask(conv_p, conv_pname, self.zeros_mask_dict, meta=None)
-
- if (self.zeros_mask_dict[conv_pname].mask is None or
- calculate_sparsity(self.zeros_mask_dict[conv_pname].mask) == 0):
- msglogger.info("__remove_structures: aborting because there are no channels to prune")
- return 0
-
- # Use the mask to prune
- self.zeros_mask_dict[conv_pname].apply_mask(conv_p)
-
- if PERFORM_THINNING:
- remove_structures(self.model, self.zeros_mask_dict, self.app_args.dataset, self.app_args.dataset, optimizer=None)
- conv_p = distiller.model_find_param(self.model, conv_pname)
- return distiller.volume(conv_p) / layer.weights_vol
- actual_sparsity = calculate_sparsity(conv_p)
- return actual_sparsity
-
- def compute_reward(self):
- """The ADC paper defines reward = -Error"""
+ def whole_network_get_obs(self):
+ """Produce a state embedding (i.e. an observation)"""
+ num_layers = self.net_wrapper.num_layers()
+ network_obs = np.empty(shape=(LayerDescLen, num_layers))
+ for layer_id in range(num_layers):
+ layer = self.get_layer(layer_id)
+ layer_macs = self.net_wrapper.get_layer_macs(layer)
+ layer_macs_pct = layer_macs/self.dense_model_macs
+ conv_module = distiller.model_find_module(self.model, layer.name)
+ obs = [layer.t,
+ conv_module.out_channels,
+ conv_module.in_channels,
+ layer.ifm_h,
+ layer.ifm_w,
+ layer.stride[0],
+ layer.k,
+ layer_macs_pct,
+ self.removed_macs(),
+ self.rest_macs()]
+ network_obs[:, layer_id] = np.array(obs)
+
+ #msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
+ #network_obs = network_obs.reshape(network_obs.shape[0], network_obs.shape[1], 1)
+ network_obs = network_obs.reshape(network_obs.shape[0] * network_obs.shape[1])
+ #msglogger.info("* obs={}".format(network_obs))
+ return network_obs
+
+ def whole_network_get_final_obs(self):
+ return self.get_obs()
+
+ def rest_macs_raw(self):
+ """Return the number of remaining MACs in the layers following the current layer"""
+ rest = 0
+ for layer_id in range(self.current_layer_id, self.net_wrapper.num_layers()):
+ rest += self.net_wrapper.get_layer_macs(self.net_wrapper.get_layer(layer_id + 1))
+ return rest
+
+ def rest_macs(self):
+ return self.rest_macs_raw() / self.dense_model_macs
+
+ def is_macs_constraint_achieved(self, compressed_model_total_macs):
+ current_density = compressed_model_total_macs / self.dense_model_macs
+ return self.amc_cfg.target_density >= current_density
+
+ def compute_reward(self, log_stats=True):
+ """Compute the reward"""
distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
-
- if PERFORM_THINNING:
- _, total_macs, total_nnz = collect_conv_details(self.model, self.app_args.dataset)
+ total_macs, total_nnz = self.net_wrapper.get_model_resources_requirements(self.model)
+ if self.amc_cfg.perform_thinning:
compression = distiller.model_numel(self.model, param_dims=[4]) / self.dense_model_size
else:
- _, total_macs, total_nnz = collect_conv_details(self.model, self.app_args.dataset)
compression = 1 - distiller.model_sparsity(self.model)/100
# What a hack!
total_nnz *= compression
- msglogger.info("Total parameters left: %.2f%%" % (compression*100))
- msglogger.info("Total compute left: %.2f%%" % (total_macs/self.dense_model_macs*100))
- # Train for zero or more epochs
- optimizer = torch.optim.SGD(self.model.parameters(), lr=self.app_args.optimizer_data['lr'],
- momentum=self.app_args.optimizer_data['momentum'],
- weight_decay=self.app_args.optimizer_data['weight_decay'])
- for _ in range(NUM_TRAINING_EPOCHS):
- self.services.train_fn(model=self.model, compression_scheduler=self.create_scheduler(),
- optimizer=optimizer,
- epoch=self.debug_stats['episode'])
- # Validate
- top1, top5, vloss = self.services.validate_fn(model=self.model, epoch=self.debug_stats['episode'])
- reward = self.amc_cfg.reward_fn(top1, top5, vloss, total_macs)
-
- stats = ('Peformance/Validation/',
- OrderedDict([('Loss', vloss),
- ('Top1', top1),
- ('Top5', top5),
- ('reward', reward),
- ('total_macs', int(total_macs)),
- ('macs_normalized', total_macs/self.dense_model_macs*100),
- ('log(total_macs)', math.log(total_macs)),
- ('total_nnz', int(total_nnz))]))
- distiller.log_training_progress(stats, None, self.debug_stats['episode'], steps_completed=0, total_steps=1,
- log_freq=1, loggers=[self.tflogger, self.pylogger])
+ self.net_wrapper.train(self.amc_cfg.num_ft_epochs, self.episode)
+ top1, top5, vloss = self.net_wrapper.validate()
+ reward = self.amc_cfg.reward_fn(self, top1, top5, vloss, total_macs)
+
+ if log_stats:
+ macs_normalized = total_macs/self.dense_model_macs
+ msglogger.info("Total parameters left: %.2f%%" % (compression*100))
+ msglogger.info("Total compute left: %.2f%%" % (total_macs/self.dense_model_macs*100))
+
+ stats = ('Peformance/EpisodeEnd/',
+ OrderedDict([('Loss', vloss),
+ ('Top1', top1),
+ ('Top5', top5),
+ ('reward', reward),
+ ('total_macs', int(total_macs)),
+ ('macs_normalized', macs_normalized*100),
+ ('log(total_macs)', math.log(total_macs)),
+ ('total_nnz', int(total_nnz))]))
+ distiller.log_training_progress(stats, None, self.episode, steps_completed=0, total_steps=1,
+ log_freq=1, loggers=[self.tflogger, self.pylogger])
return reward, top1, total_macs, total_nnz
+ def create_network_record_file(self):
+ """Create the CSV file and write the column names"""
+ fields = ['episode', 'top1', 'reward', 'total_macs', 'normalized_macs',
+ 'normalized_nnz', 'ckpt_name', 'action_history', 'agent_action_history']
+ with open(os.path.join(msglogger.logdir, 'amc.csv'), 'w') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
-def get_dummy_input(dataset):
- if dataset == 'imagenet':
- dummy_input = torch.randn(1, 3, 224, 224)
- elif dataset == 'cifar10':
- dummy_input = torch.randn(1, 3, 32, 32)
- else:
- raise ValueError("dataset %s is not supported" % dataset)
- return dummy_input
+ def record_network_details(self, top1, reward, total_macs, normalized_macs,
+ normalized_nnz, action_history, agent_action_history):
+ """Write the details of one network to a CSV file and create a checkpoint file"""
+ if reward > self.best_reward:
+ self.best_reward = reward
+ ckpt_name = self.save_checkpoint(is_best=True)
+ msglogger.info("Best reward={} episode={} top1={}".format(reward, self.episode, top1))
+ else:
+ ckpt_name = self.save_checkpoint(is_best=False)
+ fields = [self.episode, top1, reward, total_macs, normalized_macs,
+ normalized_nnz, ckpt_name, action_history, agent_action_history]
+ with open(os.path.join(msglogger.logdir, 'amc.csv'), 'a') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
+
+ def save_checkpoint(self, is_best=False):
+ """Save the learned-model checkpoint"""
+ scheduler = self.net_wrapper.create_scheduler()
+ episode = str(self.episode).zfill(3)
+ if is_best:
+ fname = "BEST_adc_episode_{}".format(episode)
+ else:
+ fname = "adc_episode_{}".format(episode)
+
+ self.services.save_checkpoint_fn(epoch=0, model=self.model,
+ scheduler=scheduler, name=fname)
+ del scheduler
+ return fname
-def collect_conv_details(model, dataset):
- dummy_input = get_dummy_input(dataset)
- g = SummaryGraph(model.cuda(), dummy_input.cuda())
+
+def collect_conv_details(model, dataset, perform_thinning, layers_to_prune=None):
+ dummy_input = distiller.get_dummy_input(dataset)
+ g = SummaryGraph(model, dummy_input)
conv_layers = OrderedDict()
total_macs = 0
total_params = 0
@@ -527,10 +793,9 @@ def collect_conv_details(model, dataset):
conv.macs = conv_op['attrs']['MACs']
conv_pname = name + ".weight"
conv_p = distiller.model_find_param(model, conv_pname)
- if not PERFORM_THINNING:
+ if not perform_thinning:
#conv.macs *= distiller.density_ch(conv_p) # Channel pruning
conv.macs *= distiller.density_3D(conv_p) # Filter pruning
- #assert distiller.density_ch(conv_p) == 1
total_macs += conv.macs
conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
@@ -540,71 +805,61 @@ def collect_conv_details(model, dataset):
conv.name = name
conv.id = id
- conv_layers[len(conv_layers)] = conv
+ if layers_to_prune is None or name in layers_to_prune:
+ conv_layers[len(conv_layers)] = conv
return conv_layers, total_macs, total_params
-from examples.automated_deep_compression.adc_controlled_envs import *
-def random_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn):
- """Random ADC agent"""
- action_range = (0.0, 1.0)
- env = CNNEnvironment(model, dataset, arch, optimizer_data,
- validate_fn, save_checkpoint_fn, action_range,
- onehot_encoding=False, normalize_obs=False, desired_reduction=None,
- reward_fn=lambda top1, total_macs: top1/100)
-
- best_episode = [-1000, None]
- update_rate = 5
- env.action_space = RandomADCActionSpace(action_range[0], action_range[1], std=0.35)
- for ep in range(1000):
- observation = env.reset()
- action_config = []
- for t in range(100):
- #env.render(0, 0)
- msglogger.info("[episode={}:{}] observation = {}".format(ep, t, observation))
- # take a random action
- action = env.action_space.sample()
- action_config.append(action)
- observation, reward, done, info = env.step(action)
- if done:
- msglogger.info("Episode finished after {} timesteps".format(t+1))
- msglogger.info("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")
- msglogger.info("New solution found: episode={} reward={} config={}".format(ep, reward, action_config))
- break
- if reward > best_episode[0]:
- best_episode[0] = reward
- best_episode[1] = action_config
- if ep % update_rate == 0:
- env.action_space.set_cfg(means=best_episode[1], std=0.4)
- best_episode = [-1000, None]
-
-
-# import os
-# import pandas as pd
-# from tabulate import tabulate
-# import apputils
-# from models import create_model
-#
-#
-# def summarize_experiment(experiment_dir, dataset, arch, validate_fn):
-# df = pd.DataFrame(columns=['File', 'NNZ', 'MACs', 'Top1'])
-# for file in os.listdir(experiment_dir):
-# if file.endswith(".pth.tar"):
-# cnt_macs, cnt_params, top1 = get_experiment_performance_summary(os.path.join(experiment_dir, file), dataset, arch, validate_fn)
-# df.loc[len(df.index)] = [file, cnt_params, cnt_macs, top1]
-# t = tabulate(df, headers='keys', tablefmt='psql', floatfmt=".2f")
-# print(t)
-# csv_fname = os.path.join(experiment_dir, "arch_space" + ".csv")
-# print("Saving results to: {}".format(csv_fname))
-# df.to_csv(csv_fname, sep=',')
-#
-#
-# def get_experiment_performance_summary(chkpt_fname, dataset, arch, validate_fn):
-# model = create_model(False, dataset, arch)
-# model, compression_scheduler, start_epoch = apputils.load_checkpoint(model, chkpt_fname)
-#
-# dummy_input = get_dummy_input(dataset)
-# perf_df = distiller.model_performance_summary(model, dummy_input, 1)
-# total_macs = perf_df['MACs'].sum()
-# top1, top5, vloss = validate_fn(model=model, epoch=-1)
-# return total_macs, distiller.model_numel(model), top1
+import pandas as pd
+def sample_networks(net_wrapper, services):
+ """Sample networks from the posterior distribution.
+
+ 1. Sort the networks we discovered using AMC by their reward.
+ 2. Use the top 10% best-performing networks discovered by AMC to postulate a posterior distribution of the
+ density/sparsity of each layer:
+ p([layers-sparsity] | Top1, L1)
+ 3. Sample 100 networks from this distribution.
+ For each such network: fine-tune, score using Top1, and save
+ """
+ #fname = "logs/resnet20___2019.01.29-102912/amc.csv"
+ fname = "logs/resnet20___2019.02.03-210001/amc.csv"
+ df = pd.read_csv(fname)
+
+ #top1_sorted_df = df.sort_values(by=['top1'], ascending=False)
+ top1_sorted_df = df.sort_values(by=['reward'], ascending=False)
+ top10pct = top1_sorted_df[:int(len(df.index) * 0.1)]
+
+ dense_macs, _ = net_wrapper.get_model_resources_requirements()
+ layer_sparsities_list = []
+ for index, row in top10pct.iterrows():
+ layer_sparsities = row['action_history']
+ layer_sparsities = layer_sparsities[1:-1].split(",") # convert from string to list
+ layer_sparsities = [float(sparsity) for sparsity in layer_sparsities]
+ layer_sparsities_list.append(layer_sparsities)
+
+ layer_sparsities = np.array(layer_sparsities_list)
+ mean = layer_sparsities.mean(axis=0)
+ cov = np.cov(layer_sparsities.T)
+ num_networks = 100
+ data = np.random.multivariate_normal(mean, cov, num_networks)
+
+ orig_model = net_wrapper.model
+ for i in range(num_networks):
+ model = copy.deepcopy(orig_model)
+ net_wrapper.reset(model)
+ for layer_id, sparsity_level in enumerate(data[i]):
+ sparsity_level = min(max(0, sparsity_level), ALMOST_ONE)
+ net_wrapper.remove_structures(layer_id,
+ fraction_to_prune=sparsity_level,
+ prune_what="filters")
+
+ net_wrapper.train(1)
+ top1, top5, vloss = net_wrapper.validate()
+
+ """Save the learned-model checkpoint"""
+ scheduler = net_wrapper.create_scheduler()
+ total_macs, _ = net_wrapper.get_model_resources_requirements(model)
+ fname = "{}_top1_{:2f}__density_{:2f}_sampled".format(net_wrapper.arch, top1, total_macs/dense_macs)
+ services.save_checkpoint_fn(epoch=0, model=net_wrapper.model,
+ scheduler=scheduler, name=fname)
+ del scheduler
diff --git a/examples/automated_deep_compression/__init__.py b/examples/automated_deep_compression/__init__.py
new file mode 100755
index 0000000..a05f8a2
--- /dev/null
+++ b/examples/automated_deep_compression/__init__.py
@@ -0,0 +1,2 @@
+from .automl_args import add_automl_args
+from .ADC import do_adc
diff --git a/examples/automated_deep_compression/adc_controlled_envs.py b/examples/automated_deep_compression/adc_controlled_envs.py
deleted file mode 100755
index d4e02df..0000000
--- a/examples/automated_deep_compression/adc_controlled_envs.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""This file contains a couple of environments used for debugging ADC reproduction.
-"""
-import random
-import numpy as np
-from scipy.stats import truncnorm
-
-
-class RandomADCActionSpace(object):
- def __init__(self, low, high, std):
- self.clip_low = low
- self.clip_high = high
- self.layer = 0
- self.num_layers = 13
- #self.means = [high-low] * self.num_layers
- self.means = [0.9, 0.9, 0.9, 0.9, 0.9, 0.8, 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5]
- self.std = std
-
- def sample(self):
- return random.uniform(self.clip_low, self.clip_high)
- action_values_mean = self.means[self.layer]
- action_values_std = self.std
- normalized_low = (self.clip_low - action_values_mean) / action_values_std
- normalized_high = (self.clip_high - action_values_mean) / action_values_std
- distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
- action = distribution.rvs(1)
- # action = np.random.normal(self.means[self.layer], self.std)
- # action = min(self.clip_high, max(action, self.clip_low))
- self.layer = (self.layer + 1) % self.num_layers
- return action
-
- def set_cfg(self, means, std):
- self.means = [0.01*m for m in self.means] + [0.99*m for m in means]
- self.std = std
-
-
-class PredictableADCActionSpace(object):
- def __init__(self, low, high):
- #self.actions = (0.51, 0.26, 0.23, 0.09, 0.24, 0.36, 0.90, 0.97, 0.98, 0.98, 0.98, 0.98, 0)
- #self.actions = (0.51, 0.26, 0.23, 0.09, 0.24, 0.36, 0.0, 0.0, 0.50, 0.50, 0.50, 0.50, 0)
- #self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.50, 0.65, 0.60, 0.00, 0.00, 0)
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.0, 0.8, 0.00, 0) # Top1 90.100000 Top5 99.420000 reward -0.113175
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.0, 0.7, 0.00, 0) # Top1 90.540000 Top5 99.360000 reward -0.124923
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.0, 0.6, 0.00, 0) # Top1 90.600000 Top5 99.340000 reward -0.128869
-
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.0, 0.8, 0.8, 0) # Top1 87.600000 Top5 98.980000 reward -0.198718
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.0, 0.8, 0.8, 0.65) # Top1 74.720000 Top5 97.700000 reward -0.447991
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0.8, 0.8, 0.8, 0.65) # Top1 39.540000 Top5 95.420000 reward -0.886748
-
- #self.actions = [0] * 13 # Top1 90.480000 Top5 99.400000 reward -0.117374
- self.step = 0
- self.episode = 0
- self.l1 = 0
- self.l2 = 0
- self.update_action_vector()
-
- def update_action_vector(self):
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0, 0.8, 0.05, 0) # Top1 89.640000 Top5 99.520000 reward -0.093653
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0, 0.8, 0.05, self.episode * 0.05)
- self.actions = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00, 0.0, 0, self.l1 * 0.05, self.l2 * 0.05, 0)
-
- def sample(self):
- action = self.actions[self.step]
- self.step = (self.step + 1) % len(self.actions)
- if self.step == 0:
- self.l1 = (self.l1 + 1) % 20
- if self.l1 == 0:
- self.l2 = (self.l2 + 1) % 20
- if self.l2 == 19:
- print("Done - exiting")
- exit()
- self.update_action_vector()
- return action
diff --git a/examples/automated_deep_compression/adc_random_env.py b/examples/automated_deep_compression/adc_random_env.py
new file mode 100755
index 0000000..127f9ee
--- /dev/null
+++ b/examples/automated_deep_compression/adc_random_env.py
@@ -0,0 +1,66 @@
+import random
+import numpy as np
+from scipy.stats import truncnorm
+import logging
+
+msglogger = logging.getLogger()
+
+
+class RandomADCActionSpace(object):
+ def __init__(self, low, high, std, mean, num_layers):
+ self.action_low = low
+ self.action_high = high
+ self.layer = 0
+ self.num_layers = num_layers
+ self.means = [mean] * num_layers
+ #self.means = [high-low] * self.num_layers
+ #self.means = [0.9, 0.9, 0.9, 0.9, 0.9, 0.8, 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5]
+ self.std = std
+
+ def sample(self):
+ return random.uniform(self.action_low, self.action_high)
+ # action_values_mean = self.means[self.layer]
+ # action_values_std = self.std
+ # normalized_low = (self.action_low - action_values_mean) / action_values_std
+ # normalized_high = (self.action_high - action_values_mean) / action_values_std
+ # distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
+ # action = distribution.rvs(1)
+ # # action = np.random.normal(self.means[self.layer], self.std)
+ # # action = min(self.action_high, max(action, self.action_low))
+ # self.layer = (self.layer + 1) % self.num_layers
+ # return action
+
+ def set_cfg(self, means, std):
+ self.means = [0.01*m for m in self.means] + [0.99*m for m in means]
+ self.std = std
+
+
+def random_agent(env):
+ """Random ADC agent"""
+ action_range = env.amc_cfg.action_range
+ best_episode = [-1000, None]
+ update_rate = 5
+ num_layers = env.net_wrapper.num_layers()
+ env.action_space = RandomADCActionSpace(action_range[0], action_range[1],
+ std=0.35, mean=env.amc_cfg.target_density, num_layers=num_layers)
+ for ep in range(1000):
+ observation = env.reset()
+ action_config = []
+ for t in range(100):
+ #env.render(0, 0)
+ msglogger.info("[episode={}:{}] observation = {}".format(ep, t, observation))
+ # take a random action
+ action = env.action_space.sample()
+ action_config.append(action)
+ observation, reward, done, info = env.step([action])
+ if done:
+ msglogger.info("Episode finished after {} timesteps".format(t+1))
+ msglogger.info("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")
+ msglogger.info("New solution found: episode={} reward={} config={}".format(ep, reward, action_config))
+ break
+ if reward > best_episode[0]:
+ best_episode[0] = reward
+ best_episode[1] = action_config
+ if ep % update_rate == 0:
+ env.action_space.set_cfg(means=best_episode[1], std=0.4)
+ best_episode = [-1000, None]
diff --git a/examples/automated_deep_compression/adc_sensitivity_analysis.ipynb b/examples/automated_deep_compression/adc_sensitivity_analysis.ipynb
deleted file mode 100644
index b73ddd2..0000000
--- a/examples/automated_deep_compression/adc_sensitivity_analysis.ipynb
+++ /dev/null
@@ -1,414 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Sensitivity Analysis\n",
- "\n",
- "\n",
- "Some pruning algorthims tune their hyperparameters based on the results of pruning sensitivity analysis. Distiller support L1-norm element-wise pruning sensitivity analysis, and filter-wise pruning sensitivity analysis based on the mean L1-norm ranking of filters.\n",
- "\n",
- "## Table of Contents\n",
- "\n",
- "1. [Load a pruning sensitivity analysis file](#Load-a-pruning-sensitivity-analysis-file)\n",
- "2. [Examine parameters sensitivities](#Examine-parameters-sensitivities)<br>\n",
- " 2.1. [Plot layer sensitivities at a selected sparsity level](#Plot-layer-sensitivities-at-a-selected-sparsity-level)<br>\n",
- " 2.2. [Compare layer sensitivities](#Compare-layer-sensitivities)\n",
- "3. [Filter pruning sensitivity analysis](#Filter-pruning-sensitivity-analysis)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load a pruning sensitivity analysis file\n",
- "\n",
- "You prepare a sensitivity analysis file by invoking ```distiller.perform_sensitivity_analysis()```. Checkout the documentation of ```distiller.perform_sensitivity_analysis()``` for more information.<br>\n",
- "Alternatively, you can use the sample ```compress_classifier.py``` application to perform sensitivity analysis on one of the supported models. In the example below, we invoke sensitivity analysis on a pretrained Resnet18 from torchvision, using the ImageNet test dataset for evaluation. \n",
- "\n",
- "```\n",
- "$ python3 compress_classifier.py -a resnet18 ../../../data.imagenet -j 12 --pretrained --sense=element\n",
- "```\n",
- "\n",
- "The outputs of performing pruning sensitivity analysis on several different networks is available at ```../examples/sensitivity-analysis``` "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import ipywidgets as widgets\n",
- "from ipywidgets import interactive, interact, Layout\n",
- "\n",
- "df = pd.read_csv('../automated_deep_compression/vgg16_cifar_sensitivity_filters.csv')\n",
- "df['sparsity'] = round(df['sparsity'], 2)\n",
- "df_filter = df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The code below converts the sensitivities dataframe to a sensitivities dictionary. <br> \n",
- "Using this dictionary makes it easier for us when we want to plot sensitivities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "from collections import OrderedDict\n",
- "\n",
- "def get_param_names(df):\n",
- " return list(set(df['parameter']))\n",
- "\n",
- "def get_sensitivity_levels(df):\n",
- " return list(set(df['sparsity']))\n",
- "\n",
- "def df2sensitivities(df):\n",
- " param_names = get_param_names(df)\n",
- " sparsities = get_sensitivity_levels(df)\n",
- "\n",
- " sensitivities = {}\n",
- " for param_name in param_names:\n",
- " sensitivities[param_name] = OrderedDict()\n",
- " param_stats = df[(df.parameter == param_name)]\n",
- " \n",
- " for row in range(len(param_stats.index)):\n",
- " s = param_stats.iloc[[row]].sparsity\n",
- " top1 = param_stats.iloc[[row]].top1\n",
- " top5 = param_stats.iloc[[row]].top5\n",
- " sensitivities[param_name][float(s)] = (float(top1), float(top5))\n",
- " return sensitivities "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Examine parameters sensitivities\n",
- "\n",
- "After loading the sensitivity analysis CSV file into a Pandas dataframe, we can examine it.\n",
- "\n",
- "### Plot layer sensitivities at a selected sparsity level\n",
- "Use the dropdown to choose the sparsity level, and select whether you choose to view the top1 accuracies or top5.<br>\n",
- "Under the plot we display the numerical values of the accuracies, in case you want to have a closer look at the details."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "def view2(level, acc):\n",
- " filtered = df[df.sparsity == level]\n",
- " s = filtered.style.apply(highlight_min_max)\n",
- " \n",
- " param_names = filtered['parameter']\n",
- " \n",
- " # Plot the sensitivities\n",
- " x = range(filtered[acc].shape[0])\n",
- " y = filtered[acc].values.tolist()\n",
- " fig = plt.figure(figsize=(20,10))\n",
- " plt.plot(x, y, label=param_names, marker=\"o\", markersize=10, markerfacecolor=\"C1\")\n",
- " plt.ylabel(str(acc))\n",
- " plt.xlabel('parameter')\n",
- " plt.xticks(rotation='vertical')\n",
- " plt.xticks(x, param_names)\n",
- " plt.title('Pruning Sensitivity per layer %d' % level) \n",
- " #return s\n",
- "\n",
- "def highlight_min_max(s):\n",
- " \"\"\"Highlight the max and min values in the series\"\"\"\n",
- " if s.name not in ['top1', 'top5']:\n",
- " return ['' for v in s] \n",
- " \n",
- " is_max = s == s.max()\n",
- " maxes = ['background-color: green' if v else '' for v in is_max]\n",
- " is_min = s == s.min()\n",
- " mins = ['background-color: red' if v else '' for v in is_min] \n",
- " return [h1 if len(h1)>len(h2) else h2 for (h1,h2) in zip(maxes, mins)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "sparsities = np.sort(get_sensitivity_levels(df))\n",
- "acc_radio = widgets.RadioButtons(options=['top1', 'top5'], value='top1', description='Accuracy:')\n",
- "levels_dropdown = widgets.Dropdown(description='Sparsity:', options=sparsities)\n",
- "interact(view2, level=levels_dropdown, acc=acc_radio);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sometimes we want to look at the sensitivies of a specific weights tensor:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def view_sparsity(param_name):\n",
- " display(df[df['parameter']==param_name])\n",
- "\n",
- "param_names = sorted(df['parameter'].unique().tolist())\n",
- "param_dropdown = widgets.Dropdown(description='Parameter:', options=param_names)\n",
- "interact(view_sparsity, param_name=param_dropdown);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Compare layer sensitivities\n",
- "\n",
- "Plot the pruning sensitivities of selected layers.\n",
- "<br>Select multiple parameters using SHIFT and CTRL."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Relative import of code from distiller, w/o installing the package\n",
- "import os\n",
- "import sys\n",
- "module_path = os.path.abspath(os.path.join('..', '..'))\n",
- "if module_path not in sys.path:\n",
- " sys.path.append(module_path)\n",
- "\n",
- "import pandas as pd\n",
- "import distiller\n",
- "import models\n",
- "import torch\n",
- "import apputils\n",
- "\n",
- "def create_macs_table(model):\n",
- " dummy_input = torch.randn(1, 3, 32, 32)\n",
- " g = apputils.SummaryGraph(model.cuda(), dummy_input.cuda())\n",
- " macs_tbl = {}\n",
- " for id, (name, m) in enumerate(model.named_modules()):\n",
- " if isinstance(m, torch.nn.Conv2d):\n",
- " conv_op = g.find_op(distiller.normalize_module_name(name))\n",
- " macs_tbl[name +\".weight\"] = conv_op['attrs']['MACs']\n",
- " return macs_tbl\n",
- "\n",
- "import math\n",
- "\n",
- "def compute_log_macs(dense_macs, sparsity_in, sparsity_out, top1_acc):\n",
- " #print(dense_macs, sparsity_in, sparsity_out, top1_acc)\n",
- " sparse_macs = dense_macs * ((1-sparsity_in) * (1-sparsity_out))\n",
- " return -1 * math.log(sparse_macs)\n",
- "\n",
- "def compute_reward(dense_macs, sparsity_in, sparsity_out, top1_acc):\n",
- " #print(dense_macs, sparsity_in, sparsity_out, top1_acc)\n",
- " sparse_macs = dense_macs * ((1-sparsity_in) * (1-sparsity_out))\n",
- " #print(dense_macs, sparsity_in)\n",
- " #print(math.log(sparse_macs))\n",
- " reward = -1 * (1-top1_acc/100) * math.log(sparse_macs)\n",
- " return reward"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = models.create_model(False, \"cifar10\", \"vgg16_cifar\")\n",
- "macs_tbl = create_macs_table(model)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "# assign a different color to each parameter (otherwise, colors change on us as we make different selections)\n",
- "param_names = df['parameter'].unique().tolist()\n",
- "color_idx = np.linspace(0, 1, len(param_names))\n",
- "colors = {} \n",
- "for i, pname in zip(color_idx, param_names):\n",
- " colors[pname] = color= plt.get_cmap('tab20')(i)\n",
- "plt.rcParams.update({'font.size': 18})\n",
- "\n",
- "def view(weights='', acc=0):\n",
- " sensitivities= None\n",
- " if weights[0]=='All':\n",
- " sensitivities = df2sensitivities(df)\n",
- " else:\n",
- " mask = False\n",
- " mask = [(df.parameter == pname) for pname in weights]\n",
- " mask = np.logical_or.reduce(mask)\n",
- " sensitivities = df2sensitivities(df[mask])\n",
- "\n",
- " # Plot the sensitivities\n",
- " fig, ax1 = plt.subplots(figsize=(20,10))\n",
- " for param_name, sensitivity in sensitivities.items():\n",
- " sense = [values[acc] for sparsity, values in sensitivity.items()]\n",
- " sparsities = [sparsity for sparsity, values in sensitivity.items()]\n",
- " ax1.plot(sparsities, sense, label=param_name, marker=\"o\", markersize=10, color=colors[param_name])\n",
- " \n",
- " ax2 = ax1.twinx()\n",
- " y2 = [compute_reward(macs_tbl[param_name], sparsities[i], sparsities[i], sense[i]) for i in range(len(sense)) ]\n",
- " ax2.plot(sparsities, y2, label=param_names, marker=\"o\", markersize=10, color=colors[param_name], markerfacecolor=\"red\")\n",
- " ax2.set_ylabel(\"Reward\")\n",
- " \n",
- "\n",
- " ax1.set_ylabel('top1')\n",
- " ax1.set_xlabel('sparsity')\n",
- " ax1.set_title('Pruning Sensitivity')\n",
- " ax1.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), fancybox=True, shadow=True, ncol=3)\n",
- "\n",
- "items = ['All']+param_names\n",
- "w = widgets.SelectMultiple(options=items, value=[items[1]], layout=Layout(width='50%'), description='Weights:')\n",
- "acc_widget = widgets.RadioButtons(options={'top1': 0, 'top5': 1}, value=0, description='Accuracy:')\n",
- "interactive(view, acc=acc_widget, weights=w)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Filter pruning sensitivity analysis\n",
- "\n",
- "Just as we perform element-wise pruning sensitivity analysis, we can also analyze a model's filter-wise pruning sensitivity. Although the sparsity levels are reported in percentage steps, the actual pruning level might be somewhat lower, because when we prune filters the minimum granularity of pruning is ```1/numer_of_filters```.\n",
- "\n",
- "\n",
- "We performed a filter-wise pruning sensitivity analysis on ResNet20-Cifar using the following command:\n",
- "```\n",
- "python3 compress_classifier.py -a resnet20_cifar ../../../data.cifar10/ -j 12 --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --sense=filter\n",
- "```\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def view_sparsity(param_name):\n",
- " display(df_filter[df_filter['parameter']==param_name])\n",
- " \n",
- "param_names = sorted(df_filter['parameter'].unique().tolist())\n",
- "param_dropdown = widgets.Dropdown(description='Parameter:', options=param_names)\n",
- "interact(view_sparsity, param_name=param_dropdown);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's look at the sparsity vs. the compute:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_filter = df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "def view_fliters(level, acc):\n",
- " filtered = df_filter[df_filter.sparsity == level]\n",
- " s = filtered.style.apply(highlight_min_max) \n",
- " param_names = filtered['parameter']\n",
- " \n",
- " # Plot the sensitivities\n",
- " x = range(filtered[acc].shape[0])\n",
- " y = filtered[acc].values.tolist()\n",
- " #y2 = [macs_tbl[name]*level**2 * math. for i, name in enumerate(param_names)]\n",
- " y2 = []\n",
- " for i, name in enumerate(param_names):\n",
- " y2.append(compute_reward(macs_tbl[name], level, level, y[i]))\n",
- " \n",
- "\n",
- " fig, ax1 = plt.subplots(figsize=(20,10))\n",
- " ax1.plot(x, y, label=param_names, marker=\"o\", markersize=10, markerfacecolor=\"C1\")\n",
- " ax1.set_ylabel(str(acc))\n",
- " ax1.tick_params(axis=x)\n",
- " ax1.set_xticks(x)\n",
- " ax1.set_xticklabels(param_names, rotation='vertical')\n",
- " ax1.set_title('Filter pruning sensitivity per layer ({}% sparsity)'.format(level*100)) \n",
- " \n",
- " ax2 = ax1.twinx() \n",
- " ax2.plot(x, y2, label=param_names, marker=\"o\", markersize=10, markerfacecolor=\"C2\")\n",
- " ax2.set_ylabel(\"reward\")\n",
- " return s\n",
- "\n",
- "\n",
- "\n",
- "df_filter['sparsity'] = round(df_filter['sparsity'], 2)\n",
- "sparsities = np.sort(get_sensitivity_levels(df_filter))\n",
- "acc_radio = widgets.RadioButtons(options=['top1', 'top5'], value='top1', description='Accuracy:')\n",
- "levels_dropdown = widgets.Dropdown(description='Sparsity:', options=sparsities)\n",
- "interact(view_fliters, level=levels_dropdown, acc=acc_radio);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/automated_deep_compression/amc-results.ipynb b/examples/automated_deep_compression/amc-results.ipynb
index 70d2471..9ff1e16 100644
--- a/examples/automated_deep_compression/amc-results.ipynb
+++ b/examples/automated_deep_compression/amc-results.ipynb
@@ -1,34 +1,154 @@
{
"cells": [
{
+ "attachments": {
+ "amc_figure2.png": {
+ "image/png": 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"
+ }
+ },
"cell_type": "markdown",
"metadata": {},
"source": [
"# AutoML for Model Compression\n",
"\n",
- "This notebook will help us visualize and review the results of the DDPG agent's sub-space search.\n",
- "It contains two visualizations of the process of discovering networks during the exploration and exploitation phases. Each discovered network is projected on a 2D subspace that maps the network's compute complexity (normalized to a percentage of the dense-network's compute budget) against its Top1 accuracy.\n",
+ "This notebook will help visualize and review the results of recreating the results of [AMC](https://arxiv.org/abs/1802.03494).\n",
+ "\n",
+ "\n",
+ "AMC trains a [Deep Deterministic Policy Gradient](https://spinningup.openai.com/en/latest/algorithms/ddpg.html) (DDPG) RL agent to compress DNNs. For the implementation of DDPG I'm using [Coach](https://github.com/NervanaSystems/coach), which is developed and maintained by a team of my colleagues at Intel AI Lab. Coach is arguably the richest open-source RL framework currently available for RL research, with loads of implemented algorithms and supported RL environments. We wrap Distiller in an OpenAI Gym environment API (https://github.com/openai/gym) so that it presents a new environment to Coach. We execute Distiller's sample application ```compress_classifier.py``` with special arguments telling it how to invoke AMC. This creates a ```DistillerWrapperEnvironment``` environment and a DDPG agent and starts the training cycle. For the exact technical details of doing this yourself, see the documentation at the top of ```examples/automated_deep_compression/ADC.py``` (note that in some cases the code uses ADC - [Automated Deep Compression](https://www.researchgate.net/publication/323141651_ADC_Automated_Deep_Compression_and_Acceleration_with_Reinforcement_Learning) - the name of the first version of the AMC paper).\n",
+ "\n",
+ "This notebook only reviews Resource-Constrained Compression. Specifically, I want to recreate the results of the first row of Table 2: using AMC $R_{err}$ on Plain20-CIFAR with a 50% FLOPs constraint. I pasted Table 2 below with the experiment we are recreating highlighted. \n",
+ "\n",
+ "\n",
+ "Plain20 is defined in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and it is essentially ResNet20-CIFAR without the residual connections. You will need to train this network first and you can do that using ```examples/automated_deep_compression/plain20_cifar_baseline_training.yaml```. The trained Plain20 achieves Top1 accuracy of 90.5% on CIFAR-10.\n",
+ "The notebook uses data captured in Distiller AMC trace files.\n",
+ "\n",
+ "## TLDR\n",
+ "\n",
+ "It doesn't work for me yet. The agent seems to learn one action: minimize sparsity (i.e. prune as little as possible). \n",
+ "\n",
+ "Your help is welcome!"
+ ]
+ },
+ {
+ "attachments": {
+ "adc_figure2.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Experiment setup\n",
+ "\n",
+ "Let's let AMC run the full-course: about 635 episodes. We *heat up* for the first 100 episodes. During this phase we don't train: the **Actor** emits actions, to which we add a strong constant noise and fill the Replay Buffer (the AMC paper refers to this phase as the *Exploration Phase*).\n",
+ "After completing the **heatup phase** (100 episodes) we start the learning phase (in AMC parlance this is the *Exploitation Phase*) in which we use an exponentially-decaying noise. \n",
+ "\n",
+ "How should the learning process look like (e.g. the reward curve)? Unfortunately, the AMC paper provides very little information about this. In the ADC paper we find Diagram 2 which provides some clues (why was this removed from AMC?). Remember that RL learning is stochastic and two executions of the same experiment can look quite different: one succeeding and another failing - so we need to run our experiments several time. I assume that ADC-Diagram 2 is a best-case scenario.\n",
+ "\n",
+ "\n",
+ "\n",
+ "### DDPG configuration\n",
+ "Actor network: \n",
+ " - Two hidden layers, each with 300 units. \n",
+ " - Final output layer: sigmoid layer to bound the actions within (0; 1). \n",
+ "Critic network (Q):\n",
+ " - Two hidden layers, each with 300 units. \n",
+ " - Actions were included in the second hidden layer. \n",
+ " - 0.01 for the soft target updates. \n",
+ "Training:\n",
+ " - Batch size = 64\n",
+ " - Replay buffer size = 2000. \n",
+ " - Exploration noise: truncated normal distribution. Constant 0.5 during exploration; exponential decay during exploitation\n",
+ "\n",
+ "### Distiller AMC experiments\n",
+ "Let's look at three executions of 50% FLOPs-Constrained Compression of Plain20:\n",
+ "```\n",
+ "$ python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --resume=plain20.checkpoint.pth.tar --lr=0.05 --amc --amc-action-range 0.05 0.80 --vs=0.8\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Notebook code\n",
"\n",
- "The Top1 value is either the Test dataset Top1 measured without any fine-tuning, or after one epoch of fine-tuning (this depends on how the AMC algorithm is configured)."
+ "Skip this part - it is necessary only for creating the diagrams. You may also toggle the code-view button."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<script>\n",
+ "code_show=true; \n",
+ "function code_toggle() {\n",
+ " if (code_show){\n",
+ " $('div.input').hide();\n",
+ " } else {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ "} \n",
+ "$( document ).ready(code_toggle);\n",
+ "</script>\n",
+ "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "\n",
+ "HTML('''<script>\n",
+ "code_show=true; \n",
+ "function code_toggle() {\n",
+ " if (code_show){\n",
+ " $('div.input').hide();\n",
+ " } else {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ "} \n",
+ "$( document ).ready(code_toggle);\n",
+ "</script>\n",
+ "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
+ "import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"import matplotlib \n",
"import csv\n",
"from matplotlib.ticker import FuncFormatter\n",
- "#from matplotlib.animation import FuncAnimation\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
"\n",
"\n",
- "import matplotlib.pylab as pylab\n",
+ "\n",
+ "#plt.style.use('seaborn') # pretty matplotlib plots\n",
+ "\n",
"params = {'legend.fontsize': 'x-large',\n",
" 'figure.figsize': (15, 7),\n",
" 'axes.labelsize': 'x-large',\n",
@@ -49,66 +169,491 @@
" if matplotlib.rcParams['text.usetex'] is True:\n",
" return s + r'$\\%$'\n",
" else:\n",
- " return s + '%'"
+ " return s + '%'\n",
+ " \n",
+ "# Widen the cells to get entire rows in the screen.\n",
+ "from IPython.core.display import display, HTML\n",
+ "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
+ "\n",
+ "\n",
+ "def plot_layer_densities(df, idx, action_type='action_history', ax=None, color=None):\n",
+ " if ax is None:\n",
+ " plt.figure()\n",
+ " ax = plt\n",
+ " \n",
+ " record = df.iloc[idx]\n",
+ " layer_sparsities = record[action_type]\n",
+ " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
+ " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
+ " ax.bar(range(len(layer_densities)), layer_densities, color=color)\n",
+ " ax.set_title(\"Ep:{} - Top1:{:.1f}%\\nMACs:{:.1f}%\".format(record['episode'], \n",
+ " record['top1'], \n",
+ " record['normalized_macs']))\n",
+ " \n",
+ " \n",
+ "def smooth(data, win_size):\n",
+ " win_size = max(1, win_size)\n",
+ " return [np.mean(data[max(0, i-win_size):i]) for i in range(len(data))]\n",
+ "\n",
+ "\n",
+ "def plot_performance(alpha, window_size, top1, macs, params, reward):\n",
+ " plot_kwargs = {\"figsize\":(15,7), \"lw\": 1, \"alpha\": alpha, \"title\": \"Performance Data\"}\n",
+ " smooth_kwargs = {\"lw\": 2, \"legend\": True}\n",
+ " if top1:\n",
+ " ax = df['top1'].plot(**plot_kwargs, color=\"b\", grid=True)\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " df['smooth_top1'] = smooth(df['top1'], window_size)\n",
+ " df['smooth_top1'].plot(**smooth_kwargs, color=\"b\")\n",
+ " if macs:\n",
+ " ax = df['normalized_macs'].plot(**plot_kwargs, color=\"r\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)\n",
+ " df['smooth_normalized_macs'].plot(**smooth_kwargs, color=\"r\")\n",
+ " if params:\n",
+ " ax = df['normalized_nnz'].plot(**plot_kwargs, color=\"black\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)\n",
+ " df['smooth_normalized_nnz'].plot(**smooth_kwargs, color=\"black\") \n",
+ " if reward:\n",
+ " ax = df['reward'].plot(**plot_kwargs, secondary_y=True, color=\"g\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"reward\")\n",
+ " df['smooth_reward'] = smooth(df['reward'], window_size)\n",
+ " df['smooth_reward'].plot(**smooth_kwargs, secondary_y=True, color=\"g\") \n",
+ " ax.grid(True, which='minor', axis='x', alpha=0.3)\n",
+ " \n",
+ " \n",
+ "def plot_2d_embeddings(top1, normalized_macs):\n",
+ " plt.figure(figsize=(15,7)) \n",
+ " plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
+ " plt.xlabel('Normalized MACs')\n",
+ " plt.ylabel('Top1 Accuracy')\n",
+ "\n",
+ " # Create the formatter using the function to_percent. This multiplies all the\n",
+ " # default labels by 100, making them all percentages\n",
+ " formatter = FuncFormatter(to_percent)\n",
+ "\n",
+ " # Set the formatter\n",
+ " plt.gca().yaxis.set_major_formatter(formatter)\n",
+ " plt.gca().xaxis.set_major_formatter(formatter)\n",
+ "\n",
+ " # Use color gradients to show the \"age\" of the network:\n",
+ " # Lighter networks were discovered earlier than darker ones.\n",
+ " color_grad = [str(1-i/len(top1)) for i in range(len(top1))]\n",
+ " plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');\n",
+ "\n",
+ " \n",
+ "INTERVAL = 100 # Animation speed\n",
+ "WINDOW = 20\n",
+ "\n",
+ "font = {'family': 'serif',\n",
+ " 'color': 'darkred',\n",
+ " 'weight': 'normal',\n",
+ " 'alpha': 0.50,\n",
+ " 'size': 32,\n",
+ " }\n",
+ "\n",
+ "# Based on these two helpful example code: \n",
+ "# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot\n",
+ "# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.\n",
+ "# Specifically, the use of IPython.display is missing from the first example, but most of the animation code\n",
+ "# leverages code from there.\n",
+ "class AnimatedScatter(object):\n",
+ " \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation.\"\"\"\n",
+ " def __init__(self, xdata, ydata):\n",
+ " assert len(xdata) == len(ydata)\n",
+ " self.numpoints = len(xdata)\n",
+ " self.xdata = xdata\n",
+ " self.ydata = ydata\n",
+ " self.stream = self.data_stream()\n",
+ "\n",
+ " # Setup the figure and axes...\n",
+ " self.fig, self.ax = plt.subplots(figsize=(15,7))\n",
+ " # Then setup FuncAnimation.\n",
+ " self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,\n",
+ " frames=self.numpoints-2, \n",
+ " init_func=self.setup_plot, blit=True)\n",
+ "\n",
+ " def setup_plot(self):\n",
+ " \"\"\"Initialize drawing of the scatter plot.\"\"\"\n",
+ " x, y, s, c = next(self.stream)\n",
+ " #self.annot = self.ax.annotate(\"txt\", (10, 10))\n",
+ " self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)\n",
+ " self.scat.set_edgecolors('gray')\n",
+ " self.scat.set_cmap('gray')\n",
+ " self.width = max(self.xdata) - min(self.xdata) + 4\n",
+ " self.height = max(self.ydata) - min(self.ydata) + 4\n",
+ " self.ax.axis([min(self.xdata)-2, max(self.xdata)+2, \n",
+ " min(self.ydata)-2, max(self.ydata)+2])\n",
+ " \n",
+ " self.annot = self.ax.text(min(self.xdata) + self.width/2, \n",
+ " min(self.xdata) + self.height/2, \n",
+ " \"\", fontdict=font)\n",
+ " # For FuncAnimation's sake, we need to return the artist we'll be using\n",
+ " # Note that it expects a sequence of artists, thus the trailing comma.\n",
+ " return self.scat, \n",
+ "\n",
+ " def data_stream(self):\n",
+ " numpoints = 0#len(self.xdata)\n",
+ " colors = []\n",
+ " xxx = 0\n",
+ " while True:\n",
+ " numpoints += 1\n",
+ " win_len = min(WINDOW, numpoints)\n",
+ " data = np.ndarray((4, win_len))\n",
+ " start = max(0,numpoints-WINDOW-1)\n",
+ " data[0, :] = self.xdata[start:start+win_len]\n",
+ " data[1, :] = self.ydata[start:start+win_len]\n",
+ " data[2, :] = [70] * win_len # point size\n",
+ " #data[3, :] = [np.random.random() for p in range(numpoints)] # color\n",
+ " # The color of the points is a gradient with larger values for \"younger\" points.\n",
+ " # At each new frame we show one more point, and \"age\" each existing point by incrementaly \n",
+ " # reducing its color gradient.\n",
+ " data[3, :] = [(1-i/(win_len+1)) for i in range(win_len)] \n",
+ " yield data\n",
+ "\n",
+ " def update(self, i): \n",
+ " \"\"\"Update the scatter plot.\"\"\"\n",
+ " data = next(self.stream)\n",
+ " self.annot.set_text(i)\n",
+ " i = i % len(data)\n",
+ " \n",
+ " # Set x and y data\n",
+ " xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]\n",
+ " self.scat.set_offsets(xy)\n",
+ " \n",
+ " # Set colors\n",
+ " self.scat.set_array(data[3])\n",
+ " \n",
+ " # We need to return the updated artist for FuncAnimation to draw..\n",
+ " # Note that it expects a sequence of artists, thus the trailing comma.\n",
+ " return self.scat, self.annot\n",
+ "\n",
+ " def show(self):\n",
+ " plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Static diagram"
+ "## Results\n",
+ "\n",
+ "Below I present the results of a single execution. There is a substantial variance between the experiment executions, but most conclude similarly to this experiment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read the results log files\n",
+ "\n",
+ "The code below reads the log file of your selected experiment. To change the path to the file you will need to open the code cell and change its content."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.set_option('display.max_colwidth', 150)\n",
+ "\n",
+ "fname = \"sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv\"\n",
+ "#fname = \"sample_logs/coach_rl/AMC-Plain20___2018.12.29-113105/amc.csv\"\n",
+ "\n",
+ "df = pd.read_csv(fname)\n",
+ "#print(df.loc[df['reward'].idxmax()])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot experiment performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x504 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "50d9908acbe8456bb31c65411b0821c2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/html": [
+ "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+ "<p>\n",
+ " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+ " that the widgets JavaScript is still loading. If this message persists, it\n",
+ " likely means that the widgets JavaScript library is either not installed or\n",
+ " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+ " Widgets Documentation</a> for setup instructions.\n",
+ "</p>\n",
+ "<p>\n",
+ " If you're reading this message in another frontend (for example, a static\n",
+ " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+ " it may mean that your frontend doesn't currently support widgets.\n",
+ "</p>\n"
+ ],
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), Checkbox(value=True, description='top1'), Checkbox(value=True, description='macs'), Checkbox(value=False, description='params'), Checkbox(value=True, description='reward'), Output()), _dom_classes=('widget-interact',))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "top1 = []\n",
- "macs = []\n",
- "normalized_macs = []\n",
- "with open('../classifier_compression/amc.csv', 'r') as csv_file:\n",
- " csv_reader = csv.reader(csv_file, delimiter=',')\n",
- " is_header = True\n",
- " for row in csv_reader:\n",
- " if not is_header:\n",
- " top1.append(float(row[0]))\n",
- " macs.append(float(row[2]))\n",
- " normalized_macs.append(float(row[3]))\n",
- " else:\n",
- " is_header = False\n",
- " \n",
- "plt.figure(figsize=(15,7)) \n",
- "plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
- "plt.xlabel('Normalized MACs')\n",
- "plt.ylabel('Top1 Accuracy')\n",
+ "plt.figure(figsize=(15,7))\n",
+ "#print(plt.style.available)\n",
+ "#plt.style.use('bmh')\n",
+ "\n",
+ "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
+ "def plot_performance_proxy(window_size=10, top1=True, macs=True, params=False, reward=True):\n",
+ " plot_performance(0.15, window_size, top1, macs, params, reward)\n",
+ " \n",
+ "#plot_performance(0.15, window_size=10, top1=True, macs=True, params=False, reward=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What do we see?\n",
+ " - The networks stay within the alloted 50% MACs compute budget.\n",
+ " - During the exploration phase (heatup) there's quite a lot of noise and we see some promising Top1 accuracy results, and as the noise is reduced and the network begins learning we see more signs of hope, but alas, the Reward and Top1 drop and never recover.\n",
+ " - What happened? The agent learns very quickly to maximize the compute budget (50% MACs) and stops producing smaller networks. But why can't it reach the high Reward values it encountered during the exploration?? There are some clues below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sample some networks\n",
"\n",
- "# Create the formatter using the function to_percent. This multiplies all the\n",
- "# default labels by 100, making them all percentages\n",
- "formatter = FuncFormatter(to_percent)\n",
+ "Let's look at the networks with the best top1 accuracy, and see if they share geometrical attributes.\n",
+ "\n",
+ "We sort the discovered networks by their Top1 accuracy and display the density of each layer in the networks. We can see that the layer densities are distributed differently in each of the networks. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1_sorted_df = df.sort_values(by=['top1'], ascending=False)\n",
+ "nrows = 2; ncols = 4\n",
+ "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
+ "for i in range(0, nrows * ncols):\n",
+ " plot_layer_densities(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='g')\n",
+ " # Fine-tune figure; make subplots farther from each other.\n",
+ " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
"\n",
- "# Set the formatter\n",
- "plt.gca().yaxis.set_major_formatter(formatter)\n",
- "plt.gca().xaxis.set_major_formatter(formatter)\n",
+ "#pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's sample networks from episodes 251 through 274 - and notice what happens from about episode 126: the first layers are very dense (~0%-10% sparse) and the following layers have higher sparsities.\n",
"\n",
- "# Use color gradients to show the \"age\" of the network:\n",
- "# Lighter networks were discovered earlier than darker ones.\n",
- "color_grad = [str(1-i/len(top1)) for i in range(len(top1))]\n",
- "plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');\n",
+ "What's going on? Ironically, the agent learns to minimize pruning actions (minimize sparsity) so it generates dense/wide layers until the environment has to step-in and increase the actions so that the final network's compute complexity remains within the 50% MACs budget. Clearly, these networks (dense/wide early layers followed by sparse/shallow layers) have low Top1 accuracies, but it's too late: the Agent has learned one action - very low sparsity. And so this goes on forever.\n",
"\n",
- "#plt.hlines(90, 1.5*10**8, 2.5*10**8, color='b')\n"
+ "Why did the Agent learn this? It's as if there is only one state to which the Agent learned one action.\n",
+ "Either the agent is confused by the observations and all states look the same, or all actions are generated by the same process."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1080 with 24 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Display networks in chronological order (of time of discovery)\n",
+ "nrows = 6; ncols = 4\n",
+ "first_network = 185 # 115\n",
+ "f, axarr = plt.subplots(nrows, ncols, figsize=(15,15))\n",
+ "for i in range(0, nrows * ncols):\n",
+ " plot_layer_densities(df, first_network + i, ax=axarr[i//ncols, i%ncols])\n",
+ " # Fine-tune figure; make subplots farther from each other.\n",
+ "f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
+ "f.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And here's the **smoking gun**: these are the actions that the DDPG agent generated, before they were constrained by the AMC algorithm (i.e. function ```DistillerWrapperEnvironment.get_action```). Here we can clearly see that the Agent learned to emit low-sparsity actions.\n",
+ "\n",
+ "Using the interactive GUI you can play back the action trajectories that the Actor emitted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "29e0b8b8a8b74f82abe960f6b59b63e4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/html": [
+ "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+ "<p>\n",
+ " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+ " that the widgets JavaScript is still loading. If this message persists, it\n",
+ " likely means that the widgets JavaScript library is either not installed or\n",
+ " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+ " Widgets Documentation</a> for setup instructions.\n",
+ "</p>\n",
+ "<p>\n",
+ " If you're reading this message in another frontend (for example, a static\n",
+ " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+ " it may mean that your frontend doesn't currently support widgets.\n",
+ "</p>\n"
+ ],
+ "text/plain": [
+ "interactive(children=(IntSlider(value=0, description='episode', max=459), Output()), _dom_classes=('widget-interact',))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "@interact(episode=(0,len(df.index)-1,1))\n",
+ "def display_ddpg_actions(episode=0):\n",
+ " f = plt.figure(figsize=(15,7))\n",
+ " ax1 = f.add_subplot(111)\n",
+ " ax2 = ax1.twiny()\n",
+ " ax2.set_xlim((1,len(df.index)))\n",
+ " record = df.iloc[episode]\n",
+ " layer_sparsities = record[\"agent_action_history\"]\n",
+ " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
+ " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
+ " ax1.bar(range(len(layer_densities)), layer_densities, color=\"gray\")\n",
+ " ax2.plot(df['top1']/100)\n",
+ " plt.title(\"DDPG Pruning Action History\\nEpisode:{} - Top1:{:.1f}% - MACs:{:.1f}%\\n\\n\\n\".format(\n",
+ " record['episode'], record['top1'], record['normalized_macs']))\n",
+ " ax1.set_xlabel(\"Layer\")\n",
+ " ax2.set_xlabel(\"Episode\")\n",
+ " plt.ylim([0,1])\n",
+ " plt.ylabel(\"Depth/Density (%)\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Network 2D embeddings\n",
+ "\n",
+ "Let's create an embedding of the networks AMC discovers over the course of each experiment session. Each network is projected onto a 2D plane mapping the Top1 accuracy versus the compute budget, and is represented by a small circle. I used gradient-color-coding to show the relative phase where each network is discovered. Lighter circles are networks discovered early in the search, darker networks are discovered later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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s2Xi8rnmfAJ5zzr1qZnvwEsnteK09u4ZoNTqe2I/WF/GSrofM7Ea8lqUP4C018tfOuePtgrfCzGJ4CWYl3iQu78Ybw/jpYbpQlwP7zeyXwCa8CWLOxksYHs5oFfwW3v26ycwuwPtAXYzXkvp74EbnXLOZ/QPeMiF/NLOf43Xx/HO8MaV/l+X8P8ObuOeLePfxpcxC51y9v2THfwLPmtkdeK2l03h9OZCLh3uBnHNrzezbeEu9zDWzX+NNoHQaXgvo83hjAsEbY7oabx3MG/3Yr2XocbHDcs7FzOwrZO9uejTXuR4vqfs7M6vES56edt4SOeAllOnxm3/IOM0f8L7kSeL9PmUa8feocy5l3nqwYeD7ZtbvnPsxMBvvtb0Lb8xoF9776DK8pZCG879478/z8BM7//d1zeCK/usDsDbz98A51+i/V7/px3IHXrL5GbxW3X/PqLvRzH4AfM7/Ymod3vjk9wH/PGiSJPy4yvFmDRaRU1E+p8rVQw899DhRHxzBUhsZdXfjL/2Qo3wFXitIJ163xCeAy7PUO2w5E7wk6dN43dN6/GNswftQPn9Q3TfgtZq8htfyshvvA2pNRp0L8D6Qx/zz3epvv5ZBS6YcQ+w/ybL9VrwJVY7kNZ+H92G51Y9vI3BNjmMe7ZIp6UcCL9l+Cm982xuy7DODjCVT8BLTr/v3oBUvgdmGt1Zn+aB9y/HWU3wFr4XnIPBrMpaS8etd419fzD/m/zJoaZSMumV4rVRDLqGBl5A8iJcoxvASg9uB1Rl10vf50iGO80G8RKXTP+8OvO6cKwbVOwevRS2Gt4TO1/ESjaNeMmXQ9givLzmS7T017HX69T6FN2FSf+b99MvO8rftHbTPan/7M8f5Hs16bbnev3hf+t/tx/pBvHGQN+AtRdKBl3S+gLd26rBLjOD93diD90XHcHWHfE/45Zv8623045+UpV4Yr7V0N97fn214CaplqXsjXvfc4JH8bdBDDz1Ovkd6HTEREckjv/taP/APzrl/LXQ8IjK2mdn1eF+MnOa8pY5OCGZWgTfT9ZectwSLiJyCNKZTRKQwJvs/NZOjiIyE7+L9PRk8zrLQrsdrKb6p0IGISOGopVNEJM/M7EN4XereCsxzzm0vcEgiIiIio0YTCYmI5N8NeJPTfEQJp4iIiJzs1NIpIiIiIiIio0YtnceotrbWzZgxo9BhiIiIiIiIFMSGDRuanHN1w9VT0nmMZsyYwfr16wsdhoiIiIiISEGYWf2R1NPstSIiIiIiIjJqlHSKiIiIiIjIqFHSKSIiIiIiIqNGSaeIiIiIiIiMGiWdIiIiIiIiMmqUdIqIiIiIiMioUdIpIiIiIiIio0ZJp4iIiIiIiIwaJZ0iIiIiIiIyapR0ioiIiIiIyKgJFToAEREREREReZ1zjgMHDtDe3k5JSQnTpk0jEBi77YVKOkVERERERE4QO3fu5N5776Wnp4dwOEwymSQQCHDppZdy1llnFTq8Y6KkU0RERERE5ASwY8cO7rzzTqLRKGVlZZgZAIlEggceeIB4PM7y5csLHOXRG7tttCIiIiIiIicJ5xy//vWviUajRCKRgYQTIBwOU1JSwkMPPUQ8Hi9glMdGSaeIiIiIiEiB1dfXk0gkCIfDWcuDwSDBYJDNmzfnObLjp6RTRERERESkwFpbW3HOHdLCOVggEODAgQN5jGpk5C3pNLPdZuayPF7MqLPczNaaWczMDpjZV80seATHvtbMtplZ3My2mtmHBpVfYmZbzKzTzNaYWeWg8rvM7Asjd7UiIiIiIiJHLpVKkUwmh63T0tKSp4hGTj5bOt8ITMp4nAH0Ar8AMLNpwO+AbcBS4FPAnwJfGeqgZvZO4BbgO8Bi4PvAbWb2Vr884J/jh8AyoBb4Ysb+7wFOA745MpcpIiIiIiJydMrLy0kmkzkTT+fcmBzPCXmcvdY515j53Mw+DoTxkkTwkswO4DrnXAp40cymAN8wsy8757pzHPpvgNudc//uP99qZsuBLwD3AzVAHXCDcy5mZj8F3u7HUA18G7jcOTf01woiIiIiIiKjpKSkhHA4TFdXF+Xl5Yesy+mco6uri6KiIsrLywsY5bEp5JjOPwV+7ZxLd0peCTzoJ5xpDwAlwNnZDmBmEbwW1AcGFT0ArPC75jYBDcBbzSwMvBnY5Ne7Afi+c27sjcYVEREREZGTxuTJkwkGg5SUlNDW1kZXVxe9vb10d3fT2tpKJBIhFApx5plnFjrUo1aQpNPMluF1of1uxuZJwMFBVQ9mlGVTi9dam22/IqDaOeeA9+K1fL4MdAFfNbO3AWcCN5rZrWb2ipndY2YThoj7E2a23szWNzY25qomIiIiIiJyVAKBAOeffz7JZJKZM2dSXl4+sFTKzJkzKSkpoaioiNNPP73QoR61QrV0/imwC3gwHydzzq11zq1wzp3mnLsG77pvBP4E+Fu8br5n4I0nvWGI49zsnFvmnFtWV1eXj9BFREREROQUsWLFChYsWMC+ffswMyoqKigqKqKxsZFEIsE111xzSLfbsSJvYzrTzKwC+CDwr34rZNoBYOKg6hMyyrJpAvpz7BcHck3t9C3gZ865Z83s+8A/OOeSZnYb8OiRXYmIiIiIiMjIMTNWr17NkiVLWLduHQcPHiQajXLeeecxd+5cgsFhF/Y4IeU96QQ+DETwZpPN9ARwtZkFMsZ1rgZ6gI3ZDuSc6zOzZ4DLgNsyilYDT2WbHMjMLgVW4HXvBa/VM70CawStXSoiIiIiIgWSTCbZunUrW7ZsIZFI4JyjpaWFvr4+zjrrrCHX8TxRFSLB+lNgjXPu1UHbbwLGAd8zswVmdgXwZeC/0jPXmtkUfx3OKzP2+wbwfjP7rJnNMbPPAe8Cvj74xGZWhre0ynXOufR8w38ErjezOcDnUEuniIiIiIgUQDKZ5Mc//jFPP/000WiUmpoaampqAHjwwQe59957ObSz6NiQ16TTzFYAizh0AiEAnHN7gbcA84ANwM3+44sZ1cLAHLzkNL3fGuBjwKeBF/CS2mudc/dnCeFreAnv0xnb/hlvvdD1wHTgM8d4eSIiIiIiIsckmUzy4IMP8uqrrw5MIgRel9uioiIqKirYvHkzu3btKnCkR8/GYqZ8Ili2bJlbv359ocMQEREREZExLJVK8dhjj/Hkk0+SSCSorKwkEolkrdvT08P48eP50Ic+lOcoszOzDc65ZcPV0/hFERERERGRAnDOsWbNGtavX09tbS2pVGqghTObSCTC/v378xjhyCjEREIiIiIiIiKnvD179rBjxw4mTpx4RBMEOefo7+/PQ2QjSy2dIiIiIiIiBbBu3TpKS0sJBAKYGcXFxcRisZz1Y7HYmFw2RUmniIiIiIhIATQ1NVFUVDTwvLKykq6uLpLJw1Z+JJFI0NvbS0lJST5DHBHqXisiIiIiIlIAkUiERCIx8LykpIRx48bR3NxMNBodSEhjsRixWIyysjIWL15cqHCPmVo6RURERERECmDx4sX09vYesq2yspLJkycTCATo6Oigra2N0tJSJk2aRF9fH0uWLClQtMdOSaeIiIiIiEgBLFq0iFQqRWdn5yHbI5EI48aNwzlHbW0tyWSS5uZmrrrqKsrKygoU7bFT91oREREREZECiEQiXHvttdx222309vZSXFyMmdHX10dnZyfjxo0jGo0yd+5czjrrLIqLiwsd8jFR0ikiIiIiIjLCUqkUiUSCSCQy5HIoNTU1fOYzn2Hbtm289NJL9Pf3M2XKFJYsWUJJSckRLaVyolPSKSIiIiIiMkJaWlp47LHHePHFF3HOEQgEWLRoEatWrWLcuHFZ9wkGg8yfP5/58+fT19fHunXruPnmm+no6CAcDjN//nxWrVpFbW1tnq9mZJhzrtAxjEnLli1z69evL3QYIiIiIiJygjhw4AC33XYbNTU11NXVEYlEiMfjNDY20tbWxkc/+tEhE8dYLMYtt9xCPB6ntLSUSCRCMpmku7ubnp4ePvCBDzBz5sw8XtHQzGyDc27ZcPU0kZCIiIiIiMhxcs5xxx13MGXKFKZMmUIkEgGgqKiIqVOnMn78eO66664hj3HvvfeSSCSoqqqiqKgIMyMUCjFu3Diqqqq4/fbbD1liZaxQ0ikiIiIiInKcdu3aRTKZpLq6Omt5XV0dnZ2dNDQ0ZC3v6elh69atlJeXZx3HWVxcTCQS4YUXXhjRuPNBSaeIiIiIiMhx2r9/P2VlZTkn/jEzKioq2LdvX9byJ554gkAgQDAYHNjmnCNzOGQ4HGbXrl0jG3geaCIhERERERGR43Qks8ymJxYarKGhgWeeeYZgMIhzjp6eHtrb2we60kYiESoqKnDOEYvFRjz20aaWThERERERkeN0+umn09bWRq6JWlOpFK2trcyaNeuwsrVr1zJp0iRisRjNzc20trZSVFREVVUVVVVVRCIRWltb6ejooLu7e7QvZcQp6RQRERERETlOkyZNorq6mgMHDhxW5pxj//79TJs2LeuYzx07djB+/HgCgQC9vb1UVFQMrO9pZkQiEcrLy3HOcfDgwZyJ7YlKSaeIiIiIiMgIeP/73093dzc7d+6kvb2dWCxGW1sbO3fupL+/n3e9611Z90ulUgNda6PRaNauuoFAgGg0etg4z7FAYzpFRERERERGQHl5OZ/85Cd57rnnePbZZ+np6aGsrIyVK1eycOFCwuFw1v3q6upob2/HOTew1Eo26f2zjQs9kSnpFBERERERGSGRSIRzzjmHc84554j3WbFiBQ8//DDgdcUdalKizNltx4qxlSKLiIiIiIicZBYuXMjEiRMxM/r6+nLWi8fjVFZW5jGykaGkU0REREREpIDMjMsuuwwzIxaLkUqlDquTTCbH5HIpoO61IiIiIiIiR8U5R319Pe3t7ZSUlDBr1qwR6fYaDocJh8N0dHRQVFQ0ML6zr6+PeDxONBolFBp7KdzYi1hERERERKRAtm7dyn333UcqlSIcDpNMJkkkElx88cVHNY5zsIqKCsyM4uJiIpEIPT09dHZ2At440aqqKvr6+pg2bdpIXUreKOkUERERERE5Alu2bGHNmjXU1NRQXFw8MOFPX18ff/jDH0gkEqxcufKYjh0MBlmyZAmbNm2irKzssLGb6e61y5cvP+7ryDeN6RQRERERERlGKpXivvvuo6am5rC1NCORCHV1dTz66KP09vYe8zkuuugiKioq6OrqIpFIAF5X3t7eXjo6Orjooouora097mvJNyWdIiIiIiIiw9i9ezfOOaLRaNbyUChESUkJmzdvPuZzhMNhPvrRjzJ79mxaW1s5ePAgr776Kp2dnUSjUSZNmnTMxy4kJZ0iIiIiIiLDaG9vJxwOD1knEAjQ1tZ2XOfZu3cvL774IuXl5UyYMIGJEycyYcIEzIxf/OIXbNu27biOXwhKOkVERERERIYRjUazLmWSyTlHSUnJMZ8jlUrxy1/+ktLS0oEEtrm5mZaWFsyMsrIy7rnnHpLJ5DGfoxCUdIqIiIiIiAzj9NNPJxaLDYy1HCyVStHd3c2CBQuO+RyvvPIKyWSSrq4uOjo6CAQCFBUVEQgEaG9vp7OzEzNj+/btx3yOQlDSKSIiIiIiMoxwOMz5559Pc3Mz/f39h5SlUimam5uZP3/+YbPOHo2mpiYSicRAq2ZRURGhUIiioiLKysowM/r6+mhqajrey8krLZkiIiIiIiJyBFauXEkikWDt2rWUlpYSDAYHWjjnzZvHO97xjuM6fiKRIJlMUlpaesjsuABmRjQapaOj47Ck90SnpFNEREREROQImBkXX3wx55xzDs8//zxtbW2UlpaycOFCqqqqjvv4zjlCodBhCWfm+UOhEIHA2OqwqqRTREREREQKqr6+nscff5y9e/cCMH36dFauXMlpp51W4MiyKy0t5dxzzx3x4x5JQmlmw86ie6JR0ikiIiIiIgXz6KOPsnbtWsyMYDAIwJ49e6ivr2flypVccMEFBY4wfyZMmEAymcQ5l7W10zlHf38/zrkCRHfsxla7rIiIiIiInDReeeUV1q5dSygUIhwOEwgECAQChMNhQqEQTzzxBLt27Sp0mHkzZcoU+vv7c47ZTG9PtwiPFUo6RURERESkIB5//HHMLGu9kvx0AAAgAElEQVSX0kAggJnx+OOPFyCywuju7iYSidDb20s8Hh9o0XTOEY/H6e3tpba2lra2tgJHenTUvVZERERERApi7969FBUV5SwPhULs2bMnjxEdv7a2Nl588UW6u7upqqrizDPPJBqNHtG+4XCY/v5+pkyZQnNz88BanalUimg0yrRp04jH4xQXF4/yVYwsJZ0iIiIiIlIQY21s4lCSySS/+tWveOmllwYm+jEzHnzwQS666CJWrlw57DGef/55gsEgiUSCqVOnkkwmSSaTBINBgsEgzjmam5s577zzRvtyRpS614qIiIiISEFMnjx5yDUn+/v7mTx5ch4jOnb33HMPO3bsoLy8nGg0SjQapbi4mNLSUh577DHWrVs35P7OOdatW8e0adNoamoiFosRDAaJRCKHJJyJRIJ58+bl6apGhpJOEREREREpiFWrVgHZWzydczjnBuqcyFpaWti6dSvFxcWHzTobDAYpKirikUceIZlM5jxGT08P8XicCRMmMGvWLPbv309DQwMtLS00NTVRX19Pb28vJSUlhEJjq8Oqkk4RERERESmIM844g8WLF5NIJEgkEgOJZvr5kiVLOOOMMwod5rBeeOEFQqFQ1mVO4PX1N4eaiTc9dtM5R01NDUuXLmXChAmEQiGKi4uZPXs2s2bNGnMJJ2hMp4iIiIiIFIiZsXr1ambOnMnjjz9OQ0MD4HW7Pf/885kzZ06BIzwy3d3dw9YxM3p6enKWR6NRampqaGtro6qqimAwyPjx4w+pU19fz+zZs4873nxT0ikiIiIiIgVjZsydO5e5c+cOdLPN1WJ4oqqsrBwyZuccyWSSioqKIY+zatUqfvvb31JWVjYwGVFaZ2cnzc3NvO997xuRmPNJSaeIiIiIiJwQxlqymbZo0SIeeeQRIpFI1jVH+/v7CYVCnHbaaUMeZ8GCBezbt49nn32W4uJiioqKKC8vJxaL0dLSwrvf/W7GjRs3WpcxajSmU0RERERE5DiUlZVx7rnnEovFDpssKJFIEI/Hefvb3z5sUr1161Y2btxIKBQilUrR09PDnj17aG9v58Mf/vCYGN+aTV6TTjOrNbObzKzBzOJmtsvMPj6oznIzW2tmMTM7YGZfNbPgERz7WjPb5h93q5l9aFD5JWa2xcw6zWyNmVUOKr/LzL4wMlcqIiIiIiKnkosvvpiVK1fS29tLLBYjHo/T29tLKpXiyiuvHHZ8an19PWvWrKGkpISysrKBls6KigrMjDvvvHPI5WVOZHnrXmtmZcAfgf3AB4F6YBIQzKgzDfgdcDfwceAM4AeAAX87xLHfCdwCfB64H3g7cJuZtTjn7jezAPAL4JvAPX7dLwJ/7e//HuA04P0jd8UiIiIiInKqMDNWrVrF8uXL2blzJ729vYwbN44ZM2Zk7XI72EMPPURRURGBQID29nb6+vowM5xzA+t0/v73v2f16tV5uJqRlc8xnX8NlABvd87F/W27B9X5FNABXOecSwEvmtkU4Btm9mXnXK5pof4GuN059+/+861mthz4Al4SWgPUATc452Jm9lO8xBQzqwa+DVzunMu9cI6IiIiIiMgwwuEwc+fOPap9enp6aGhooKKigtbWVsBbQiWddDrnSKVSrFu3jhUrVlBZWTnMEU8s+exe+27gceDf/W6zW83s38ysJKPOSuBBP+FMewAvWT0720HNLAK80a+X6QFghd81twloAN5qZmHgzcAmv94NwPedc5uP7/JERERERESOXiwWIxQK0dXVBbyecILXgmpmBAIBnHOsXbu2kKEek3wmnacD7wEqgHfgtU6+H/heRp1JwMFB+x3MKMumFq/FNtt+RUC18+Zefi9ey+fLQBfwVTN7G3AmcKOZ3Wpmr5jZPWY2IduJzOwTZrbezNY3NjYOe8EiIiIiIiLDKSsro7+/n0QikXOyoXTyuXnz2Gsry2fSGQBagI8659Y7534FfA64yu/iOqqcc2udcyucc6c5567x47kR+BO88aJhvDGk2/BaP7Md42bn3DLn3LK6urrRDllERERERE4BkUiEiRMnDiSWQxmLkwnlM+k8AGx3ziUytr3o/zwto87EQftNyCjLpgnoz7FfHC/RzeZbwM+cc88Cl/r/TgK3+c9FREREROQk1NrayrZt23jllVdIJBLD75AHy5cvx+ugObSysrI8RDOy8jmR0GPAxWYWcs6l0/P0vMG7/Z9PAFebWSBjXOdqoAfYmO2gzrk+M3sGuAwvYSRjv6eyTQ5kZpcCK4Cl/qYAXksnQAStXyoiIiIictJpbm7mV7/6FQcPHqS0tJRkMkk8Huecc87hoosuOqJZZkfLvHnzBmapzdbamZ5QaMWKFQWI7vjk81X9Jt4Msv9jZnPN7GJ/223OuVa/zk3AOOB7ZrbAzK4Avgz8V3rmWjOb4k9CdGXGsb8BvN/MPmtmc8zsc8C7gK8PDsJfuuU7eDPkpmfR/SNwvZnNwevy++gIX7uIiIiIiBRQa2srt9xyC319fcyaNYtJkyYxdepUpk2bxqZNm1izZs0RtTSOllAoxMqVKweSy8Gcc0SjUd74xjcWILrjk7ek0zm3Cbgcbxba54AfAr/EWyYlXWcv8BZgHrABuNl/fDHjUGG8FtJxGfutAT4GfBp4AfhT4Frn3P1ZQvkasMY593TGtn8GeoH1wHTgM8dxqSIiIiIicoL53e9+R3l5OdXV1Ye0aEYiESZPnszLL7/M/v37CxghXHTRRSxcuBAzI5VKDSyV4pyjpKSET37yk8OO+TwRWSGz+bFs2bJlbv369YUOQ0REREREhhGLxfj2t7/NrFmzCAaDWes0Nzczfvx43vWud+U5usM1NDTw9NNP89prrxGNRlmyZMlA99sTiZltcM4tG65ePsd0ioiIiIiI5F1HRweRSGTIpK24uJjm5uY8RpXb5MmTufLKK4evOEYo6RQRERERkZNacXExiUQi5yQ94C1FUlxcnOfIsjtw4ABr165lx44dpFIp6urqOO+885g/f/6Y7F6rWVpFREREROSkVlFRQXV1NZ2dnTnrdHZ2cvbZZ+cxquyef/55br31Vvbu3UtVVRV1dXXEYjHuvfde7rrrroJOdnSslHSKiIiIiMhJ75JLLqGxsZF4PH7IduccTU1NhEIh5s2bV6DoPK2trfzmN7+hqqqK8vJyQqEQgUCAkpISqqur2b17N+vWrStojMdCSaeIiIiIiJz0zjjjDFavXs2ePXs4ePAgra2tNDU1sWfPHpxzfOQjHyn4RD3r1q0jGo0SDocPKzMzSktLWbt27Zhr7dSYThEREREROSWcddZZzJkzh02bNnHgwAHC4TDz589n5syZJ8RYyZ07d1JUVJSzPBKJ0NbWRldXF+Xl5XmM7Pgo6RQRERERkVNGNBplxYoVhQ4jq6EmOhrL1L1WRERERETkBJBMJunt7c1Z3tfXR1FREWVlZXmM6vgp6RQRERERESmw3t5e2tra6O7uJpFIHFbunKOtrY3y8vIx1xqq7rUiIiIiIiIFtnXrVgCqqqpoamqirKyMkpISzIx4PE5nZyf9/f00NTUVONKjp6RTRERERESkwPbt20dRURHV1dUUFRXR3NxMe3s74E0gVFlZSW9vL93d3QWO9Ogp6RQREREROQm1tbXx0ksvEYvFqKmpYf78+VmX4pDRk0ql2L59O7t27SIQCDBjxgzOOOMMAoHDRzkWFxcTDAZpamqivb0dMxuol0gk6OvrIxgMjsl7qKRTREREROQkkkgk+M1vfsOOHTuYP38+paWlvPjii/z2t7/lsssuY/HixYUO8ZSwfft27rrrroHxmYFAgI0bNxKJRLjqqquYOHHiIfVnzJjBU089RW9vL4FA4JBxm845Ojo6CAQCLFiwIK/XMRKUdIqIiIiInER++ctfAvDZz372kFaxV199lV/84hdEIhHmzZtXqPBOCdu3b+fnP/85oVCISCSCmeGcI5lM0tPTw49+9CM+9alPUVFRgXOOvr4+Jk6cSCqVIhgMHjZRUPp5f38/F154YSEu6bgo6RQREREROUkcOHCA/fv3c/311xMMBg8pmzBhAldccQW//e1vmTt37pibAXWsSKVS3HXXXYTD4UPuQbq7bH9/P6lUiscee4xQKMTGjRsHtg1u4cxkZoRCIXbv3k1VVVW+LmdEaMkUEREREZGTxKZNmzjrrLMOSzjTZsyYQTKZ5ODBg3mO7NSxY8cO+vv7c96DYDBIIpFgw4YNvPzyy0yfPp158+ZRW1uLc27Y4/f09Ix0yKNOLZ0iIiIiIieJnp4eJkyYkLPczKisrByTM6COFfX19TkTTvDugZlRWlp6yLjOWCx2SL10AuqcG9gnlUpRUVExOoGPIiWdIiIiInLckskkXV1dhEIhSktLCx3OKau8vHzIdRxTqRTNzc1jMnEZK46023LmPUilUnR1deGcI5VK4ZzL2eoZiURGJM58UtIpIiIiIscsFovxxz/+keeeew4zo7+/n9raWs4//3zmzp1b6PBOOWeffTa33nor559/ftbkZPv27ZSWljJ+/PgCRHdqmDVrFuvWrRtooRwsnVhmfjnT3d1NOBwmEonQ1dUFcMj4znQS6pxj3bp1zJkzJz8XM0KUdIqIiIjIMYnFYvzgBz+grKyMCy+8kLKyMlKpFAcPHuTee++lpaWF8847r9BhnlJqa2uZM2cOt99+O1deeSVlZWWAl7Ts3r2b++67j3e/+90FjvLkNmvWLEpKSujp6SEUOjTdcs7R399PIBA45EuBdKtmetvgCYUyu9fu378/D1cxspR0ioiIiMgxefjhhykvL+fss88e+IAcCASYPHkylZWVPPzww8yZM4eampoCR3pqedvb3sZDDz3ETTfdxPTp0yktLeXAgQPE43He+c53MnPmzEKHeFIzMz784Q/zgx/8YCDBTCeMyWQSeD35TCelxcXF9Pf309PTM5Bg5jp2X18fnZ2dlJeX5+2ajpeSThERERE5aolEgueff543velNWT8gl5SUcNppp7F+/Xouu+yyAkR46goEArz5zW/m/PPPZ8eOHcTjcc4880xmzpypZVLypK6ujuuvv55169axfv16enp6CIfDVFVVkUgk6OzspKGhgWnTpmFmRCIRotEovb29h3WphddbOtPLprS2to6ppFNLpoiIiIjIUWttbaW4uJiSkpKcderq6mhoaMhjVJKpuLiYhQsXsmzZMmbNmqWEM89KS0u5+OKL+Yu/+AvC4TBTpkyho6OD6upqFi5cSCqVYteuXbS3txOPx6msrCSVSg20iGZOKJS5DcbeZEJq6RQRERGRoxYKhejv7885WQp4M9oOHtMmcqoJh8OcfvrpbN++nblz5xKNRgGYPXs2ra2tNDY2EovFCAQCnH766ezcuXNg38xWz/TPSCQy5LI4JyK1dIqIiIjIUauqqiISidDc3Jyzzr59+8bcLJty6nHO0dfXNzDecjRUVVUxbty4gYQTvISyurqaOXPmsGDBApxzA2t1Dh7XmfnvysrKMddqra+eREREROSomRnnnXceTz75JKtWrTqsu9/+/ftpbm5m8eLFBYpQZGh9fX08+eSTPPPMM/T29gIwc+ZMLrjgAqZPnz6i52ppaaGysjJneSgUoqSkZMiZac0M5xwHDhwY0djyQUmniIiIiByTpUuX0tTUxEMPPcSsWbOora0lkUiwd+9eGhsb+dCHPkRRUVGhwxQ5TDwe54c//CHxeJy6ujqi0SipVIr29nZ+9rOfcfnll7No0aIRO18wGBwYj5nLcOVpo9kiO1rUvVZEREREjomZsXr1aj7wgQ9gZrz00kvs3r2b2bNnc/311zN58uRChyiS1e9//3v6+vqYOHHiQJfXQCBAVVUVU6ZM4Te/+Q2dnZ0jdr65c+fS0dGRszwWi9HX13dExxprXWtBLZ0iIiIicpymTp3K1KlTCx3GmJNIJNi8eTNbtmwhkUhQV1fHsmXLGD9+fKFDO6n19fXx/PPPM3369KwJXFFRERUVFWzYsIGLLrpoRM45b948HnzwQZqbmw9btzaVSrF//35mzZrFli1bhj3WUDNGn6jU0ikiIiIikmdNTU3ceOONbNq0iRkzZnDmmWfinOO2227joYceGpitVEZec3MzkUiEcDics040GqW+vn7EzhkKhbj66qtpbGykvr6ejo4Oenp6aGxsZPv27UyePJnq6uqB+tnuf3pbIDD2Uji1dIqIiIiI5FF/fz8/+clPWLJkCfPnzx/YPn36dBYuXMivfvUrKisrWbp0aQGjPHmlJ+QZinOOYDA4ZJ2+vj42bdrEc889R3d3N+Xl5SxZsoSFCxdmXSpo/PjxfPrTn+a5557jhRdeoLe3l6qqKq644grOOOMMHnvsMQKBAOFwmHg8njXGcDg8JpchGntpsoiIiIjIGPbiiy8ybty4QxLOtGg0ygUXXMATTzyh1k685K+9vZ2WlpYRm0Cnrq6OVCo1sDxJNj09PcyePTtneWdnJ9/97nfZuHEj06ZNY+nSpUyePJmnn36a73//+wOz4Q4WjUaZMGECgUCAzs5OGhoauPvuu7nvvvt4wxveQCqVIhQKUVpaSigUGlg6JRwOU1paOhD/WDP20mQRERERkTFsy5YtnHHGGTnLJ06cSDKZpKmpaUwmGCPBOcezzz7L2rVr6e3tJRgMkkwmWbJkCRdeeOGQXWOHEwwGWbFiBRs2bGDSpEmHdVft6uqit7d3yOV+7rzzTurq6g5Zh7asrIwJEybw0ksv8ctf/pKrrrrqsP02btzIAw88QFlZGRMnTsTM6O/vZ/v27WzZsgUzI5FIUFRUdMianuCN/ezv7x+TM0Ir6RQRERERyaPhEgczo6ioiP7+/jxGdeJwznH//fezc+dOFixYQE1NDWZGZ2cn27Zt40c/+hEf+chHjivxXLVqFQcPHmTPnj1UVFRQUlJCMpmks7OTrq4urrrqqpz36MCBA7S0tGRNSs2MuXPn8tBDD9HS0nLIOM2uri7uv/9+qqurD4k9FApRUVFBR0cHZkYymSQejxOJRAa6AqdSKeLxONFolNbW1mO+7kJR91oRERERkTwaP348Bw4cyFne29tLR0cHVVVVeYzqxLFnzx62bNnCeeedR21t7cAMs+Xl5QPjXNeuXXtc5wgEArz3ve/liiuuoLi4mNdee4329nbmzZvHn/3ZnzF9+vSc+77yyitMnDgx54Q+wWCQCRMm8Morrxyy/dlnnyUajeZMlsvKykilUkyePJlIJEJ3dze9vb309PTQ19dHdXU1oVDoiJdWOZGopVNEREREJI+WLl3KLbfcwqJFiwbG6WV67rnnmDt3LsXFxQWIrvDWrVvHrFmzsiZnZsbs2bN55plnuOCCC45rzcr0sYYau5lNKpUa9ryBQIBUKnXItn379g05CVAgECAUCtHd3c2kSZNIJpMkEgnMjEgkQjKZpLm5eUyuf6uWThERERGRPKqpqeHcc8/lnnvuYe/evQMTBvX09LB27Vp27drFm970pgJHWTivvvoqtbW1OcsrKyvp6+vLOVnPaJs8eTLNzc05J3pyztHY2MiUKVMO2R4KhY5o1ty2tja6uroIBAIUFxdTVFREIpGgoaGBcDjMggULRuxa8kUtnSIiIiIieXb++eczbtw4nnjiCR5++GGKioro7u5m/vz5XHfddZSVlRU6xIIJBoNDjmdNpVIkk8mCLR0ya9YswBvbma3Vcc+ePZSVlR2WdM6bN4+9e/fmPG4ikQCguLiY1tZWmpubB8b2JhIJKioq6OzsZN68eSN4NfmhpFNEREREpAAWLVrEwoULaWtrI5FIMG7cuDE5M+lImzt3Lvv376empiZreUNDA1OmTCESieQ5Mo+Z8Z73vIef/OQndHV1MX36dIqLi+nt7WX37t3s37+fa6+99rD95s+fz4MPPkhPTw8lJSWHlKVSKTo7Oxk3btzAbL3V1dUD40bT43wBWlpaxtysxupeKyIiIiJSIGZGVVUV48ePV8LpW7ZsGQ0NDTQ2Nh5W1tPTw5YtW1i1alUBInvd5MmTue666yguLuaRRx7h/vvv59FHH6WiooKPf/zjWZPCYDDINddcQywWo729nd7eXvr6+ujq6qKlpYXp06fT2dnJmWeeyeTJk+nq6uK1116jubmZaDTK4sWLmThxIuvXry/AFR8f06Kzx2bZsmVuLN5wEREREZET3e7du7njjjuYMGECkydPJhgM8tprr1FfX88FF1zAueeeW+gQB6RSKfr6+ohEIjlntM0Uj8d57rnneP7554nH49TW1rJixQpCoRB333038+fPxzlHT08P8XicUChEeXk5ZkZLSwvxeJxrrrkmD1c2PDPb4JxbNlw9da8VEREREZETyowZM7j++ut59tln2b59O6lUiilTpnDdddcNOclQIaQn/DlSRUVFLF++nOXLlx+yvbGxkb6+Ptra2qivr6e/v59oNEpfXx/JZJKpU6cCjMlZjZV0ioiIiIjICaekpIRVq1YVvCvtSHLO0dDQQFNTE5FIhFmzZg10q66trSUYDLJ9+3ZmzZpFVVXVwNIsXV1dvPzyy5gZ73jHOwp5CcdESaeIiIiIiMgo27dvH2vWrKGzs5NAIICZ0d/fz9KlS3nzm9+MmeGc4/TTT6eqquqQfcvKypg7dy7PP/8806dPL9AVHDslnSIiIiIiIqPowIED3HbbbYA3oVC6BTMYDLJx40a6uro455xzAG8d0lgsxquvvkp7ezvOOcrLy5kwYQI1NTVs3rz5sK65JzolnSIiIiIiIqPo/vvvBzhsbdF08rlt2zbq6uooLS2lubmZ3bt3U1lZyYQJEwgEAnR2drJlyxbKyspoaWnJe/zHK29LppjZl8zMZXm8IaPOcjNba2YxMztgZl81s+ARHPtaM9tmZnEz22pmHxpUfomZbTGzTjNbY2aVg8rvMrMvjNzVioiIiIjkTywWY/fu3dTX19PX11focCRDe3s7Bw4cIBjMntaYGWZGfX09sViMXbt2MW7cONrb29m1axc7d+4kFosNLKPS29ub5ys4fvlu6dwNDJ7fuBHAzKYBvwPuBj4OnAH8ADDgb3Md0MzeCdwCfB64H3g7cJuZtTjn7jezAPAL4JvAPX7dLwJ/7e//HuA04P0jcoUiIiIiInkSj8f53e9+x+bNm6mursY5R1tbG4sWLeLSSy8lHA4XOsRTXkdHB6FQaKBVM5f+/v6B8Z6tra2kUinSy1t2dHTQ0dFBZWUlra2t+Qh7ROU76Uw65w7mKPsU0AFc55xLAS+a2RTgG2b2Zedcd479/ga43Tn37/7zrWa2HPgCXhJaA9QBNzjnYmb2U7zEFDOrBr4NXO6cS47EBYqIiIiI5EMikeDHP/4xZWVlvO9976OkpATwZjp9+umn+elPf8rVV1+ds4VN8qOkpIRkMnnIWM7B0uM2zYxkMkkqlSKRSJBKpQBvWZZQKERbWxttbW0454ZNYk8keete65tqZvv8x/1mdl5G2UrgQT/hTHsAKAHOznYwM4sAb/TrZXoAWOF3zW0CGoC3mlkYeDOwya93A/B959zm470wEREREZF82rBhA+FwmAsvvHAg4QRvptOLL76Y/v5+Nm3aNMQRJB+qq6upqKgYSCAHc84RCARYtGjRQMIZj8cPqZ9Kpejr6xto/Uy3gI4V+Uw61wEfBd4GfBBoBh4zszf75ZOAwa2gBzPKsqnFa63Ntl8RUO28O/JevJbPl4Eu4Ktm9jbgTOBGM7vVzF4xs3vMbEKuCzCzT5jZejNb39jYOPwVi4iIiIiMkvXr17No0aKsLV7pJGb9+vUFiEwymRlvectbcM4dlnimt9XU1FBaWgp43WxzSZcFAvluOzw+eYvWOXefc+7nzrlNzrnHnHMfBh7DH1s5yude65xb4Zw7zTl3Dd513wj8Cd540TDeGNJteK2fuY5zs3NumXNuWV1d3WiHLSIiIiKSU2trK+PHj89ZPn78+DE50+nJaPbs2bz97W8faKlMJBL09/eTTCaZNGkSV199NYFAIGtimildNtYmEyr0kilPAu/2/30AmDiofEJGWTZNQH+O/eJArt+ybwE/c849a2bfB/7BOZc0s9uAR48ifhERERGRgohEIvT29lJWVpa1vLe3l0gkkueoJJdFixYxd+5cNm/ezGuvvUYkEmH+/PlMnOilMkfTqNXR0UE0Gh2tUEdcoZPOJcBe/99PAFebWSBjXOdqoAfYmG1n51yfmT0DXAbcllG0Gngq2+RAZnYpsAJY6m8K4LV0AkTI/zhXEREREZGjNn/+fLZt28bSpUuzlm/fvp0FCxbkOSoZSiQSYcmSJVnLQqEQ4XB4oAU0m0AggJmNuS8TjijBMrO/8md6PWZm9m1/vcxZZnaWmf033qQ+/+FXuQkYB3zPzBaY2RXAl4H/Ss9ca2ZT/HU4r8w49DeA95vZZ81sjpl9DngX8PUsMZQB38GbITfub/4jcL2ZzQE+h1o6RURERGQMWLFiBS+++CINDQ2Hle3du5dt27ZxzjnnFCCyw3V0dPDUU0/xyCOPsHHjRq0lmsPixYuHXF4lHA5TVFREZWVlniM7Pkfa0vlZ4F/NbA1ws3PukWM41yS81sg6oB14HrjUOfcwgHNur5m9BW8Jkw1AG3Az8H8zjhEG5uAlp/j7rTGzjwF/D/wbsAu41jl3f5YYvgb8f/buOzqqK0/0/XdXlkoB5YCyhAKSQGCJaGyiwdgmODTY7rZxT9ue6enbM2967p383p01d72ZO/dOWDNvpt/rHrudsI2NDdhNMGCSAQtJgAJBQshIAgmhWMqlSvv9IVSDUJUkQBHvz1pey5x9zqlfSQWrfue392/vllKeuePYXwPvAEX0V1R/eB/vTVEURVEURZniOjo6aG1txWg0EhkZOa22nPAkLCyM559/np07dxIZGUl8fDxSSqqrq2lqamLr1g73SLgAACAASURBVK0EBQVNaoxOp5Mvv/ySS5cuYTabcblcuFwu9u3bx9q1a8nNzZ3U+KaagQqmwWDA4XAM2TJFCEFAQMC0++yONulMAJ4Efgf4SghRA/waeEdK2TiaG0gpXxzFOfnAkmHGq4EhP2Ep5Tv0J44j3f9nHo61AM+MdK2iKIqiKIoyPTU1NbF//37q6urw9fXFbrej1WpZvnw58+Z53JlvSrPZbJSVlXHlyhVcLheLFi1Cq9XS0NC/ocOcOXPIysqaElMw9+zZw7Vr1wgNDcVisWC1WhFCIKVk//799PT08Nhjj012mFPG5cuXCQwMpKOjA4PB4N4aZeBn5uPjQ3NzMy6Xa1p1sB1V0nl7jeVeYK8QIhLYBvwE+BshxB76q5+Hxy1KRVEURVEURbkPTU1NvP322wQHB5OWlubuENrT08Phw4fp7OycVknP9evX+fDDD9FoNO6k4+bNm9hsNp599lnS09MnOcL/1NLSQkVFBUFBQdy6dQuDwYCfn587gbLZbBw7dozExERiY2MnPL7Ozk5aWlrQ6/VERUVNiSTObrdjMpkICwujt7cXq9WKlBK9Xo+vry96vZ6+vj6cTueUiHe07rmRkJSyQQjxGRAC/BdgNbBRCHGF/mmtZ8c4RkVRFEVRFEW5L/v27SMkJITQ0FD3MSEEZrOZhIQETp48ydy5cwkMDBzmLlNDR0cH27dvx2AwDKli6nQ6Pv/8c7Zt20Z0dPQkRThYSUmJuzJnNBrR6/XuMSEERqMRjUbDjh07+MUvfjFhU0bb2tr47W9/S21tLTqdDiklGo2GZcuWsWjRokmduhoSEoLFYsHHxwez2ezeu3OA3W7HaDSi0012P9h7M+r0WAhhEEK8JIQ4ClwGFtJf7YwC4ulfE7l9XKJUFEVRFEVRlHtksVior68nONhzP0y9Xs+MGTM4d+4c0P+Fvry8nOLiYmpra91TG6eKM2fOoNPpPE6bHTh+4sSJSYjMs66uLveaRG9Jkk6nw2azUV1dPSExWSwWfv3rX1NbW4sQYtC+mceOHePAgQMTEoc38+fPp6enx+Nnb6BCn5eX93Cu6RRC/DP9DXa0wPvA70spL91xSoMQ4s+Aoa2zFEVRFEVRFGUStLa2Yjabh52GaDKZaGxs5Pjx4+Tn5xMYGIjJZMJisaDValm7di2+vr4IIYiIiJjUCtOFCxeGfX2DwUBlZSVSyimRlAQGBrrXz3qLRwjhXo+amJg47jEdOHAAu90+5DMxMO36/PnzPPLII4SHh497LJ7U1tbidDqxWCz4+/u7f99Op5Ouri76+vqIi4ublNgexGj/1iwE/hjYIaXs9XJOM/3NhhRFURRFURRl0hkMBux2+7DnOJ1OWltbaWpqYtmyZe7pjFJKbt26xSeffILZbEan09Hb28v8+fNZsWIFWq12It7CIANTK70ZWCvpcrkmJb675eTkcOLEiRETdSHEhCTzvb29VFVVDZsAu1wuCgoKePrpp8c9nrs5nU73lGStVktra6s7VpfLhdlsRgjBqVOnSE5OnvD4HsSoptdKKRdLKd8ZJuFESumQUn41dqEpiqIoiqIoyv2Ljo52T0n0REpJe3s7bW1tLFy4cND6OSEEkZGRzJ8/H61Wy6pVq1i+fDnV1dV89NFH7mmjEyk4OBiHw+F13Ol0Yjabp0TCCf2VzszMTOx2u9epygNTW1NSUsY9HovFMuwemANu3bo17rF40t7ejsvlwmQy0dnZiZQSp9OJ0+kE+n+//v7+HvdlnepGlXQKIf5ICLHNw/FtQog/HPOoFEVRFEVRFOUBaTQaHnvsMW7evDmk4jlQyRRCEBcX53V7kejoaPr6+mhvb8fPz49FixbR0dHBxYsXJ+ItDLJ48WIcDofX9X42m428vLwJj2s4mzdvJjAw0N2F9U4DCWdiYuKE7CdqMBhwOp3DrtWVUmIymcY9Fk9sNhvQPy18oGItpXT/19vbS1dXlzsJnU5GW8f+Kf17dN7tKvAb4J/HLCJFURRFURRFGSN5eXl0dnZSUFDAjBkzMJlMOBwOOjs78fHxISYmBh8fH6/XCyHw9/enp6eHwMBANBoNKSkpFBYWkp2dPYHvBDIyMjh37hw3b97EaDS6K5oul4u+vr5BSXFhYSGXL1/G4XAQFhbGggULSElJmfC1nkII3nzzTX7zm99gsVjcW70MVIqDgoJ47rnnJiSW4OBg/Pz86Ozs9Ppz0Ol05OTkTEg8dwsICBiUZN5p4M8Oh2PYz+tUNdqkMwao8XD8xu0xRVEURVEUZRqSUtLd3Y1Wq52WX2ZHIoRg1apVzJ8/n6KiIvf2HStWrCA5OZmDBw9isVi8Xj8wPffOSmhISAglJSUTEf4gGo2Gl156iSNHjnD27Fk0Gg1CCOx2O5mZmaxbt46bN2+yY8cOYmNjmT9/PgaDgaamJvbt20dcXBybNm2a8MTTZDLx5ptvUlFRQWFhIZ2dnQQEBJCXl0dqauqE7TcphGDlypV88cUXHpstDaybnKy9Tge61o7UNXkqNIm6V6NNOpuBDKD6ruOZQNtYBqQoiqIoiqKMP4fDwbfffktRURF2ux2n00loaCiLFy8mKytrssMbc0FBQaxZs2bI8Tlz5rB9+3avyU9raysul2vQtis2m23QnpMTSavVsmbNGlasWEFDQwNSSsLDwzEajVitVnbs2MHChQuJjIx0X+Pv709cXBwnTpzg22+/ZcmSJRMet0ajISMjg4yMjAl/7TtlZWXR3t7OsWPHAAZt6eLn58err746aWtiLRaLuxmUNwPTqKeb0Sadu4F/EkLUDGyVIoTIBP4B2DVewSmKoiiKoihjz+Fw8OGHHyKEYNOmTUREROByubh27RrHjh3j1q1brFq1arLDnBBRUVFER0dz/vx5cnJyBiUcXV1dnD17lszMzEHVpZqaGmbPnj0Z4brpdDpiYgZPOCwuLiYiImJQwnnn+Tk5OeTn57No0aIJqy5ORUuXLiU7O5uioiIaGhowGAxkZ2cza9asSf253N3I6m4Dyeh0/N2NNun8M2A+UCaEuHn7WBRQeHtMURRFURRFmSbOnDmDEILNmze7v8BqNBqSk5OJiori/fffJzU1ldjY2EmOdGK88MIL7Nq1i0OHDhETE4PRaKS5uZmWlhays7NJSEhwn3vr1i1qa2tZt27d5AXsRWVl5ZBE9E4D1dqWlhbCwsImKqwpKSAggJUrV052GINERkYihHBXO++ueA7sJTrc73iqGlXSKaXsFEIsBdbTn3wCnAUOSCknvl+0oiiKoiiKcl+klBQVFfH00097rJj4+voyf/58CgsLvzdJp16v5wc/+AHNzc1cvHiRvr4+TCYTbW1ttLW1cf36daSU1NfX09zczJYtW5gxY8Zkhz2ElHLEKtidTXyUqUev19Pb63mXSpfLhUajYdmyZRMc1YMb9S6ssj/V3nv7P0VRFEVRFGUaslqt9Pb2EhUV5fWchISESdkSZLKFhoby+OOPu/+8cuVKiouLqampQaPRkJ2dzZw5czAajZMYpXczZ86koaGB6Ohoj+NdXV1YrdZB61OVyWez2bhy5Qp1dXWj2g7l0qVLJCYmTkBkY2fUSacQwh9YDcQDgzYyklL+/RjHpSiKoiiKoowDjUbj3nDeW8MUu90+LdeNjTVfX1+WLFkyKY137kdubi6//OUvSUlJISAgYNCYlJILFy6Qk5MzaU2QlMGklJw6dYpvvvkGs9mMw+HA4XCMeE1ZWRnr16+fVl1sR5V0CiHmAQcAM2ACOoAZQC/QAqikU1EURVEUZRowGo1ERUVRVVVFamqqx3PKy8tJSUmZ4MiUBxUYGMjatWs5fPgwmZmZxMfHo9PpaG1t5fLlyzidTlasWDHZYSq3nThxgqKiIpKTkzEajbS1tdHZ2TnidU6nk6amJsLDwycgyrEx2krn/wb2Az+hf4uU3NvH3wf+dhziUhRFURRFUcbJ4sWLOXToENHR0fj5+Q0aq6ur49KlS7zxxhuTFJ3yIObNm0dQUBCnTp1y7+Xp6+tLbm4uixYtUlXOKaK3t5dTp04xa9Ys9x6wZrN5UPMgp9PpXn+r0WjQarUIITAYDFit1kmJ+36NNumcD/wXKaVDCOECDFLKciHEfwP+P+C34xahoiiKoiiKMqYyMjJoamri/fffZ968eSQkJOBwOCgvL6e8vJxnn312yjXKcTqdtLe3o9VqCQgImFZTCydaQkICCQkJ7mnUer1e/bymmIsXLxIQEOBOOAG6u7vdvye73U5+fj7l5eVA/9/ZRYsWodPp6O3tJTAwcFLivl+jTTpdQN/t/28CZgLlwC0gaRziUhRFURRFUcbRY489xqxZsygsLOTgwYNotVqSk5N58803p9QXWpvNxokTJzh//jw6nQ6Hw4HZbGbx4sXk5OSoZGoYWq3W67pdZXJ1dHQMqToPNBGy2Wz86le/or293T3W2NhIaWkpr7/+uvvBy3Qy2qTzAjAHqAIKgD8VQjiAN4Gr4xSboiiKoiiKMo6ioqLYsGHDZIfhlc1m491338XPz89dfZVSUldXx8mTJ2lqauKJJ56Y7DAV5Z4NNA66k8FgwGazceTIkUEJ54C2tjZOnDjB8uXL6enpwWw2T1S4D2y0bcn+Dhh4TPJ/AonAUWAd8IfjEJeiKIqiKIryPXfy5EnMZjNr1qxxT/cVQhATE8OmTZu4cOECN27cmOQoFeXeZWZm0t7ePijx9Pf3R6vVcunSJa/XXbx4ESHEtKtgjyrplFLul1LuvP3/V6WUKUAMEC6lPDKeASqKoiiKoijfPy6Xi3PnzpGbm+txCq3JZCI7O5vCwsJR39Nms9HS0jKqDqGKMp78/Px45JFHqK2tdSeeA5/z4bZNcTgcCCEwmUwTEudYGXF6rRBCT//WKPOklGUDx6WU9eMZmKIoiqIoyr1oamqivLwcm81GaGgos2fPVp06p7Genh6klISEhHg9JyYmhuPHj494r46ODo4ePcrly5cxmUxYrVZCQ0N5/PHHmTVr1liGrSij9sQTTyCEoKioiMDAQLRaLQ6Hg+TkZHcDobslJydPcJRjY8SkU0ppF0LcAORI5yqKoiiKoky0vr4+du3axY0bN0hLS8PHx4fS0lK++uor1q9fT1ZW1mSHqNwHrVaL3W7H5XKh0XienGez2UacZtje3s5bb71FQkICGzduxMfHB5fLxfXr19mzZw8rV65k/vz54/EWHlrd3d0UFhZSXFxMX18fAQEBLFq0iOzsbHS60baMUYQQPPHEEyxdupRLly7R3d2NTqdj1apVXL16dUjFU6/Xs3LlShYsWDBJEd+/0X4q/gX4CyHEq1JK23gGpCiKoiiKMlpSSnbs2IHZbOb1118f9IX31q1b7Nq1C5PJREpKyiRGqdwPHx8fIiIiuHbtmtfqzpUrV0hLSxv2Pvv37yc5OZm5c+e6j2k0GuLj4wkKCmLfvn2kpaVNq6Ysk6mxsZF33nnHva7QaDTS3d3NwYMHKSwsZNu2bYO2AVFGZjabycvLAyA3N5fa2lpef/11Dh48SFVVFUIIkpOTeeKJJwgODmbt2rWTHPG9G23SuQJ4HLghhCgDuu8clFJO3bZniqIoiqI8tGpqaujo6GDz5s1DqmERERGsXr2ao0ePqqRzHFksFgoLC6msrMTpdDJz5kwWLFhATEzMA997yZIlfPXVV0RERODn5zdorLa2lurqap588kmv13d2dlJdXc1zzz3ncTwgIIC4uDjOnz/Po48++sDxPuxcLhcffPABGo1mUGJpMBiQUmKxWPjyyy+9/ryVkR09etTdUOiFF15wV/KdTicGg2HabhE02qSzC9g7noEoiqIoiqLcq5KSErKzs71Ov0xKSuLrr7+mpaVl2LWB05XFYsFqtRIQEICvr++Ev/6VK1fYvXs3iYmJ5OXlodVquXnzJjt27CAnJ4dVq1Y90P0zMjJoaWnhk08+Yfbs2cTGxuJwOKisrKS2tpYtW7YMSUbv1NTUREhIyLBreyMiImhoaHigOL8vKisrcTgcHpvYCCEwGAyUl5fT3d2tKsf3oa+vj9LSUlwuF4GBgRiNRlwuF0IIhBA4HA7a2to4duzYtNsqaFRJp5TyxfEORFEURVEU5V719PQQFxfndVyj0eDv709PT89DlXRWVFRw/Phx2tvb8fX1pbOzk+TkZFatWkVwcPCExGCxWNi9ezcrVqwgNDTUfTwoKIikpCQOHjxIZGQkmZmZD/Q6jz76KGlpaRQWFlJYWIhWq2XWrFk888wzIybaOp0Om234lWF2u12tQxylq1evDjuu0WgwGo1cv36d9PT0CYrq4dHQ0IBGo0Gv12M0GgEGPVDT6XT4+flRWlr6cCadiqIoiqIoU1FgYCAtLS1exx0OBxaLBX9//wmManydO3eOY8eO8fjjjxMfH49Go6Gvr4+ysjLefvtttm3bNigJHC9FRUUkJiZ6fC2TycS8efM4ffr0AyedAGFhYaxfv/6er5s5cyY9PT20t7cTGBjo8Zxr166xYsWKBw3xe8Hlco3qPClV/9H7IaXE4XAM+zDFZDLR1NSEw+GYVg9LRrVPpxDishDikrf/xjtIRVEURVEUT3JycigrK8Nut3scLy8vJzw8nBkzZkxwZOOjp6eHgwcPsmnTJhITE91VEKPRSG5uLo888gj79++fkFgqKyuJj4/3Oj5z5kyampqwWq0TEo8nWq2WhQsXkp+f7/EzcvnyZRwOB6mpqfd8b6vVSkdHB06ncyxCnRbi4+OHXVMopaSvr4/o6OgJjGp6s1qtFBcXc/r0aSwWC1JK999rl8uFw+EY9BkTQqDRaIbdy3MqGm16vPOuP+uB+UAe8MsxjUhRFEVRFGWUoqOjiY+PZ8+ePTz55JPudWRSSq5evcrx48d5+eWXJznKsVNcXExiYqLXJDozM5PCwkLa2toICgoa11hcLtew25UMfDkebXVsvCxbtoy2tja+/PJLUlNTCQ0NxWq18t1339HZ2ckrr7zidU2wJ1VVVZw8eZK6ujoMBgMul4u5c+eybNmySVlXO5Fmz57N/v37sdvtHtfJ2mw24uPjvVaVlf8kpeTIkSOcOXMGHx8fhBDuvyt9fX20t7djt9vd07+1Wi0zZswYMv12uhjtms6/8nRcCPGXwMQsHFAURVEURfFg48aNHD58mLfffpvY2FhMJhM3b95ECMHWrVuZOXPmZIc4ZpqamoiKivI6rtPpiIiIoKmpadyTzujoaOrr672uIW1pacFkMuHj4zOucYxECMGGDRu4ceMGRUVFXLhwAaPRyLx588jKyhq2ydDdzp07x9GjR1m6dClPPfUUWq2W9vZ2zp49y1tvvcWPf/zjh7qBjk6nY+vWrXz44YeDuqm6XC53Irpp06bJDnNaOHz4MMXFxYSFhQ2aJiuEoK2tjaqqKq5fv47D4UAIQUREBJmZmZhMJmJiYqZdF9sHnQj8MXAG+KMxiEVRFEVRFOWeabVa1q5dy2OPPUZVVRU2m428vDxiY2On3Rezkej1+hEb4/T19d1TInW/Fi5cyIcffkhycvKQxNLlclFSUkJeXt6U+B0IIYiNjSU2Nva+79HZ2cmhQ4f4wQ9+MKjSHBgYyMqVKzlx4gSHDh166JOu+Ph4Xn/9db755hsuXryIy+XCZDKRm5vL4sWLJ/0hw3TQ1dVFYWEh4eHhQ2YLdHZ2cvnyZerq6tzHpJQ0NDTQ09PDggULhl3HPlU9aNKZBUyvCcWKoiiKojyUfHx8yMrKmuwwxlV6ejr79+9n3rx5HpM5i8VCe3v7AyVXoxUdHU1ubi4HDx4kJyfHneQ3NzdTWlqKwWBg0aJF4x7HRDl37hyzZs3yOrU5NzeX999/n97e3oc+8QoNDWXz5s1s2rRpxGnWylAXLlzAx8dnyM/NZrNhs9mor6/3eF1HRwcdHR0T8lBprI0q6RRC/Mvdh4Bo4EngrbEOSlEURVEURRkqMTERvV5PYWHhkCqizWbjyJEjLFiwYMK6Wi5fvpyIiAhOnz7NyZMn0Wg0+Pr6kpeXx6JFix6qZOTmzZskJSV5Hff19SUoKIjm5uYJSfqnAiHEQ/U7niidnZ0eHxrZ7Xba29vd3X/1er37IUd7ezs2m43GxkbCwsImNN6xMNp/kfLu+rMLaAT+K/CrMY1IURRFURRF8UgIwYsvvsgHH3zAjRs3yMjIwNfXl8bGRi5evMisWbN47LHHJjSmjIwMMjIysNlsuFwujEbjlJhSO9a0Wu2IHUPtdrtKwpQR+fv7e9xWRgiBTqdDp9ORlpZGeHg4bW1tAGRlZdHU1ITNZpuWew6PtpHQ4vEORFEURVEURRmZv78/b7zxBhUVFVy4cAGr1UpwcDBbt26d1K0qDAbDpL32REhNTaW4uJiMjAyP483NzfT19RERETHBkSnTTVZWFl9//TX+/v6DHlIYjUb8/f3Jzc2lpaWFgwcPurf60ev1ZGRkEBcXNy0f6ox2eq0voJVSdt513B9wSil7xiM4RVEURVEUZSitVsvs2bOZPXv2ZIfyvZGZmcmRI0eoqKggLS1t0Jjdbuf48eMsXLhQVTqVEfn5+ZGbm0tpaSlBQUHuz8zAnrZ9fX2UlJQMusZut1NaWorZbJ6Wn7HRTq/9GDgC/PNdx18DVgEbxzIoRVEURVEURZlKdDodL730Etu3b6e6upr09HRMJhMNDQ2UlpYSHx/P0qVLJztMZZS6urqoq6tDSklMTAx+fn4T+vpPPPEEGo2GwsJC9z6d3d3duFwuysvLvV5XUVFBSEgIUsppVfEcbdK5GPhzD8ePAH85duEoiqIoiqIoytQUERHBT3/6U4qLizl37hw2m43Q0FA2bNhAfHy81yRASklLSwtOp5OgoKCHfiryVNbb28v+/fuprKwkOjoaIQR79uwhJSWF9evXj2vnYSkl9fX1nDp1ilu3bmEymVizZg3QX92srKykurqa9vZ2r/dob29Hq9VOq4QTRp90+gF2D8ftQMDYhaMoiqIoiqIoU5fJZGLOnDn4+flht9sJCwtj5syZHpMAKSWFhYXk5+fjdDrR6/V0d3eTnZ3NypUrMZlMk/AOvr9sNhvvvfce0dHR/O7v/q7752+1Wvnmm2949913+fGPf3zPDwUcDge9vb0YjUav10op+fzzzyktLeXSpUs0NzdjNBopLy8nMjKSn/zkJwQGBvLdd99hMpno6uryeB+TyeSxCdFUN9qksxzYAPyvu45vAirGNCJFURRFURRFmYKcTif79u2jrKwMPz8/NBoNPT09mM1mNm/ePKiRk5SSAwcOUFNTw9q1a4mKikIIQWdnJ/n5+fzmN7/htddeU4nnBDp37hx+fn6sWbNm0EMCk8nE6tWr2bVrF2fPnmXx4tH1ULVYLBw7doyLFy+i0WhwOp0kJiayYsWKIU29jh8/Tn5+PocOHRrUBfnGjRukpaWh0Wj4+c9/DvRvjVRWVubxNRMSEqblZ2a0Sec/AG8JIUKBQ7ePPQH8HHhjPAJTFEVRFEVRvr+6uro4d+4clZWVuFwuoqKiyMvLm7TusFJKPvnkExobG0lKSnLvhSqlpKOjg/fee4/XXnvNHd+NGzeoqKjghz/8IUaj0X0ff39/Vq9ezcGDB/nmm2/c0yuV8Xf+/HlWr17tsSothGDBggUcOHBgVElnS0sLb731FiaTiZkzZ6LT6XC5XFgsFt599122bNni3tfV6XRy6tQpTp486XHbnYqKClJTU6mqqkKn05GQkEBzczM3b94cdF5ERARJSUnujrbTyWi3TPngdqfa/4v+vTmhf5/OX0gp3xuv4BRFURRFUZTpob29naKiIsrLy93TTnNzc0lNTb3n9WfV1dV88sknJCUlkZubi16vp7a2lvfee49FixaxbNmycXoX3tXW1nLjxg3i4+PRaDTu40IIAgMDcTgcHDp0iB/+8IcAFBYWMm/evEEJ553XLFiwgB07drBy5cpp2Y10OrJYLMM+tIiIiMBisYzqXp9//jlms5nAwED3MY1GQ2BgIAaDgZ07d/KLX/wCrVZLXV0dPT09dHZ2er3fpUuXKC4uRkqJw+EgNzeXzs5Orl+/jpSS2NhYAgICcDgc7gce08moI5ZS/hL4pRAi9vafr49bVIqiKIqiKMq0UVNTwyeffEJGRgZPP/00JpOJ69evc+TIES5cuMDmzZsHJWrD6ezs5NNPP2XdunXExsa6j0dFRZGVlcVnn31GaGio1/0yx0tRUREBAQFe38eMGTOoqqqiu7sbs9lMY2Mj2dnZXu8XFBSEEIKenh78/f3HK2zlDiaTic7OTo8PAqC/uj6aqauNjY20tLQwc+ZMj+M+Pj50dnZy+fJlsrKycDgcOJ3OYe9pt9sHVTBtNhtms5n09PRBx6ZjEyGAUf3tF0LECCESoT/ZHEg4hRCJQoiY8QxQURRFURRFmbqsViuffvopzzzzDGvWrCEyMhKr1UpPTw9paWk0NjZy8uTJUd/v7NmzJCcnD0o4B5jNZpYsWcLp06fH8i2MisVi8ZqsQP/eqUaj0d0ARq/X09fX5/V8p9OJ3W6fllWr6SozM5PS0lKv4yUlJWRlZY14n4aGBvc2J97o9Xrq6uoACA0NxcfHZ9iKdmxsLDNnzkRKiV6vB/qnbt/538B9p6PRPXKCd+nfj/NuK4Df3M8LCyFWCiGcQoirdx1fKIQ4LYSwCiFuCiH+Vggx4pwDIcQ2IUSFEKJPCFEuhHjZw+tdFkJ0CiF2CyFm3DW+UwjxJ/fzXhRFURRFUb6vSkpKiIuLIzExkc7OTj744AN27txJY2MjFosFm83GyZMn3V/AR3LlyhVSU1O9jiclJXHr1i2sVutYvYVRMZvN2Gw2r+MulwubzebeciM9PX3Y/RarqqqIjIwc1y06lMEWLlzIxYsXqagY2ge1srKSsrIyFi5cOOJ9Wltbh/0sQH/COJBkBgQE2zqggAAAIABJREFUEBcXR1pamsdzzWYzM2fOJCYmBiEEQgiMRiO9vb1cu3aN6upqrFar+6HHw9y9dj7w+x6OnwL+972+qBAikv5E9iAw647jsfQ3KvoMeP322NuAAP50mPttAt4C/hjYDzwNvCeEaJVS7hdCaICPb8e65/a5f8Ht9alCiOeBeGDLvb4XRVEURVGU77OqqioyMzOx2Wx89NFHJCQkuDe+B1iwYAFVVVVs376d119/naCgoGHvN7C1iDcajQadTjfidMWxNm/ePPbu3eueFnu3zs5OwsPDCQgIcJ//7bffcvXqVVJSUgad29HRwYkTJ3jqqacmJHalX2BgIC+99BI7duygqKiIWbNmIYSgsrKStrY2XnzxRWbMmDHsPc6ePUt+fj52ux2n0+mxeimlxGq1DkoyN2/eTENDAzqdjvLycmw2G0IIoqKiWLx4MU8++SS+vr7u60tLS2lubnZfX1tbS1hYGNnZ2Q910mnwcq4euKeevbcTwA+Af7t97aw7hn8P6AB+R0rpAi4KIWYCfy+E+BspZbeX2/43YIeU8p9u/7lcCLEQ+BP6k9AQIAz4FymlVQixnf7EFCFEMPCPwHop5cT+66UoiqIoijLNDVR0ysrKCAgIYP78+YPGhRCkpKRgsVg4deoUTz/99LD3i4yM5MaNG4SHh3scb2pqQqfTTXiFcNasWfj5+dHY2Eh4ePigxLOnp4empia2bt3qPubr68tLL73ERx99xOXLl0lLS3M3RLp06RKPP/74sBVdZXxER0fz85//nIqKCqqrq4H+ByPp6ekjNnRqbW3l4MGDpKenU19fT3Nz85DPAvQ31QoICCAm5j9XIc6YMYOf/vSnHDhwgPT0dJxOJxqNhqCgINatW0dqaip2ux2Xy8X169dpaWkhPDycGTNmIKXEYrHQ3NxMXV3doPtOF6NNOouBHzK02vgK4H1itGd/BUjgf9LfDfdOS4GDtxPOAQeA/weYBwxZECCEMAB5wP9719AB4N9uT81tBuqBJ4UQvwXWACW3z/sX4D+klBfu8X0oiqIoiqJ8782cOZPvvvuOmzdvkpOT4/W8jIwMPvvsM5566qlh18Ll5eXxySefMHv27CFNXaSUFBQU8Mgjj4y6MdFY0Wg0/OhHP2LHjh1UVVXh7++PRqPBZrNhtVrZtGkTCQkJg66Jjo7mZz/7GaWlpZSXl+N0OomKihpVxVcZP1qtltmzZzN79ux7uq6wsJDQ0FBMJhPx8fFcuXKF+vp6AgICMBgMOBwO2tvbMRgMvPLKK0M+54GBgWzZsoW+vj46OjowGo3uyjjgXt9rsVhYunQpVquVpqYmoH9/ztTUVK5cuTJkD9DpYLRJ5/8AvrzdTOjOfTqfBTaM9sWEECuA3wXmSSmlh39wouifsnunhjvGPAml/3003HW8ATACwVLKJiHEC/RXNP8ZOA78rRDiKSAL+LkQ4h3gMaAMeENKectD/G9we1/SuLi44d+soiiKoijK98AjjzzCv//7v6PT6QZ9gb6b2WzG5XJht9sxGAxez4uJiSE7O5vPP/+cpUuXEhcXhxCClpYWCgoK6OvrY8mSJePxVkbk4+PDtm3baGhooKKiArvdTnh4OLNnz/baEMhoNJKXl0deXt4ER6uMtZqaGvcWKVqtlvT0dCwWC7du3aKjowOdTofD4WDbtm3D/l0wGo2YTCaKioq4cOECTqeT2NhYlixZgkajISMjg8uXL9PY2Oi+prq6moiICDIyMib8gctYGO0+nfuFEBuA/w788vbh88AmKeW+0dxDCBFK/7Ta16SUdyeI405KeRpYdEc8gfRXUJ+jv4Krp3+q79/SX/0csr5TSvkr4FcAubm5028ytaIoiqIoyhjz9/fnySefZN++fe5phZ50dXWh1WpH1X1z9erVhIeHc/r0aQ4ePIhOp8PlcjF//nyWLVs26R08IyMjiYyMxGKx0N3dTU9Pz7BJhvJw0Gg0g9ZTCiEICgpyV62llJSVlQ37UAXg2rVrbN++nY6ODneH45qaGi5cuIDL5cJqtRIbG0tYWBgNDQ20tLQAcOvWLYKCgh7qSie3k8tRJZheZAHRwG/vqHBqACGEcNA/VfcmEHnXdQM7uN70ct9mwOHluj6g1ct1/wB8KKU8J4T4D+CvpJROIcR79FdCFUVRFEVRHgrt7e00Njai1WqJjY0d86Rtzpw53Lp1i7KyMncHzrtdunSJuXPnjmqPQSEEc+fOZc6cOXR1deFyufDz8xtxzd1Eqa6u5vDhw7S0tODr60t3dzdRUVGsXr3a696NyvSXkpJCRUWFu9p5t66uLgwGg9dx6G84tX37dvdDmN7eXlwuF4GBgdhsNpqbmykoKKCjo4OwsDDy8vKYOXOmOyG9ceMG0dHRXpsYTVX3vTGQECIEeA14XUrpuf/vYIXA3Tvk/pT+hj7rgev0J6Y/EkJo7ljXuQ7oob+yOoSU0iaEKATWAu/dMbQOyPfUHEgIsZr+qucjtw9p6K90Qn/TpOlXs1YURVEURblLW1sb+/fv58aNG0RFRWGz2WhtbSU3N5fHH398TKfpLV++nP/4j/+gsLCQRx55xP2FWEpJRUUFVVVVvP766/d0TyEE/v7+YxbjWKioqGD37t1kZWUxf/58NBoNTqeTGzdu8MEHH7B161bi4+MnO0xlHOTm5pKfn09wcDB+fn6DxpxOJ/X19SxZssTrgxUpJfv27aO3txchBDabDYfDQXNzM35+fhQVFXH8+H/Wvurq6igtLWXz5s2kpKRw5coVrFYrGo1mWiWccB9J5+11mW8Am+jvNPvZaK673Xl2ULMeIUQjYBto4iOE+CXwM+DXQoh/BJKBvwH+daBz7e1utl8Dfyal3HX7Vn8P7BRCFNDfQOgp+tebPuMhfj/6mw69LKUc2LH3BPAzIcRl4I9QlU5FURRFUaa59vZ2fvOb35Cbm8vGjRvd1c22tjYOHDjAnj172LRp06gqj6Oh1+t59dVX2bVrFx9//DFxcXFotVpu3LiBr68v27ZtG7YCNB04nU6++OIL8vLyCA4Odh/XarXEx8djNBrZvXs3P//5z8fs56rcu76+Pi5evEhraytGo5HZs2cTEhLywPf18/Pjueee47PPPiMkJITg4GC0Wi0dHR00NTWRlJTEggULPF7rdDrZvn07ZWVllJeX09bWhtFoJDExkezsbKxW66CEc4DL5WLfvn38/u//Pq2trTidTlwul4dXmNpGlXTeWdWkf4qsL7AN2H5Xp9kHIqW8LoR4gv6GP2cBC/1rKP/yjtP0QBoQeMd1u4UQPwH+HPhfwDVgm5Ryv4eX+Ttgt5TyzB3H/hp4Byiiv6L6w7F6T4qiKIqiKJPh2LFjZGdns2jRokHHg4KCeP7553nrrbe4fv36mDZH9PX15eWXX6alpYXvvvsOl8vFkiVLhp1y2tTURFNTE3q9noSEhElfrzmc8vJy/Pz8BiWcd4qIiHBvxZGYmDjB0SkA586d49ChQ8TFxREVFUVHRwdvv/02SUlJbNiw4YE/X7NmzeKNN97gzJkz7o7E4eHhPPPMM+59Pz05dOgQ58+f59ixY+6ksbOzk+bmZhYuXEhNTY3X1+zt7eXGjRtkZWXR19c34prRqWjYpPN2VfNNYCPwLfB/01/ZbAPOPmjCKaX87/Q3J7rzWD7gtSWZlLIaGPLblFK+Q3/iONJr/szDsRY8VEUVRVEURVGmI5vNxuXLl3nzzTc9juv1eubPn8/Zs2fHpSN/SEjIiJWlW7dusXfvXtra2oiMjMRqtbJr1y7y8vLGfOrvWGlubmbGjBlexwcayzQ3N6ukcxJcvHiR48eP8+qrrxIaGuo+vnLlSn7729/y+eefs2XLkF6h9yw4OJgnn3ySJ598clTn2+12CgsLyc/P91il7OrqcjcU8qa3t5fy8nLS0tIGNTOaLkaqdB4C/glIl1K60281XUBRFEVRFGXq6uzsxNfXF7PZ7HHc5XLhdDqpqanh7NmzpKWlDVmjNp6ampp47733WLx4MRkZGe71ae3t7Rw+fJjOzk42bBj1rnwTRq/X43A4hj3HbrdP6Wrtw0pKydGjR9mwYcOghBP6f28bNmzgl7/8JTdv3iQqyttOjOOjvr4eq9VKT0+Px/GOjg6SkpLIz8/3OC6EIDY2lsLCQqSUZGZmjme442KkR0jfAL8H/A8hxMoJiEdRFEVRFEV5QEajkd7eXpzOIf0Uqaqq4t/+7d8oKSkhODiY0tJS/vVf/5U9e/aMmFCNlcOHD5Obm0tWVtaghiiBgYE888wzXL16lZs3vW1cMHnS0tKor6/3+HOF/gpzY2Mjs2bNmuDIlIHPi7fKvVarZe7cuZSUlExkWED/ek5vnxnobxgUERHhdRr6nDlzsFqtdHR0UF9fPy0LgMMmnVLKFcA8oAH4WAhRLYT4m4Hh8Q5OURRFURRlMlgsFgoKCvj222/57rvvpt10Nj8/P/f6wjvV1NTwxRdfMHfuXB577DHmzJnDvHnzWL16NY2NjXz22aj6Qz6Qrq4uamtrycrK8jhuMBjIysri3Llz4x7LvQoJCSE+Pp6ysrIhnwmn00lJSQmZmZleK8zK+BnYK3W4hCwwMJDe3t4JjKpfREQEvr6+XivgLpeLvr4+tmzZwpw5c9Dp+iejmkwmlixZwvLlyyktLQWYdv8WDRixkZCUshL4r0KIvwCeo7+ZkBZ4XwjxIfCplPL6+IapKIqiKIoy/vr6+vjiiy+4du0aiYmJGAwGzp8/j8PhYNOmTeOy/nG8LFu2jN27dxMeHu6ebnj06FGysrI8Tj+cP38+x44do66ublz3mmxvbycgIGDYKaihoaFcunRp3GJ4EM8++ywfffQRx48fJy4uDl9fX3ciHR0dzfr16yc7xO+lgIAAWlpacLlcXtcDNzc3T8oWPGazmeTkZDIzMykuLh4ynpubi1arpbS0lOzsbFavXo3NZsPHx4empiZOnTpFd3c3AFFRUcNWTaeqUW+ZIqW0AR8BHwkhZtHfYOhP6e8WO702ilEURVEURbmLy+Xio48+wmw288orr7iTIikl1dXVfPzxx7zyyitERkZOeGxOp5NLly5RVFREe3s7vr6+zJkzh5ycHEwmk8drkpOTWb16Ne+//z5JSUmEhoa69+j0RKvVEhcXx/nz58c16fTx8aGnpwcppdeqVHd3Nz4+PuMWw4MwGAy88sorVFdXc/78eRobGwkMDOSFF14gJiZmWk59fBiEh4fj5+fHlStXSE9PHzLe19dHaWkpr7322iREB5s2baK2thatVkt5eTnd3d1otVpycnIwm82888472Gw2oL/CaTQaCQoKYv369djtdqD/4VBqaiparXbYvz9T0T3v0wnu6ucfCyH+nP79MBVFURRFUaa1q1ev0tvby1NPPTWoUiKEIDExkby8PI4dO8bWrVsnNC6bzcYHH3yA1WolKSmJtLQ0ent7uXTpEvn5+bz66qsEBQV5vHbu3LmkpqZSUlJCVVUVPj4+w3aFNZvNWCyW8XorQP+WLX5+fl63FZFScvHiRVaunLrtRAY+E6pD7dSyZs0aPv30U/R6PUlJSe6krKOjg927d4/Zfp3349atW9hsNmbOnElMTAxSSjQaDQ6Hg127drkTTgCr1YrVaqW9vZ3vvvuOhIQEenp6SEtL4+bNmyQmJn4/ks4Bt6ufH49RLIqiKIqiKJOmuLiYrKwsr0lZeno6+fn59Pb2TmgVbt++fWg0Gh599FH3l0x/f3/Cw8O5evUqH330Eb/3e7/n9Quoj48PixYtYtasWbz99tvDflnt6uoa9+mHQgiWL1/O3r17mTFjxqCEWUrJqVOn0Gg0pKSkjGscU4mUkqtXr5Kfn09zczN6vZ7MzEzy8vImtKvwdJeQkMBzzz3H3r170el0REZG0t3dTV1dHXl5eaxYsWLSYtu5cycOh8Nd5TcYDLhcLrRaLXV1dV6vKy8vZ/369XR0dHDp0iV0Ot2wU4inqgdKOhVFURRFUR4WnZ2dZGRkeB03GAz4+vpO6NTP7u5uLl++zBNPPOExUUxOTqa2ttZr1fBOISEhBAYGcvPmTaKjo4eMu1wurl+/Pib7GI4kLS2Nnp4eduzYQXx8PNHR0VitVsrLy/H39+fll1+edl+q75fL5WLnzp3U1tYSFBREVFQUDoeDy5cvU1BQwMsvv0xMTMxkhzltJCUl8bOf/YyamhpaW1sxGo288MILGI3GSYvp1q1bdHZ2YrPZkFJSWVlJW1sbgYGBI25/4nA46OjoID8/H61WO2X3sB2JSjoVRVEURVHo7/hqsVg8JmTQv/9iT0/PhHYmrampISwsDIPB4HFcCEFUVBSVlZWjmuq5evVqdu7cidFoHDTN0G63U1JSQnR09Lit53S5XHR1dSGEwM/Pj3nz5pGRkUFJSYm7uvfss89+79ZFnjx5kvr6ehITE93JhNFoxGw2Yzab+fDDD/nDP/xDr58BZSghBAkJCSQkJEx2KABcuXIF6J/mW1BQQEREhLshkFarJSgoiLa2No/XJiYm0tHRQVhYGBkZGTgcjmn590MlnYqiKIqiKEBOTg5Hjx4lIyPD45e68vJyEhISJnRq7Wim0Wm1Wlwu16jul5SUxMaNG/nyyy/x9fUlMDAQu91OQ0MDaWlpPP3002P+hdbpdPLtt99SWFiIw+HA5XJhNptZuHAhubm5LFy4cExfbyIMdA+9c4/R+71Pfn4+sbGxHn/PAQEBdHR0UFpa6rUBlDL1tbe3A/17ia5Zs4auri5aW1vx8/NDq9WybNkyvvjiiyHX+fv7M3fuXDQaDWFhYXz33Xde/32a6lTSqSiKoiiKAsyaNYvTp09z7Ngxli1b5t4rT0pJTU0NBQUF/OhHP5rQmKKiomhqanJXRDxpamoiLS1t1PdMS0sjJSWFyspKmpubMRgMPPvsswQEBIxV2G5Op5OPP/6Y3t5eli1bRkhICFJKGhsbKSwspL6+ng0bNkyLL9FSSi5dusTp06e5efMm0N8xddGiRcydO/e+3kNTUxMajcZrB2Lor8BXVFSopHMaczqd9PX1kZ6ezunTp2ltbXWPhYWFERgYyIYNG8jPz6exsRGtVktqaipLly6lpaWFzs5OysvLSUlJweFwoNPpvl+NhIQQgcBaKeUnYxSPoiiKoijKpNBoNLz00kvs2bOHd999l6SkJPR6PfX19dhsNrZs2UJUVNSExhQSEkJkZCRVVVWkpqYOGW9sbKSrq2vYtaieaLVaj9tKjLVz587R3d3NqlWr3JU8IQQRERGsXr2aAwcOUFlZ6fG9TSVSSr766isuX75McnIy2dnZCCFoamrixIkTVFdXs3HjxntOAkZTyRZCTMt9GZX/FBgYiE6no6CgYFDC6evrixCCvXv3kpmZycaNGzEajWg0GhoaGjh9+jRSShYtWoSfnx8BAQEUFxezePFinE6n+8HYdPCgkSbQv3enSjoVRVEURZn2jEYjP/jBD2htbaWyshKHw0FmZibJycmTVlXYuHEjb7/9NlarleTkZMxmMzabjerqaq5evcrGjRu5cOECNpuNsLAw4uPjp0wFpKCggHnz5nlMrPR6PbNnz6agoGDKJ51VVVVcunSJhQsXuvdvhf5KZ0hICAUFBVy4cIHs7Ox7um9oaCh9fX3Y7fZB971Td3f3iM1mlKnNaDQipaShoWHQcSGE+6FCaWkp169fJzQ0FIfDQV1dHTabjaioKEwmEzdu3KCkpMT9d0lKORlv5b4Nm3QKIYJHuH7GGMaiKIqiKIoyJQQHB0/YWsOB6bu1tbUAxMfHExcX504cZ8yYweuvv86pU6c4duwYLpcLKSWpqakkJSWxa9cuwsPD0ev1nD59GiEEGzduJD4+fkLi98blctHS0kJERITXcyIjIykuLp7AqO5Pfn4+8fHxHhNDrVZLYmIi+fn595x0GgwGsrKyqK2tJSoqasjDAqvVSkdHh5paO811d3fT3d095LjL5UKn05GQkMC8efMwmUzU1dVhNBp59NFHqa6uxuFw4HA4qKmpAfqn4w5cN52MFG0zMFwaLUYYVxRFURRFUbxoamri008/xeVykZSUBMCePXswGo08//zz7g6z/v7+rFu3jieeeAKbzYZOp+Pzzz/HYrGwcuVKd2dTKSW3bt3i448/nvStNu6s4nibQupwOB64Gc9EqKurY/HixV7Hw8LCKC4u9rjOTkpJc3Mz3d3dBAQEEBw8uKazZs0a3n77berq6ggJCcHHxweXy0VbWxvNzc2sX79e7dU5zbW0tHjseq3VanE6nSxbtoyvv/6akpISd1Mws9nMM888Q3x8PH19fQDu/Wu1Wu2Umc0wWiMlnV3A3wFnvYwnA/86phEpiqIoiqJ8D3R2dvLee++xZMkSsrKy3F8ily1bRklJCe+99x5vvPHGoC+rA01nbty4wfXr13n88ccHJW1CCCIjI7HZbBw6dIjXXnttwt/XnbGkpKRw7do1r9Nnq6urp/zUWuj/uQ83nXFgbebdicCVK1c4dOgQXV1dGAwG+vr6CA4OZt26dcTFxQFgMpn4nd/5HU6fPk1RURF9fX1IKUlMTOTFF1+c9Iq18uA0Gg1arZaoqCh3EyqA3t5ehBCcPn2auro6XnjhBYKCgpBSUlFRwa5du/jJT36Cj48PjzzyCL6+vkD/522godB0MVKkpUCvlPIrT4NCiLljH5KiKIqiKMrD78yZM8yaNWvIlEwhBDk5Oe4Or8uXLx9y7blz54iNjfVaJZw5cybl5eW0t7cTGBg4HuGPyuLFi9m5cydRUVH4+/sPGmtpaeHKlSv8+Mc/nqToRi8pKYmGhgave6E2NDQM2ROyrKyMvXv3EhYWRkhICEIIpJR0dHSwfft2tm7d6r6f0WhkxYoVLF++3F3Jng4VYGV0zGYzGo2GBQsW8M0337ibCQkh0Gq1hIWFsXTpUmprazl79ixGo5G5c+cyb948ysrKmDVrFtXV1cyePRsfHx+EEFit1mlVAR8p6TwEhA4z3oZqIqQoiqIoinLPSktLee6557yOz507l71793pMOjs6OoZM07yTVqvFbDbT0dExqUlnQkICy5cvZ//+/aSkpBATE4PL5eL69etcu3aNDRs2EBo63FfNqWHRokVs376diIgId7VpgNVq5dq1azz77LPuY3a7nb1797qbwAwQQhAYGIhWq2XPnj38wR/8waDqqBACo9E4/m9ImVDR0dHurYIee+wxOjs7aWlpISgoCKfTSXJyMu++++6gKujx48dZvnw5eXl52O12Lly4wIULF3j00UdJTk722nhqqho26ZRS/vUI47XAi2MakaIoiqIoyvdAd3f3sAlhYGAgPT09Hsf8/Py8jkH/9Luenh6P68gmWm5uLgkJCRQVFVFSUoIQgsTERNauXTupCfG9mDlzJitXruTIkSPExsYSERGBEILGxkZqa2tZvHixe00uwOXLlzGZTF733zSbzbS1tVFTUzOkQqo8fGw2Gy6Xi66uLiIiIjCZTERERODj44PD4eDgwYODEs4Bx44dIzExkfDwcPexU6dOkZCQQEtLC9HR0RP5Nh7I9JkIrCiKoiiK8hDx9/entbV10BfKO7W2tg6ZkjogJyeH3bt3k5SU5LGhSENDA0FBQcNWQydSaGgo69atm+wwHkhubi4xMTHk5+dTUlICQGxsLC+++OKQhk3Nzc3DVqKEEJhMJpqbm+876ZRSYrfbEUJMuaqX1WqlvLzc3TwpPT19ysU4kQYaBiUlJXHmzBlaW1tZtWoVp0+fZsmSJVy8eNHrtQUFBaxdu9b9Zykl5eXllJWVPZxJpxAiHfg/gIGNgi4B/ySlvDwegSmKoiiKojzMcnJyOH/+/KAvlHc6f/48OTk5Hsfi4+MJCgqipKSE7OzsQev/2trauHjxIs8///y4xP19FhkZyaZNm0Y8z2g0uruQeuNyue5rKq3L5eLs2bMUFBTQ1tYGQFRUFIsXL2b27Nn3fL+xJKXk6NGjfPvtt+j1eneDpS+//JLVq1ezYMGCSY1vssTFxaHVarlw4QIxMTEsXLiQtrY2Wltb6evrw+l0erwuNjaW+Ph4DAYDa9asobCwEIvFQm9vL729vRP8Lh7MqJJOIcRm+tdulgCnbx9eApQKIbZIKT8fp/gURVEURVEeSgsWLODXv/41Z86cITc31504OhwOCgoKaGpqYuPGjR6vFULw4osvsmvXLr7++muioqLQ6/W0t7fT0dHBM888M2i6pzKx0tPTOXHiBKGhoR63i3E4HHR1dZGSkuLxepfLRVVVFY2Njej1elJTU5kxYwYul4udO3fS3t7O4sWLiY6OxuVyUVNTw+HDh6mvr2f16tXj/fa8+vrrrykqKkKv16PRaNzvXafT8fXXX6PRaL7Xe46mp6dTXV1NbW0t9fX12Gw29zRsq9XqPi84OJhnn30WvV7PlStXKCoqIiMjg2eeeYZvvvmG6OjoaVXlhNFXOv8e+Ecp5Z/ceVAI8XfA/wRU0qkoiqIoinIPfH192bZtG7t376a4uNg9zfLatWtERkby6quvel0TCGAwGNiyZQstLS2Ul5djt9vJzs4mPT19Wm2l8DAKCQkhMTGRxsZGwsPDB02BdrlcNDY2Mm/ePHx8fIZcW11dza5du9DpdMyYMQOHw8H/z959R9V15Yme/56buIRLRiByBpEkklBE0bJky5bl0JaDbJdddrlqdVWHqX7TPfPeH2/1qhd6XndN18xUVVc722XLSbbVLiNZOSABAgESQWSRc7yEm8/8gbglDBeQRBDS/qyl5cXZJ/wOF/D5nb33b586dYqoqChCQkIYGBhg79699pcUSqWSyMhIAgMDOXz4MDExMUuyzMrIyAj5+floNJopQ74nks8TJ06Qmpq6IJV5ZVmmsbGRgYEBtFrtPVVsZ2hoyP4ioaqqipSUFCRJIjIyErPZTEZdYRNhAAAgAElEQVRGBhcuXADG5/u++OKLnD9/nqKiP69a+f3335OZmcnOnTtRKBSkpKQs1e3ckbn+RQoG3ppm+9vAL+YvHEEQBEEQhAeHh4cHL7/8sr0gjSRJbNmy5bYquvr4+LBx48YFjFK4E08++SSHDh2iqakJNzc31Go1ZrMZvV5PdHT0tMOqW1tb+fTTT0lOTsbPz8++fdWqVVRUVHDmzBl27tw5bdKm1WpJTk6moKBgSZLO8vJyVCrVtHOM4c9rndbW1hIXFzev166treXIkSOYTCb7NpvNxsaNG8nOznYY02KRZRmVSkVtbS0wPuc3KioKvV6PQqEgKyuL0dFRSkpKyMzMpLq6elLCOeHy5csEBQWRmJg44wupe9Fck848YDVQ84Pta4DCeY1IEARBEAThAbNixQqHBYWE5Umj0XDw4EFaWlooLi5Gr9fj6elJeno6AQEB0x5z6tQpYmJiJiWcMN6bmZSUxJkzZ2a8ZmhoKGVlZfN1C7dFr9djtVpn7MWUJAm9Xj+v162rq+Ozzz7DZrMhSZI9wZRlmYsXL2IwGBzOm14sE/c9MXezvb2dNWvW2Oe8vv/+++zevZvNmzejVqt59913HZ4rLy+P+Pj4xQp93sw16fw98C+SJEUznoACrAN+CvwnSZLSJnaUZfnK/IYoCIIgCIIgCMuPJEmEhIQQEhIy677Dw8O0tLSwbds2h+eKjIyksrLS4fmsVuu0c0gXg06nm9OwWUcVme+ELMt8++239uTtVpIkYbPZKCwsZN26dUu6PI9arZ40lFqWZdrb2wkNDcVoNNLZ2cn777+Pn58fr7/+On19fQ7P1d/fvyDDkxfaXH8qP2F8iO1/A07d/PffgJCbbZcZ7/G8vAAxCoIgCIIgCMJ9bWRkBGdn5xkTion1PR2pq6sjKipqIcKbVWJiIhaLBVmWp2232WzYbLZ5ja+9vZ3R0VGHw2cnej5LSkrm7Zp3IigoCDc3t0kJt8FgoKurC61Wa69i3N3dzdDQ0IzD6319fbFYLAse83yba9K5apZ/Cbf8VxAEQRAEQRCE2+Dq6srY2JjD5TNgPDEdHR1ldHR0SltfXx8VFRULuixJV1cXJ06c4MiRI1y4cIHh4WF7m6urK2vXrsVqtU5ZLsZms2G1WtmxY8e8FrkaHBxEoVDMOGfTarXO2HO4GJRKJRs3bmTTpk32++/s7MTDwwOLxUJ6erp939LS0hk/w/Xr1y/LQmFziliW5aqFDkQQBEEQBEEQHlRubm4EBwfT2tpKaGjolHabzUZraytxcXEcPnyY5ORkQkNDsVqt1NfXU1lZyZ49e6bMB50PZrOZL774ghs3bqDVapEkidraWs6ePcuGDRvYunUrkiTZK6vm5eWhUqmw2WwolUosFgs7duwgMzNzXuNycXFx2LM6QaFQ4ObmNq/XvRNbtmyhr68PZ2dnKisr6e3tRZIkFAoF69evZ2RkhKtXr1JYWMhrr73Ghg0buHTpkv3+FAoFmzZtIjg4eInv5M5Is31Q9h0lKRz4JZB4c1MZ8M+yLN9YiMDudRkZGXJhoaihJAiCIAiCIMyP1tZWPvroI5KTk/H19bX34FksFioqKlAqlbz88su0trZSUFBAS0sLkiQRHR1NZmbmbVU9vh2ffPIJbW1tuLu7T+pVtFqtDA4OsnnzZtavX2/fbjAYqKysZHR0FHd3d+Lj4xdk+RKbzcY///M/YzQap53LKssykiTx4x//+J4p1NXa2srFixfp7Oykp6eHsbExPvroIx555BHc3Nxobm7GxcWFoKAgbDYbNTXjdVyjo6Pp6+ujpaWF9PR0fvWrXy3xnYyTJKlIluVZF1+dU9IpSdJWIAeoA87f3LwZiAR2y7J87s5DXZ5E0ikIgiAIgiDMt4l1OpVKJZ6enlitVjo7O4mKimLfvn1oNJpFjae7u5u33nprUhJ8K7PZzNDQEL/85S+XpMBNcXExOTk59gRzwsTXERERPPfcc4se12xGR0f51a9+hcVi4de//jUwPi82LCwMs9lMUVERKpWKNWvWkJyczOeff47ZbOb5559HpVLxj//4j0t8B+PmmnTOdUDw/wDelWX5Zz+4yO+A/wmsn/YoQRAEQRAEYcHYbDYMBgNqtXpBepKExRceHs5f/dVfUV9fT1dXF2q1mv379+Pp6bkk8Vy9ehUnJyeH8ybVajVKpZKGhgaio6MXOTpITU3FaDRy8uRJJEnCYrHYez2joqJ46qmnFj2muWhoaECSJFQqFYmJiaxduxZnZ2eamprQ6XS88sorVFZWolQqMZvNREREkJaWtmTVie/WXJPO1cAr02z/V6B43qIRBEEQBEEQZjU2NkZubi7FxcXYbDYsFgsRERFs3LiRsLCwpQ5PuEsKhYLo6OglSeJ+aHR0dNZER6lUYjAYFiyGvr4+8vLyqKysxGq14ufnR1ZWFqtWrUKSJNatW8eaNWu4du2afd5kUlIS3t7eCxbT3ZooGCXLMnv27OHo0aNcu3bN3q7Vann00UeJiIhAlmVCQkIoKirC29ub2NjYpQr7js016RwGVgLXf7A9CJjfFV4FQRAEQRAEh0ZHR3nnnXdYuXIlzz77LN7e3pjNZq5fv87nn3/Oww8/THJy8lKHKdwnfHx87PMKpyPLMiaTacF6Ymtra/niiy/w8fEhPDwcpVKJXq+3J2nPPPMMCoUCrVY774WKFoLRaKSsrIympiZUKhUmk4kLFy5MSjhhfF7sV199xZtvvolOp0OlUqFSqWhrayM+Pn6Jor9zc006jwB/kCTpx0DuzW2bgN8B3yxEYIIgCIIgCMJUx44dIzw8nK1bt9q3qdVq/P39WblyJUeOHOHo0aMEBQWxdu1aoqKiZlxSQhBmsnr1as6cOYOrq+u0czYNBgPOzs4EBQXN+7VHRkb44osviIyMnFSB1sfHBy8vL+rq6rh48SKbNm2a92svhKKiIr777jt6enrsVYpdXV25cuXKtPvbbDby8vJISUmhpqaGzMxMWltbFznq+THXQcF/y3gv52lg7Oa/k0Al8L8tTGiCIAiCIAjCrUZHR6mqqiIrK2vS9vLycr744gs8PT3Zv38/jz/+OP7+/vzpT3/i2LFjsy4rIQiOuLq6smnTJgYGBjCbzfbtsiwzNjbG8PAwe/fuXZAXG1euXMHDw2PaJU8UCgWBgYHk5+dPWRf0XlRWVmZ/IXT69Gmqq6sxm80YDAaMRqPD47q7u3Fzc6OsrIxPP/2UoKCgZfn7PNd1OgeBxyRJSuDPS6aUy7JcsWCRCYIgCIIgCJN0d3fj6+uLs7OzfVtvby9nz57lkUcemTTEMS4ujoiICP70pz8RFBQkhtwKd2zz5s1otVrOnDmDQqFAqVRiMplwdnbmwIEDRERELMh1a2pq8PDwcNju6uqKzWajv78fHx+fBYlhPsiyTE5ODhcuXECv//PMRL1ej4eHByqVCovFMu2xHh4e9iJho6OjXL58mY0bNy5K3PNpxqRTkqR6IFOW5V6Am0mmSDQFQRAEQRCWgEKhmPJwWlJSQnx8/LRz6jQaDenp6Vy6dEkkncIdkySJtWvXkp6eTmNjI2NjY3h6ehIYGLigQ7d/uAyKo9ju9Z6/jo4O9Ho93d3dk7b39PQQEBBAYmIipaWl0x6bkZGBSvXnlK2uro7NmzcvaLwLYbbhteHA4i+4IwiCIAiCIEyxcuVK9Ho9/f399m2NjY0zVqwNDg6mq6tr0tBIQbgTSqWSyMhIEhMTCQoKWvC5wuHh4QwNDTlsHxsbw2az4eXltaBx3K3R0dFph9AODg6iVCrZsWMHISEhk9okSWLHjh14eHigUChwdXVdrHAXxFwLCQmCIAiCIAhLTKVSkZGRwalTp9i3b5+9B2R4eJi+vj4kSWLlypWTHlAlSVoWvUGCMGFwcJD29na8vb3p7e2dMqQcxntB29vbSU9Pn7bA0b3Ew8MDZ2fnKb+HKpUKSZIwmUw89dRTdHd309jYiEajITExkZGREbRaLQaDAa1Wy8jICBEREfblVpaTuSSdAZIkzbifLMtt8xSPIAiCIAiCMIMtW7bw5Zdf8sc//pFVq1ZhNBo5d+4cvr6+yLLMxYsXCQoKYvPmzWg0Gtrb2/H09LTPCxOEe9XQ0BBff/01zc3NODk5IcsyNpuNyspKVq5ciZ+fn33JlK6uLnQ6HVu2bFnqsGfl6+uLt7c3QUFBtLS02LdP9BQ3NTUREhKCXq/H09MTWZZpbW0lLCyM69evExsba09AMzMzJw23XS7mEnHxDG0SICOG4AqCIAiCICwKhULB008/zbVr1/j222+JiIggPDwchWJ81pTFYqG6upqcnBx2795NYWEhWVlZYtkU4Z42MjLCv//7v2Oz2XBzc7P/vKrVaoxGI729vfblQjw9PcnKyiIjI2PRejllWaampoYLFy7Q2dmJUqkkPj6ejRs3zqmI0RNPPMHAwAC5ubm0t7cD40uimEwmvv/+e4KDg0lLS8PPzw+r1Up1dTVlZWXExcUhSRJJSUkkJCTQ0dFBVFTUQt/uvJtL0vk00LfQgQiCIAiCIAhzI0kS7e3tBAYGEhkZOalNpVKxatUqCgsL+frrr7FYLERERHDy5En6+vpwcXEhOTmZkJAQkYgK94xz585htVrRarWTtisUCvsQ06effpr4+Hj7C5bFIssyhw8fpry8nM7OTsbGxpAkiY6ODq5evcozzzxDXFzcjOcIDQ3lpZdeQqVSMTg4SG9vL25ubqhUKnQ6HWlpaYSEhNDV1YWLiwsZGRmkpaXZh+SmpqZisVgYGRlZFkvE/NBcks5cWZa7FjwSQRAEQRAEYU5kWaakpIS1a9dO2y5JEmFhYdTX12Oz2fjDH/5AREQEHh4ejI2N8eWXX+Ll5cWBAwemPOQLwmIxmUy0tbVhsVgoLi6eMm9zgiRJKJVK8vLySEhIWOQo4fLly1y7do2GhoZJczJ7enrQ6/V8/vnn/OIXv8Dd3X3G84SHh+Pp6UlTUxMqlco+N/OFF16guLiY7777DqVSyWuvvUZXVxfHjx+nqakJgLCwMHbt2sWqVauQJAmbzbboyffdmC3pFDPOBUEQBEG4J4yNjWEwGHB1dUWj0Sx1OEvKbDZjNptxcXFxuI+bmxsGgwEnJyceeughnJyc7G3x8fEUFxfz2Wef8dJLLy1GyIJgZ7FYOHHihD3RlGUZs9mMm5ubw2OUSiUDAwOLGOU4WZY5f/48bW1t0xbjMhqN6PV6CgoK2Llz54zn6uvro6urCz8/P8bGxtBoNEiSRHl5OZcuXQJg37596PV63nvvvUkFgxobG3n33Xf5yU9+gpeXF1ar9b5KOsWYC0EQBEEQllRzczNnzpyhubkZjUaDyWQiLi6Obdu24e3tvdThLQm1Wo0kSRiNxknJ5K3GxsawWq2sX79+yj6SJLFmzRqOHj1Ka2srQUFBixH2FBaLhbKyMkpKStDr9bi6urJ69WpSUlLuicJHPT09XLlyhb6+PrRaLUlJSURFRYlhyXfBZrPxySef0N/fT3R0NBqNBpvNxrVr12Zcl9Nms83YKz/RW5qbm4ter0epVJKQkMDmzZvnNOdyOh0dHZw9e5bBwUG8vb0ZGRlBr9dPGd7a39/P9evXZ0w6e3t7+f3vf095eTkNDQ1YLBaCgoJIS0vj8uXL9v1CQkI4cuTItBVqLRYLZ86c4dFHH70nfj9ux2xVaectfZYk6SDw10AkoAVuAP8O/Fq++dpAkqQs4NdAGtAPvAf8Z1mWZ6wLLEnSK8A/ML6uaAPwj7Is//GW9u3A/wcEAyeBV2RZHril/QvgsizL//Pu71QQBEEQhB/q6uqisLCQ3t5enJycSE5OJi4ubtY39dXV1Xz11VfExsayc+dOlEolJpOJxsZG3n77bV555RX8/PwW6S7uHZIkkZiYSEtLi8OiIi0tLajVaodD/hQKBaGhoZSXl8+adE5U0xweHkan0xEYGHjXidfY2BgffvghGo2GtLQ0vL29GRgYoKSkhPz8fF566aUZe74WkizLHD9+nJKSEmJiYggMDGRsbIxjx46h0Wh4/vnnl/26iUulsrKS7u5uIiMj7T9DCoUCnU6H0Wh0mFjabDbS0tKmbTOZTLzzzju0tLTQ1tbG2NgYKpWKzs5OysrKeOGFF4iIiLitOE+dOkVubi5dXV3o9XoUCgXu7u6EhITQ2dmJwWCYtP9sSxJ99dVXlJSUUFNTY9+2cuVKDAYDo6Oj9m1OTk7U19c7PE9dXd2ySzhhcdfp7AL+EagCjMBm4LeAFfhXSZJCgOPAl8DrQAzwDuO9rX/v6KSSJD0BvA38EsgB9gIfSJLUJ8tyjiRJCuAQ8L+Ab27u+38Cf3fz+KeBMODZeb5fQRAEQXjgybLM999/T2lpKSEhIfj4+GAymTh16hSnTp3i4MGDDpMii8XC119/TXp6+qTF3zUaDTExMWg0Gr755ht+/OMfL9bt3FM2bdrE22+/jU6nY8WKFfbtsizT2NjIwMDArEnbRIGWmVRUVHDy5EkA3N3dGRgYQKlUsmvXLmJjY+84/m+++YbAwEC2b99uTz58fHyIjIzkwoULfPnll7z88st3fP67cenSJWpra3nyyScn9RJPFGg6dOgQr776qujxvAP5+fl4e3tP+d4FBARQW1uLSqWatCSILMuYTCYkSSI1NXXacx49epSGhgYaGxvt28xmM52dnej1ej7++GN++ctfOhwV8EPl5eWcP3+eqqqqST2OIyMjDA0N2Zc+mWjT6XRTCnrdanBwkNbWVurq6qa0aTQaFAqFvfdUlmXUajVms3nac2k0mmW55u6iDQSWZfmYLMtfy7JcKctyvSzL7wPfA1tv7vJTYAh4TZblclmWvwb+C/BzSZJmepX0n4BPZVn+tSzL12VZ/l/AYeB/v9nuA/gBv5FluQr4I5AAIEmSN/AvwI9m600VBEEQBOH2Xbp0iaqqKrKzs4mLiyMgIIDQ0FDWrVuHr68vf/zjHx0+QJWXl+Ph4TEp4bxVSEiIfY7Ug8jX15cXXniB6upqLl68SF1dHdXV1Vy4cIHm5mZsNtu0QwFv1d/fj6+vr8P20tJScnJy2LBhA0899RS7du3imWeeISsriyNHjlBeXk5rays3btxAr9fPOfa+vj6amprIzs6eknxIksTGjRvp7e2lo6NjzuecL1arlYsXL7Jp06ZphyVnZGQwPDxsL/Ai3J7+/v5p5yK7uLgQHh5uH8JqMBjs/9RqNT/60Y+m7QU1Go1cvXrVvpzKD42OjjI6OsrVq1fnHOOpU6doamqadojrRHw6nQ4Yrxbt5eXFunXrHJ6vp6eHsbGxKb+LIyMjGI1GoqOjJ+2blJTk8FxJSUnLsnrtksw+lcatBTYCp29u3gh8L8vyrd/Fo4ALMO1rDUmSNEDmzf1udRRYJ0mSEugB2oA9kiSpgYeA0pv7/QZ4S5blsru/K0EQBEEQrFYrQ0NDjI6O2h/eU1JSphT+kSSJ6OhozGbztG//YXw+laenp8NrKRQKfHx8liQxuVcEBwfzN3/zN+zZswdnZ2cMBgO+vr4kJyfz6quv4uXlRXNz87THjoyM0NbWxurVq6dtN5vNHDt2jN27d08aTitJEsHBwezcuZOvv/6ab775htOnT/Pb3/6WTz/9lP7+/lnjrq+vJyoqyuEwQYVCQWxsrMOfjYXU3NyMq6urw5cdkiQRFRVFeXn5Ikd2f9BoNFgslmnb3N3diY+Px2q1EhcXR0JCAk8++SS/+MUvHM7L7OzsxGq1OjwnQHd3N9XV1XOKb3R0lP7+foaHhx3uMzg4iIuLC+7u7kRERLBr164Z541qNJpJvbcTmpub0Wq17Nq1Cy8vL9zc3Kiurmbr1q3T/vz5+PiwefPmSZVvl4vFHF6LJEkeQCugYTzh/a+yLP/mZvNKIPcHh3Tc0jYdX8bv4Yf/t+kAnABvWZa7JUl6hvEezf8bOAv8d0mSHgWSgF9IkvQekA1cA96QZbnTQfxvAG/A+Fo7giAIgiCMMxqNnDt3jitXriBJEhaLBQ8PDwCHw2clSSIwMJCjR4/y5JNPEhgYOKldqVTO+mBltVqnfZh7kCgUCuLj44mPj5/S9uijj/LRRx8hyzKhoaEoFApkWaa3t5fCwkKys7Mdzk28fv06fn5+Dos1+fv74+Pjw7Zt24iNjcVoNFJQUMC7777Lq6++OuMLA5vNNuvnplKplqRHx2QyzToMU6vV3lbPrvBnycnJVFRUOPy5GxwcJC4ujn379s35nLMNc76dYdAWi2XW/W02GxqNhoyMDLZu3Up4ePiM+wcFBdmT1KGhIft2FxcXTCYTJSUlHDx4EEmS6OzsxGQy8ZOf/ITCwkIqKiqQJImEhATS09Opq6sjLi5u2Q2xXey/0npgDeO9lxsYT/7aZFl+eyEvKsvyRcDe530z+f1/gacYny+qZnwO6X9nvPdz2vmdsiz/AfgDQEZGxvL6pAVBEARhgRgMBt555x3UajWpqam4urpis9no6emhqqqKuro6h8Vu1Go1sizz8ccfk5mZyZYtW+xtcXFxXL16ldjY2GkfAo1GI729vQ4LhAwODmIwGPDw8Hhg16IMCgri4MGDHD16lLKyMvs6nbIss23bNtasWePw2P7+/lmrA/v6+tp7Np2cnNi8eTNWq5WTJ0/y1FNPOTwuMDCQS5cuOaxWKssyDQ0N7Nq1a453On98fHzo7e3FarWiVCqn3aenp4fg4OBFjuz+kJmZSUFBAQMDA1NeTIyMjNDT08Mjjzwy5/MFBASgUChQqVQOezt9fX3nPP/Yzc0NlUplr5Q9HZ1OR3JyMs8888yczqlQKMjOzkav13PixAn7fM3w8HCKi4tJSUkhPz+foqKiSW179uwhLS0NWZbp6enh/Pnz+Pj4zOmlzb1mUaO9OXS29uaXVyVJ8gJ+xXhxn3Yg4AeH+N/8b7uDU/YAFgfHGYE+B8f9M/CxLMtXJEl6C/gvsixbJUn6gPGeUEEQBEEQ5uj06dNoNBri4+MnVaNcsWIF7u7u5Ofn4+/vP21Rm/7+fkJCQoiOjubEiRP4+/vbe+yCg4PR6XTU1NQQExMzKTmx2WyUl5eTnJw8ZUH56upqzp07R39/P66urgwNDREbG8v27dsnPeRarVauX79OfX09siwTHh5OQkLCsnuYm01gYCCvvvoqvb29DAwMoNVq51R9VqvVMjY2NuM+Y2NjUxL6rKwsfvOb3zA2Njbls5kQFBSEVqvl2rVrpKSkTGmvrq7GYrHMWJxlofj4+ODn50dNTc20vcfDw8PcuHGDxx57bNFjux+4urry0ksv8dFHH9mLXUmSxOjoKHq9ftpRDzPRaDSkpKTYP5cfcnFxsS/FMxcKhYLMzEwGBwdpaGiY0q5UKvH392fjxo1zjhFg48aN9t+/mpoaent78fLyQq/XU1ZWRl5e3qT9b9y4wVtvvcUbb7yByWSiuLgYtVo9p6rf96Kl/quqYHz5FBgfWntQkiTFLfM6dwOjQPF0B8uybJIk6TLwMPDBLU27gbzpigNJkrST8V7P9FtimJhQMDHsVxAEQRCEOTCbzZSWlpKRkTFtEqPValm5ciWNjY0kJiZOahsdHaWtrY309HScnZ1JSUkhNzfX/qAvSRIHDhzg/fffZ2BggODgYJydnRkaGqKpqQlfX19279496ZxFRUWcOnWKmJgYEhMTiYmJwcXFhcuXL/POO+/wyiuv4O3tTVtbG5988glOTk72uVPnz5/n2LFjPPvss/flNBofH5/bWq9w1apVnDp1ivXr10+ZkwvjCWdra+uUHk1nZ2c8PDwYHBx0mHRKksT+/fv54IMPGBwcJDU1FTc3N0ZGRigtLeXKlSu88MILS1Ydds+ePXzwwQfYbDZiY2NRqVTIskxHRwe5ubls3bpVLJlyFwICAvjrv/5rKisrqa6uxmazkZSUxOrVq+dcYfZWe/bsob29HZVKRXt7O2NjYyiVSry9vfHz8+O5556b9mfYkezsbKqqqpBlmba2NnuPp6urK2FhYWRlZd1WYgzjP/N79+4lKyuLvLw8Ojo6aG5uJiIiguPHj097jNlspqCggPT0dAIDA4mPj2dgYICAgAAsFsuyekG2aJFKkvRfgfNAPeNJXjbjFWbfvbnL74C/BP5dkqR/AaIYX2Ll/5FleeTmOYIYX2fzH2RZ/urmcf8EfCFJUgHjBYQeBZ4Eprx+kiTJDfg98IIsy8abm88BfylJUiXwt4ieTkEQBEGYs4GBATQajcPkAsaTnerqamJjY+3DaXt6euy9XBOVLENCQrh48SImk8n+gKjT6XjzzTepqKigpKSEtrY2PD092bt3L1FRUZOSkrq6Oo4ePYpOp8NkMtHQ0MD58+cJCgpi//79qNVqcnJyeOyxx/joo4+IjIyctMZncHAwvb29fPLJJ7z++uuzDi293+l0OpKSkjh16hQ7d+6c9IBrMpk4efIkaWlpUz57WZYZGxub9SF/xYoVvPbaa+Tm5vL222/bj01ISODVV1+9rQR5vvn7+/Pyyy/z/fffU1JSgqenJ6Ojo6jVanbs2DFt76xwe1QqFcnJySQnJ9/1udRqNa+99holJSXk5uYyNDSEUqkkMTGRTZs23fbPkkaj4fXXX+fs2bNcvnx50tIo27dvv6vP38/Pz95L/uGHH1JSUjJpnc4fam9vty8T1draSl9fH/7+/hgMhiVbx/ZOLGZ67M54whcEGBhPPv/h5jZkWW6WJGkX4wV/ioABxudP/udbzqEG4gCPiQ2yLH8tSdKPgf8D+L+ABuAVWZZzponhfwBfy7Kcf8u2/wq8BxQy3qP64t3eqCAIgiA8KJRKJRaLxeHcPBgfxipJEsePH8fNzc2eVK5Zs2bSfExJkiatVzdBpVKRkpIy44Ned3c3n332GevXryckJMQeS3p6OiUlJXz00UccPHiQCxcucPbsWeODbNkAACAASURBVPsQyh/y8fFBr9dz8eJF9u7deyffkmXJZrPZq3XqdDr792/Pnj0cOXKEQ4cOERMTY1+n8/r166SkpLBjx44p56qrq5ux+uutvLy82Lt3L4888ghGoxGNRuNwHuViW7FiBS+++CJDQ0P09/ej1WpZsWKFWJvzHqVSqcjIyCAjI2NezqfRaHjooYfYvn07er0elUqFq6vrvH7+q1evpqqqatq/exMm5shXVFQA439zFQrFHfUIL6VFSzplWf4b4G9m2SeP8QJDjtpvAFM+aVmW32M8cZwthr+cZlsv0/SKCoIgCIIwOy8vL5ycnBgYGHCYZHR3d7Np0yb7fKuJ5OWHD289PT24urre0cPUmTNnSEhImDIsVqlUkpaWxqlTp6itrcXd3Z3S0lKHi8zDeC9XaWnplKRzZGSEgYEBnJyc8PHxuS+SD5vNxqVLlygoKLC/PHByciIrK4usrCyUSiX79++nu7ubq1evotfr8fDwwM3NDW9v7ylzywYGBsjJyWHHjh239f1RKBQz9pYvNlmWaWxspKOjA5VKRUxMjL0as/BgUSqVM1ZivhurVq3CZrMRExNDVVXVtPukpqbaiwvB+MuQoKAgh8sN3auWz0BgQRAEQRDuOZIksXHjRi5cuMCaNWumPAh1d3czODjI6tWrCQwM5LPPPiM5OXlKQmKz2SgrKyMzM/O2kzmTyURNTY3DJRYm1lXMzc1lYGAASZJmTGydnJwwGo32r/v6+jh+/DgNDQ326q9arZbNmzfPy9DApWKz2fjss8/Q6/VkZ2fj5+eHLMt0dXVRVFREW1sb+/fvR5Ik/Pz8JvVqpqen8/HHH1NRUUFKSgrOzs40NTVRVlbGli1bpszfXU5aWlr46quvkGUZf39/zGYzJ06cICoqiscff3zZ9TAJ9y61Wk1sbCxWq5Xu7m76+ibXQM3IyGDlypU0NDQgSRIeHh6EhoayZ8+eJYr4zomkUxAEQRCEu5Kenk5PTw+XL18mKCgIT09PLBYLXV1d9Pf38/zzz6PVagkJCWH16tUcP36cNWvWEBgYiEKhsM/v1Gg0ZGVl3fb1DQYDGo1mxmRAp9PR39/Prl27yM3NRa/XO5yzOdGbB9Db28u7775LcnIyL774IhqNBlmWaW1t5eTJkwwPD7N+/frbjvleUFpaysDAAA8//LB9SKskSfj7+7Nr1y6+++47KisrSUhImHKsp6cnb775JjU1NVRWVmKxWPDz8+OnP/2pw3VZl4POzk4+/vhj1q5dO2mYttlsprCwkI8//piXX355WVYPvV2yLGM2m1GpVA/E/S6FsbExOjs7UavVvPzyy9TU1NDY2IhGoyE5ORkPDw/7GrtRUVHIsozFYqG4uHjZLdkjkk5BEARBEO6KJEns3r2bpKQk8vPzaWxsRKVSkZCQQGpqqr1QEMDOnTsJCAjg4sWLXLhwAaVSiVarJTMzk3Xr1t3RfD5nZ2dMJhNGo9Fh4qnX61EoFPj5+REXF0dNTQ1eXl5TelUnqlVOzAs7evQoq1evnrTcgiRJBAcH89hjj/HFF1+QlJSETqe77biXWkFBAcnJydN+z1UqFUlJSVy+fHnapBPGh8TGxcURFxc343VaW1vtQ6tDQ0MJDg6+Z4cmnz59msTExCnDtNVqNVlZWRw7doyamppZ73k5Gx0d5dy5cxQWFtoL6MTExLBjxw78/f1nOVq4HZ9++inDw8McP36cmJgYYmNjCQsLsyf53d3d+Pv74+TkZP8bplKpKCoqWnZL9oikUxAEQRDuEzabjf7+fmC8J2qxC7IEBwfP6e17UlISSUlJGAwGbDYbzs7Od5WETAxRq62tnXZYpyzLVFRU2OdlxcTEUFNTQ3V1NZGRkfYhwWazmRs3bmC1WsnIyGBwcJCWlha2bds27XV1Oh2RkZEUFxeTnZ19x/HPxGq1YrVaUavV856odXV1sXLlSoftgYGB5OfnO2yfzcDAAJ9//jl6vd5+nYKCApydnXnmmWeWtDrtdAwGAw0NDTz55JPTtisUCmJiYiguLr5vk069Xs9vf/tbrl27RmFhIYODgzg5OZGQkEBtbS0HDx6cVPxLuHNDQ0M0NjbS3NxMR0cHHR0d5OXl4e7ujtVqZWBgALVazbPPPsvp06fx8PAgNjZ2qcO+YyLpFARBEIRl7tZiMDDeE2exWEhPT2fz5s337FpuWq120tcGg4H+/n7UavVtF+rZsmUL7777Lm5uboSGhtqPtVqtFBUVYbPZ7MmlSqVi9+7d5OXlkZ+fbx9KOzAwwIoVK3j11VdxcnKipaUFX1/fGb9/AQEBdHV13e6tz6qhoYFz587R1NQEjC9wP1HcZ74KiExUHnZ0f2az+Y5fXIyNjfHee+8RHh7Ohg0b7J9HSkoK9fX1vPfee7zxxhtz7iE2Go1cvXqVzs5OVCoVcXFxhIeHz2siPjIyglarnfH76+7uTmNj47xd817z2WefkZeXR15enn2b0WikuLiYzs5OFAoFf//3f3/P/k1ZTpqamjAajbS0tNi3mc1ment7J33d19eHTqejq6uLvr4+NmzYYO+BXk7ET4wgCIIgLGOyLHP48GGGhobYu3evffhbb28vubm5fPLJJzz//PML1uvZ29trr/AZFhY2JZGci+HhYU6cOEFlZSWurq6YzWY0Gg2bNm1izZo1c0os/Pz8ePHFF/nyyy8pLy/H398fi8VCS0sLoaGh/OhHP+J3v/sdw8PDuLm5odFoyM7OxmAw0N3djdls5tKlSzz33HP2KqoajWZSQaHpGAyGea8iWVhYyIkTJ9DpdISEhKBQKDAajRQUFFBZWckrr7wyL9eMjo6mvr7e4fDZ+vr6O+5ZKSwsxMvLa8rxE0WdhoaGyM/PZ+fOnbOeq6ysjD/96U8EBgaycuVKTCYT3377LRqNhueee27e5pC6uLhgMBgwm80Ov796vX5ZrY14OwYHB2lubqawsHDa9ra2Nrq7u+3Fo4S7I8sywKwJvJOTEx4eHvT09GCxWByO6LjXiVnBgiAIgrCMVVZW0t3dzRNPPDFpvpWPjw979+7FYrFQVFQ079ft7e3lvffe4+233yYvL4+zZ8/y61//mpycnNt6Cz88PMxbb73F2NgYu3btYvv27ezatYvk5GTOnj3LqVOn5nyuwMBA/vIv/5LHHnuM0NBQYmJieP3113nuuefw8PBg/fr1nDlzBoPBYD9Gq9USEBBAbW0tKSkpk3regoKCGBsbm9TzcCtZlqmpqXGYtN2J/v5+vv/+e/z8/NDpdPa5XRPLtAwPD3PmzJl5udaGDRu4du0ag4ODU9r6+vqorKy8o8JOAMXFxURFRTlsj4qKoqSkxGG71WpFlmUaGhrIycnh0UcfZefOnSQmJpKamspTTz1FcHAwH374IRaL5Y5i/CFnZ2fCw8Opr6+ftn3i855puZ3lrK2tjb6+vhm/n5WVldTV1S1iVPev4OBgtFotMTEx9m3+/v6sWrWK2NhYXFxc0Ol09tEbE7q7u5dlYSfR0ykIgiAIy9jly5fJyMiY9m25QqFg7dq1nD17lrVr187bNQcGBnj33XeJiooiNTXV/gA0NjZGaWkpn332GQcOHLD3UBqNRlpbW5FlmYCAAFxdXe3nOnnyJP7+/pOWHpEkCV9fXzZv3szJkydJSUnBz89vTrFJkkRERMS0886ys7Mxm80cPnyY8PBwPDw80Ov1NDQ0kJCQwO7duyftr1Ao2LBhA2fOnGHv3r2TihTJssyVK1dQKBQzJle36/Lly7i5uU3b0yZJEu7u7hQVFbFt27a7HuIYHBzMzp07+e6774iOjiY0NNS+PmV9ff2knvPbNTo6OmOPoJubG6Ojo5O2WSwWCgoKyMvLY3h4GMA+n/CH8z8lSSItLY2Ojg4qKyvnbemarVu38sEHH+Dm5kZQUJB9+8QwbY1Gs6zn1c1EkqRZkxmlUrnoc8XvV15eXgQGBmIymUhNTSU8PByA9vZ21Go1mZmZGI1GOjo68PX1JTIykoaGhmWZcIJIOgVBEARhWevq6uKhhx5y2B4UFERPTw+yLM/b/LfTp08TEhJCdHT0pO3Ozs6sXbuWM2fOUF9fT1hYGCdOnKCkpAQPDw+USiV9fX1ER0ezZ88eVCoVFRUVDuN3cnIiLCyMwsLCeVmXTpIkdu7cSVZWFlevXmVoaIiAgAAeeughvLy8pj1m3bp1DA0NcejQIVatWoWfnx9jY2NUV1djtVp54YUX5nVeYWNj44xLv6jVahQKBQMDA/j6+t719VJTU+3f44mex4iICH7yk5/YCy/dCVdXV/R6vcNiQXq9ftLLB7PZzHvvvWfvVZ4Ypm2xWLh69So6nW7a4j2xsbFcvXp13pLOlStXcuDAAQ4fPsy1a9dYsWIFZrOZ5uZmQkJCePHFF5ftQ/9sQkND8fLyQqvVThoNcKuUlBRWrVq1yJHdv3bt2sXvf/97kpKSOHv2LA0NDfY2tVrN5s2bcXd3x2azERkZSVhYGG1tbdhstiWM+s6IpFMQBEEQljG1Wo3JZHLYbjKZ5rVnwmQycf36dYdz8RQKBeHh4Vy+fJmzZ89is9nYtWuXfdkUk8lEZWUl77zzDo8//jguLi4zzgP19fWlubl53uKH8aqzGzdunNO+kiTx8MMPk5aWRlFREbW1tTg5OZGdnU1sbOy8JyAKhWLG4cmyLGOz2eb1ut7e3uzatWvezgfjyWxtba3DpLOuro41a9bYvz59+jS9vb0oFIpJSbxarcZms5Gbm0tQUNCU3lNXV1eHCdKdCgsL46/+6q+oq6ujs7MTpVLJrl277rlqu/PNxcWFhIQEtm3bxtGjR+1zDickJCTg6+s7rz37DzpfX18kSaKwsHBSwgnjL2JOnz7Nk08+iVKppKOjA39/f8LCwu7ZJYdmIpJOQRAEQVjGYmNjuX79Ohs2bJi2vbKyktjY2Hl7SBkZGUGtVs+YKHp6elJbW2t/WL81QdJoNKxevZqCggLKysowmUwz9sKaTKZ5L9RzJ/z8/KYMv10IcXFxFBYWTlrb9FZGoxGNRuOwZ/ZekZGRwZUrV6isrCQuLs7+M2Cz2aitraW1tRWLxcLHH39MREQERUVFUxLOCQqFAoVCQXl5+ZQ5pj09PXfVI+vIxPIot863exDs27fP/j3Ny8ujt7cXV1dXUlNTiYqK4kc/+tF929O7FCaGNFdVVU3bLssyZWVlZGVlUVZWRm1tLRs3blyW1YOXX8SCIAiCINitXbuWd955h4iIiClrLvb29nL58mUOHDgwb9fTarWYTCasVqvDHtSxsTGMRiMZGRkOH1Dj4uI4d+4czs7OdHd3s2LFimn3a2lpISMjY97iv9elpaVx4cIFxsbG7FV0J9hsNoaGhti0adM939Oh1Wo5ePAgH3zwAVVVVQQGBiJJkn1oYHh4OBEREVitVioqKjCbzVPu91YTx97KYrFQWVnJE088sdC388DQaDS88cYbVFRUEBUVxeDgIBqNhqysLDIyMmb8jITb19fXh8lkwmw2O9xncHDQXlhrbGyM2traZTmvWCSdgiAIgrCM+fr6sn//fr766iuioqKIjo5GoVBQX1/P9evX2bNnD8HBwfN2PWdnZ4KDg2lubrYXvvih+vp6ZFnG29vb4Xk8PDwwGo3s2LGDc+fOsWnTpim9pzdu3GB4eJikpKR5i/9e5+LiwrPPPsuhQ4dwcXHBxcUFhUKBwWBgZGSE6OjoO64ou5hkWeb48eN4enqyZcsWhoaG6O/vp6Ojg3379uHh4YEsyzQ1Ndl7u2c7362J9vDwMOfPnycwMHBSZU/h7imVSpKTk+dtnqzgmCzLaDQaVCqVw6rBOp1u0u9He3s78fHxixXivBFJpyAIgiAsczExMfzsZz/jypUrlJaWIssywcHBvPnmm3h4eMz79bZu3cqhQ4fw8PCYNMxTlmWqq6vp7+/H2dkZg8HgsILpxDzUNWvWoNfrOXnyJOHh4Xh7e2M2m2lpaWFkZISDBw/eE8NrF1NERAQ/+9nPKCgooKKiAqvVip+fHw8//DBRUVH3fC8nQG1tLd3d3Tz//PP2oYBffvkla9eutSecly5dorW1lYSEBAYGBmacq2qz2ejv7+f48eOYTCZ6e3tJS0tj+/bty+L7IQjT8fHxwWazER0dzfXr16fdJzExEbVazfr166mpqZl2iaPlQCSdgiAIgnAfcHNzIzs7m+zs7AW/VmhoKI8//jjffPMNPj4++Pj4YLFYaG1txdnZGScnJwIDA2loaHBYYbW+vt4+12/Lli0kJSVRWFhoXy5g7dq19oetB5GHhwcPPfTQjJWJ72WFhYWkpaXZE86JFwnbtm0Dxj//9vZ2Hn74YTQaDQaDgbKyMiRJmpJEWq1WJEnipZdeQq/Xo1KpCAsLQ6PRLPp9CcJ8mhg5IssyQ0NDk4aQKxQK1q1bh5ubG/n5+cD4XOmOjg5RvVYQBEEQhAdDfHw8kZGRlJWV0dHRgaurK5mZmYSFhfHpp5+i1WppbGykqalpyvDHnp4eysvLefXVV+3bfHx8ePjhhxf7NoQF0tfXx7p16+xfWywWlEqlPQktKysjJSXFnjiuWrWKnp4eOjo6Jq0XKcsysixz4MCBeR0mLgj3iieeeII//OEPbN++3Z54ajQaIiIiMJvN6PV62trakGWZkZERdu7cKQoJCYIgCILw4NBoNKSlpU3ZnpWVxVdffcXGjRu5dOkSdXV1BAcHo1AoaG9vp6Ojg/T09CmFj4T7h0ajYXR01P61VqtFpVLR19eHh4cHPT09BAUF2dsVCgXZ2dm0tLRQUVHBwMAAFouFtWvXkpWVdc9X6xWEuWpubub06dM0NjYiyzK+vr5s27aN3NxcewVnSZJQKpX09PRQUFBgn9M5MDBAb28vfn5+S3wXt08knYIgCIIgzKuIiAiSkpIoKioiNTUVm81GR0cHBoOB4eFhoqKiRK/mfS4xMZGKigp7L7ckSSQnJ1NaWsrmzZunPUaSJEJCQggJCWFwcJDTp08vyjI1grBY8vLyyMnJoaCggPr6emw2G0FBQTQ3N5OWloZOp+Po0aPU19fT1tbGyMjIlHN0dnaKpFMQBEEQBAHgoYceIigoiIsXL9LR0YFCocDT05PNmzeTnp4u1vq7z6WmppKXl0dlZSWrVq0CIDMzk08//ZSLFy/i7e1NW1ubwyGzzc3NhISELGbIgrCgOjs7OXr0KF988QXDw8P27Q0NDTQ3N2Oz2cjOzsZkMlFTU+PwPBqNxuFyVfcykXQKgiAIgjDvJEkiMTGRxMREzGYzsiyjVqtFpdEHhLOzMy+++CIff/wx5eXlxMbGolKp8PLy4saNG5jNZq5cuYK/v/+UYlEjIyNUVlby0ksvLVH0gjD/zp8/T2lp6aSEc4LFYuHChQusWLECPz8/nJycMBqNU/aTJInw8HBiYmIWI+R5JZJOQRAEQRAW1EwVaGVZpqGhgc7OTpRKJbGxsXh6egLj63Tm5eXR0tKCJElERESwbt06AgMDFyt04S6sWLGCn//851RWVlJTU4PNZiM8PJwnnniC9vZ2Dh06RE5ODikpKQQGBmKz2WhsbOTq1ats3Ljxgf+cZVmmo6ODkZERdDodK1asEC9tlrG6ujrq6+sdtre0tDA8PEx8fDw9PT2cPXsWq9U6aZ/09HRUKtWyrGotkk5BEARBuEsTSzr8cMioLMu0tLTQ2NgIQFhYGMHBweLB8aampia++eYbVCoVoaGhGI1Gzpw5Q0REBJ6enly7do3ExESSk5ORZZnGxkb++Mc/snXrVjIzM5c6fGEOlEolSUlJJCUlTdoeGRnJT3/6U3Jycrh8+TImkwlJkvDz8+Ppp58mOjp6iSK+N1RWVpKTk8PIyAhWqxWlUom7uzuPPfYYkZGRSx2esID279/P0NAQ7u7uVFVVMTg4iIuLC3FxcTg7O/Pcc885XIrqXiaSTkEQBEG4AzabjeLiYvLz8+np6QHGk8oNGzYQExNDX18fn3/+OaOjo6xcuRJJkrh8+TLOzs4888wz+Pj4LPEdLK2Ojg4+/fRTdu/eTXR0tD0RN5vNHD58mBs3bvD444+j1WrtxyQlJREWFkZOTg6BgYGTqp/KskxnZyd6vR43NzcCAgJEcn+P8/Ly4vnnn1/qMGbU0tJCXl6evdJoaGgoWVlZhIWFLdg1S0tLOXLkCDdu3JhUAdjNzY2hoSEOHDiwLIdXPuiioqKIjIzkypUr07YHBwej0+nQarXs27ePt956i4SEBGw2GwqFAqVSiUajWZZFhACkiRK8wu3JyMiQCwsLlzoMQRAEYQGMjo6Sn59Pfn4+JpMJjUbDunXryMrKwtnZGZvNxueff053dzeRkZH4+PjYK7TW1dWRmppKaWkpMTExxMTE2JMfWZapra2lurqaN954Azc3tyW+06Vz6NAhQkJCSE9Pn9L2ySefEB4e7rBHp6ysDKPRyJNPPglAbW0tx44dw2Aw4ObmxsjICBqNhp07dxIfH7+g9yHcv/Ly8sjNzSUlJYXIyEgkSeLGjRuUlJSQnp7Oli1b5v2aZrOZf/qnf6KmpmbaOX0uLi7Exsbyd3/3d6IY1zLT2dnJ7373Oz7//PMp8zpVKhVPP/00zz33HOHh4fzrv/4rIyMj9iVUhoaGcHZ2xsnJCbVazd/+7d/i7u6+RHcymSRJRbIsZ8y2n+jpFARBEIRbDAwM8Pvf/56qqiqKiooYHBzEw8ODqqoq8vPzefPNN6mqqqK7u5v09HR7FUGlUklQUBA+Pj5cvHgRX19fYmNjJ51bkiRiYmIYHBykoKCA7du3L8UtLrmxsTFu3LjBI488MqVtYkhydna2w+PDwsI4duwYANevX+ebb74hKSnJPudNlmV6eno4cuQIFotlytBOQZhNc3Mzubm57N+/f9LLoaSkJCIjI/n6668JDg4mKipqXq9bWVmJwWCYNuGE8RdiBoOB+vr6B34I8nLj7+/P7t27kWXZvmSK1WolODiY9evXk56ezurVq/nwww8xGo1oNBry8vK4ceOGfZ1OX19ftm7dyvvvv8/Pf/7zJb6j2yOSTkEQBEG4xYcffsi5c+coLi62b+vu7ubo0aN0d3fj6uqKLMtER0dPW7Zeq9USFhY2bYXCCdHR0eTm5j6wSefo6CguLi5oNJpp22VZnnFo7ERiabPZ+Pbbb0lNTcXb23tSu5+fH+np6Xz33XfEx8ejUolHHmHu8vLyWLNmzbSjEVxcXEhLS+PSpUvznnT29fUxMDAw4z4jIyP09fXN63WFxbFu3TqCgoKIioqyD9n29fVl27ZtxMfHI0kS9fX1yLJMbm4uTU1Nk47v6ekhJyeHffv22f+OLhfiL7AgCIIg3NTW1kZXVxclJSXTtl+5coW0tDQ0Gs2kJOeHVqxYQVtbm8N2Nze3SXO1HjQuLi6Mjo7ahy7fSpIkAgICaGlpcThvrqWlheDgYGpqatBqtQ4/Cw8PD3Q6HVVVVSQmJs77fTyIbDYbtbW19orCUVFRhISE3HfzZ2/cuDHt0O8JkZGRnD9/ft6v6+TkhLOz84z7aDQanJyc5v3awuIICQmZdTkgm802JeGcMDIyQmtrK6Wlpaxfv34hQlwQYjC4IAiCINzU2NhIXV0djuodyLJMXV0dNpvN4T4T+83UrtfrcXV1vet4lytnZ2fCwsIoKyubtj09PZ0rV65gNpuntBkMBsrLy1m3bh19fX2zzmvS6XSiV2ietLW18Zvf/Ibvv/+erq4uOjo6+PLLL/m3f/s3+vv7lzq8eSXL8oxzJid62+fbqlWr0Ol0DpN4pVKJk5PTlKH7wv1ltr9ZnZ2ddHZ2LlI080MknYIgCIJw02zJ4gSdTkd3d7fD9vb29hmHc9bW1pKamnpHMd4vtm7dSm5u7pQk32w209TUhCzLHD16lMbGRqxWKxaLhbq6OnJyckhNTSU8PBytVjttYnork8kkeoXmQV9fHx999BFxcXFs3ryZVatWkZCQwLZt21ixYgXvvfceBoNhqcOcN8HBwfaljqbT2Ng4qXryfPH09CQ+Pp7w8PApiefEWrVpaWmz9oYKy5ckSbN+vi4uLstqaC2I4bWCIAiCYBcWFkZ0dDRnzpyZtF2lUuHn54dCoSAyMpKMjAwKCwvx9vZGrVZP2lev19Pa2opGo6GiooL4+Hh7j4nNZqOqqoqenh575dUH1cqVK/mLv/gLvv76a7RaLSEhIZhMJqqrqwkLC+OnP/0pNTU15Ofnc+bMGSRJIjQ0lEceecTeyxMXF8fRo0enHaYL4wlsZ2cnzzzzzGLf3n0nNzeXsLAwAgMDJ22fGGI7MDDAlStX2LBhwxJFOL+ysrL4j//4DyIiIiYt2wPjLzKKiorYsWPHglx7//79mM1mnJ2d6enpwWAw4OLigre3NwkJCezZs2dBrivcOyamBuj1+iltEwXpUlJSliCyOyeSTkEQBEG4KTAwEF9fX1JSUrh69SoKhYLMzExWrVo1qbhHU1MTMTEx5OXlERoaip+fn33JlObmZh599FFCQ0M5fPgw3377rf1Bvb29HW9vb1555ZUFe0ttMpkoLS2ltLSU4eFh3NzcWL16NatXr3ZYuGephIWF8Ytf/IK6ujo6OztRqVRkZ2fb52gmJyeTnJxs7wn9Yc+Pi4sLq1evprS0lNTU1Em9y1arlatXr5KQkGAfYnvjxg1sNhvBwcEEBATMGJvVasVoNOLk5DRtwagHiSzLXLt2bcbCV2FhYZSWlt43SWdUVBRJSUl89dVXpKenT1oypaioiP+fvTuPb+pMFzz/O5IlS/K+yCs2Nt4xtrGxMSaslxAIhlSApFIJVIDst1Ld1VXddWd6puePmXs/n/u59zPTPV3T3ZVbNwlJSEIICQECSSBA2A14A2y8AN7wbsubvEqWdOYPl9U4lmwWL9h+v38lOkc6r4wln+d9n/d5oqKiSEhImJRr11n4UgAAIABJREFUu7i48Morr9DU1ERBQQFdXV34+PiQnp6Ov7//pFxTeHJ4enrS2trK2rVrOXHixIhKxpIk8dRTT+Hq6jrud9iTRvTpfESiT6cgCMLs1N7eznvvvUdJSQk6nQ6TycT58+ftM86urq4sX76czMxMtmzZQkFBAQ0NDSgUCqKjo8nMzCQgIMD+es3NzfZ00fnz5xMQEIDBYMBkMuHt7T2hvTp7enr45JNP8PLysld0bW9vp6CgAKPRyK5du6Z0L6nNZqO6uprOzk60Wi3R0dGjVoYn4hpHjx7l9u3bhIaGotPp6O/vp66ujgULFrBhwwaOHTtGXV0dUVFRKBQKKisr8fb25vnnnx9VhMhgMHDhwgVKS0vtQe6iRYtYuXIl3t7eEzr2mcJisfCP//iPPPfcc07P6evr4/Lly/z7f//vp3Bkk6+8vJycnBxqa2uRZZl58+aRmZnJwoULZ13xJOHJkJOTw8mTJ+39hisqKmhra8PNzY3Y2FhUKhXZ2dlPTBGhB+3TKYLORySCTkEQhNmrp6eHb7/9lqtXr/L5559js9lGnbNp0yZ27tzJM88888CvW1xczPfff093dzdmsxmtVktYWBjPPffchKxg7Nu3j8DAQFauXDnihliWZS5cuEBzczO//vWvH/s6D6K0tJQTJ06g0+nQ6/V0d3fT1NTEU089xfLlyyf8hr21tZXr16/T1dWFp6cnixcvxsfHhw8//JD58+ezevVqe8Brs9nIz8/nypUrvPHGG3h4eGCz2bh8+TLnz5/Hy8uLyMhIIiMjMZvN3Llzh6qqKnbv3o1er5/Qcc8Esizzz//8z6xcudLppEVzczPV1dW89dZbUzy6qeFstf1+PT095OfnU1FRgUKhICEhgcWLF4s9xcJDGRwc5C9/+QvNzc309vaOyODQaDQEBgby1ltvPTFtoB406HwyRisIgiAITxB3d3c6Ozu5du2aw4AThmajY2NjWb9+/QMFUDk5ORw/fpzvv/+euro6YCiNbtGiRTQ0NPD2228/VkDT0tJCS0sL27Ztc1iA5KmnnuLPf/4zra2tkx44lZWV8d1337Ft2zbCw8Ptj7e3t3Po0CHMZjNr166dkGsN9/TU6/WsX79+xLHc3Fzc3d1Zt27diJ/JcNp0R0cHly9fJiUlhf379yNJEgkJCUiSRE1NDcXFxTz11FMkJyfj5ubGoUOHePvttydk3DOJJEmkpqZSWVlJUlLSqOOyLI/bYmSmG+8zXlxczOHDh+nu7qajowNJkrhz5w6nT59mx44dTtv/CMLPqVQqXn/9dY4fPz4i40KWZRITE8nOzn5iAs6HMfNGLAiCIAhToKOjY8yS9B0dHVgslgeqjtrT08PJkyf54osvRhSGsFgsXL9+HavVir+/P++88w6yLGM0GpFlGU9PzzHbNtyvsrKSmJgYp/sPlUolMTExVFZWTmrQKcsyJ0+e5Pnnnx8RcAL4+vryq1/9ivfee4+MjIxHTi22WCzk5+eTn5+PwWBApVIRFxdHVlYWwcHB9vNu3LgxatX3fhkZGXz44YfcvHmTpKQkwsLC7MdiYmJobGzkwoULrF+/ngULFlBaWkp9ff2kVC190i1fvpy//OUv3L17lwULFth/Ly0WC6WlpdhsthlX2GSi1NfX880331BZWTli/113dzc6nY5PP/2U3/72t3h5eU3jKGemnp4erly5QkFBAWazGZ1OR1ZWFkuWLHni9qhPJI1Gw/bt2+nv76ehoQFJkggODp7RVYtF0CkIgiDMOW1tbXR0dKDRaAgJCXEY2KlUKnQ6HV1dXQ5fQ6VSIUnSA804FxQUUFVV5bASIUBJSQmNjY2cOnWKmzdvYrFYgKGV0KVLl7J8+fJxg0+bzTbuOUql0unK7US5d+8eKpXK6cqOu7s7cXFx3Lx585GKzgwODvLpp58iSRIrVqwgODgYk8lEWVkZn376KZs3b7YXeOnu7h4zbdnHxwez2cyCBQtGBJzDgoODWbBgAeXl5aSnpxMUFERDQ8OcDDrd3d3Zs2cP33zzDadOnUKv1yPLMs3NzURERLBr164J3687U/z00080NTWNCDiH9fX10dHRwZUrV9iwYcM0jG7mam1t5YMPPqCtrY3W1lYGBwdxdXXFYDBw7do13nzzzRnXNmQsZrOZW7duUVtbi1qtZuHChYSFhREVFTXdQ5sQIugUBEEQ5oyGhgYOHTpEa2sr3d3daDQa3N3dWb9+PRkZGSPOzcjIoKioiMbGRoevtXDhQiIiIh6osmldXR1VVVVOj1utVgwGA0VFRSxatMi+ItLV1cXNmzepra3lpZdeGjOoDA0NJT8/355u+nOyLFNZWTnpq1FdXV34+/uPmY6o1+sxGo2P9Po//fQTrq6ubNiwwX4NnU5HWloa8+bN48iRI4SHh+Pm5oZOp6Ozs9PpCpPRaEShUBAREeH0epGRkZw6dYr09HSsVusDrzzPRj4+Prz22mu0tLRQV1dn/9nN1QJL8D8/V84mp2BokquoqEgEnQ9BlmX27dtHdXX1iJ9tf38/VVVVDAwM8PXXX0/ZHvXJVlpayldffUV9fT2lpaVoNBqWLFlCUFAQe/bswcPDY7qH+NhE0CkIgiDMCQ0NDbz//vucPn2a27dv21f8AgIC6O7upre3lzVr1tjPz8jI4NKlS9y5c4c7d+6MeK2AgABWrlzJ008//UDX1mq146ZFqdVqYmNjRwRIw1Vo8/LyKCoqIiUlxenzw8PDcXFxobi42OG+u+LiYlQqlcMVvYmk0+nGDSi7uroeKbV2cHCQ69ev8+KLLzoMagMCAoiMjKSgoICVK1eSnJxMQUGB01XXgoICJEka1YfxflqtFrPZzODgIA0NDWzZsuWhxz3bBAQEjKjQPJfZbDZkWR4zg8BqtdqzF4QHU1FRQXd3t9NgvqmpyR6QzvS05ZqaGr744gv27dtHU1OT/fELFy6wZs0aBgcH+Xf/7t/N+NZNc3e6ThAEQZhTvvrqK06dOkVZWdmIG8SWlhYOHDjAuXPnRqS/urm58frrr/Pcc8+xc+dOEhMTiY+PZ+vWrfzqV7/iV7/61QOnWaakpJCamur0uI+PD56enqPadwD21aSrV6+OeQ1Jkti+fTvnzp3j3Llz9sDPaDTaH9u+ffukt3mIjIyks7OTlpYWh8eHU8gcBcbjMRgMuLm5jXmTGRkZSW1tLQCpqak0NTWRk5PD/dX6ZVmmpKSE69ev4+3tPaIH6891dnbi5uZGUVER8+fPn9OresJoSqUSnU437sSFo8+24FxVVRWtra1Oj8uyjMlk4t69e1M4qslx/PhxvvvuuxEB57CzZ89y584d/umf/okvv/yShoaGaRjhxBArnYIgCMKs19raisFg4Pbt2w6P9/b2UlFRQUFBAatXr7Y/HhQUxB//+EdKS0spLi7GYrEQGxtLSkrKmDeZPxcZGUlQUBDLly/n8uXLI46p1Wo2bNhg7yHpiK+vLzdu3Bj3OgEBAbzxxhtcunSJDz/8EKvVilKpJDk5mTfeeGNKAialUsnq1av5+uuvefnll0dc02QycejQIeLi4vDx8Rn3tQwGA01NTbi4uBAREYEkSYzX6k2WZVpaWvj444/x9/dn48aNnDt3joKCAuLj41EoFFRUVDA4OMiOHTuoq6ujsLCQwMDAUQG5LMuUl5ejUCgwGo2zJpVPmFhZWVl0dHRQXV096thwAZgVK1ZM/cBmsAdp6SjL8gOd9yTr7e2lpaWFsrIyp+fk5ubi5uZGS0sLt2/fZt26dU9Mj86HIYJOQRAEYdZrb2+nq6trzBuUe/fuOdy/6eLiQlJS0iOtzA2TJInXX38dm81GTEwMubm59Pf3ExwcTHJyMgqFYsy0V4vF8sAl8r29vcnOzmbTpk0MDg7aCx5NpfT0dAYHB3n//fdZsGCBPYW5pKSEhIQENm3aNObz29raOHr0KAaDAb1ej8Vi4fDhwyQlJdHf3097e7vTlaPbt28THh5OcnIydXV1fPvttyxcuJDExESqqqqQZdlejVaSJPz9/SksLKSwsJBFixbZK2IODg5y69YtWltbefrpp1m8ePGMbFMgTL7MzExu3LiB1WqlsbHRnkqrVquZN28e0dHRxMfHT/MoZ5aIiAj0ej1tbW0Ojw+nxU/2doHJNjAwgMViwWq1Oj1nOAOns7OTnp4eTp06xbx582bcexffnoIgCMKs5+rqOu6eSjc3N6eN7yeCu7s7v/vd77hz5w7Jycn09vYSHBzMsmXLuHTpEk1NTSxYsMDhc5uamoiJiXmo60mSNK0tBbKyskhNTaW4uJjOzk58fHx4++23x91/1dnZyd69e4mPjycrK8u+j6m/v5/8/Hx0Oh0XL14kOzt71B6n+vp6Ghoa+M1vfoNWqyUyMpL09HQ+++wzfH19WbVq1ajrqVQqdu3axfHjx/nhhx/sRZAMBgMLFizg3/7bfzurKmQKE0+lUvHmm29y8uRJrl+/bp/ccnFxITMzkzVr1szpAlSPIjo6Gp1Oh6enp8M94gEBAcybN++BMiaeZO7u7qhUKjQaDQMDAw7P0ev19i0hFouF1tZWzp07x86dO6dyqI9NBJ2CIAjCrBcWFoaHhwd+fn5OZ85TU1PH3Hc5ERQKBXFxccTFxY14PCsri48//hi9Xj+qSmF3dzfV1dW8+uqrkzq2yaDRaEhPT3+o55w9e5bIyMhRPyOtVktWVhYnT55kcHCQQ4cOkZaWRnBwMAMDA5SUlFBWVsbWrVtHTDBotVo2bdrEoUOHSE9Pd3jz7+rqyrZt2+jt7aWurg5ZlgkNDZ0VFSOFqaFWq9m8eTPPPPMMBoMBSZIICAiY8cVfpotCoWDnzp3s3bsXg8GAwWCwt0wJDAwkICCAF198cbqH+dhcXV2JiopiyZIlXLp0adRxpVLJ0qVL6e/vtz/W29tLTU3NVA5zQoigUxAEQZj1lEola9euxWg0cuDAAXuTcbPZjMlkYt26dcybN2/a+i8GBQWRnZ3NsWPHCA4ORq/XA0N7GhsaGsjOziY4OHhaxjaVzGYzpaWlbN682eFxpVJJTEwMXV1dJCYmkpeXx08//YQsywQHB7Nnzx6HabfDvVhbWloICgpyen03N7dRwa4gPAy1Wk1ISMh0D2NWCA4O5t133+XixYvcuHGDwcFBtFotmZmZLF26dNzslZli06ZNVFdX09fXx40bN+yrmm5ubmRnZ6PRaFCr1ajVanp6epBlecx03CeVCDoFQRCEOSErK4u2tjZ7A3uz2YxarcZmsxEYGMiuXbumfO/j/RYtWkRYWBi5ubn2np4RERE899xzD1QAyGw2c/PmTfsMeFhYGCkpKbi6uk7quCdSX18farV6zCJN3t7e3Lt3b8Q+2y+++IKkpKQxK4RqNBrRtkIQZhgvLy+ys7PJzs6e7qFMGl9fX/vWgzVr1lBXV4darSYoKIju7m4qKipQKpXo9XpCQkJGVFmfSUTQKQiC8ABkWaa+vp7c3FwMBgMajYbk5GQSExNFcZEZoquri6KiIm7dukVBQQF9fX0oFApiYmJYt24dFRUVLFq0aFrH6OXl9cC9P+9XXV3NwYMHCQ8PJz4+HkmSKC8v5+zZs2zfvp2oqKhJGO3E02g0mEymMQsn9fX1jQpK9Xo99+7dc1qspb+/n7a2NtG2QhCEJ5Jer+ff/Jt/w3fffcfVq1cxm80UFRUxODhoP8doNOLr60tkZOSMmkwcJu6UBEEQxiHLMkePHuX27dsEBQXh7++P2Wzm4sWLnD17ll27donefTPAwYMHuXjxIrm5ufbHbDYb5eXlGAwGlEol0dHRD9UKxRlZlmltbaW7uxs3NzeH7TgmSnt7OwcPHmT79u0jChElJydz7949Dhw4wO7du+0pu08yjUZDeHg4NTU1TgPlyspKMjIyRjy2ZMkS/vKXv5CZmemwUNGVK1eIjY11WBBoeEKptLQUs9lMSEgIiYmJ01qESRCEuSkhIYErV65w9+7dEQHnsPb2dgICAmbkdospCzolSfojsA2IBySgGPgHWZZ/+Nl5mcB/AdKADuAj4D/Jsjxm8rIkSbuB/whEAFXA38uy/Nl9x/8G+O/APOA0sFuW5c77jn8F5Mqy/E+P8z4FQZh9zp8/T01NDampqSOKQgQEBFBfX8++fft49913RXXCKWKxWGhra0OSJPz8/B6oUEdXVxf19fUUFhY6PN7W1kZdXR03btwgMzPzscZXWVnJ4cOH6ezspLe3116BccuWLZPSNuHatWukpqY6rHwbHh7O0qVLuXLlClu2bJnwa0+G1atXs3//fry8vPD397c/LssyJSUlmEwmEhMTRzzH29ubFStWsG/fPjZu3EhUVBSSJNHT08PVq1cpLS3ltddeG3Wt3t5ePv30U1paWmhra8NqteLt7c13333H888/P+o6c9mjfO4EQXg4Wq0Wq9VKX1+f03NaW1tJSEiYwlFNjKlc6fwb4EMgF+gD3gCOSZK0WpblSwCSJIUBPwJfA28CMX99jgT8r85eWJKk54EPgP8AfA9sBj6RJKldluXvJUlSAF8A/zdw5K/n/u/AH//6/BeA+cBLE/yeBUGY4SwWC1evXiUxMdHhTVZoaChtbW3cvXuX2NjYaRjh3GGxWDh79iyFhYVoNBpkWcZsNpORkcGKFStQKpUYjUbq6+uBoYBruAVKc3MzHR0dY+7pu337NtXV1Y8VdN65c4fPPvuMH374YUSj+LCwMLq7u3nxxRcfq9+nI6Wlpfz61792ejw1NZX33ntvxgSdYWFh/OIXv+Dw4cP4+/vb+3TW1tai1Wp59dVX7fty77d8+XK8vLw4e/Ys3377LRqNht7eXhISEnjttddGVaK12Wx8+OGHVFdX09LSYn+8s7MTtVrNoUOH0Ol0REZGTvp7fpJZLBbOnDlDfn4+CoUCWZaRJInMzExWrlwpgk9BmEBWq3XcLTszsYgQTGHQKcvysz976O8kSdrI0OrncI3gvwWMwOuyLNuAW5IkhQL/LEnS38uy3Ovk5f8OOCDL8n/56/+X/XXF9H9hKAj1A/TAn2RZHpAk6TOGAlMkSfIF/jOwabzVVEEQxjZccW02rfjV19fj6uo6Zp8+Pz8/SktLRdA5iSwWC5999hkKhWJEYZ329nauXLlCbW0tLi4uVFVV4efnBwytXsbGxpKdnY2Li4vDQOV+KpVq3HPGYrPZOHjwIEeOHKGxsXHEsdraWg4dOoRKpSIhIWFC9wGbzeYx+4vqdDpMJtOEXW8qxMXF8Yc//IFbt27R2NiIRqPh+eefJzw8fMw05cTERBYuXEhXVxdmsxkvLy+ne5/Ky8tpa2sbEXAOM5vNNDQ0cOLECd55550Je18zjcVi4eOPP8ZgMKBSqewBptVq5erVq9TV1fHKK6/Mqu98QZhOfn5+uLi44OLi4nSS1N/fn4iIiKkd2ASYtj2df1199ATuDySfAk7+NeAc9gPw34BU4KKD11EDGcB7Pzv0A/DfJUlSAgagAXhWkqRjwHrgxl/P+xPwvizLxQ8w5reAt2BoBl0QhP+Z8paTk0NDQwMw1P4hKyuLRYsWTWs10IkwVkGTYUql0uHeC2HiFBQUYLVaeeaZZ0bc4Pr6+rJ27Vr279/PvHnzWLt2rf3fa3BwkPLycj7++GN27tyJp6cnHh4eTiv/paWlPdYqZGVlJZ2dnaMCzmEGg4G2tjbKysomtGCRn58fdXV1xMTEODxeV1c3Ik11plCpVCxevJjFixc/1PMkSXqgPda5ubkOA85hPT099n25c7VfZ0FBgT3gvP+7XKlUolAoqK+vp6SkZNoLcAnCbKHRaEhISKC1tZWKiopRx11dXfH392fp0qXTMLrHM51TU/8b4A385b7HgoGmn53XdN8xR/wZCp4dPc8V8JVlWQZeZGjl8y7QA/yjJEnZwCLgv0mS9JEkSZWSJB2RJCnQ0YVkWf6LLMvpsiynz4SCDIIw2WRZ5sSJE5w8eRJfX1/WrFnDmjVr0Ov1nD59muPHjzP08Zu59Ho9RqNxzHSW7u7uMXv/CY8vLy+PxYsXO1xRKSkpwd/ff9QKokqlIjExEVmWKS0tJSMjg2effdZhOuDixYvR6/VER0c/8hjb2trsEy/OVFdX09bW9sjXcCQtLY3Lly/bMw3uJ8syly9fJi0tbUKvORv09vY+UAuV+5uyzzU5OTkoFAqHk4eSJCFJEpcvX56GkQnC7LVp0yYiIiKIiYmxZ2pIkoSvry8pKSls3LgRT0/PaR7lw5uWlU5Jkn7DUND5nCzLdVNxTVmWLwPL7huDF0MrqNsZ2i+qYmgP6T8ytPop9ncKwjju3r1LSUkJqampI9IS9Xo9Pj4+FBYWUlZWNiM3vA/z9PQkPDychoYGwsLCRh3v6+vDYDCQmpo6DaObOwwGg9PAvrS01OlqmCRJREREkJuby1tvvYXBYMDLy4srV67Q0tKCTqcjIyOD8PBwXn/99cdamXd1dR13RWysdM9HlZKSQnFxMYcOHWL9+vX26q1Go5EzZ84wMDDAkiVLJvSas4Gvry+urq5Og8rhCY65usopyzKdnZ1jvn+lUjnhkyiCMNfpdDr+9m//lrNnz9qzfGCoeOHTTz/9WJOj02nKg05Jkv4D8H8yFHCe+tnhRuDndxWB9x1zxABYnDzPBLQ7ed7/A3wuy3KBJEnvA/+HLMtWSZI+Ac6N/04EQcjJySEsLMzhPjgXFxfCw8PJycmZ0UEnQHZ2Nu+//z5Wq5WQkBDUajWyLNPW1kZVVRUbNmwYc0+d8PhUKhVmsxmtVjvicVmW6enpGXPW18PDA6PRiFKpZOfOnVRXVxMfH09raysajYasrCySkpIeaz8nQGxsLEFBQWg0GgYGBkYdd3FxITIycsI/D0qlkpdffpkzZ87wL//yL/ZelG1tbSQnJ7Nz507RS9aBZcuWcfv2bbq6uhxmZHh6ehIZGTnqd26ukCQJpVJpLxzkiCzLopDQGKqrq7l48SL19fVIkkR0dDRPPfUUgYEOE+oEwU6r1fLss8/yzDPP0NfXh4uLy4z/LprSv0KSJP1fwO8ZKtrjKLC7BPxakiTFffs6NzJU7dZhnXtZls2SJOUCG4BP7ju0EbjiqDiQJElPM7TqOTz1q2BopRNAzfSmHQuzWHNzM0ajEa1WS2ho6Izf79jQ0DDmvoLhAjsznbe3N2+++SanT58mPz8fV1dXzGYzfn5+PP/886KA0BSIjY3l9u3bpKSkjHhckiTUajUDAwNO/yAPDAyMSFGKjIyclIqkOp2OxYsXs2XLFr755psRqZsKhYLNmzeTkJDgsI/k41KpVGzYsIG1a9fS1DS02yQwMHBGNhCfKuHh4URFRWEymairqxuRQu/p6UlwcDAbN258qNfs6uoiNzeX27dvY7FYCAwMJCMjg8jIyBn5fR8TE0N1dbXTnqVWq1W0lXHi5MmTXL16lebmZnp7h8qXNDQ0UFJSwubNmx96r7IwNymVylmTbTGVfTr/X+Bt4GWgXJKk4ZXJflmWu/76338Gfgv8qyRJ/xmIAv4e+P+GK9f+tZrtaeA/yrL8zV+f98/AV5IkXWOogFA2Q1VxR9WHlyTJnaGiQztkWR4u53ce+K0kSaXAHxArncIEq66u5vvvv6enp8deSVKhULBu3boJb58w1cbasznWDPlM4+XlxbZt2zCZTHR3d6NWq2fknoqZavny5Xz66aeEhoaOKooTEhJCTU2N0x6YdXV1JCcnA0PFhQoKCrhw4QJ9fX0olUpSUlJYsWLFAxWfGc+WLVsYGBjA19eXoqIiDAYDPj4+pKSkEB0dzfbt2x/7GmNRq9Wi0N0DkiSJl156iR9++IHCwkIGBwexWq24urri6+vLCy+88FAFmCorK/nqq6+Ii4vjb/7mb3B1daW2tpZjx44RFRXFpk2bZtz34cqVK6moqMBqtY5a0bRYLNhsNrKysqZpdE+u0tJSrly5wt27d0fstTYYDHR1dXHs2DFCQkIICAiYxlEKwtSSpqrIhyRJzi70sSzLu+87bxlDLUzSgE5gL/CfhlcsJUmKAKqAPbIsf3Tf83YztE90+Pjfy7L8qYNx/DdgQJbl/3DfY37AR8AahlZUd8qyfG+s95Oeni7n5eWNdYogAEM3Il9++SWRkZH4+fkhSRKyLGM0GqmoqGDNmjVkZGRM9zAfyYEDB7DZbA73OsJQuxGLxcKOHTumeGSTo76+nmvXrtHY2IiLiwvx8fEsWbJEpNZOkZKSEr799lsiIyOZP38+sixTXV1t74eZlJQ0Km2tvr6e27dv884776BWq/mXf/kXysvLuXz5Mq2treh0OpKTk0lJSWHPnj3MmzdvQsba0tJCXl6ePehMT08nONhZPTxhug0MDFBVVYXFYiEgIOCh0x97enr4H//jf5CdnU1oaOiIY2azmUOHDrFkyZIZ+V1/69Ytjhw5MipF22q18uKLL87Y/WWT6c9//jM3btygp6fH4XG9Xs+qVavYunXrFI9MECaeJEn5siynj3veTK8sOV1E0Ck8CFmW+dOf/kRISIh9n9X9+vv7KSoq4g9/+MOMTIOrra1l//79pKWlodFoRhwzmUwUFBTwwgsvzPjm6rIsc+rUKa5fv05oaCje3t5YrVZaW1tpbW3lV7/6FfPnz5/uYc4JPT09FBQUcO/e0LxgZGQkqamptLW1ceDAAXQ6Hf7+/siyTGtrK4ODg7zyyisEBgZy4MABjh07xokTJ0a97vz589m6dSt/93d/J/Y/Cg/t/PnztLS08PTTTzs8Xl9fz5kzZ/jtb38741Y7Yehzl5eXR2VlJZIkERMTQ1pa2pj9i+cqq9XKP/zDP3Dnzp0Rjw9POMNQRkJCQgJ//OMfp2OIgjChHjToFH9ZBWES3bt3D6vVio+Pj8PjWq0Wb29vioqKSE8f9/P6xAkLC2PVqlWcP3+e0NBQhlsJGQxaFwqkAAAgAElEQVQG6urqWL58+YwJOHt7e6murkaW5VGTBDdv3qS4uJj09PQRe5t8fX3x9/fnwIEDvPvuu2LFcwq4u7uzatWqUY/rdDp+//vfU1payr1795AkiZSUFGJjY1EoFPT19VFWVsa5c453T9TU1NDS0kJpaemMT3kXpl5lZeWYFaxDQkIwmUwYjcZJ2dM72dzd3e0tsYSx3b+YI0kSXl5eeHl52Sez+vr66O7unvHtxAThYYmgUxAmUUdHB25ubmPObGu1WtrbnRVZfvItW7aMsLAwcnJyKCoqAoYKdLz88stO026fJCaTiePHj1NeXo63tzcKhYKOjg5CQkL4xS9+gaenJ5cuXSIqKsphMQ0/Pz98fX0pLCxkxYoV0/AOhGFKpZJFixY5bFTf0NBAV1eXw6qyw27evElZWZkIOoWH5mjP4/2GK8E66qUqzC4uLi54eXnh5uaGj48PFouFxsZGBgYGkCQJT09P9Hq9qAkgzDki6BSESeTq6jpu83GLxTIqNXWmCQ0N5YUXXpiQ17LZbAwMDKBWqyc9zdFqtbJv3z5sNhvp6en2lhk2m426ujo++OADduzYgdFodJgePSwgIICysjIRdD7BZFl+oJUFsfogPIrQ0FBqamqc7glua2vDarWKQGOOeOqpp2hvb6e3t5fGxv/Z8U+WZbq6uujv70etVmM0GsXvhDBniKBTECZRVFQUR44cYWBgwGFgabPZMBgMopgAQ43sz58/b18ttdlsxMXFsWrVqkmr8FdcXEx/fz+LFi0asRqtUCgIDw/HbDZz7do1lErlmKvVSqVy3MkFYXoN78V1dXXFZDI5PCcpKclpBVxBGEtGRgYffPABSUlJo4IIWZa5cuUKaWlpoqflLCfLMpcuXeLChQtIkoTBYHB4ntlsxmg0kpuby7p166Z4lIIwPUTQKQiTSK1Wk5mZyc2bN4mPjx+xcmez2aioqLBXtZ3L2tvb+fDDD/Hz87MXJRocHKSxsZG9e/dOWqGe3NxcgoODnQaUoaGh3Lx5Exja8+lsz2Z7e/uoipXCk0Wn0xEbG8uqVav48ccfRx0PDw8nICCAhQsXTsPoRhoYGCAvL4+CggL7793ixYvJyMiY8c3BZys/Pz/WrFnDV199RVZWFjExMbi4uNDU1MS1a9ewWq2sXLlyuocpTLIjR45QWFhIY2Mj/v7+DA4OOj23s7OTyspKEXQKc4YIOgVhkq1Zs4aBgQEKCwvR6/VotVpMJhMGg4HQ0FC2bds23UOcdt988w0hISEjAjeVSkV4eDju7u4cPHiQ3//+9xO+SmA0GscMFrVaLYODg2RkZFBdXc3ChQtHBagmk4mGhgaeeeaZCR2bMPG2bt1Kc3MzHh4e5OTkjGiZsnjxYl599dVpr1xrNBrZu3cv7u7uxMfH4+bmRl9fH3fu3CE/P589e/ZMSD9RYeItXboUPz8/Ll++zOnTp1EoFLi5uZGenk5mZua0/24Jk6u+vp6ioiIqKytxcXEZt0qxJEkoFIopGp0gTD/xDSgIk0ySJJ599lkyMzMpLCyks7MTX19fNm7cKPr2MdTPsK2tzWn/Ol9fX+rq6rh9+zYJCQkTem2NRoPJZHK6ejQ8S7169Wo++eQTSkpKmD9/Pu7u7siyjMFgoKKigmXLlj10X7+Zzmq1cuPGDfLz82lvb0etVrNw4UKWLl3qtFrzdNNoNPzmN78hLy+PBQsW0N/fj1KpJCkpiZUrV465b3eqfP311wQGBhIVFWV/TK1W4+3tTVVVFQcPHuTNN9+cxhEKY4mKiiIqKgqLxYLVakWtVs/IFinCwxueyLLZbJjNZiRJGjOd39/fn8TExCkepSBMHxF0CsIU8fX1FWk0DjQ0NODj4zPmjK+Xlxd1dXUTHnSmpKRQXFzsdOWosbGRhIQENBoNu3fv5sKFCxQUFGCz2bBarfj5+fHss88+ESmZU2lwcJDPPvsMWZbJysoiKCiIvr4+bt26xfvvv89LL71EeHj4dA/TIbVazfLly1m+fPl0D2WU5uZmDAaDw5YwABEREZw/f56GhgZCQkIe+vX7+vq4ceMGzc3NqFQq4uLiiIqKEkHRJHBxcRErm3NMc3MzfX199v/v7OwkICCAurq6UQXKdDod7u7uLF68eKqHKcwg3d3d5OXlUV5ejiRJxMfHs2TJEtzd3ad7aI9EfCMKgjCtlEolVqt1zHNsNtukFOBIS0vj6tWrNDQ0jNrb2d7eTkNDA6+99howFKysW7eOtWvX0tvbi1KpnLON0U+fPo1Op2PTpk32n5lWq2XVqlWEh4dz4MABfve73zlsMSM4V11djV6vdzoBI0kSer2e6urqhw46CwsLOXnyJNHR0YSHh2MymTh16hSnTp3i5ZdfnpG9I4XHJ8syTU1NdHV1odVqCQsLEymfj8jV1XXE36mOjg5cXV2ZP38+7e3t9Pf3o1Ao8PPzw8fHhx07dsz4yvXC5CkpKeHrr7+mtbWVxsZGJEmipKSECxcu8OKLLxIXFzfdQ3xoIugUhAkynFKjVqvFH+2HEBERwbFjx7BYLA5XBmRZpq2tbVJWibVaLbt37+bzzz+nubkZX19fJEmy93N8+eWXR1XOVSgUeHh4TPhYZgqTycSNGzfYtWuXwxWyiIgIgoODKS4uJi0tbRpGOLM9yD6wh23rcufOHX766SdeffXVEUXLMjIyuHr1Kvv27eOdd94RK3NzTFVVFT/88AMDAwN4e3vT29uL2Wxm7dq1pKamTvfwZpy0tDRqamro6emxP9bU1ISbmxteXl74+/ujUqnIysoiMzNTTPQITjU1NfH111+Tl5dHb2+v/fH29nb79p6333570ir7TxbxF0YQHlN7ezsXLlzg1q1bwNBN4aJFi1i5cqUo+PEAPDw8iImJobKykpiYmFE33bW1tbi7uzvtfzesvb2dgoICDAYDWq2WpKQkIiMjx72J9/X15d1336Wqqoq7d+9is9lIS0sjISFBtDdwoKmpCV9f3zED7+joaKqrq2dl0Gmz2ZBleVJ+N8LDw7lw4QKyLDv8vZVlmdbWVtauXftQr3v+/HnWr18/qkq2JEksW7aMqqoqSktLSUpKeqzxCzNHZWUlX331FcuWLSMsLMz++2YwGDh79iwmk4lly5ZN8yhnlqSkJE6fPo23tzednZ32x3t7ezGZTERFRfHSSy/NyBUqYWqdO3eOysrKEQHnsJ6eHmpqarhw4QLbt2+fhtE9OhF0CsJjaGxsZN++fYSEhLB8+XJcXV0ZGBigtraWf/3Xf2X37t3o9foHfr3hFYy5tsdqy5Yt7Nu3j5s3bxIcHIybmxsDAwM0NzdjNpvZvXu305+JLMucPn2avLw8QkJCcHd3p7+/nyNHjuDh4cErr7wybhqsJEksWLCABQsWTMbbm1WcBUT3e5TVuCddeXk5p0+fprW1FQB3d3fWrFlDamrqhGU2BAcH4+XlRU1NDREREaOO37t3D51ON+4EzP2MRiNtbW3ExMQ4PSc5OZni4mIRdM4Rsixz/Phxli9fPup3yd/fn/Xr13Ps2DFSUlJEi56HoFKpeO2119i7dy89PT20tLRgtVpxd3fHw8OD+fPnz7mCc8KjuXPnDs3NzU6PNzY2Ul5ePoUjmhgi6BSERyTLMl9++SUxMTEEBQXZH9doNMTExKDVavn66695++23x71Jr62t5dKlS/aVNj8/P5YuXTrhzcQtFgulpaUUFhbS39+Pt7c3GRkZD7QiOJlcXV3Zs2cPpaWl5Obm0tDQgFarJT09nZSUlDH3Bl69epXi4mKysrJGnBceHs6dO3fYv38/r7322pwL5CdLUFAQbW1tY/YtraioGDPImWlOnz7NmTNnOHv2LBUVFdhsNsLCwqivr6ekpIQdO3ZMWOD5wgsvsHfvXnp7ewkPD8fNzY3e3l5qa2tpbW1lz549D/W7bDKZ0Ol0Y47P3d3daYVNYfapra1FlmWn7aLc3d0JDQ2lqKiIpUuXTvHoZjY/Pz9+//vfc+zYMQoKChgcHKS/v5+Ghgb753jbtm1zrvic8HBsNhsWi8XpcavVOm4tjCeRCDoF4RFVVVUhy7LTmcvQ0FBqampoaGgYsxdkYWEhP/74IxEREaxevRoXFxfa29u5du0apaWl7NixY0ICz+7ubj7++GMA9Ho9Hh4e9PX1cfjwYYKCgvjlL385rXu6lEolixYtYtGiRQ/8HKvVysWLF0lOTh4VmEqSRExMDFeuXKG2tvaJraY602g0GhYtWsSFCxfYsGGDw3Tourq6GZf240xtbS3nzp3j008/pb+/f8Tjn3/+OQAJCQmkp6dPyPU8PT1ZvXo1ly5dIicnB4vFgkajITU1lW3btj30fmIPDw96enro6+tzuuLf2Nj4xLa5ESZeR0eHff+6Mz4+PnR0dEzhqGaP2tpabt68SU1NzYjAoa+vj/b2dg4dOoRer3+oLChhbvH09MTT03NEmvbPj8/E7Vui2okgPKL6+np8fHyc/uGWJAk/Pz/q6+udvkZHRwcnTpxgyZIlhIeHo1Kp7M9LTU2lt7eXixcvPvZYZVlm//79uLu7k5CQgF6vx8vLi+DgYJKTk+ns7OTEiROPfZ2pVldXh1qtdnojLkkSgYGBFBcXT/HIZrenn36ajo4ODh8+TF1dHRaLBaPRyOXLlzl69CgvvPACrq6u0z3MCXHu3DmuXr06IuAcZrPZOHv2LGfPnp2Qaw0MDPDRRx+Rn59PRkYG27Zt45lnnsHPz4/q6upHmnzSaDTExcWRn5/v8LjZbKawsJAlS5aMeLy/v5/i4mIKCwupr6+fdenSc5lGoxnR2sOR/v5+UVn1EZ09e5bGxkZcXFzQ6/UEBQXh7++PWq3GZDLR1tbG5cuXp3uYwhNs1apVTrOFhifUnbXWepKJoFMQHpFCoRj3Rsxms42Z1ja8D9FRmqJCoSAqKorc3FxsNttjjbW2thaj0TiiYMT911mwYAE3b95kYGDgsa4z1Uwm07htOdRq9Yx7X086V1dXdu3aRXR0NKdOneJPf/oT+/btw2QysWfPnlm1N7a2tpaqqiqnx+vq6ujt7WVwcPCxr3X06FG8vb3ZunUrMTEx6PV6YmJi+MUvfkFAQACHDh16pNddu3YtN27c4OrVqyPG2d7ezsGDB4mIiLBnY1itVr7//nv+63/9r+Tm5nLr1i2+/PJL3nvvvTEn0ISZY8GCBXR2dmI0Gh0et1qtVFVVkZiYOMUjm/lkWaampgZ3d3cCAwMZHByku7sbm81GSEgIAQEBdHZ2UlZWNt1DFZ5gKSkpREdHk5qaOiJDRafTkZqaSkxMzIzcgy/SawXhEUVFRXH58mViYmIcBpZWq5XW1tYxb8BramoIDg52etzT0xNZlunq6nqs9Lfy8nL8/Pycrsqq1Wq8vLyoqqoiISHhka8z1fz8/Ojq6hozuO/u7iY6OnqKR/ZkaWlp4erVq9TU1CDLMhEREWRmZj5WuXWVSsWyZctmfYVLSZLGnDgaPva4e4Y7Ojqorq7m1VdfHfVakiSRlZXFJ598Qmtr60On5Xl7e7N7926+//57cnJyCA4OxmQy0dHRwdKlS1m5cqW9+NOhQ4cwGo1s2bLFXkRm+Eb6s88+49VXXx2xh12YeYbbdly4cIF169aNWNG0Wq1cunSJyMhI/P39p3GUM5Msy/b9ePdP0vT09NDe3k5ISAje3t6PPZEszG5KpZLdu3fz008/4ePjY58sHP7srlq1aka25hNBpyA8oqCgIPz8/KiqqiIqKmrEMVmWqaioICwsDF9f30e+hizLD1QtdDwWi2Xc1DylUjnmxvUnkZ+fH3q9noaGBocVPYcr4P7yl7+chtE9GfLz8/nxxx8JCQmxT4AYDAb27t3LunXrJmwv4mwVGxtLXFwcOTk5Do9HRETg6+v72Puh7969S0REhNPXUSqVREVFcffu3UfaC+br68uOHTvo7OyktbUVFxcXwsLCRlyvtraW2tpatmzZMuL7QpIkIiIiMJlMnD59mh07djz8GxSeKCtWrGBgYIAjR44QERFh79NZWVlJWFgYzz///HQPcUZqb28Hhib6fk6WZZqamoiIiBD7OYVxKZVKnn76adauXWv/vfL19Z3RrdxE0CkIj+HFF1/ko48+4saNG4SEhKDT6ejt7aW+vh5Jkti1a9eYz4+OjqaqqmpU/7xhXV1d9lXIxxEcHDxmiqDNZqOzs3NGlnPftGkTH3/8MbIsExISglKpRJZlOjs7KS0tZdWqVbi7u0/3MKdFXV0dp0+fHpWi4+HhQWBgIGfOnCEwMJCwsLBpHOWTbeXKldy8eZOysrJRhVVcXV1Zu3Yt69ate+zrWK3WcQNXFxeXx65Y6O3tPaoARXV1NZcuXaKiosLeTiMuLm5UFkdUVBQ3btygp6dnzn6mZgtJkli/fj1Lly7l+vXrdHZ24uHhwa9//esZ+XfgSVFaWkp3d7fTrTdWqxWTycT8+fOneGTCTKVUKmfNJIUIOgXhMXh4ePD2229z8+ZNCgoK6O3txcPDg6ysLBYtWoRKpRrz+UuWLOHKlSuEhISMCiytVit3795l2bJlj73SmZiYyA8//EB3d7fDojtNTU34+fk9VrrldAkKCmL37t388MMPXLx4EQ8PD0wmEwqFgnXr1rF48eLpHuK0ycnJYd68eQ6rlg73e7x8+TIvvfTSNIxuZtDr9fZVn4KCAsrLy7FarcyfP5+MjAx0Oh0//fQTLi4uxMbGPvJ1goODyc3NdZrZIMsydXV1j91qYTit1sXFBT8/P/Ly8jh79izR0dFs3LgRpVJJW1sbt2/fpq6ujrVr19oDTxcXF9zd3enu7hZB5yzh5eXF6tWrp3sYs8bAwMC4+7ttNttjZUAJwkwlgk5BeExqtZr09PRHSlP08PBg69atHD58mNDQUIKDg1EqlbS3t3Pv3j3CwsLIzMx87DGqVCq2bt3KN998w/z589Hr9SgUCgYHB2lsbKSlpYU9e/Y89nWmS2BgILt27aKzs5OOjg40Gg1BQUFzvjdnRUXFmL+XgYGBXLt2bQpHNDMtXryYkJAQjh07RmVlJS4uLvj7+5OYmIi/vz/Nzc0cOXKETZs2PXLxlfDwcBQKBXfv3nVYtbCqqorBwcFRqfwPqre3l1OnTlFaWoq7uztmsxmFQkF/fz8rV64cMTHh7++Pr68v165do6SkxN7GyGaz0dfXZ9/rKQjCSHq9Hm9vb3s6pCMeHh5iv6wwJ4mgUxCmWVxcHK+//jpXr17lxo0bWCwWAgMD2bhxI3FxcRMWOMXFxfHKK6/w008/ce3aNdRqNWazmbi4OJ577rlZMfPqKHVwLhuverJCoRAFLR6Qv78/HR0dPP3006NuGIOCgli2bBnfffcdcXFxj7S/U5Iktm3bxqefforRaCQxMRGNRoPJZKKkpITCwkJefvnlR/o+6O3t5YMPPiAkJIRt27ah1WqRZZkzZ87g7+/vcCVcoVAQHx9Pfn4+CxcuRKFQUFtbi5+fn/iMCYITiYmJHD9+3P739efc3Nxwc3MTfaOFOUkEnYLwBPD39yc7O5vs7OxJvU54eDi7du2ir6+PgYEB3NzcZk0/RWG0oKAg2tvbne7RGuuYMNLdu3fRarVOVyh8fHzw8PCgvLz8kVc7g4OD2bNnD+fPn2ffvn2oVCrMZjOxsbHs3r37kdPfz5w5Q3Bw8IisCUmS6OnpIT4+3unzvL29sVqtDAwM0N/fT15eHtu3b3+kMQjCXKBSqdi8eTNHjx7l3r17I/qhenh4MG/ePLZv3z7ns3CEuUkEnYIwB+l0OoerG3NBQ0MD169fp7u7G09PT9LS0mZt4LVs2TJOnDiBv7//qIp3VquV2tpa1q9fP02jm1na29vHLeg1Xlrdg/D392fbtm0MDg7S39+PRqNBrVYzODhIQUEBBQUFdHd3o9VqSU5OJi0tbUTLi58zm83cunWLrVu3jjo23CbFmeH2D5cvX6azs5PNmzfPqh6sgjAZUlJScHV1tddRGM448ff3Z/PmzaJwmzBniaBTEIQ5wWKx8NVXX1FbW0tQUBBarZbW1lY+/vhjoqKieP7552d0KXJHEhISuH37Njdu3CAiIsLe67Wjo4Oamhrmz58/bmEas9lMYWGhPdjR6XSkpKSwZMmSOTVx4Sxd7n4mk2nCMgdUKpW9EFl/fz/79u2z7x/39fWlu7uboqIicnNz2bVrl9OU166uLrRarcN/q5CQEHsRMUfa2tpQqVQsXbqUxMTEcQujCYIwJD4+nri4OFpbW+nr68PT03NWbGERhMchgk5hQpjNZgYHB9FqtTOyYe1c1d3dTW5uLsXFxQwODuLj48PSpUvte7hmk2+//ZbOzk4yMzNHvLf58+dz69Ytfvjhh0lPb55qkiTxi1/8gsLCQnJyciguLgaGKlY+9dRTpKWljZnm1dvby0cffYRWq2XhwoV4enrae/nl5eWxe/dueyA728XFxXHixAmngeVwUS5HK4qP69tvvyUwMJDVq1fb/728vLyYN28eBQUFHDhwgLfeesvhv+Vwiq6jqrjx8fH2ImY/D1otFgvl5eWsXbt2TleAFoRHJUnSjKwILwiTRQSdwmOpqanh3Llz3Lt3DxcXFyRJYvHixaOqIQpPnoaGBj799FP8/PxYsGABarUao9HImTNn7EVLHrfh/ZOiq6uLsrIysrKyRgXTSqWShIQErly5wtq1a2fd760kSaSlpZGamorJZAKG+ks+yJ6iw4cPo9frSUpKsp8/vK/x9u3bHDhwgLfffntO7E9yc3MjJSWF/Px8li5dOuKzYbVayc/PJyEhAU9Pzwm9bldXF1VVVbz++usOf86pqakUFxdTW1vrsDiJl5cX7u7u1NfXM2/evBHH3N3dWbFiBefPnycyMpLQ0FBcXFwwGAxUVVURGRnJkiVLJvT9CIIgCHPT7LijFKZFUVER3333HfPmzSMjIwOlUkl/fz81NTWUlZXxxhtv4ObmNt3DnJOam5spKCigq6sLd3d3Fi9eTGhoqP2m1WKx8PnnnxMVFTWi6bBGo8Hf35+ysjJOnz7Nhg0bpustTKiysjICAgKcBtEqlQo/Pz/Ky8tJTU2d4tFNDUmSxtz793Pt7e3U1dWxefNmh8FOTEwMlZWV3Lt3b840Ot+wYQNHjx7lxx9/JDw8HHd3d3p7e+0/g8lYKa+urmb+/PlOU1slSSIqKoqKigqHQackSaxYsYIzZ87g6+s7alJlcHAQtVqNu7s7BQUFWK1WAgMDyc7OJjo6ek5MKAiCIAiTTwSdwiPp6+vj2LFjLFy4cERgqdVqWbBgATU1NXz//fe88MIL0zjKucdms3H48GHu3r1LcHAwbm5uGI1GDhw4QFBQEL/85S9RqVTcunULnU43IuAcplAoWLBgAQUFBaxduxa1Wj0N72RimUymcVdtVSqVfSVQGAp2hvvGOiJJEiEhIfagaC5QKpVs3bqVlpYWCgsLMRqNeHp6snPnToKCgh779VtbW+no6ECr1RIaGmpvaTNeqrtSqRyz9U1SUhIdHR0cPnyY2NhYAgMDGRwcpLKyks7OTnbt2iXSAAVBEIRJJYJO4ZEUFhbi6+vrdCUzNDSUwsJC+vr6Zl264pPsxIkTNDU1kZWVNSJYCA8Pp6SkhMOHD/Piiy9y9+7dMYsaaDQadDodjY2NsyKg8PPzo6ioaMxzuru7nRZUmavGW+Uar/rpbBUQEDChWQC1tbWcOHECo9GIXq+np6cHk8nEqlWrmDdvHqdPn8ZqtTqdAKiurmbt2rVjXmPVqlUkJiaSl5fH3bt3gaHV6vT09IdaARcEQRCERyGCTuGR1NfX4+Hh4fS4i4sL7u7utLS0EBERMXUDm8P6+/u5fv36qIAThlYvExISuHTpEh0dHdhstnEDiuFVltkgLi6O48eP21emfq6jo4PBwUGioqKmYXRPpuFgx9lKmyzLNDY2kpaWNg2jmz1qa2v54osvWL9+PQkJCfafdUNDA0ePHmXJkiX4+/tz/fp1h/sr7969y8DAANHR0eNey9fXF09PT8rKyrBarbS3t3P16lWWLFnCihUrZs0ebkEQBOHJM7vKUwpTZrx0LuCB0sKEiXPnzh38/PycpsMqlUoCAgIoLS0lPDyczs5Op69lNpsxGo0TkjL4JHBxcSE7O5vi4mIMBoN9dW64h+HNmzdRqVQcO3aM5ubmaR7tkyEgIMBeMMiRmpoaJEmasL6NsixTVVXF0aNHOXDgACdPnsRgMEzIaz+pZFnmu+++Y8OGDSQmJo74vgwJCeGVV17h/PnzrF+/nhs3bnD27Fn757a3t5ecnBzOnDnDCy+8MO53rSzLHD58mJKSErZs2cI777zD22+/zYsvvkh9fT379+/HarVO6vsVBEEQ5i4xrSk8ktjYWM6ePes0KOnv72dgYICQkJApHtnc9SD7Fl1cXDCZTGRlZfHTTz/R1dU1quG9LMvU1NQQHx+PVqudzCFPqcTERNRqNT/++CN37txBpVLR3d2Nr68vsbGxKJVK2tvb+eijj1i6dOm46YpzwdatW9m7dy+9vb1ER0fbW6ZUVFRw7949Xn311QkpNNPf388XX3xBb28v8fHx6PV6DAYDe/fuZdGiRWzcuHHSC9rIskx5eTlXr16196dMTEwkIyNjzKyOx9HU1MTAwADx8fEOj3t6ehIbG0tNTQ1vvvkmOTk5HDhwALPZjFKpJCkpiddff/2B+v+Vl5fT2NjIzp07RxQl0uv1PP/88xw8eJC8vDwyMzMn7P0JgiAIwjARdAqPZOHChfz444+0tLSMKkBhs9moqakhIyNDpGtNIT8/P7q7ux324xvW29uLv78/Go2Gbdu28c033zBv3jyCgoJwcXGhp6eHuro6LBYLzz777BS/g8kXExNDdHQ09+7dY//+/cTHx4/oM+np6UlAQAAFBQXo9XoWLVo0jaOdft7e3v0SVYEAAB3tSURBVLz11ltcuXKFCxcu0Nvbi0ajITk5mTfffHNUb8dHdfDgQby8vMjOzrb/7kZHR5Oamsrx48e5cOECq1atmpBrOWKz2fjyyy9pamoiODiYhIQEe6Gd3NxcduzYMardyERob28nMDBwzIA6ODiY9vZ23N3dWb9+PU8//bR9f+fDBOJ5eXksXbrUYRVchUJBVlYWp06dEkGnMOV6enrIy8ujqKgIk8mEt7c3S5YsISkpSdxDCMIsIj7NwiNRKpXs3LmTTz75BKPRiL+/v33lqKWlhdDQUNasWTPdw5xTIiMjkWWZ9vZ2hwVxjEYjRqORhIQEYGi1+tVXX+XixYvk5OQgyzI6nY709HSWLVuGq6vrVL+FKSFJErW1tfj4+IwIOIep1WrCwsK4cOHCnA86Yag/5bp161i3bt2YExqPqr6+nra2NjZs2DDqtV1dXVm3bh2HDh0iKyvLaduQ+8myTENDA21tbajVansP2rGcO3eOtrY2kpOT7WmqWq0WT09PfHx82L9/P7/73e8mvJKzRqOht7d3zHN6enpGfBYlSXqkG/Hm5maeeeYZp8fDwsJoa2sT2yKEKdXc3My+ffuYP38+a9asQafTYTAYyM/Pp7CwkJ07d86KCuqCIIigc07o6emhs7MTtVqNXq+fsJvGgIAA3n33Xa5fv87Nmzcxm834+fnx3HPPERUVJfq7TQGj0Uhubi5lZWVYLBY8PT0pKioiLi6O/7+9e4+vqrrzPv75nVzIIYEASQhBCEGIoAFKgACWp4jgBaUgVqhO1adQW53aeTrPq9fpbXqbjm2n1daxMz6Ot/ryAogtjDcUtaIVVBAVHGIkGtBgIEAiJCaQ23r+2Dv0cDgnAczJOUm+79drvTxnr7X3XiesmPPb65abm0sgEMA5x4EDBygrK+PSSy897gtrXl4eS5cuxTlHa2trn3mqXFpa2uGQxMGDB/Puu+9SX19PRkZGN9YsscXid3rHjh2cddZZUQOdzMxMBg8ezO7duztdLKeyspJVq1ZRU1PD/v37CQaDDBkyhBkzZnDhhRdGvEdLSwubN29mwoQJEfOzsrLYt28f27dvj7iQzycxatQoamtrOXDgANnZ2Sfkt7W1sX37dr7whS984nu1D62PNlS4qamJQCCg/29Lt2lra+Ohhx5i5syZx/1up6enk5+fz4YNG1i3bh2LFi2KYy1FpKv0jW+YfVRNTQ1PPfUUu3btIj09naamJlJTU5kzZw6TJk3qknsEg0HOPfdczj333C65npy8Xbt2sXLlSrKyshg+fDjJyckcOnSIlJQUKioqKC8vJyMjg8bGRvr378/ixYs566yzIl7rdHtPeqqOtp8A7+eRlJRES0tLN9aqb2pqaup07nBaWhpNTU0dlvnwww+5++67WbduHe+9996x4+np6dTU1FBXVxdx3+B9+/aRmpra4dZOWVlZ7Ny5s8uDzuTkZGbNmsXatWu56qqrjtuCqq2tjSeeeIJhw4Z1yYJe48aNY8eOHVGHKe/YsYPCwkIFndJtysrK6N+/f8SHSWbGjBkzWLlyJRdccIG2XhPpBfrOt8w+5uDBg9x9993k5eUxY8YMkpOTcc7x0UcfsX79eurq6pg1a1a8qymnqaGhgZUrVzJ27Njj5tX179+fnJwcSktLmTp1KgUFBaSnp3dpD3dvkJeXR21tbcTtU8D7+TrnYraAjPxNVlYWFRUVTJw4MWJ+W1sb+/fv73QP1TVr1vDss88eF3CCN4/5kUceITMzk9mzZ58wB/1khgybWcxWdp05cyYNDQ3ccccdFBUVkZubS11dHdu2bSM7OztioHw6SkpKuOuuuxg7duwJC7wdPHiQTZs2sXTp0i65V0/R1tbGO++8w5tvvklDQwODBg1iypQp5Ofn6/+X3eC9997rcEu1YDDI0KFDqaysPO6BqXOOPXv28MYbb1BXV0dGRgbFxcWcccYZ+ncTSWCauNFLPfnkkwwfPpz8/PxjPVhmxuDBg5k4cSIvvPAChw8fjnMt5XS9/vrrDBo0KOJCLsnJyYwaNYo333yTgoIChg4dqj/EYaZPn86+fftobm4+Ia/9C82UKVM67A2VrvGpT32K3bt3c+jQoYj5O3fuZMCAAeTm5ka9xkcffUR1dTU7d+6MmN/c3My2bdt45ZVXTsjLycmhoaGBo0ePRr3+oUOHyM/P7+STnB4zY968edxwww2kp6dTWVnJ0aNHWbJkCVdffXWXza3Oysri8ssv55FHHmHdunVUVFSwe/dunnvuOR588EHmzp0bs8+YiBoaGrjzzjt57rnnyMzMZMyYMSQnJ/OnP/2JVatWafuYbuCc63T+cPsUkXYtLS2sWrWKhx9+mEAgQH5+PklJSaxevZqVK1dqdIpIAlPQ2QsdOnSIysrKqNuVpKWlHVuhU3qmd955p8M5iQMGDKCpqanDvTj7sry8PEpKStixYwe1tbXHvtQ0NjZSXl6OmcV0tVT5m2AwyLx583j00UeprKw89m/R0tLCjh072LhxIwsWLOjwGocOHaKhoaHDvYP37dtHdXX1Ccf79etHUVER77///nFfbtvV19dz4MABiouLT/GTnZrMzEzmzJnD4sWLmT9/PmeccUaX36OwsJAbb7yRrKwsXn31VTZt2kS/fv24/vrrY/75Es3DDz/MoEGDuOCCCxgzZgx5eXmMHz+eSy65hPr6ep5++ul4V7HXGzlyJB988EHU/KamJvbu3Xvcd5nHH3/82EOZyZMnM3r0aCZPnswVV1xBc3Mzjz32WHdUXUROg4bX9gK1tbVs3ryZnTt34pwjMzOT9PT0DntpBgwYEPELmPQMra2tHT4hNjMCgUCHX8L7urlz55Kdnc2LL75IeXk5SUlJOOeYMmUKs2fP7rWr9yaikpISgsEgGzZsoLm5mfT0dGpraxk2bBjXXnsteXl5HZ4fDAZJS0vrsMyAAQOiDqe+6KKLuOeeeygrK2PEiBFkZGTQ0tJCdXU1H3zwAQsXLjxuvmVPlpGRwezZs/v0Q5WqqioOHDjAZz/72RNGgSQlJVFSUsLjjz/OnDlzetVexYmmqKiI9evXU1lZGXFLoq1bt3LmmWcem+ZQV1dHaWkpV1111Qnfb5KSkvjMZz7DihUrOHz4cNTfdRGJHwWdPVxpaSlr164lLy+PM888EzNjz549NDQ0dHhe+xc76ZlGjhzJnj17ou6T2NjYSGtrK5mZmd1cs55l0qRJTJw4kfr6elpbWxkwYICG1MbJhAkTKCoqorq6miNHjpCZmXnS+4Dm5OQwcODAqD0nZsbUqVMpKSmJeH5aWhrXXXcdmzZtYsuWLTQ2NuKcY+zYsTHbo1Pi5+233yY/Pz/qg7v2uYTvvfceRUVF3Vy7viM5OZklS5awatUqJkyYwPjx4wkGg9TU1LBt2zYOHjzI8uXLj5UvKysjPz8/6hYqqampjBo1irKysqi/6yISPwo6e7CDBw+ydu1aJk+efNxTvYyMDKqrq6mrq4u4EEr7FhrTp0/vzupKFyopKeH1118nNzf3hB4e5xyVlZUUFxf3qRVpT5eZacGgBGFmHc7d7Oi8BQsWcPjwYVatWnXcsPJAIMC8efMYOXIko0aNinqN1NRUzjvvPGbPnk1zczPJycnar7KXam5u7nTP19TUVM0P7AYFBQUsX76cjRs3smLFClpaWsjIyGDq1Klcfvnlx/19a2pq6nREw8msdC0i8aFvpD3YK6+8wvDhw08YRhIIBCgoKKC0tJTi4mJSUlJoaGhg//79tLS00NjYSHJycqd73kniGjJkCHPnzmXDhg2MGDGC7OxsAoEA9fX17Nmzh+TkZObMmRPvaop0m7PPPpvFixeTnJxMVVUVFRUVpKenU1RURH5+Ptdcc81JLahlZtqMvpfLzc1ly5YtUfPbV0zOycnpxlr1XTk5OVx22WUsWrSow8WFsrOz2b59e4fXqq6u5pxzzolFNUXkE1LQ2YPt3LmT8ePHR8zLz8/nyJEjbNy4kWAwyNGjR8nJySE1NZXm5mYaGxvZvn17l+3XKd1v+vTpZGVl8eKLL/Lyyy+TlJREv379KCkp4dxzz+30Sb5IbzNlyhSKiop46623qKqqIhgMUlRU1CX7XErvcc455/DUU09x8ODBiFvx7N69m/T09KiL8UlsmFmHD4bGjh3LY489RlVVVcR53lVVVdTX11NYWBjLaorIaVLQ2YO1tbVFfSJoZhQWFlJZWUl6ejrFxcXHlf34449Zt24dqampUQNXSXxjxoxhzJgxNDc309raSr9+/bQ9ivRp/fr1Y+rUqfGuhiSwlJQUFi5cyKOPPkpxcTEjR44kKSmJ5uZm3n33XUpLS7n22mvjXU0JEwgEWLhwIWvWrGHmzJmMHj362IJ5u3btYtOmTVx22WUaFi+SoLo16DSz2cA3gclAPvAj59y/hJWZAdwCTAFqgXuBHzrnOtw0y8yWAd8DCoAK4OfOuQdC8ucCfwBGAM8Cy5xzH4XkrwY2O+d+9Yk+ZDfKy8ujpqYm6oJAlZWVJCcnM27cuBMCkfT0dMaMGcMzzzwTMV96lpSUFPVsioicpLPPPptgMMhf/vIXtm7dSjAYpKGhgYKCApYtW8bQoUPjXUWJoLCwkKVLl/Lss8/y8ssvM3DgQOrq6hg0aBBLlixh9OjR8a6iiETR3T2dGcAO4EHgd+GZZjYSWA88AnwFKATuBgz4p2gXNbPFwF3At4Angc8C95lZjXPuSTMLACuA3wBr/bI/AL7tn78EGAVc2SWfspvMnDmT1atXM2zYsBMCjra2Nt5//32GDx8eNaAcPHgwFRUVVFdXn9biHSIiIj1V+yI2hw8fprGxkQEDBtC/f/94V0s6UVBQwHXXXUdNTQ319fVkZGR0uG+1iCSGbg06nXNPAE8AmFmkHsWvAoeB65xzbcD/mNkZwK/N7OfOuY+jXPo7wErn3C3++7f9HtPv4gWhWUAOcKtz7oiZPYAXmGJmQ4CbgUs7601NNAUFBUycOPHYXlbZ2dmAt1F6RUUFycnJHa70ZmakpaV1ur2KiIhIbzVw4EDt69gDDRkyRMGmSA+SaHM6ZwFP+wFnu3XAbUAx8NfwE8wsFSgBbg/LWgf8wcySgAPAh8AlZvYYcCHwpl/uVuBO59xbnVXOzK4HrgdvoZ5EcNFFF5GXl8fGjRt56623CAQCBINBZsyYQW1tLdXV1VHPdc7x8ccf64+tiIiIiIjETKIFnXnAS2HH9obkRZKN9zn2hh3fC/QDhjjn9pvZUrwezd8BG4CbzGwBMAH4upndC8wGtgPXO+f2hd/IOXcHcAfAtGnT3Kl9tNgwMyZNmsSkSZM4cuQIbW1tBINBzIx9+/Zx7733MmLEiIgb3u/fv58hQ4ZEXL1PRERERESkK/SZJb6ccxudczOdc6Occ/8b77PfBnwJb75oCt4c0jK83s8eJy0tjf79+x+bw5mbm8u4ceMoLS3l6NGjx8o55zh48CAVFRVcfPHF8aquiIiIiIj0AYnW01kFhG+olhuSF8kBoCXKeUeBmijn/RZ40Dm31czuxFtJt9XM7sPrCe0VFi1axPr169m6dSuZmZmkpKRQX19PIBDgyiuvTJhhwiIiIiIi0jslWtD5EnCtmQVC5nXOBxqA1yOd4JxrMrPNwMXAfSFZ84GXIy0OZGYXADOB9s3cAng9nQCp9KIe4EAgwMUXX8x5551HeXk5TU1NZGVlkZ+fr21SREREREQk5rp7n84MYKz/NhUYZmaTgXrnXDnwn8A/AP9lZjcDY4CfA//evnKtv5rts8D3nHN/9q/1a2C1mb2Kt4DQAuBzwMIodbgduNo51z7m9AXgH8ysFPgGvains11aWhoTJkyIdzVERERERKSP6e4evWl4PZav4y0M9DX/9Z0AzrkPgIuAs4HX8BbtuQNvT812KcA4ILP9gHNuDfBl/3rbgRuAZc65JyPU4ZfAGufcKyHHfgo0AluAfODrn/BzioiIiIiICGDOJcQirD3OtGnT3JYtW+JdDRERERERkbgws9ecc9M6K9dr5i6KiIiIiIhI4lHQKSIiIiIiIjGjoFNERERERERiRkGniIiIiIiIxIyCThEREREREYkZBZ0iIiIiIiISMwo6RUREREREJGYUdIqIiIiIiEjMKOgUERERERGRmDHnXLzr0COZ2X5gd7zrIZ9INnAg3pUQOQlqq9JTqK1KT6G2Kj1JIrfXUc65nM4KKeiUPsvMtjjnpsW7HiKdUVuVnkJtVXoKtVXpSXpDe9XwWhEREREREYkZBZ0iIiIiIiISMwo6pS+7I94VEDlJaqvSU6itSk+htio9SY9vr5rTKSIiIiIiIjGjnk4RERERERGJGQWdIiIiIiIiEjMKOkVERERERCRmFHRKj2Bms81srZntNjNnZj+MUGaGmW00syNmVmVmN5lZ0klce5mZlZnZUTN728yuDsufa2alZlZnZmvMbFBY/moz++4n/5TSG5jZt81sk5nVmtlHZvZXM5sfoZzaq8SVmV1rZq/5bbXRbzffMDMLKaN2KgnFbzutZlYedlxtVeLOzH7if08NT2NDyvTJtqqgU3qKDGAH8B1gb3immY0E1gNlwFTgq8ANwC86uqiZLQbuAm4HPgXcCdxnZpf4+QFgBXAPMA3IBn4Qcv4SYBTwm0/06aQ3mQvcDZwPTAc2Ao+Z2az2AmqvkiCqgZ8DnwaKgF/6778OaqeSeMxsGPBH4Omw42qrkkh2AXlhqQL6eFt1zikp9aiE98v8w7Bj/wpUAoGQY18DPgbSO7jWRuDBsGMPA8/7r3MAB6T5778KPO6/HgK8D0yI989EKbETsA34bch7tVelhEzAn4E/+6/VTpUSJuF1lDwD/BPwE6A8JE9tVSkhUnjbjJDfZ9uqejqlt5gFPO2caws5tg7oDxRHOsHMUoESv1yodcBMf6jDAeBD4BIzSwEuBN70y90K3Omce6vLPoX0Ov7Tx4F4f1Daqb1KQjHPdLy2+Rf/sNqpJJIf4X2p/lWEPLVVSSQjzKzST0+a2adD8vpsW1XQKb1FHicOu90bkhdJNpAc5bx+wBDnPSJaCnwXKAfqgZvMbAEwAbjNzO41s/fMm3Oa+8k/ivQy3wcGcfzGzmqvkhDMLNPM6oGjeE/S/905d6ufrXYqCcHMzgf+HrjWbz/h1FYlUbwKLAcWAH8HHAReNLML/fw+21aTu/uGIj2Nc24jMLP9vZllArcBV+AN80kBCoGb8J4mXRmHakoCMrMb8YLORc65yu64p9qrnKI6YDLeU/ZP431J+dA5d1csb6p2KifLzLKB+4HlzrkT1nSINbVVORXOuSfCDr1oZiOAb+PN5YzlvRO6raqnU3qLKmBY2LHckLxIDgAtUc47CtREOe+3eOPqtwIX+K9bgfv89yKY2beAf8MLOJ8Jy1Z7lYTgnGtzzpU757Y5524Hfs3fFrRQO5VEMAEYjrcgW4uZtQD/DIzx338BtVVJbJuAAv91n22rCjqlt3gJuNCfP9duPtAAvB7pBOdcE7AZuDgsaz7wsv+LeRwzuwDvKdLP/EMBvCdHAKnod0oAM/sZ8GPg0ggBJ6i9SuIKAGn+a7VTSQSbgYl4PfLt6XbgA//146itSmKbgtdeoS+31XiuYqSkdLIJb8uU9j82H+INF5gMjPXzRwKH8ZaTLgIW4Y2j/2XINc4A3gYuDzm2GO/p0T8C44Bv+O8viVKHcmBGyLFb8VbTG4c3/GdNvH9WSvFNwO+ARr9tDQtJmSFl1F6V4p6An+I97T7TbxNf8dvl7/18tVOlhEycuHqt2qpSQiTgZryt087E+576B6ANWOjn99m2Gvd/HCWlk0nAHLxV68LT8yFlZuIthHEEb3L1TUBSSH6Bf86ysGsvA94BmvD2TbomSh1uA34TdiwLeBRvXtQLQH68f1ZK8U1R2qkD7g0rp/aqFNcE3OJ/MWkEaoHX8JbuD22HaqdKCZeIsC2F2qpSIiTgIbwtUY7i7YX8DDA3rEyfbKvmV0RERERERESky2nsuYiIiIiIiMSMgk4RERERERGJGQWdIiIiIiIiEjMKOkVERERERCRmFHSKiIiIiIhIzCjoFBERERERkZhR0CkiIhJHZva8md0Z8v5eM3umG+7rzOyaWN9HREREQaeIiPRofpDmzOzXYcdH+MfnxKlqp+sfgaXxroSZLfN/fnvNLCUsL8fMjvr5/yvCud81s1Yz+7cOrn+pmT1lZgfNrNHMyszsdjM7KxafR0RE4kdBp4iI9AZHgK+b2aiuvKh5Ujov2XWcc4ecc7Xdec8OtAItwMKw48uBqkgnmJkBXwH+FfiimaVGKPPPwKNAOfA5YDzwJaAJ+JeuqryIiCQGBZ0iItIbbATexAt0ojKzcWb2uJnV++lRMxsbkr/MzFrM7Hwzex04ClxgZj8xs3Iz+7yZ7TSzBjNbY2YDzexzfi9dnZmtNrPMkOtNMbMnzazav99mM5vfSR2PDa81swK/N/GEFFI+1z9nv1+Hl8xsdtg1zzezbWZ2xP/v+afws70bL4hsv5YBXwbuilJ+HpAB/BQ4AFweVpepft4PnHNfc85tcM7tds695Jz7OnCDXy7FzG42s0q/V7XKzFacQr1FRCRBKOgUEZHewAHfAv7OzKZFKmBmQeBpIA04z08ZwLqw3rgA8CvgG3g9cFv843nAF4ErgEuAWcBqvADs8/6xzwDfD7nWQGAlcD4wBXgK+O9TGEL6gX/f9lQAbAeeD/lMfwEG+PcvBp4A1pvZ2X6Z4cBjwGt+Hb4J/P4k7w9wJ17gne+/Px/I9j97JDcADzjnWoA/+u9DXQs0ADdHOjmkl/f/4P1crwEKgUXAy6dQbxERSRDJ8a6AiIhIV3DOvWhma4HfAHMiFPkCkANMdc4dADCzq4BdwFXAfX45A77pnHux/USvc49+wBdDzl0F/D0wzDm33z+2Aq+nr71Oz4fV4YdmthBvzuYvTuIztQJ7Q+pxP5CKNyQV4Eq8wPZKP8gD+IWZzcML9v4vcCNej+NX/DI7zOz7eMNbO+Wce9/M1gPXAT8GrgfuBxrDy5rZUOAyYKp/6H6/PoXOuZ3+sbOAd51zTZ3cehTwDrDBOeeA94HNJ1NnERFJLOrpFBGR3uS7wCwzWxQhrwjY0R40Ajjn9gFlfl6oSMHNntBz8YLBve0BZ8ixoe1v/AV3/sPM3jazj8ys3r/XKc89NbMfAfOBBSG9gSXAMOCjkCHD9Xg9roV+mXOAV0OCUoC/nuLt7wC+ZGa5eMNl/ytKueXAdufcdgDn3B7gWbxA9dhHOcl73gNMBMr9BYauiDQ/VEREEp+CThER6TWcc+8A/w9veOzpjuZpdc4diXC8Ofx2UY6F/m29Fy8A/I7/38nAG3i9lSfNzD6PN2x3sXPu3ZCsAFDqXzc0nU3IPMwu8Jh/rweAre1BZVgd2xcQKvbnxbaYWQtwIccvKFQGjOksgHTOvQGMxhs23YQ3JPgNMxvYVR9KRES6h4JOERHpbX4KDOf43jWA/wHOMbPs9gN+z9044K0Y1WU28B/Ouf/2A7Uq4MxTuYCZzcALXr/snAvvodziX++wc648LH3ol9kBTDezpJDzZp1KHfxe0rvxhg5H6+WchzfndBbHB8DFQJC/LSh0P9Afb87sCcxscMh9651zf/YXGJqGF0yfdyp1FxGR+FPQKSIivYo/3PWXePMZQz0I7AdW+qvKTgVWAHvwFvuJhTLgajObaGaTgYeApE7OOcbMhgFr8YaaPmtmw9qTX+QBoAJ43Mwu8le7nWFm3zOzxX6Z/8Sby3qHmZ3tz/fsdD5pBD/zr/PHKPk34M2/3OSceyskvYk3f/QGAOfcFv9avzCzP5jZbDMbZWbnmtkteD3VmNm3zexqMysys9F4W6q04s3zFBGRHkRBp4iI9Ea34C2ec4xzrhG4CG8blBeADcDHwPyTWNTmdC3H+1v7KrAGWMepLYYzHsjFWwyoKizhDwM+D6/H8x68gOxPwHRgt19mD94+m9Pxhvb+nii9jB1xzjU75w74ixsdJ2QBoVVRTl8JzDGzQv9aPwYW4807XYMXnN+Lt1hT++q/h/16bsJbsfdy4ArnXNmp1l1EROLLvAXhRERERERERLqeejpFREREREQkZhR0ioiIiIiISMwo6BQREREREZGYUdApIiIiIiIiMaOgU0RERERERGJGQaeIiIiIiIjEjIJOERERERERiRkFnSIiIiIiIhIz/x9RA5ySVK2bagAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1 = df['top1']\n",
+ "normalized_macs = df['normalized_macs']\n",
+ "plot_2d_embeddings(top1, normalized_macs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Accuracy contour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"BIN_SIZE = 2# 0.5\n",
"NUM_BINS = int(100 / BIN_SIZE)\n",
@@ -118,7 +663,7 @@
"idx_bins = [-1] * NUM_BINS\n",
"\n",
"draw_what = \"accuracy contour\"\n",
- "#draw_what = \"mac contour\"\n",
+ "draw_what = \"mac contour\"\n",
"if draw_what == \"accuracy contour\":\n",
" for i,accuracy in enumerate(top1):\n",
" bin_id = int(accuracy // BIN_SIZE)\n",
@@ -149,7 +694,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Video animation"
+ "### Video animation"
]
},
{
@@ -160,86 +705,6 @@
},
"outputs": [],
"source": [
- "# Based on these two helpful example code: \n",
- "# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot\n",
- "# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.\n",
- "# Specifically, the use of IPython.display is missing from the first example, but most of the animation code\n",
- "# leverages code from there.\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.animation as animation\n",
- "import numpy as np\n",
- "\n",
- "from matplotlib import animation, rc\n",
- "from IPython.display import HTML\n",
- "\n",
- "INTERVAL = 100 # Animation speed\n",
- "POINT_AGING_SPEED = 20\n",
- "\n",
- "class AnimatedScatter(object):\n",
- " \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation.\"\"\"\n",
- " def __init__(self, xdata, ydata):\n",
- " assert len(xdata) == len(ydata)\n",
- " self.numpoints = len(xdata)\n",
- " self.xdata = xdata\n",
- " self.ydata = ydata\n",
- " self.stream = self.data_stream()\n",
- "\n",
- " # Setup the figure and axes...\n",
- " self.fig, self.ax = plt.subplots(figsize=(15,7))\n",
- " # Then setup FuncAnimation.\n",
- " self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,\n",
- " frames=self.numpoints-2, \n",
- " init_func=self.setup_plot, blit=True)\n",
- "\n",
- " def setup_plot(self):\n",
- " \"\"\"Initialize drawing of the scatter plot.\"\"\"\n",
- " x, y, s, c = next(self.stream)\n",
- " self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)\n",
- " self.scat.set_edgecolors('gray')\n",
- " self.scat.set_cmap('gray')\n",
- " self.ax.axis([min(self.xdata)-2, max(self.xdata)+2, \n",
- " min(self.ydata)-2, max(self.ydata)+2])\n",
- " \n",
- " # For FuncAnimation's sake, we need to return the artist we'll be using\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat,\n",
- "\n",
- " def data_stream(self):\n",
- " numpoints = 0#len(self.xdata)\n",
- " colors = []\n",
- " xxx = 0\n",
- " while True:\n",
- " numpoints += 1\n",
- " data = np.ndarray((4, numpoints))\n",
- " data[0, :] = self.xdata[:numpoints]\n",
- " data[1, :] = self.ydata[:numpoints]\n",
- " data[2, :] = [70] * numpoints # point size\n",
- " #data[3, :] = [np.random.random() for p in range(numpoints)] # color\n",
- " # The color of the points is a gradient with larger values for \"younger\" points.\n",
- " # At each new frame we show one more point, and \"age\" each existing point by incrementaly \n",
- " # reducing its color gradient.\n",
- " data[3, :] = [(1-i/(numpoints+1)) for i in range(numpoints)] \n",
- " yield data\n",
- "\n",
- " def update(self, i): \n",
- " \"\"\"Update the scatter plot.\"\"\"\n",
- " data = next(self.stream)\n",
- " i = i % len(data)\n",
- " \n",
- " # Set x and y data\n",
- " xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]\n",
- " self.scat.set_offsets(xy)\n",
- " \n",
- " # Set colors\n",
- " self.scat.set_array(data[3])\n",
- " \n",
- " # We need to return the updated artist for FuncAnimation to draw..\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat,\n",
- "\n",
- " def show(self):\n",
- " plt.show()\n",
- "\n",
"a = AnimatedScatter(normalized_macs, top1)\n",
"plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
"plt.xlabel('Normalized MACs')\n",
@@ -249,6 +714,98 @@
"a.ani"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Performance Evaluation\n",
+ "OK, one thing stands-out: the best Top1 we see during our traversal of the compressed network sub-space is below 80%, while the AMC paper reports a Test accuracy of 86%. The appendix to this notebook explains why this is so, but for now let's see if this is a problem: after all, what we really care about is reproducing the 90.2% Top1 score AMC reports on the fine-tuned network.\n",
+ "\n",
+ "So let's get to it: Here are the results of fine-tuning for 30, 50, and 60 epochs. At 60 epochs of fine-tuning, the Top1 is 88.42-90.18 = -1.76% off of the original Top1. Here too, I am not able to get the paper's results (too many epochs; Top1 is not up to par).\n",
+ "\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=30\n",
+ "```\n",
+ "==> Top1: 87.790 Top5: 99.370 Loss: 0.400\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=50\n",
+ "```\n",
+ "==> Top1: 88.310 Top5: 99.440 Loss: 0.402\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=60\n",
+ "```\n",
+ "==> **Top1: 88.420** Top5: 99.390 Loss: 0.416\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Appendix: on the relative pruning robustness of Plain20 and VGG16 (CIFAR)\n",
+ "\n",
+ "The diagrams below show the relative pruning sensitivity of Plain20 and VGG16 at 20% filter sparsity.\n",
+ "\n",
+ "It is evident that VGG16 is much more robust compared to Plain20."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_plain20_sensitivity = pd.read_csv('../sensitivity-analysis/plain20-cifar/plain20_cifar_sensitivity_filters.csv')\n",
+ "df_vgg16_sensitivity = pd.read_csv('../sensitivity-analysis/vgg16-cifar/vgg16_cifar_sensitivity_filters.csv')\n",
+ "df_plain20_sensitivity['sparsity'] = round(df_plain20_sensitivity['sparsity'], 2)\n",
+ "df_vgg16_sensitivity['sparsity'] = round(df_vgg16_sensitivity['sparsity'], 2)\n",
+ "\n",
+ "def plot_sensitivity(df, level, acc, name):\n",
+ " filtered = df[df.sparsity == level]\n",
+ " #s = filtered.style.apply(highlight_min_max)\n",
+ " \n",
+ " param_names = filtered['parameter']\n",
+ " \n",
+ " # Plot the sensitivities\n",
+ " x = range(filtered[acc].shape[0])\n",
+ " y = filtered[acc].values.tolist()\n",
+ " fig = plt.figure(figsize=(20,10))\n",
+ " plt.plot(x, y, label=param_names, marker=\"o\", markersize=10, markerfacecolor=\"C1\")\n",
+ " plt.ylabel(str(acc))\n",
+ " plt.xlabel('parameter')\n",
+ " plt.xticks(rotation='vertical')\n",
+ " plt.xticks(x, param_names)\n",
+ " plt.title('{} Pruning Sensitivity Per Layer (at {:.0f}% sparsity)'.format(name, level*100))\n",
+ "\n",
+ "plot_sensitivity(df_plain20_sensitivity, 0.2, 'top1', name=\"Plain20 (CIFAR)\") \n",
+ "plot_sensitivity(df_vgg16_sensitivity, 0.2, 'top1', name=\"VGG16 (CIFAR)\") "
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -273,7 +830,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.7"
}
},
"nbformat": 4,
diff --git a/examples/automated_deep_compression/automl_args.py b/examples/automated_deep_compression/automl_args.py
new file mode 100755
index 0000000..e15907c
--- /dev/null
+++ b/examples/automated_deep_compression/automl_args.py
@@ -0,0 +1,36 @@
+def add_automl_args(argparser, arch_choices=None, enable_pretrained=False):
+ """
+ Helper function to make it easier to add command-line arguments for AMC to any application.
+
+ Arguments:
+ argparser (argparse.ArgumentParser): Existing parser to which to add the arguments
+ """
+ argparser.add_argument('--amc', dest='AMC', action='store_true', help='AutoML Compression')
+ group = argparser.add_argument_group('AutoML Compression Arguments')
+ group.add_argument('--amc-protocol', choices=["mac-constrained",
+ "param-constrained",
+ "accuracy-guaranteed",
+ "mac-constrained-experimental"],
+ default="mac-constrained", help='Compression-policy search protocol')
+ group.add_argument('--amc-ft-epochs', type=int, default=1,
+ help='The number of epochs to fine-tune each discovered network')
+ group.add_argument('--amc-save-chkpts', action='store_true', default=False,
+ help='Save checkpoints of all discovered networks')
+ group.add_argument('--amc-action-range', type=float, nargs=2, default=[0.0, 0.80],
+ help='Density action range (a_min, a_max)')
+ group.add_argument('--amc-heatup-epochs', type=int, default=100,
+ help='The number of epochs for heatup/exploration')
+ group.add_argument('--amc-training-epochs', type=int, default=300,
+ help='The number of epochs for training/exploitation')
+ group.add_argument('--amc-reward-every-step', action='store_true', default=False,
+ help='Compute the reward at every step')
+ group.add_argument('--amc-target-density', type=float,
+ help='Target density of the network we are seeking')
+ group.add_argument('--amc-agent-algo', choices=["ClippedPPO-continuous",
+ "ClippedPPO-discrete",
+ "DDPG",
+ "Random-policy"],
+ default="ClippedPPO-continuous",
+ help="The agent algorithm to use")
+ # group.add_argument('--amc-thinning', action='store_true', default=False,
+ # help='Perform netowrk thinning after altering each layer')
diff --git a/examples/automated_deep_compression/comparing_model_spaces.ipynb b/examples/automated_deep_compression/comparing_model_spaces.ipynb
deleted file mode 100644
index 42b20ef..0000000
--- a/examples/automated_deep_compression/comparing_model_spaces.ipynb
+++ /dev/null
@@ -1,1290 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 103,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style>\n",
- " .dataframe thead tr:only-child th {\n",
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- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import seaborn as sns\n",
- "#master___2018.07.24-2*\n",
- "df = pd.read_csv(\"../classifier_compression/logs/master___2018.07.24-235532/arch_space.csv\")\n",
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JJpG4HuZprak2GyxubvLWK6+yVKuxqW0m7nuYM0/8Dk40xsVzP2OnlqNptRk+doKN1XkaYYvAUWjTwbcVsUQUwzaxb3pe8f4MkVSM0tUNIq6i4rU4P3eJWrOO53l861vfui18W1tb48Jbb7FzdZbHTp6iJ31jps61a9dYWl7n2tImKpbFUja6WcZo1klaDql4Gq01q7UCpXqF9fw6BwfG6eofo17eoVHNk9suoMI2yTAgYhoUCmUWWpovPHryenDUaDVY3Fpmen2OdlShQ4WZcTC7YphJB1Nr2oHXSY8MA88PaLWaJMwkyjRIpJI0jAZBvY3r+VT8BgubK7x98QIHDhzg7NmzH+o78kkg4ZEQQgghhBBCfIbkcjm+9/3vcXljnpblMXrsIPcdGMK6aVepMAzJbeWYPv8Ob719Abul+fGLL3Lq/vsZGhnhxIkTHD9+nHg8Trvd5sqVK0xPT7O1XaDtB4QaLFORiNocnZhgamqKwcFByuUyMzMzXL58mVx5h5bXJtABpjKJ2Q4DXX0cP36cqakpksnkh3K9Ozs7/OQnP2FreRl3K4dXrmB4bSKWjR8GlFDspJMsXLxIanCQxx9/nImJiQ+l70ajwY9+9BIXF7bJDh7iyPH776pdNBbn8LFT/OAv3kStzJHuSvH7X/86meTtxauVUqTjCca6uthwYiwvb9KdSOG1mtRrFUqFbdY3Fim4BQ6cuo+1tXny1W0aXg2SNvF4EtO2cZt1qrUapmkS3V0qB2DFIqSPDFC5ukE6Gic0DBZzq/z456+QTCb5xje+cctuaD/72c/YuTbP0eHRW4KjQqHA6toacyub2PEsATah18Ro1IibNql4+vr1DCe6aJS3aNYrVBoVMokMiUxvJ6DMVQgqZRKxKD3KodB0cdIWa9vrHBwcA2B+fZnFnTVc7eHrACsdRUdNjGTk3ZuGbVq0PR9tKJQy8f2QttfuhJ1KEYvHqAUBQRDgqZAmbc7NXODgyBinTp360N7PTwoJj4QQQgghhBDiM6JSqfD8889zaX0WuzvGww8/jGndOtNnY2OD5eVlmuUyul5lpCtKbmWbzXyeYHUVf/IYxbl53nz9dQKtAUWh1maz1MQNDaLxJIZh4nttWvVtFreqXHhnmlqlSDQWpaZbbFXzRFIxUtkUpuXg+j7bhRzzc6vM51Y499ZbPHD6NI8++uivtW38xYsXee2VV6jPL2JWqxwbGWbs4DjpePzGLBXXZaNQYHZ1ldzyKj8sFlm+/36++MUv/tpL9V577TXm1/LYie67Do4AwiBg5sIv6Q01oRFhKNXF9uYWmYm9dz4LAp/Z2VmKFZcHJj9HwWuzvrLItB3BNE1y9U16Dx6gXC1QrZeoNsqopI1pG0Tj7y490zRbDcxalYgTwTBuvBeRZBSnL41bcskmEignZPraZQ6OjHH48GFOnjwJdN6drdU1wlqdyalTt4xxYWGeta0iynJwA0U8HqFW3sb2PFKZ3luONQ2DvliaNbdBsVYkk+iEUPFUltxKGycI8Zptal7IgVQ3W40GW/kcgz0DuO02xUaZ7WoBPwKGYRIaYGWit7xLShmYhiIMNCqi8MMQz/NwIhFQCmUYRKNR2qFLq96m0qyRiSZZ2lhlZmaGhx9++K6f56eBhEdCCCGEEEII8QnXbreZnZ1ldnaWWq1Gu93Gsizi8TiHDh3ixIkTxONxXnnlFS7MX6JlBxw+egKv3ca0OrNMtNbMz8+zurCAt72NAwxnu+gbGkZNHuPSpWtEqyHdtk22VuON119nPdBsWEmOP3CWwycfobt/+JbApdWss7pwlRde/gHN5jYq5XP/mdOc+sJDpLK371xVzpdYm1/m7fVpam6dWq3Gl7/85Q8U4ly8eJGfvvQSlekZjvb387mpKaw9lsTFHYcjQ0McGRri2vo6b0/PML279O4rX/nKBw6vyuUyV2evsV5ocObJJ+6p7fL8ZRobq8Rdl5Of+zzri1dY31jn4PhBbMu+7fi1tXW2CxW0GaGrb4AuDfWFq2wsXiOv23gZk2RPFwuzlyjVC5jxCC3dJpnsun4O23bwPY9WO6Ber5NK3fp84oMZitsr+J5PPJPAwObc9HnGRw5w4sQJDMNgbm6O2laO8cGhW55ZuVyiWK5SqjbAThN1HNxGBeU2iEcTGOr255t1Ymw2yjSbNTzfw7Zs2m4Tx45gYqLDELcdkLIdsiqk0mywXdzB9dps10uEBgRKY0QsAtvAcG6PP0zDIAxD3t1ILAxCwjDE2H1P7IiN23JRtiIINa72WFhbYmFhQcIjIYQQQgghhBCfDLVajfPnz3P58hVy5Tq5skvT9Qm0xlSKiG1waW6VF1/8Ee22y4V3LrDRzHPg2CF+8dKrWIZFV28XI4cP0PBarMzP4edyHOrrp+89u0odPXqQt9+cZqjZ5HAqzUS6i+25ZUaHuwnDkHRX720hjxON0241SfR3k8vl6BoaoGX5KHvvMCjTkyXTk6W4XeDSL8+jL2kcx+HJJ5+8p/uSy+V47ZVXqEzP8PChwxwZGrqrdhPDw3SnUrz0qwtctSwGBwc5derUnRvu4fLly2wW6/QNjeFE774odxD4rC1dg9wGk2NHiMfjxBJpqo02ua0tRkZGbzle65DNzQ1KtRZ9I4dQKFBwbHScuXM/ZccJGR4/Sa1aptlu4Ic+gamIODGUeu/zitGoVWi1WiQTKZRxIzgzbQsrHcNtt+lK9dGolyiWSqzlNlhaWuLQoUPkcjla1Sp940duOe/OTp5qvYVh2jQ9j0wyS724gem5xBJ77+hnKIO4FaHse7TaTWzLxm+3sFAYhokOPbwgwA9CetMpStUCxVqZMAyptGqEBmhTgakwnL3raClloMOAMAwxTYsw1ATBjfAIpbAsC21D0ApwA49SrUKhULheq+uzQsIjIYQQQgghhPgE2t7e5vnnn2duPc9msUUi28foifvJdPVi2jaB77OxPM+br/yA7dwKTb9IXVfIDPfQiAWEQZt23WWnXmR+fp7yToGuqMmj9x0ns8cuWtFohEx3iuWNHdydAlEzyVce/RIX1xfJL88zk/gFp848Sb1Wxmu30VpT2tlicXGGopfn1Je/TKmYY7NQZWbmMmceemjfWT1dfd2cOvsg7/zsLZwLEQ4dOsTo6Oiex76X1pqXX36Z2vwCk/0Ddx0cvas7leLsseO8OjvLLzO/4NChQx+ovs3GxgalqsuBqbsb97t2ttbxiwWSlk0m0VmmlspkqeXXKZcrjIzcenytVqNSa6CVRSJxY7ZQNOIQMSw8t4infXS9Stt3wTbwgzZx5/ZnbBgmyjDxbq7/cxM74eA3m6BB2waRuMNqboO1tTUOHTpEqVSiXa+Tec/9qtWq1Bud8IhAQRigfB/btPecdXT9GkybahDgem1SgO95EAZYVoTQ9VFKEQYhUdNE+x71RoNQadx2m9DQKAM0oOx9irArhVKA1ihAownDALgRCpmmgWcq/NCn5bqouKLpNqlUKvT09Ow79k8bCY+EEEIIIYQQv5VarRZLS0s0m02CIMBxHAYGBujr6/u4h/ax29nZ4S//8rtML+fR0S5OP/lFEqlbZwoVchvMX/kVQQoSqT6aBQ8zahI70E1kKEU06qADTX2nzNrsMm61RLQZp5ArkTm09xbsw8N9vD67QrWoeOr0cZLpNA/EjvPjX73BbKvJzuYaAT6hDtDAxsIcpXae3qkjKMtgcOwQC1cvUqrUKJaKdHd173uNmZ4sY5PjrC3luHTp0l2HRysrK+RWVrBrNe6fOnnX9/Rmo729jG5tsrO2xqVLl3j00Ufvqb3WmkKhQK3VJpXpunODm1SKO1Cv0pu+0S4aS5D3Amq12m3H12o1Wm2fWPz2ZxZzHIKqix94+C0f13PB7GxTb+yzq51pWQShj+97t4VHZtTG1XV8t000GQPfI1/Ms7OzA3R20dOhxnzPDLRms0mz1QYjhmWb+H67EwKZ7x9JGEoBIVqHAGhC0BrTNPG1BmUShhodhEQMC7fdxt+9LA0ow+j87deom6UMA5Qi1CFBEKBUZwlbEAQf+JyfRBIeCSGEEEIIIT4yQRDQarXwPI9IJEI0Gr1j/Zrt7W0uXbrE7Ow18tUWrh+gNZimIhOzGRseZGpqiomJiY9kW/ffds1mk+eff56ZlQJWepATD5697Z4Wtje5eO5VNmorpIZ7GRsfpfnGT6lWapTKFdq1KulUCssyO0Wvu23wIhRrbeaW17Ask7Gxwdv6jjoRKl4T07VJd/fiBwFX1pbYLOXZrG6yFW6THRrAdhw812W7uUld1XDCCgtX3yGRSJNIpqk0Sqyvb7xveAQwdHCEpcvzzM3PU61WSaX2Lhh9s8uXL+NubnFiZHTPGkd36/jYAX506RJXrlzhkUceuafaR77v47bbgIFtR+6p31qlDG6TVPbGrBbTtgnCEM/3bju+1Wrh+QGRePS27xw7An5A221hRmz80EdFFAb73xfT7IQ7e4UjCtVJZXRnFzZtBjTdFo1GAwDbtlGmgRcEODe9k2EYEuoQxbuBELBbbP39BDrcnR3UOZdhGKCMTogE8G6opHfPq/SNrEgDIWACod6/k9u+2ntM10MxDZZpfqaWrIGER0IIIYQQQoiPwNbWFtPT08xem6Xte4S7NXhiTozJyUmmpqbo7r49OHjzzTf5xetvsFVxyVdd0r2DJHu6MEyDdqvFlY1lVvLzXFte5/DoRZ599lni8buvJ/NpMD09zcJGAT+S5r4HPn9bcOT7HpfefI1cY43MaD+x3gxzMxeo57eImAFJrXFCiPoeyWiESrWGXa/jpG1UVHF1NUc4G5DJpEinb53NUq1W8ANNJB7H9ducn7/KajNPrUujdEhsKMvhRx/CNEw2rsySHOklkR3E6opSLhdpVhokW2mCdpudnR1arkv0PbNbbhaJOvQO95OrF7h8+fJdFSne2trCK5cZO3zkjse+n950mqjW1EslyuUy2ezetXn2opRCAaF+n9BiH4HvQRAQubkw9m7QslesobXezT9u/zbmRDE0eG0XM3K3Yce74c4eY2v7GIaBGbH2/L6rq4tIPE65VqO/68bMKcuyME2Dtu4UpFbKBsMg9P33HUkr8AijMRzb2T2PA5ZF0HZRhkIHGq01Sin8MMC0LAw6YZOlTHzlozVob59ZQjpEq059pVBrTFPdFkgHYYjSnbtvWzamMkjE4mTeUxPs007CIyGEEEIIIcSHZmdnh1deeYXVzXUKrSKFVhnLsTEtk8ALCIsBq+VNLrxzgYNjB3nqqaeuzyb5+c9/zs/feItruTrDh47x8CNHiSdunWkyeeohttaXWbxykersCu32/8e3v/1tYrHYx3G5v3FhGDIzM8NWqcnkQ4/sufQot7ZEpVlAJWzsbIytxVkixW0SQYAVcxjpHaRWLWKjSMdihG4bKxrFNi3qtoebsVks5OlbXOGB+4/fcu5Wy8UPQpxIlAvzV1lqblN1fE5MHGO1tE0tYtJuNYnFk7RqdbygTXagn2g2Raq3m+LaJpVWEV3zqDbiNBr19w2PAHqH+tm4uEixWLzj/Wk0GtQrFcwgJPUhvBNdqSSVeoN8Pn9P4ZFlWSQSCSKWotmoEYvffc2kzlIrRXjTbJm262Jbna3jb+/LxjQUwR5BTDwSxdYGQcvF7urGNq1OIMP+S660DlEGtxTLfpffcIkYJk4iRqNYxQpC4rHY9QC3v7+faDpNrli4JTyKxxMkYlHaNR/f11iRNNq08P36vuMIwpCm74GZIuZ0rtt2YmDZtNotUqZFEHjo0CNU4AOxSBTbtolGHJptF2Wa6MAnbPnXQ6Zb++h8pgyFDgOM3ZDrZqEfQNAJkBzDoifbTW/v7cXhP+0kPBJCCCGEEEJ8KFZXV3n+Bz9gqbRKnRajhw9w4sgDxJM3ZgZVSxVWri0xu7jE9tUChUKBr3/96+TzeV4/d55ruQYnH/4CvYMje/ZhWhbDBw7TOzDM+Z//hMvLOV544QW+9a1vfeAt1T9JFhcX2dwpQSRBV+/AnsesLcylnZhBAAAgAElEQVRS9cpEe9PkV5ewC9sMZ7uxtSana3h+gGlF8AOfZrOF1hoDSDkRMlEHo1ezmFvn0uwCRw6PkU7emH1UrzcwQ0Wl3aTdKFBxPCaOHca2bSzDBB0S7i53Cv0ATYhhdX7tNAyT7tFhCqvrNCsVcpub+N77zzwBsJ0IQRjguu4dj22322jfJ3rXs2zeXzQSoeR7tNvte27b19dHMrZKubhzT+FRLJ6k5kSptxqkdusYNRs1HNskuceyvWQygWNblJq3BzG2ZRExHNyGixOLEbGjnaLPJoRBsGf4GAQBpjKwrFvvYRiEtMsNkrE00XScwkqOpGfSO9JDb28vABMTE7w92M/yhYscPzh+fdlgOp0mGY+SL+VRRNEalB0hQOMFHrZ5+/Mqug0CO0IilsLa/T4SjaEiDu3AR5s2tmXge22qnouKREjE4sScGGknSbVdx/B8AkAFmrDpY8Zv7kcT6ABldMZoGJ2d1dRNoZAOQ4LARzc8ElYEKzSYGDvE0aNH3+cJfjp9tqIyIYQQQgghxEcil8vx/A9+wGx+gfhQmqeee5rJzx2/JTgCSGXTTJ05xReeexqdsbi8Ncf3v/99XnvtNRZ36hy9/+F9g6ObRZwopz//RfJNWFhZZ21t7aO6tN8q8/Pz5CotRg5O7Pl9ubBDsZTDw6XRqmCV8gxluuhOpsnEk0S1Rb1WJ+JE8QKN22rd0l4pg6GuNKlMjIbX4sLla7d8v5OvEHUVvg7IG3UOHj5wvfaLfs86puszV25auqWUomtkEM8IaLpN6o39Z568S4edujd3M9PDNE1QBkEQ3vHYuxEEIRjGB6qtdfDgQfoycTZXFu+pXSrTBbE4pXoV6CxLKxd3SMUj9Oyx1DOVShF3bNxmA8+/NeQylEHMSWKFJioAx4yi/RDLtPH2COM0Gt9rY5nqtpo+rXwVW5kksikCz8cMwWu6jPQNXS9m3t/fz/CBA9jZDJeXblx3f38fqUQUpUMc26DZqBNLpAmdGI3W7e+AFwZst6qE0ThdqZsLjhsYpo2KOrQCn1jUxrYUa8VtjHiC3kwP/dke+uJZzNDo/DEVeCFBuYm+aTbX9XfEMNBhiGWaRN5Tn6rttlEhBE2PhBljoLufod4BJicnb39wn3ISHgkhhBBCCCF+LVprXnrpJeaLy3SN9XLq86fv+Mu2HbF58AsPY3dHubBwiZ/94g0CM8rQgcN33W8kGmX08CTbVZfp6elf9zI+EZrNJq4XkkjvvYSqViniBk2UY6FaTZKWRc/uLmzdqQwxbBr1BkoZaK0JdLhbwPjmZVKKkYFuAh2wUyjR2p1143k+lVId1fAxoiZOIko8cWNpWBAEoAyM3R20TNvGUCa++55AwzCx41E85VHIF+54za1Gk4hp3bbz114SiQROMkEz8K+P+9dRrNUwY7F7WrL2romJCQZ70rj1Evncxl236x0cgXSG7UoRz/eolAqY2iebSuxZJ8y2I/T29pKMRSgXdq5/HuqQXLlAdvgAI90HqOcKRO0oETOC4Wu8Zuv6Lmbv8tptTEMRsSNY1o2FSoHr0VgvknQSZEf6KKzksH2Drmw3w32DHDhw4Pqxjz/+OL0Th1nY2iRX7DzfaDRGf18v/b0ZvFaFttvAjiQgGqPpubS9G89Ka81arYjvREkkM6Ti6RvjCDy038aOxwhMA9OxCaMWJbeBaUfoSmeJOlH6sz30J7uJahPDBwINbkBQau720dk5jd1/T5mmgR2xseybrtn3abtt2sUGqUicGDaPnX6Ys2fPEoncWxH0TwMJj4QQQgghhBC/luXlZTYLOfxIyNTDp+66nWEYfO6xB1kprrOS26Grd+Cel54NHzxCse4zN79wfcenm4VhyMLCAj/84Q/5zne+w5//+Z/zne98hx/96EcsLy+jP0BB44+T5+0WH98nnPM9j0CHNOpVzGad7uSNX7yjdoSUEyMSmJRLlc61h2BHIijLxPVu7ORlRywcy8RvttgqlQBYWFijx45jKSiHDbr7bgQZzXaLltYY0RhOtBMoJbqyOGaU+tattYo8z8WORQjNkFq9hufdvoPYzTaW1uiOZ6/Pbnk/hmHQ29uLlUyS2x33B9VwXapui0gyuWdocye2bXPmzENMjGS5evEcbbd150Z0lq11D46i01muri6ws7lKXzbO+Pj49V3H3mt0dISudJRyIUer1QlIVne2aDsOo5NTHBybJEaUsNomm+gmaHqYGLRqN2b9hDrEbTVwbJPYTUXoQz+gPLdF3I6SGegmaPsEFZd2ucGZk6d58MEHb5kV1t/fz5lHHqF/6hivX55mfWcbgMOHjzDU10XMNrB0m3qjSizVTRhPUa6XCMOAIAxZruWpKo2KpxnqHr5+Xq1DKvkNnIhJNBYjO9iHF42w4TaId2eJ+D6teg2Ag4NjHOgaJGHGiIQWljLQrk9QaeEV6rTbHphmZxdHw8C2rVtqb4VBSKPWwC81sXxF3DP50iNPcPLYFFNTU3f1HD9tpOaREEIIIYQQ4tdy6dIl8s0iYycO3nMRWTtik+hKsbFUpu0277lvJxoj1dVD3W1SLBavF+5tt9tcvHixU1y6mGOnWcL124Q6xFAGUcvhncuXGOzpZ2pqipMnT94y0+K3VSQSwVQKf59dqpRSuM0GvlsnErdIx2/dLW2gq5fGlku+WgfTJ51wiMVi1Ot13HqDhOOgjM6spLhtoz2frWIRv+pSzVU4nhkiTFW4EjZI3rQksVirEsbiZLr7rr8D2aFBcvML1Eo5/KaLFev8ct6o1UjEIxhkcf02jUZj352rqqUKbqXJwKFeJib2Xqr3XkeOHGHp4kWurq1xoL//rtrs5dr6GnZvL4cOH/7A78apU6dYWFigUp/lV6+/wqmHn8CJ3nl3wMPH7mdrdYGZ11/l9PAAw4OH6O/fu8YVQDqd4cDoCM2Wx8bqAun+QZa2N+HQESZOPEDEidJq1gnyi9R3NsjEMhSrRXTCpGXWcWJxmvUatmUSi0WvF+b2Gi6VhRxOaJLJZohnU2xfW8Uoe5w+dh/3HZvixIkTt43noYceotls8rYyeOvyVTZ2djg+Ps7RoxO02m3emVlA+wFtO4kZT9IOPJYK6zSVInBiqGSWAwMHsHfrLoWBRzm/AX4L329wYGwE09LM57Yw0wnMUDPQnWZzbZFkuovunn6Ojh2m5bWZ2Zoj8AK0MnHrHl7bw2hEMLpjOKkYtmUSj8Wv1zry2h71UgWv1MRuaTJ2mqcf/yKPP3yWr371q5+J2mp7+e3/t6MQQgghhBDit1a9XmdxeYmqX+fBQ2Mf6Bxdgz20zQW2N9YIw/CeAygr4hAEjetFjavVKs8//zzzG0ts1XcwYjZjxw6Q7s52dn3zA0o7BZbnllld2GR5e435+Xl+53d+53r49NsqkUgQi1hUijt09w3e9r0dcQg9n6DRINE9cNvm7vGIw2hPP+2NFfLNGnmrQiKRwnEcWm2XZrtNPBrFc30cw6Ttt1meX0PFuzke6+Px41NsbW/jNDya9Qp2pJe271Fu1Al7B8h0913vy4rYZAYGqC1XqK5t0zUxiu+3cZs1upIRonaKUGu899muffnKAv2pHo4fP37XAc7Ro0f55cAAO6trLOW2OPg+oct+qs0ml9fXSZ48ycmTJ++5/buUUnzta1/DdV0uza5w7tUfcfjE/fQPHdj3PQ+DgFJ+i1azgTM6QMFt4mTvvC38oUOHqVSr5OeW+Om5nxE/forDh4/TvVtY/XOPfAH1hoGtLObXLuNYBs12E9fzaVYqRJNR4k6UVCqF32zT3K7g7lRJOnFS2TSxdJKN6QXMasDE+BG+8PDjPP3003uGKUopnnzySbLZLL9Mp9iZX+Slt87Rm0rhpJOMjPQwt7RGtZrHxaTpNvAI8cKQdCTDZN8Y0UgMrQOatQq18jZG2IagxdDQAC4eqZ4+Hjh9CleHJC2bufMXqFar1PMN1rfWiMcTZCNRBiIZ3EaTpt8mNEK0B2HQRnkhfs3H7krQ9hoEOqBVb+FVmqhmgBNajPUO8o0vP8vXvvJVHn/88dvqQH2WSHgkhBBCCCGE+MCq1Sot3yWZTWM7H6wOSDKTxIxatNtNvLZ7fdnT3Qp8D2O3wG+9Xue73/0uM+vXaFoeJ594gO7+ntvaZHu7OHjsMDsb21x5+xLNhRbe9zyee+65PbdD/21x9OhRzr8zzdXleQ5MTN0WQHT1DmDj4Jbr6OG9l+Rl4kn6onGoNrB9i621bUKlCcOAutck4bbZWN0h3KxjhJouz2HiSDdf+9wDdKdSdGWzON42XqtBuVRgu1nHT6ZJdvUQeU9dop6xEUqbmxS38hiORdv2STgW6VSKoOFBCPvN41iYuUZ9u8zE6PF7CnAikQhnH3uMH5fLvHH5CtlEkkwiceeGuzzf57XpS0SGhzl26j6Ghobuuu1eYrEYzz33HKm//mtmrs4xf+0dFq9eon/4AKlMF9HYjR3VqqUCuY1l4jZ88ZEpTHWCsNXiwtw8m4UCJ8cPkU3uvXNbq92m7Ths+00iYwNUvWZnJ7JKkVS6i1S6izNPfIVr0+dJRlJcmb9AY6tIw9/GjFuEcRsjncJfLmNog1jEIWMnsO0IjUqNynqeZGjz0NRpvvyFL91V2Hrq1CkOHDjAW2+9xezVq9TzefxqHdP3iLWb5Le2ydca1G2TtrZwIg62VszMv0PEUCg/wFIapT2cqE2mK0PN1hw9dZKBI4c4+9hjTE5OcuHCBQbHD7I6v8DS7Cw7G1vkWgU8PyQSj9JNL0ajiqs92nRmH+m2D01NPd8k0CFKg6EUMcOmL93HA1OneO6bz/HEE0/cUtPps0rCIyGEEEIIIcQH1m63CQlvKTR7rxzHwbYs6vUKvte+p/Ao8H2qpQKjAzHi8TgvvPACVzfm8GKah588i/0+W7Yrpegb7ifdneGtl3/JldVrvPjii3zzm9/8wNfyURsZGWF4oJeF3CL5rXX6hm6tAxSNJ+jpG2R12aRcKDHWd/usG9/3iSiYGhwk1tXFTqVC1XMptmvUGz7l9RL1zSqHWhHShk1vdoDnHnmEvky2s5zNiZFKpbCMkPn1RdxEmuTQKAOj47f1FU0lGT5+jNbbb7Hxq6tkxjKkJ8cYHBhk8co8ZiR227sT+AHzl2bJr2wx1X+Er37lq6TT6dvO/X5OnDjB8vIyV5pNXrpwnsdPTDHQ1XXHdtVmk9emL1GPxxk6Nsljjz12T/3uJxqN8uyzz3LkyFXefvtt1ja3KVQ22NxewvWDzjG2STIW4b4DWUaHBzhz5gzj4+NcvnyZ1157jfLiEj965wJZx6EnnSEZi6GUot5qUahUyNdrxAb6OfPt50ikUlSqNTYLVS69+QpaWSRSGWw7gg41juUwkhkm5SvqzSINv0W77OPmy/i7dcBcx8aJRUlE42SdFCO9h3j49EN8+ctf5nOf+9xdzxDMZDJ86Utf4uzZs6yurrKzs0OlUsF1XVZXV5mfn2d5bZNq3SVfrFAulaj6HrYOiJqaeDRCd7aPobERRg8d4uDRCY4dO8b9999PKpUC4MyZMzzwwAOsrKyQy+XY2Nhgc3OTWq2GUopMJkMikWB7e5u5uTm2cjm28jmKpSKtZgsdhkQjUQb6+nnggQf40pe+xIMPPkhfX98dru6zQ8IjIYQQQgghxAdm2zYGimD3F+APYmBgkIgJ5fwOpnlvv6JsrS2RsGFsZIhSqcTi+hJl3eDRJ5543+DoZk7U4YEnH+YXL77G/PIC6+vrDA8P37nhx0ApxdTUFHOrW8xfvkBXbz/We7YXHxmfZOaNn7CTK+Af9m8JZzSaarlAzDbpznbR39/Pgb4+CrUa1UaTYqnIzNY1Uq7FZCSOYdtkstnrtZP8IEBrTbPaomZrVDaNth1812VrbYlMdx8RJ4qhDILAp1YtUa5u46Qc+poJKAc05gusVnzcVoP48CDJ3Zk09WqN9flVtpbXSUcS3D90jK88/TTj4+Mf6F49/fTTBEHAvGXz1zPTjHf3cHx0dM+ZO03XZW5jg+nVFSIjwwxOTvK7v/u7H/ostMnJSY4ePcr6+jpra2vs7OzQbHZqfSUSCXp7exkdHWVw8MaSxOPHjzM6Osr58+e5euUK9UKBzVqdoFYBwIxEsEYGGchmOTo5yenTp+nq6qJcLjM9Pc3s7CzlSo16yyMImhiO4tCpA6TPnmB8fJzu7m4WFxe5cuUK8/Pz1Jr1ThHzEGKxKP19/Zw8eZIHH3yQycnJD7zTWDQaZWJi4rbaVWEYksvluHbtGvPz8xSLRVqtFlprHMehp6eH3t5eRkZG6Ovro7+/f88ljKZpMj4+flfvS7lcZmVlhVKphFKKnp4e+vr66Orquudls58VEh4JIYQQQgghPrBEIoFjRqiVNwn8ANPaexew95OMJYhoaLhNGrUK0fjdLTEKw5CVuSsMpSJMTU0xPT3Ndr3IwROHiNzjErpoPMbYxAF25ne4dOnSb214BJ0wYWZmhtrVVX71+ivc/8gXbgmQDh49SaKrn9pGkatX5jh2fALTMtFoKsU82muRTEav7yCmlKInlaI7mcSrtDicHaDb7GfAjjC9s0Mhv8OfvfoqhgKtFCWliSa6qcYMznzhUQDy+QKVZoPtlWsEoUZrjaEU0YhJX9JmYugIceskjUKVUrHEwpVFWs0mm2WT1ysevuejQuhP9nBf/1EOjo7x6KOP3hKi3CvLsnjmmWc4PzjIm6+/zsbaGosXzpOwLLpTKRzbxvMDyvUapWYLu6eb5H0nmbzvPh5//PGPbPmiUoqRkRFGRkbuuk0ymeSJJ57g0UcfZWtri+3tber1OlprYrEYfX19DAwM3DLmTCbD2bNnOXv2LNVqlUKhgOd5WJZFNpslk8lcr1d0//33A51/phqNBkEQYBgGsVjsIy8kbxgGg4ODDA4O8sQTT3ykfb0rk8nsW6Rd7E3CIyGEEEIIIcQHlk6nGRkaYbW2xcbSGqNH7r02yPriKmM9w4Qxk+m3fs6DT36VeCL1vm201sy8/UtMr8bQ2AA9PT0srSxRDRqcHr/zlu57GT18gNdm5pmbn+Ox+mMk7qFOzm+Sbdt8/etfx3X/X2aWtnnrtR9x+Pjn6BkYRimFE43ywGNf4e3vbtPMVZlVcwwO9xMGLvgumUSE4eHhW2YktVpt5udXaRUbTKWHePaph/jJhfN0Dw8xeOQI6UwGHYZYts14Nkt6bo7F+gaDPf30DvXjtlw2NzfJ5/N4nkeoQyzTIh6PMzg4SHd3N8roBBW5tU2K6zukzV4ePPogEcfBNAyiEYfJyUmmpqbo6bm9TtUHYRgGDz74IIcPH+bixYtcvXqVVqXCTr2O9gOUE8Hs6yWbiHPo8GFOnjx5T6HOb5pt24yOjjI6em/veCqVur7E6/0YhnF9JpgQN5PwSAghhBBCCPFrOXnyJP8/e/cdHHd6oHf+eTsCjZxB5EhEhiExw+FwNCMOKU5crbxa+3at2tWtfaXaO9fa5z2X93xln311Z1fZ5T+89p3l0p3OJ9try1qVNmgkSpMDh5oAajhDIhA5B4LIqfN7fxBDTWADDRCN+P1UoYju/r1vP42qmUI9eH/v2zPSp6HugU2XR5FIRCN9Q2qsrFd2epaGJmd17a2XVHfiEeUdKbnvSU4rSwvqbruuwOy46oszdenSJY2OjmrOv6j8ksK4b1f7PG9ykrIKczW3vKiRkRHV1dVtaZ6dkJqaqq997WvyXr6snqEJ9d+4qu6bXhWV1ygjO1d5hSXKqqzT3Ie/UMAfUMfYmHzpySo+kqP8ylK53G4FAkEtL69qfPyO5mcXleNIUmNSns4fO3Z382W3RycfPaNvfOMbMsYoGo3K6by7suyjjz7Sy2++qs7WmzrxpRalZaarvKJc5RXl6+YOBUMa6OhVy7FTerT5tL785S8rFArJ4/HI4/Ek7Bj0zMxMPf7443rsscc0MzOjmZkZBYNBOZ1OZWZmKjc391CfpAVshPIIAAAAwAOpqqpSfnquJsfvqPdml6qbj8Y9tv2DG0q2XpUUFuvXfu3X9PLLL8vTM6C+j66q64ZXxeXVSsvMlnE4FPCvanJkQAt3JpSXlqSa0hw988zTys/P18DAgELRsFJTNrex8uf5UnwKLS4rEAg80Dw7IS0tTV/72tfU2dmp9vZ2jd2e1u3xW5oabFckYhUILCkpJ1u+hVlVJ2fKuHwKrjrU/nGfIjYqh4y8DqeyHckq8+arKr9AxysqFAiH9c7NNiXV1X5mY+RPiiPp7m1OU1NTCt8I66O3W1Xfcky5R9bfXHh5cUk3f3FdmcanxrJanT9/Xl6vd0dPt3M4HMrNzVVubu6OvSdwEFAeAQAAAHggTqdTTz75pJYuL6u7o19WUs0GBVI0GlV7603NjtzR0ZwqPfHEE/J4PHr22WdV1tamtrY2jU/NaHr0lqYHo7KSnMYoI8Wt6oocHT1aq5MnT97btyQajd7dZ8f5YJvdGoeRtXdXRO0HHo9Hx48f17FjxzQyMqKuri4tLy8rGAzKPlSl69evyzs3r/xoVBWFhZpeWNDi6qqC4bDcTqeSPB5VFOSr5kiRvC6X+m/f1ntd3XJVV6rh1Kl7e+F8njFG58+fl7VWrg6Xuj+4qT6fR8XVZSooOXLvljhrraYnpjTaO6SFqTmVZBaqsfyonnnmGXm93p38UQF4AJRHAAAAAB5YeXm5nvryeemN19XfOaTbIxMqq63QkfLiz2yiHQoENdI/rOGeQbmCRrXZlXrm6afvHYntcDh07NgxNTc3a3R0VL29vfc28PV6vSosLNTRo0e/UDx41/bNCQaCD/Q5QsGQXA7nvis2jDEqLS1VaWnpZ55//vnndfnyZU10dqpjZFQ5SUl6qLpaZbm5cq9thLzs96t7bEzdY6Pyu9zy1B9V0+nTG25e7HQ6dfHiReXl5enjjz/W5OyUJjpH1P1hh1wel4zDoXAwJJ8rSQWpOaorK1V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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ff847d9e160>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def add_results(df, plt, cmap):\n",
- " # create data\n",
- " x = df['MACs'].tolist()\n",
- " y = df['Top1'].tolist()\n",
- " z = df['NNZ'].tolist()\n",
- " z = [n/30000 for n in z]\n",
- " plt.scatter(x, y, s=z, c=x, cmap=cmap, alpha=0.4, edgecolors=\"black\", linewidth=2)\n",
- "\n",
- "# Change color with c and alpha. I map the color to the X axis value.\n",
- "plt.figure(figsize=(20,10))\n",
- "add_results(df, plt, cmap=\"Blues\")\n",
- "add_results(df2, plt, cmap=\"Reds\")\n",
- "add_results(df3, plt, cmap=\"Greens\")\n",
- "\n",
- "# Add titles (main and on axis)\n",
- "plt.xlabel(\"Compute (MACs)\")\n",
- "plt.ylabel(\"Accuracy (Top1)\")\n",
- "plt.title(\"Network Space\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "<matplotlib.figure.Figure at 0x7ff8479e5128>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Change color with c and alpha. I map the color to the X axis value.\n",
- "plt.figure(figsize=(20,10))\n",
- "\n",
- "add_results(df4, plt, cmap=\"Oranges\")\n",
- "\n",
- "# Add titles (main and on axis)\n",
- "plt.xlabel(\"Compute (MACs)\")\n",
- "plt.ylabel(\"Accuracy (Top1)\")\n",
- "plt.title(\"Network Space\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style>\n",
- " .dataframe thead tr:only-child th {\n",
- " text-align: right;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: left;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Unnamed: 0</th>\n",
- " <th>File</th>\n",
- " <th>NNZ</th>\n",
- " <th>MACs</th>\n",
- " <th>Top1</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
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- " <td>10.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>BEST_adc_episode_001_checkpoint.pth.tar</td>\n",
- " <td>3243400</td>\n",
- " <td>75744988</td>\n",
- " <td>10.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2</td>\n",
- " <td>BEST_adc_episode_006_checkpoint.pth.tar</td>\n",
- " <td>4893669</td>\n",
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- " <td>26.48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>3</td>\n",
- " <td>BEST_adc_episode_114_checkpoint.pth.tar</td>\n",
- " <td>8249014</td>\n",
- " <td>161158222</td>\n",
- " <td>78.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4</td>\n",
- " <td>BEST_adc_episode_008_checkpoint.pth.tar</td>\n",
- " <td>5780922</td>\n",
- " <td>158955054</td>\n",
- " <td>71.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>5</td>\n",
- " <td>BEST_adc_episode_108_checkpoint.pth.tar</td>\n",
- " <td>7041071</td>\n",
- " <td>176417822</td>\n",
- " <td>80.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>6</td>\n",
- " <td>BEST_adc_episode_044_checkpoint.pth.tar</td>\n",
- " <td>9410752</td>\n",
- " <td>227796580</td>\n",
- " <td>86.64</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>7</td>\n",
- " <td>BEST_adc_episode_077_checkpoint.pth.tar</td>\n",
- " <td>6231760</td>\n",
- " <td>178635022</td>\n",
- " <td>81.33</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Unnamed: 0 File NNZ MACs \\\n",
- "0 0 BEST_adc_episode_003_checkpoint.pth.tar 1880678 40981916 \n",
- "1 1 BEST_adc_episode_001_checkpoint.pth.tar 3243400 75744988 \n",
- "2 2 BEST_adc_episode_006_checkpoint.pth.tar 4893669 130690152 \n",
- "3 3 BEST_adc_episode_114_checkpoint.pth.tar 8249014 161158222 \n",
- "4 4 BEST_adc_episode_008_checkpoint.pth.tar 5780922 158955054 \n",
- "5 5 BEST_adc_episode_108_checkpoint.pth.tar 7041071 176417822 \n",
- "6 6 BEST_adc_episode_044_checkpoint.pth.tar 9410752 227796580 \n",
- "7 7 BEST_adc_episode_077_checkpoint.pth.tar 6231760 178635022 \n",
- "\n",
- " Top1 \n",
- "0 10.00 \n",
- "1 10.01 \n",
- "2 26.48 \n",
- "3 78.21 \n",
- "4 71.42 \n",
- "5 80.34 \n",
- "6 86.64 \n",
- "7 81.33 "
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 92,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Relative import of code from distiller, w/o installing the package\n",
- "import os\n",
- "import sys\n",
- "import torch\n",
- "module_path = os.path.abspath(os.path.join('..', '..'))\n",
- "if module_path not in sys.path:\n",
- " sys.path.append(module_path)\n",
- "import distiller\n",
- "import models\n",
- "import apputils"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {},
- "outputs": [],
- "source": [
- "dataset = 'cifar10'\n",
- "dummy_input = torch.randn(1, 3, 32, 32)\n",
- "arch = 'vgg16_cifar'\n",
- "checkpoint_file = \"../classifier_compression/logs/master___2018.07.25-205658/BEST_adc_episode_044_checkpoint.pth.tar\" \n",
- "\n",
- "model = models.create_model(pretrained=False, dataset=dataset, arch=arch)\n",
- "apputils.load_checkpoint(model, checkpoint_file);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "metadata": {},
- "outputs": [],
- "source": [
- "t = distiller.weights_sparsity_summary(model)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <th>Name</th>\n",
- " <th>Shape</th>\n",
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- " <th>Cols (%)</th>\n",
- " <th>Rows (%)</th>\n",
- " <th>Ch (%)</th>\n",
- " <th>2D (%)</th>\n",
- " <th>3D (%)</th>\n",
- " <th>Fine (%)</th>\n",
- " <th>Std</th>\n",
- " <th>Mean</th>\n",
- " <th>Abs-Mean</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>features.module.0.weight</td>\n",
- " <td>(61, 3, 3, 3)</td>\n",
- " <td>1647</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>features.module.2.weight</td>\n",
- " <td>(61, 61, 3, 3)</td>\n",
- " <td>33489</td>\n",
- " <td>33489</td>\n",
- " <td>0</td>\n",
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- " <td>0.0</td>\n",
- " <td>0.08</td>\n",
- " <td>-1.19e-02</td>\n",
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- " <th>2</th>\n",
- " <td>features.module.5.weight</td>\n",
- " <td>(116, 61, 3, 3)</td>\n",
- " <td>63684</td>\n",
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- " <td>0.07</td>\n",
- " <td>-8.42e-03</td>\n",
- " <td>5.25e-02</td>\n",
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- " <th>3</th>\n",
- " <td>features.module.7.weight</td>\n",
- " <td>(112, 116, 3, 3)</td>\n",
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- " <td>-1.06e-02</td>\n",
- " <td>4.48e-02</td>\n",
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- " <th>4</th>\n",
- " <td>features.module.10.weight</td>\n",
- " <td>(184, 112, 3, 3)</td>\n",
- " <td>185472</td>\n",
- " <td>185472</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>0.0</td>\n",
- " <td>0.05</td>\n",
- " <td>-6.52e-03</td>\n",
- " <td>3.98e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>features.module.12.weight</td>\n",
- " <td>(177, 184, 3, 3)</td>\n",
- " <td>293112</td>\n",
- " <td>293112</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>-5.51e-03</td>\n",
- " <td>3.21e-02</td>\n",
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- " <tr>\n",
- " <th>6</th>\n",
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- " <td>(209, 177, 3, 3)</td>\n",
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- " <td>0</td>\n",
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- " <td>-2.65e-03</td>\n",
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- " <th>8</th>\n",
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- " <td>(487, 487, 3, 3)</td>\n",
- " <td>2134521</td>\n",
- " <td>2134521</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>-9.06e-04</td>\n",
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- " <th>11</th>\n",
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- " <td>1155942</td>\n",
- " <td>1155942</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.01</td>\n",
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- " <td>1016298</td>\n",
- " <td>1016298</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>6.56e-02</td>\n",
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- " <tr>\n",
- " <th>14</th>\n",
- " <td>Total sparsity:</td>\n",
- " <td>-</td>\n",
- " <td>9410752</td>\n",
- " <td>9410752</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.00</td>\n",
- " <td>0.00e+00</td>\n",
- " <td>0.00e+00</td>\n",
- " </tr>\n",
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- "</table>\n",
- "</div>"
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- "text/plain": [
- " Name Shape NNZ (dense) NNZ (sparse) \\\n",
- "0 features.module.0.weight (61, 3, 3, 3) 1647 1647 \n",
- "1 features.module.2.weight (61, 61, 3, 3) 33489 33489 \n",
- "2 features.module.5.weight (116, 61, 3, 3) 63684 63684 \n",
- "3 features.module.7.weight (112, 116, 3, 3) 116928 116928 \n",
- "4 features.module.10.weight (184, 112, 3, 3) 185472 185472 \n",
- "5 features.module.12.weight (177, 184, 3, 3) 293112 293112 \n",
- "6 features.module.14.weight (209, 177, 3, 3) 332937 332937 \n",
- "7 features.module.17.weight (487, 209, 3, 3) 916047 916047 \n",
- "8 features.module.19.weight (487, 487, 3, 3) 2134521 2134521 \n",
- "9 features.module.21.weight (447, 487, 3, 3) 1959201 1959201 \n",
- "10 features.module.24.weight (298, 447, 3, 3) 1198854 1198854 \n",
- "11 features.module.26.weight (431, 298, 3, 3) 1155942 1155942 \n",
- "12 features.module.28.weight (262, 431, 3, 3) 1016298 1016298 \n",
- "13 classifier.weight (10, 262) 2620 2620 \n",
- "14 Total sparsity: - 9410752 9410752 \n",
- "\n",
- " Cols (%) Rows (%) Ch (%) 2D (%) 3D (%) Fine (%) Std Mean \\\n",
- "0 0 0 0.0 0.0 0.0 0.0 0.26 -2.40e-03 \n",
- "1 0 0 0.0 0.0 0.0 0.0 0.08 -1.19e-02 \n",
- "2 0 0 0.0 0.0 0.0 0.0 0.07 -8.42e-03 \n",
- "3 0 0 0.0 0.0 0.0 0.0 0.06 -1.06e-02 \n",
- "4 0 0 0.0 0.0 0.0 0.0 0.05 -6.52e-03 \n",
- "5 0 0 0.0 0.0 0.0 0.0 0.04 -5.51e-03 \n",
- "6 0 0 0.0 0.0 0.0 0.0 0.04 -6.98e-03 \n",
- "7 0 0 0.0 0.0 0.0 0.0 0.02 -2.65e-03 \n",
- "8 0 0 0.0 0.0 0.0 0.0 0.01 -1.06e-03 \n",
- "9 0 0 0.0 0.0 0.0 0.0 0.01 -9.06e-04 \n",
- "10 0 0 0.0 0.0 0.0 0.0 0.01 -7.74e-05 \n",
- "11 0 0 0.0 0.0 0.0 0.0 0.01 2.33e-05 \n",
- "12 0 0 0.0 0.0 0.0 0.0 0.01 -2.21e-04 \n",
- "13 0 0 0.0 0.0 0.0 0.0 0.09 -4.34e-07 \n",
- "14 0 0 0.0 0.0 0.0 0.0 0.00 0.00e+00 \n",
- "\n",
- " Abs-Mean \n",
- "0 1.90e-01 \n",
- "1 5.79e-02 \n",
- "2 5.25e-02 \n",
- "3 4.48e-02 \n",
- "4 3.98e-02 \n",
- "5 3.21e-02 \n",
- "6 2.94e-02 \n",
- "7 1.72e-02 \n",
- "8 1.09e-02 \n",
- "9 9.68e-03 \n",
- "10 8.77e-03 \n",
- "11 8.45e-03 \n",
- "12 8.53e-03 \n",
- "13 6.56e-02 \n",
- "14 0.00e+00 "
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- " <td>6.07e-02</td>\n",
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- " <th>3</th>\n",
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- " <tr>\n",
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- " <td>0.0</td>\n",
- " <td>0.02</td>\n",
- " <td>-1.36e-03</td>\n",
- " <td>1.24e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>features.module.21.weight</td>\n",
- " <td>(369, 311, 3, 3)</td>\n",
- " <td>1032831</td>\n",
- " <td>1032831</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.01</td>\n",
- " <td>-1.17e-03</td>\n",
- " <td>1.05e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>features.module.24.weight</td>\n",
- " <td>(390, 369, 3, 3)</td>\n",
- " <td>1295190</td>\n",
- " <td>1295190</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.01</td>\n",
- " <td>-1.11e-04</td>\n",
- " <td>8.75e-03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>features.module.26.weight</td>\n",
- " <td>(116, 390, 3, 3)</td>\n",
- " <td>407160</td>\n",
- " <td>407160</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.01</td>\n",
- " <td>3.76e-05</td>\n",
- " <td>8.66e-03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>features.module.28.weight</td>\n",
- " <td>(303, 116, 3, 3)</td>\n",
- " <td>316332</td>\n",
- " <td>316332</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.01</td>\n",
- " <td>-1.54e-04</td>\n",
- " <td>8.87e-03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>classifier.weight</td>\n",
- " <td>(10, 303)</td>\n",
- " <td>3030</td>\n",
- " <td>3030</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.08</td>\n",
- " <td>-1.55e-05</td>\n",
- " <td>6.21e-02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>Total sparsity:</td>\n",
- " <td>-</td>\n",
- " <td>5780922</td>\n",
- " <td>5780922</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.00</td>\n",
- " <td>0.00e+00</td>\n",
- " <td>0.00e+00</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Name Shape NNZ (dense) NNZ (sparse) \\\n",
- "0 features.module.0.weight (42, 3, 3, 3) 1134 1134 \n",
- "1 features.module.2.weight (42, 42, 3, 3) 15876 15876 \n",
- "2 features.module.5.weight (104, 42, 3, 3) 39312 39312 \n",
- "3 features.module.7.weight (93, 104, 3, 3) 87048 87048 \n",
- "4 features.module.10.weight (244, 93, 3, 3) 204228 204228 \n",
- "5 features.module.12.weight (161, 244, 3, 3) 353556 353556 \n",
- "6 features.module.14.weight (237, 161, 3, 3) 343413 343413 \n",
- "7 features.module.17.weight (341, 237, 3, 3) 727353 727353 \n",
- "8 features.module.19.weight (311, 341, 3, 3) 954459 954459 \n",
- "9 features.module.21.weight (369, 311, 3, 3) 1032831 1032831 \n",
- "10 features.module.24.weight (390, 369, 3, 3) 1295190 1295190 \n",
- "11 features.module.26.weight (116, 390, 3, 3) 407160 407160 \n",
- "12 features.module.28.weight (303, 116, 3, 3) 316332 316332 \n",
- "13 classifier.weight (10, 303) 3030 3030 \n",
- "14 Total sparsity: - 5780922 5780922 \n",
- "\n",
- " Cols (%) Rows (%) Ch (%) 2D (%) 3D (%) Fine (%) Std Mean \\\n",
- "0 0 0 0.0 0.0 0.0 0.0 0.29 -6.32e-03 \n",
- "1 0 0 0.0 0.0 0.0 0.0 0.10 -1.46e-02 \n",
- "2 0 0 0.0 0.0 0.0 0.0 0.08 -9.57e-03 \n",
- "3 0 0 0.0 0.0 0.0 0.0 0.06 -1.11e-02 \n",
- "4 0 0 0.0 0.0 0.0 0.0 0.05 -6.46e-03 \n",
- "5 0 0 0.0 0.0 0.0 0.0 0.04 -5.12e-03 \n",
- "6 0 0 0.0 0.0 0.0 0.0 0.04 -6.94e-03 \n",
- "7 0 0 0.0 0.0 0.0 0.0 0.02 -2.80e-03 \n",
- "8 0 0 0.0 0.0 0.0 0.0 0.02 -1.36e-03 \n",
- "9 0 0 0.0 0.0 0.0 0.0 0.01 -1.17e-03 \n",
- "10 0 0 0.0 0.0 0.0 0.0 0.01 -1.11e-04 \n",
- "11 0 0 0.0 0.0 0.0 0.0 0.01 3.76e-05 \n",
- "12 0 0 0.0 0.0 0.0 0.0 0.01 -1.54e-04 \n",
- "13 0 0 0.0 0.0 0.0 0.0 0.08 -1.55e-05 \n",
- "14 0 0 0.0 0.0 0.0 0.0 0.00 0.00e+00 \n",
- "\n",
- " Abs-Mean \n",
- "0 2.22e-01 \n",
- "1 7.41e-02 \n",
- "2 6.07e-02 \n",
- "3 4.74e-02 \n",
- "4 3.85e-02 \n",
- "5 3.07e-02 \n",
- "6 2.89e-02 \n",
- "7 1.83e-02 \n",
- "8 1.24e-02 \n",
- "9 1.05e-02 \n",
- "10 8.75e-03 \n",
- "11 8.66e-03 \n",
- "12 8.87e-03 \n",
- "13 6.21e-02 \n",
- "14 0.00e+00 "
- ]
- },
- "execution_count": 102,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "checkpoint_file = \"../classifier_compression/logs/master___2018.07.25-205658/BEST_adc_episode_008_checkpoint.pth.tar\" \n",
- "\n",
- "model = models.create_model(pretrained=False, dataset=dataset, arch=arch)\n",
- "apputils.load_checkpoint(model, checkpoint_file);\n",
- "distiller.weights_sparsity_summary(model)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/automated_deep_compression/ddpg/README b/examples/automated_deep_compression/ddpg/README
new file mode 100644
index 0000000..8893c59
--- /dev/null
+++ b/examples/automated_deep_compression/ddpg/README
@@ -0,0 +1,36 @@
+
+Note: this code is originally from OpenAI Spinningup and contains
+a few changes to conform to the requirements of AMC.
+In any case it is currently temporarily added to the Distiller
+source repository and may in the future be removed. It is not
+directOpenAI Spinningup and contains
+a few changes to conform to the requirements of AMC.
+In any case it is currently temporarily added to the Distiller
+source repository and may in the future be removed. It is not
+directly used.
+
+https://github.com/openai/spinningup has an MIT license:
+https://github.com/openai/spinningup/blob/master/LICENSE
+
+
+The MIT License
+
+Copyright (c) 2018 OpenAI (http://openai.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/examples/automated_deep_compression/ddpg/__init__.py b/examples/automated_deep_compression/ddpg/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/automated_deep_compression/ddpg/core.py b/examples/automated_deep_compression/ddpg/core.py
new file mode 100755
index 0000000..8bc0e25
--- /dev/null
+++ b/examples/automated_deep_compression/ddpg/core.py
@@ -0,0 +1,45 @@
+import numpy as np
+import tensorflow as tf
+
+
+def placeholder(dim=None):
+ return tf.placeholder(dtype=tf.float32, shape=(None,dim) if dim else (None,))
+
+def placeholders(*args):
+ return [placeholder(dim) for dim in args]
+
+def mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
+ for h in hidden_sizes[:-1]:
+ x = tf.layers.dense(x, units=h, activation=activation)
+ return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
+
+# Neta
+def amc_mlp(x, a, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
+ x = tf.layers.dense(x, units=h[hidden_sizes[0]], activation=activation)
+ x = tf.layers.dense(tf.concat([x, a], axis=-1), units=h[hidden_sizes[1]], activation=activation)
+ return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
+
+
+def get_vars(scope):
+ return [x for x in tf.global_variables() if scope in x.name]
+
+def count_vars(scope):
+ v = get_vars(scope)
+ return sum([np.prod(var.shape.as_list()) for var in v])
+
+"""
+Actor-Critics
+"""
+def mlp_actor_critic(x, a, hidden_sizes=(400,300), activation=tf.nn.relu,
+ output_activation=tf.tanh, action_space=None):
+ act_dim = a.shape.as_list()[-1]
+ act_limit = action_space.high[0]
+ with tf.variable_scope('pi'):
+ pi = act_limit * mlp(x, list(hidden_sizes)+[act_dim], activation, output_activation)
+ with tf.variable_scope('q'):
+ #q = tf.squeeze(mlp(tf.concat([x,a], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)
+ q = tf.squeeze(amc_mlp(x, a, list(hidden_sizes)+[1], activation, None), axis=1)
+ with tf.variable_scope('q', reuse=True):
+ #q_pi = tf.squeeze(mlp(tf.concat([x,pi], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)
+ q_pi = tf.squeeze(mlp(x, pi, list(hidden_sizes)+[1], activation, None), axis=1)
+ return pi, q, q_pi
diff --git a/examples/automated_deep_compression/ddpg/ddpg.py b/examples/automated_deep_compression/ddpg/ddpg.py
new file mode 100755
index 0000000..e92747d
--- /dev/null
+++ b/examples/automated_deep_compression/ddpg/ddpg.py
@@ -0,0 +1,327 @@
+# Code from https://github.com/openai/spinningup
+# Changes are marked with "Neta"
+
+import numpy as np
+import tensorflow as tf
+import gym
+import time
+from spinup.algos.ddpg import core
+from spinup.algos.ddpg.core import get_vars
+from spinup.utils.logx import EpochLogger
+
+import logging
+msglogger = logging.getLogger()
+
+class ReplayBuffer:
+ """
+ A simple FIFO experience replay buffer for DDPG agents.
+ """
+
+ def __init__(self, obs_dim, act_dim, size):
+ self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)
+ self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)
+ self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)
+ self.rews_buf = np.zeros(size, dtype=np.float32)
+ self.done_buf = np.zeros(size, dtype=np.float32)
+ self.ptr, self.size, self.max_size = 0, 0, size
+
+ def store(self, obs, act, rew, next_obs, done):
+ self.obs1_buf[self.ptr] = obs
+ self.obs2_buf[self.ptr] = next_obs
+ self.acts_buf[self.ptr] = act
+ self.rews_buf[self.ptr] = rew
+ self.done_buf[self.ptr] = done
+ self.ptr = (self.ptr+1) % self.max_size
+ self.size = min(self.size+1, self.max_size)
+
+ def sample_batch(self, batch_size=32):
+ idxs = np.random.randint(0, self.size, size=batch_size)
+ return dict(obs1=self.obs1_buf[idxs],
+ obs2=self.obs2_buf[idxs],
+ acts=self.acts_buf[idxs],
+ rews=self.rews_buf[idxs],
+ done=self.done_buf[idxs])
+
+
+"""
+
+Deep Deterministic Policy Gradient (DDPG)
+
+"""
+def ddpg(env, test_env, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
+ steps_per_epoch=5000, epochs=100, replay_size=int(1e6), gamma=0.99,
+ polyak=0.995, pi_lr=1e-3, q_lr=1e-3, batch_size=100, start_steps=10000,
+ act_noise=0.1, max_ep_len=1000, logger_kwargs=dict(), save_freq=1):
+ """
+
+ Args:
+ env_fn : A function which creates a copy of the environment.
+ The environment must satisfy the OpenAI Gym API.
+
+ actor_critic: A function which takes in placeholder symbols
+ for state, ``x_ph``, and action, ``a_ph``, and returns the main
+ outputs from the agent's Tensorflow computation graph:
+
+ =========== ================ ======================================
+ Symbol Shape Description
+ =========== ================ ======================================
+ ``pi`` (batch, act_dim) | Deterministically computes actions
+ | from policy given states.
+ ``q`` (batch,) | Gives the current estimate of Q* for
+ | states in ``x_ph`` and actions in
+ | ``a_ph``.
+ ``q_pi`` (batch,) | Gives the composition of ``q`` and
+ | ``pi`` for states in ``x_ph``:
+ | q(x, pi(x)).
+ =========== ================ ======================================
+
+ ac_kwargs (dict): Any kwargs appropriate for the actor_critic
+ function you provided to DDPG.
+
+ seed (int): Seed for random number generators.
+
+ steps_per_epoch (int): Number of steps of interaction (state-action pairs)
+ for the agent and the environment in each epoch.
+
+ epochs (int): Number of epochs to run and train agent.
+
+ replay_size (int): Maximum length of replay buffer.
+
+ gamma (float): Discount factor. (Always between 0 and 1.)
+
+ polyak (float): Interpolation factor in polyak averaging for target
+ networks. Target networks are updated towards main networks
+ according to:
+
+ .. math:: \\theta_{\\text{targ}} \\leftarrow
+ \\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
+
+ where :math:`\\rho` is polyak. (Always between 0 and 1, usually
+ close to 1.)
+
+ pi_lr (float): Learning rate for policy.
+
+ q_lr (float): Learning rate for Q-networks.
+
+ batch_size (int): Minibatch size for SGD.
+
+ start_steps (int): Number of steps for uniform-random action selection,
+ before running real policy. Helps exploration.
+
+ act_noise (float): Stddev for Gaussian exploration noise added to
+ policy at training time. (At test time, no noise is added.)
+
+ max_ep_len (int): Maximum length of trajectory / episode / rollout.
+
+ logger_kwargs (dict): Keyword args for EpochLogger.
+
+ save_freq (int): How often (in terms of gap between epochs) to save
+ the current policy and value function.
+
+ """
+
+ logger = EpochLogger(**logger_kwargs)
+ # Neta - logger is giving me some problems, so ignore
+ # logger.save_config(locals())
+
+ # Neta - disable seeding
+ # tf.set_random_seed(seed)
+ # np.random.seed(seed)
+
+ # Neta - we pass the gym environments to DDPG, instead of using a factory method
+ # env, test_env = env_fn(), env_fn()
+ obs_dim = env.observation_space.shape[0]
+ act_dim = env.action_space.shape[0]
+
+ # Action limit for clamping: critically, assumes all dimensions share the same bound!
+ act_limit = env.action_space.high[0]
+
+ # Share information about action space with policy architecture
+ ac_kwargs['action_space'] = env.action_space
+
+ # Inputs to computation graph
+ x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim, obs_dim, None, None)
+
+ # Main outputs from computation graph
+ with tf.variable_scope('main'):
+ pi, q, q_pi = actor_critic(x_ph, a_ph, **ac_kwargs)
+
+ # Target networks
+ with tf.variable_scope('target'):
+ # Note that the action placeholder going to actor_critic here is
+ # irrelevant, because we only need q_targ(s, pi_targ(s)).
+ pi_targ, _, q_pi_targ = actor_critic(x2_ph, a_ph, **ac_kwargs)
+
+ # Experience buffer
+ replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)
+
+ # Count variables
+ var_counts = tuple(core.count_vars(scope) for scope in ['main/pi', 'main/q', 'main'])
+ msglogger.info('\nNumber of parameters: \t pi: %d, \t q: %d, \t total: %d\n' % var_counts)
+
+ # Bellman backup for Q function
+ backup = tf.stop_gradient(r_ph + gamma*(1-d_ph)*q_pi_targ)
+
+ # DDPG losses
+ pi_loss = -tf.reduce_mean(q_pi)
+ q_loss = tf.reduce_mean((q-backup)**2)
+
+ # Separate train ops for pi, q
+ pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
+ q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr)
+ train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
+ train_q_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q'))
+
+ # Polyak averaging for target variables
+ target_update = tf.group([tf.assign(v_targ, polyak*v_targ + (1-polyak)*v_main)
+ for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
+
+ # Initializing targets to match main variables
+ target_init = tf.group([tf.assign(v_targ, v_main)
+ for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
+
+ sess = tf.Session()
+ sess.run(tf.global_variables_initializer())
+ sess.run(target_init)
+
+ # Setup model saving
+ logger.setup_tf_saver(sess, inputs={'x': x_ph, 'a': a_ph}, outputs={'pi': pi, 'q': q})
+
+ def get_action(o, noise_scale):
+ a = sess.run(pi, feed_dict={x_ph: o.reshape(1, -1)})[0]
+ msglogger.info("spinup_ddpg: pi a={}".format(a))
+ a += noise_scale * np.random.randn(act_dim)
+ msglogger.info("spinup_ddpg: pi a={} after adding noise".format(a))
+ return np.clip(a, -act_limit, act_limit)
+
+ def test_agent(n=10):
+ for j in range(n):
+ o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
+ while not(d or (ep_len == max_ep_len)):
+ # Take deterministic actions at test time (noise_scale=0)
+ o, r, d, _ = test_env.step(get_action(o, 0))
+ ep_ret += r
+ ep_len += 1
+ logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
+
+ start_time = time.time()
+ o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
+ msglogger.info("spinup_ddpg [after reset]: o={} r={} d={}".format(o, r, d))
+ total_steps = steps_per_epoch * epochs
+
+ # Main loop: collect experience in env and update/log each epoch
+ for t in range(total_steps):
+
+ """
+ Until start_steps have elapsed, randomly sample actions
+ from a uniform distribution for better exploration. Afterwards,
+ use the learned policy (with some noise, via act_noise).
+ """
+ if t > start_steps:
+ a = get_action(o, act_noise)
+ else:
+ a = env.action_space.sample()
+
+ # Step the env
+ o2, r, d, _ = env.step(a)
+ msglogger.info("spinup_ddpg: o2={} r={} d={}".format(o2, r, d))
+ ep_ret += r
+ ep_len += 1
+
+ # Ignore the "done" signal if it comes from hitting the time
+ # horizon (that is, when it's an artificial terminal signal
+ # that isn't based on the agent's state)
+ d = False if ep_len==max_ep_len else d
+
+ # Store experience to replay buffer
+ replay_buffer.store(o, a, r, o2, d)
+
+ # Super critical, easy to overlook step: make sure to update
+ # most recent observation!
+ o = o2
+
+ if d or (ep_len == max_ep_len):
+ """
+ Perform all DDPG updates at the end of the trajectory,
+ in accordance with tuning done by TD3 paper authors.
+ """
+
+ # Neta
+ # stats = ('Peformance/Validation/',
+ # OrderedDict([('act_noise', act_noise)]))
+ #
+ # distiller.log_training_progress(stats, None,
+ # self.episode, steps_completed=self.current_layer_id,
+ # total_steps=self.amc_cfg.conv_cnt, log_freq=1, loggers=[self.tflogger])
+ # Neta: noise decay
+ if t > start_steps:
+ act_noise = act_noise * 0.97
+
+ # Neta: don't learn while in heatup
+ if t > start_steps:
+ for _ in range(ep_len):
+ batch = replay_buffer.sample_batch(batch_size)
+ feed_dict = {x_ph: batch['obs1'],
+ x2_ph: batch['obs2'],
+ a_ph: batch['acts'],
+ r_ph: batch['rews'],
+ d_ph: batch['done']
+ }
+
+ # Q-learning update
+ outs = sess.run([q_loss, q, train_q_op], feed_dict)
+ logger.store(LossQ=outs[0], QVals=outs[1])
+
+ # Policy update
+ outs = sess.run([pi_loss, train_pi_op, target_update], feed_dict)
+ logger.store(LossPi=outs[0])
+
+ logger.store(EpRet=ep_ret, EpLen=ep_len)
+ o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
+
+ # End of epoch wrap-up
+ if t > 0 and t % steps_per_epoch == 0:
+ epoch = t // steps_per_epoch
+
+ # Save model
+ if (epoch % save_freq == 0) or (epoch == epochs-1):
+ logger.save_state({'env': env}, None)
+
+ # Test the performance of the deterministic version of the agent.
+ test_agent()
+
+ # Log info about epoch
+ logger.log_tabular('Epoch', epoch)
+ logger.log_tabular('EpRet', with_min_and_max=True)
+ logger.log_tabular('TestEpRet', with_min_and_max=True)
+ logger.log_tabular('EpLen', average_only=True)
+ logger.log_tabular('TestEpLen', average_only=True)
+ logger.log_tabular('TotalEnvInteracts', t)
+ logger.log_tabular('QVals', with_min_and_max=True)
+ logger.log_tabular('LossPi', average_only=True)
+ logger.log_tabular('LossQ', average_only=True)
+ logger.log_tabular('Time', time.time()-start_time)
+ logger.dump_tabular()
+
+ # Neta - this is required because we are using the same environment for training and testing
+ #o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
+
+if __name__ == '__main__':
+ import argparse
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--env', type=str, default='HalfCheetah-v2')
+ parser.add_argument('--hid', type=int, default=300)
+ parser.add_argument('--l', type=int, default=1)
+ parser.add_argument('--gamma', type=float, default=0.99)
+ parser.add_argument('--seed', '-s', type=int, default=0)
+ parser.add_argument('--epochs', type=int, default=50)
+ parser.add_argument('--exp_name', type=str, default='ddpg')
+ args = parser.parse_args()
+
+ from spinup.utils.run_utils import setup_logger_kwargs
+ logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)
+
+ ddpg(lambda : gym.make(args.env), actor_critic=core.mlp_actor_critic,
+ ac_kwargs=dict(hidden_sizes=[args.hid]*args.l),
+ gamma=args.gamma, seed=args.seed, epochs=args.epochs,
+ logger_kwargs=logger_kwargs)
diff --git a/examples/automated_deep_compression/fine_tune.yaml b/examples/automated_deep_compression/fine_tune.yaml
new file mode 100755
index 0000000..9729ea1
--- /dev/null
+++ b/examples/automated_deep_compression/fine_tune.yaml
@@ -0,0 +1,15 @@
+lr_schedulers:
+ training_lr:
+ class: StepLR # ReduceLROnPlateau
+ step_size: 10
+ gamma: 0.3
+ #mode: max
+ #patience: 5
+ #factor: 0.1
+
+policies:
+ - lr_scheduler:
+ instance_name: training_lr
+ starting_epoch: 0
+ ending_epoch: 200000
+ frequency: 1
diff --git a/examples/automated_deep_compression/ppo-amc-results.ipynb b/examples/automated_deep_compression/ppo-amc-results.ipynb
new file mode 100644
index 0000000..0f2f1ee
--- /dev/null
+++ b/examples/automated_deep_compression/ppo-amc-results.ipynb
@@ -0,0 +1,550 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using Clipped PPO for Automated Model Compression\n",
+ "\n",
+ "\n",
+ "Continuing on the work of [AMC](https://arxiv.org/abs/1802.03494) we replace DDPG with Clipped PPO.\n",
+ "\n",
+ "Results are interesting and encouraging as there is learning. However, this is less sample-efficient compared to DDPG, and therefore takes longer.\n",
+ "\n",
+ "We search for a 50%-MACs-constrained (FLOPs-constrained) Plain20. From Greedy Search algorithm we know that there exists a 50%-MACs-constrained Plain20 that can provide Top1=90%. The current fine-tuned Plain20 model from our PPO experiments has a Top1=89%."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Experiment setup\n",
+ "\n",
+ "\n",
+ "### Clipped PPO configuration\n",
+ "\n",
+ "### Distiller Clipped PPO AMC experiments\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Notebook code\n",
+ "\n",
+ "Skip this part - it is necessary only for creating the diagrams. You may also toggle the code-view button."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<script>\n",
+ "code_show=true; \n",
+ "function code_toggle() {\n",
+ " if (code_show){\n",
+ " $('div.input').hide();\n",
+ " } else {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ "} \n",
+ "$( document ).ready(code_toggle);\n",
+ "</script>\n",
+ "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML('''<script>\n",
+ "code_show=true; \n",
+ "function code_toggle() {\n",
+ " if (code_show){\n",
+ " $('div.input').hide();\n",
+ " } else {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ "} \n",
+ "$( document ).ready(code_toggle);\n",
+ "</script>\n",
+ "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib \n",
+ "import csv\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
+ "\n",
+ "\n",
+ "\n",
+ "#plt.style.use('seaborn') # pretty matplotlib plots\n",
+ "\n",
+ "params = {'legend.fontsize': 'x-large',\n",
+ " 'figure.figsize': (15, 7),\n",
+ " 'axes.labelsize': 'x-large',\n",
+ " 'axes.titlesize':'xx-large',\n",
+ " 'xtick.labelsize':'x-large',\n",
+ " 'ytick.labelsize':'x-large'}\n",
+ "pylab.rcParams.update(params)\n",
+ "\n",
+ "\n",
+ "def to_percent(y, position):\n",
+ " # Ignore the passed in position. This has the effect of scaling the default\n",
+ " # tick locations.\n",
+ " if y < 1:\n",
+ " y = 100 * y\n",
+ " s = \"{:.1f}\".format(y)\n",
+ "\n",
+ " # The percent symbol needs escaping in latex\n",
+ " if matplotlib.rcParams['text.usetex'] is True:\n",
+ " return s + r'$\\%$'\n",
+ " else:\n",
+ " return s + '%'\n",
+ " \n",
+ "# Widen the cells to get entire rows in the screen.\n",
+ "from IPython.core.display import display, HTML\n",
+ "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
+ "\n",
+ "\n",
+ "def plot_layer_densities(df, idx, action_type='action_history', ax=None, color=None):\n",
+ " if ax is None:\n",
+ " plt.figure()\n",
+ " ax = plt\n",
+ " \n",
+ " record = df.iloc[idx]\n",
+ " layer_sparsities = record[action_type]\n",
+ " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
+ " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
+ " ax.bar(range(len(layer_densities)), layer_densities, color=color)\n",
+ " ax.set_title(\"Ep:{} - Top1:{:.1f}%\\nMACs:{:.1f}%\".format(record['episode'], \n",
+ " record['top1'], \n",
+ " record['normalized_macs']))\n",
+ " \n",
+ " \n",
+ "def smooth(data, win_size):\n",
+ " win_size = max(1, win_size)\n",
+ " return [np.mean(data[max(0, i-win_size):i]) for i in range(len(data))]\n",
+ "\n",
+ "\n",
+ "def plot_performance(alpha, window_size, top1, macs, params, reward, start=0, end=-1):\n",
+ " plot_kwargs = {\"figsize\":(15,7), \"lw\": 1, \"alpha\": alpha, \"title\": \"Performance Data\"}\n",
+ " smooth_kwargs = {\"lw\": 2 if window_size > 0 else 1, \"legend\": True}\n",
+ " if macs:\n",
+ " ax = df['normalized_macs'][start:end].plot(**plot_kwargs, color=\"r\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " #ax.set_ylim([0,100])\n",
+ " df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)\n",
+ " df['smooth_normalized_macs'][start:end].plot(**smooth_kwargs, color=\"r\")\n",
+ " if top1:\n",
+ " ax = df['top1'][start:end].plot(**plot_kwargs, color=\"b\", grid=True)\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " df['smooth_top1'] = smooth(df['top1'], window_size)\n",
+ " df['smooth_top1'][start:end].plot(**smooth_kwargs, color=\"b\")\n",
+ " if params:\n",
+ " ax = df['normalized_nnz'][start:end].plot(**plot_kwargs, color=\"black\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
+ " df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)\n",
+ " df['smooth_normalized_nnz'][start:end].plot(**smooth_kwargs, color=\"black\") \n",
+ " if reward:\n",
+ " ax = df['reward'][start:end].plot(**plot_kwargs, secondary_y=True, color=\"g\")\n",
+ " ax.set(xlabel=\"Episode\", ylabel=\"reward\")\n",
+ " df['smooth_reward'] = smooth(df['reward'], window_size)\n",
+ " df['smooth_reward'][start:end].plot(**smooth_kwargs, secondary_y=True, color=\"g\") \n",
+ " #ax.set_ylim([0,100])\n",
+ " ax.grid(True, which='minor', axis='x', alpha=0.3)\n",
+ " \n",
+ " \n",
+ "def plot_2d_embeddings(top1, normalized_macs):\n",
+ " plt.figure(figsize=(15,7)) \n",
+ " plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
+ " plt.xlabel('Normalized MACs')\n",
+ " plt.ylabel('Top1 Accuracy')\n",
+ "\n",
+ " # Create the formatter using the function to_percent. This multiplies all the\n",
+ " # default labels by 100, making them all percentages\n",
+ " formatter = FuncFormatter(to_percent)\n",
+ "\n",
+ " # Set the formatter\n",
+ " plt.gca().yaxis.set_major_formatter(formatter)\n",
+ " plt.gca().xaxis.set_major_formatter(formatter)\n",
+ "\n",
+ " # Use color gradients to show the \"age\" of the network:\n",
+ " # Lighter networks were discovered earlier than darker ones.\n",
+ " color_grad = [str(1-i/len(top1)) for i in range(len(top1))]\n",
+ " plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');\n",
+ "\n",
+ " \n",
+ "INTERVAL = 100 # Animation speed\n",
+ "WINDOW = 20\n",
+ "\n",
+ "font = {'family': 'serif',\n",
+ " 'color': 'darkred',\n",
+ " 'weight': 'normal',\n",
+ " 'alpha': 0.50,\n",
+ " 'size': 32,\n",
+ " }\n",
+ "\n",
+ "# Based on these two helpful example code: \n",
+ "# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot\n",
+ "# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.\n",
+ "# Specifically, the use of IPython.display is missing from the first example, but most of the animation code\n",
+ "# leverages code from there.\n",
+ "class AnimatedScatter(object):\n",
+ " \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation.\"\"\"\n",
+ " def __init__(self, xdata, ydata):\n",
+ " assert len(xdata) == len(ydata)\n",
+ " self.numpoints = len(xdata)\n",
+ " self.xdata = xdata\n",
+ " self.ydata = ydata\n",
+ " self.stream = self.data_stream()\n",
+ "\n",
+ " # Setup the figure and axes...\n",
+ " self.fig, self.ax = plt.subplots(figsize=(15,7))\n",
+ " # Then setup FuncAnimation.\n",
+ " self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,\n",
+ " frames=self.numpoints-2, \n",
+ " init_func=self.setup_plot, blit=True)\n",
+ "\n",
+ " def setup_plot(self):\n",
+ " \"\"\"Initialize drawing of the scatter plot.\"\"\"\n",
+ " x, y, s, c = next(self.stream)\n",
+ " #self.annot = self.ax.annotate(\"txt\", (10, 10))\n",
+ " self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)\n",
+ " self.scat.set_edgecolors('gray')\n",
+ " self.scat.set_cmap('gray')\n",
+ " self.width = max(self.xdata) - min(self.xdata) + 4\n",
+ " self.height = max(self.ydata) - min(self.ydata) + 4\n",
+ " self.ax.axis([min(self.xdata)-2, max(self.xdata)+2, \n",
+ " min(self.ydata)-2, max(self.ydata)+2])\n",
+ " \n",
+ " self.annot = self.ax.text(min(self.xdata) + self.width/2, \n",
+ " min(self.xdata) + self.height/2, \n",
+ " \"\", fontdict=font)\n",
+ " # For FuncAnimation's sake, we need to return the artist we'll be using\n",
+ " # Note that it expects a sequence of artists, thus the trailing comma.\n",
+ " return self.scat, \n",
+ "\n",
+ " def data_stream(self):\n",
+ " numpoints = 0#len(self.xdata)\n",
+ " colors = []\n",
+ " xxx = 0\n",
+ " while True:\n",
+ " numpoints += 1\n",
+ " win_len = min(WINDOW, numpoints)\n",
+ " data = np.ndarray((4, win_len))\n",
+ " start = max(0,numpoints-WINDOW-1)\n",
+ " data[0, :] = self.xdata[start:start+win_len]\n",
+ " data[1, :] = self.ydata[start:start+win_len]\n",
+ " data[2, :] = [70] * win_len # point size\n",
+ " #data[3, :] = [np.random.random() for p in range(numpoints)] # color\n",
+ " # The color of the points is a gradient with larger values for \"younger\" points.\n",
+ " # At each new frame we show one more point, and \"age\" each existing point by incrementaly \n",
+ " # reducing its color gradient.\n",
+ " data[3, :] = [(1-i/(win_len+1)) for i in range(win_len)] \n",
+ " yield data\n",
+ "\n",
+ " def update(self, i): \n",
+ " \"\"\"Update the scatter plot.\"\"\"\n",
+ " data = next(self.stream)\n",
+ " self.annot.set_text(i)\n",
+ " i = i % len(data)\n",
+ " \n",
+ " # Set x and y data\n",
+ " xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]\n",
+ " self.scat.set_offsets(xy)\n",
+ " \n",
+ " # Set colors\n",
+ " self.scat.set_array(data[3])\n",
+ " \n",
+ " # We need to return the updated artist for FuncAnimation to draw..\n",
+ " # Note that it expects a sequence of artists, thus the trailing comma.\n",
+ " return self.scat, self.annot\n",
+ "\n",
+ " def show(self):\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Results\n",
+ "\n",
+ "Below I present the results of a single execution. There is a substantial variance between the experiment executions, but most conclude similarly to this experiment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read the results log files\n",
+ "\n",
+ "The code below reads the log file of your selected experiment. To change the path to the file you will need to open the code cell and change its content."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.set_option('display.max_colwidth', 150)\n",
+ "\n",
+ "fname = \"sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv\"\n",
+ "#fname = \"sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv\"\n",
+ "df = pd.read_csv(fname)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot experiment performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x504 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0e374e6486bb4619b145838f78ab3eac",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/html": [
+ "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+ "<p>\n",
+ " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+ " that the widgets JavaScript is still loading. If this message persists, it\n",
+ " likely means that the widgets JavaScript library is either not installed or\n",
+ " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+ " Widgets Documentation</a> for setup instructions.\n",
+ "</p>\n",
+ "<p>\n",
+ " If you're reading this message in another frontend (for example, a static\n",
+ " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+ " it may mean that your frontend doesn't currently support widgets.\n",
+ "</p>\n"
+ ],
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), Checkbox(value=True, description='top1'), Checkbox(value=True, description='macs'), Checkbox(value=False, description='params'), Checkbox(value=True, description='reward'), Output()), _dom_classes=('widget-interact',))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15,7))\n",
+ "#print(plt.style.available)\n",
+ "#plt.style.use('bmh')\n",
+ "\n",
+ "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
+ "def plot_performance_proxy(window_size=10, top1=True, macs=True, params=False, reward=True):\n",
+ " plot_performance(0.15, window_size, top1, macs, params, reward)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_performance(0.15, window_size=10, top1=True, macs=True, params=False, reward=True, start=0, end=600)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What do we see?\n",
+ "- If we zoom in on the first 600 episodes, we see how the reward starts rising as the agent learns to retain as many compute resources (MACs) as possible. Does this occur with other models?\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sample some networks\n",
+ "\n",
+ "Let's look at the networks with the best top1 accuracy, and see if they share geometrical attributes.\n",
+ "\n",
+ "We sort the discovered networks by their Top1 accuracy and display the density of each layer in the networks. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1_sorted_df = df.sort_values(by=['top1'], ascending=False)\n",
+ "nrows = 2; ncols = 4\n",
+ "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
+ "for i in range(0, nrows * ncols):\n",
+ " plot_layer_densities(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='g')\n",
+ " # Fine-tune figure; make subplots farther from each other.\n",
+ " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
+ "\n",
+ "#pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Network 2D embeddings\n",
+ "\n",
+ "Let's create an embedding of the networks AMC discovers over the course of each experiment session. Each network is projected onto a 2D plane mapping the Top1 accuracy versus the compute budget, and is represented by a small circle. I used gradient-color-coding to show the relative phase where each network is discovered. Lighter circles are networks discovered early in the search, darker networks are discovered later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1 = df['top1']\n",
+ "normalized_macs = df['normalized_macs']\n",
+ "plot_2d_embeddings(top1, normalized_macs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Video animation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "a = AnimatedScatter(normalized_macs, top1)\n",
+ "plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
+ "plt.xlabel('Normalized MACs')\n",
+ "plt.ylabel('Top1 Accuracy')\n",
+ "#a.ani.save('amc_vgg16.mp4', fps=10, dpi=80) #Frame per second controls speed, dpi controls the quality \n",
+ "rc('animation', html='html5')\n",
+ "a.ani"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/automated_deep_compression/presets/ADC_ClippedPPO.py b/examples/automated_deep_compression/presets/ADC_ClippedPPO.py
new file mode 100755
index 0000000..464155b
--- /dev/null
+++ b/examples/automated_deep_compression/presets/ADC_ClippedPPO.py
@@ -0,0 +1,73 @@
+from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters
+from rl_coach.architectures.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
+from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.filters.filter import InputFilter
+from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.schedules import LinearSchedule
+
+####################
+# Graph Scheduling #
+####################
+
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2048)
+schedule_params.evaluation_steps = EnvironmentEpisodes(5)
+schedule_params.heatup_steps = EnvironmentSteps(0)
+
+#########
+# Agent #
+#########
+agent_params = ClippedPPOAgentParameters()
+
+
+agent_params.network_wrappers['main'].learning_rate = 0.0001 # 0.0003
+agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh'
+agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense(64)]
+agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense(64)]
+agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh'
+agent_params.network_wrappers['main'].batch_size = 64
+agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
+agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
+
+agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
+agent_params.algorithm.num_consecutive_playing_steps.num_steps = 1024
+agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
+# The entropy coefficient is a regularizer. A policy has maximum entropy when all policies are equally
+# likely and minimum when the one action probability of the policy is dominant. The entropy coefficient
+# is multiplied by the maximum possible entropy and added to loss. This helps prevent premature convergence
+# of one action probability dominating the policy and preventing exploration.
+# Source: https://medium.com/aureliantactics/ppo-hyperparameters-and-ranges-6fc2d29bccbe
+# This value sets the regularization penalty
+agent_params.algorithm.beta_entropy = 0
+agent_params.algorithm.gae_lambda = 0.95
+agent_params.algorithm.discount = 1
+agent_params.algorithm.optimization_epochs = 10
+agent_params.algorithm.estimate_state_value_using_gae = True
+
+# Distributed Coach synchronization type.
+agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
+
+agent_params.pre_network_filter = InputFilter()
+agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
+ ObservationNormalizationFilter(name='normalize_observation'))
+
+###############
+# Environment #
+###############
+env_params = GymVectorEnvironment()
+env_params.level = '../automated_deep_compression/ADC.py:DistillerWrapperEnvironment'
+
+vis_params = VisualizationParameters()
+vis_params.dump_parameters_documentation = False
+vis_params.render = True
+vis_params.native_rendering = True
+vis_params.dump_signals_to_csv_every_x_episodes = 1
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+ schedule_params=schedule_params, vis_params=vis_params)
diff --git a/examples/automated_deep_compression/presets/ADC_DDPG.py b/examples/automated_deep_compression/presets/ADC_DDPG.py
index eb03ed7..d7e9523 100755
--- a/examples/automated_deep_compression/presets/ADC_DDPG.py
+++ b/examples/automated_deep_compression/presets/ADC_DDPG.py
@@ -3,14 +3,13 @@ from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
-from rl_coach.environments.gym_environment import GymEnvironmentParameters, GymVectorEnvironment
-from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.environments.gym_environment import GymVectorEnvironment
from rl_coach.exploration_policies.truncated_normal import TruncatedNormalParameters
-from rl_coach.schedules import ConstantSchedule, PieceWiseSchedule, ExponentialSchedule
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+
from rl_coach.memories.memory import MemoryGranularity
from rl_coach.base_parameters import EmbedderScheme
from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.filters.filter import InputFilter, OutputFilter
steps_per_episode = 13
@@ -18,10 +17,10 @@ steps_per_episode = 13
# Block Scheduling #
####################
schedule_params = ScheduleParameters()
-schedule_params.improve_steps = EnvironmentEpisodes(400)
-schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(3) #3) # Neta: (1000)
-schedule_params.evaluation_steps = EnvironmentEpisodes(1) #1) # Neta: 0
-schedule_params.heatup_steps = EnvironmentEpisodes(100) #120*steps_per_episode) # Neta (2)
+schedule_params.improve_steps = EnvironmentEpisodes(800)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(5)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1) # Neta: 0
+schedule_params.heatup_steps = EnvironmentEpisodes(100)
#####################
# DDPG Agent Params #
@@ -29,10 +28,22 @@ schedule_params.heatup_steps = EnvironmentEpisodes(100) #120*steps_per_episode)
agent_params = DDPGAgentParameters()
agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(300)]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300)]
+agent_params.network_wrappers['actor'].heads_parameters[0].activation_function = 'sigmoid'
agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(300)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(300)]
agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
-agent_params.network_wrappers['actor'].heads_parameters[0].activation_function = 'sigmoid'
+
+agent_params.network_wrappers['actor'].optimizer_type = 'Adam'
+agent_params.network_wrappers['actor'].adam_optimizer_beta1 = 0.9
+agent_params.network_wrappers['actor'].adam_optimizer_beta2 = 0.999
+
+agent_params.network_wrappers['critic'].optimizer_type = 'Adam'
+agent_params.network_wrappers['critic'].adam_optimizer_beta1 = 0.9
+agent_params.network_wrappers['critic'].adam_optimizer_beta2 = 0.999
+
+
+agent_params.network_wrappers['actor'].learning_rate = 0.0001 # 0.0001
+agent_params.network_wrappers['critic'].learning_rate = 0.001
# agent_params.network_wrappers['critic'].clip_gradients = 100
# agent_params.network_wrappers['actor'].clip_gradients = 100
@@ -40,21 +51,25 @@ agent_params.algorithm.rate_for_copying_weights_to_target = 0.01 # Tau pg. 11
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
agent_params.algorithm.heatup_using_network_decisions = True
agent_params.algorithm.discount = 1
+
+# Shadi
+agent_params.algorithm.use_non_zero_discount_for_terminal_states = True # <===========
+
+# See : https://nervanasystems.github.io/coach/components/agents/policy_optimization/ddpg.html?highlight=ddpg#rl_coach.agents.ddpg_agent.DDPGAlgorithmParameters
# Replay buffer size
agent_params.memory.max_size = (MemoryGranularity.Transitions, 2000)
+#agent_params.exploration = AdditiveNoiseParameters() #
agent_params.exploration = TruncatedNormalParameters()
-agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
- (ConstantSchedule(0.5), EnvironmentSteps(100*steps_per_episode)),
- (ExponentialSchedule(0.5, 0, 0.996), EnvironmentSteps(300*steps_per_episode))])
+agent_params.algorithm.use_target_network_for_evaluation = True
+#agent_params.exploration.evaluation_noise_percentage = 0.01 # Neta new
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
-agent_params.network_wrappers['actor'].learning_rate = 0.0001
-agent_params.network_wrappers['critic'].learning_rate = 0.001
+
##############################
# Gym #
##############################
env_params = GymVectorEnvironment()
-env_params.level = '../automated_deep_compression/ADC.py:CNNEnvironment'
+env_params.level = '../automated_deep_compression/ADC.py:DistillerWrapperEnvironment'
vis_params = VisualizationParameters()
diff --git a/examples/automated_deep_compression/presets/ADC_PPO.py b/examples/automated_deep_compression/presets/ADC_PPO.py
new file mode 100644
index 0000000..26da3c5
--- /dev/null
+++ b/examples/automated_deep_compression/presets/ADC_PPO.py
@@ -0,0 +1,67 @@
+from rl_coach.agents.ppo_agent import PPOAgentParameters
+from rl_coach.architectures.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.environment import SingleLevelSelection
+from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
+from rl_coach.filters.filter import InputFilter
+from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2000)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+schedule_params.heatup_steps = EnvironmentSteps(0)
+
+#########
+# Agent #
+#########
+agent_params = PPOAgentParameters()
+agent_params.network_wrappers['actor'].learning_rate = 0.001
+agent_params.network_wrappers['critic'].learning_rate = 0.001
+
+agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(64)]
+agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(64)]
+agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(64)]
+agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(64)]
+
+agent_params.input_filter = InputFilter()
+agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter())
+
+# Distributed Coach synchronization type.
+agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
+
+###############
+# Environment #
+###############
+env_params = GymVectorEnvironment()
+env_params.level = '../automated_deep_compression/ADC.py:DistillerWrapperEnvironment'
+
+vis_params = VisualizationParameters()
+vis_params.dump_parameters_documentation = False
+vis_params.render = True
+vis_params.native_rendering = True
+vis_params.dump_signals_to_csv_every_x_episodes = 1
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+ schedule_params=schedule_params, vis_params=vis_params)
+
+########
+# Test #
+########
+# preset_validation_params = PresetValidationParameters()
+# preset_validation_params.test = True
+# preset_validation_params.min_reward_threshold = 400
+# preset_validation_params.max_episodes_to_achieve_reward = 3000
+# preset_validation_params.reward_test_level = 'inverted_pendulum'
+# preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
+#
+# graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+# schedule_params=schedule_params, vis_params=VisualizationParameters(),
+# preset_validation_params=preset_validation_params)
+
+
diff --git a/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv b/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
new file mode 100644
index 0000000..3ebdf50
--- /dev/null
+++ b/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
@@ -0,0 +1,3153 @@
+episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
+1,55.59,-7.471664447521032,20262528,49.96875,44.97377622377623,BEST_adc_episode_001,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.53125, 0.796875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.64060384, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5325509, 0.8, 0.2, 0.2, 0.2]"
+2,48.040000000000006,-8.566173991573633,14448384,35.63068181818182,44.96705217859064,adc_episode_002,"[0.5625, 0.1875, 0.75, 0.75, 0.1875, 0.75, 0.1875, 0.59375, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.6113952, 0.2, 0.77715176, 0.8, 0.2, 0.78101426, 0.2, 0.6172896, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2]"
+3,33.17,-11.076577929272247,15780096,38.91477272727273,55.44311457772996,adc_episode_003,"[0.375, 0.3125, 0.1875, 0.75, 0.75, 0.4375, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.4096109, 0.32922018, 0.2, 0.8, 0.8, 0.4927334, 0.2, 0.2, 0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+4,21.609999999999996,-12.914522498134303,14284800,35.22727272727273,44.15344271113502,adc_episode_004,"[0.1875, 0.1875, 0.75, 0.75, 0.6875, 0.6875, 0.75, 0.1875, 0.1875, 0.375, 0.1875, 0.78125, 0.1875, 0.5625, 0.1875, 0.1875, 0.546875, 0.1875, 0.1875]","[0.2, 0.2, 0.8, 0.8, 0.6931433, 0.70121616, 0.8, 0.2, 0.2, 0.37824214, 0.20681375, 0.8, 0.2, 0.57309604, 0.2, 0.2, 0.5597267, 0.2, 0.2]"
+5,49.980000000000004,-8.342554246614586,17512704,43.1875,55.0564819795589,adc_episode_005,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.78125, 0.71875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375]","[0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.7320402, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4382071]"
+6,29.01,-11.890704529531282,18808704,46.38352272727273,42.41863905325444,adc_episode_006,"[0.6875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.328125, 0.21875, 0.1875]","[0.7162616, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.33758944, 0.22015022, 0.2]"
+7,29.479999999999997,-11.707539967068247,16219584,39.99857954545455,40.98305540613233,adc_episode_007,"[0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.75, 0.25, 0.1875, 0.1875, 0.1875, 0.65625, 0.1875, 0.78125, 0.796875, 0.1875, 0.1875, 0.546875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.26608127, 0.2, 0.2, 0.2, 0.67828786, 0.2, 0.8, 0.8, 0.2, 0.2, 0.54987895, 0.2, 0.2]"
+8,40.5,-9.979001044738254,19219392,47.39630681818181,45.683162990855294,adc_episode_008,"[0.1875, 0.75, 0.1875, 0.3125, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.8, 0.2, 0.34389165, 0.7206159, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2]"
+9,26.8,-11.937110398634719,12085632,29.803977272727273,31.145777299623457,adc_episode_009,"[0.1875, 0.75, 0.75, 0.75, 0.1875, 0.1875, 0.75, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.265625, 0.796875]","[0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2705093, 0.8]"
+10,53.36000000000001,-7.760424979658303,16835328,41.51704545454545,39.95763851533082,adc_episode_010,"[0.5, 0.1875, 0.1875, 0.75, 0.4375, 0.1875, 0.1875, 0.1875, 0.3125, 0.25, 0.1875, 0.78125, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.796875]","[0.5087335, 0.2, 0.22310375, 0.8, 0.4402622, 0.2, 0.2, 0.2, 0.33992076, 0.26379427, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8]"
+11,24.099999999999998,-12.385581016714568,12216384,30.126420454545453,22.16581495427649,adc_episode_011,"[0.75, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.6875, 0.1875, 0.71875, 0.59375, 0.1875, 0.796875, 0.796875, 0.1875]","[0.8, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5640245, 0.2, 0.7181329, 0.2, 0.7211405, 0.60042065, 0.2, 0.8, 0.8, 0.2]"
+12,41.23,-9.893052831358784,20450304,50.43181818181818,52.2626412049489,adc_episode_012,"[0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.75, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.37657455, 0.2, 0.2, 0.2, 0.8, 0.62348944, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.75581264, 0.2, 0.2, 0.2, 0.2, 0.2]"
+13,42.78999999999999,-9.525124647257512,17011584,41.95170454545455,47.88528778913395,adc_episode_013,"[0.5, 0.1875, 0.1875, 0.4375, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.51191026, 0.2, 0.2, 0.49817657, 0.7572117, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2]"
+14,39.870000000000005,-10.089310421432874,19368576,47.76420454545455,44.213959117805274,adc_episode_014,"[0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.359375, 0.640625, 0.1875, 0.1875, 0.3125, 0.1875]","[0.2, 0.2, 0.2, 0.71005416, 0.2, 0.2, 0.2, 0.70436376, 0.2, 0.2, 0.2, 0.2, 0.8, 0.3624721, 0.65216035, 0.2, 0.2, 0.32427475, 0.2]"
+15,27.969999999999995,-11.856257500430672,14078592,34.71875,37.52353415814954,adc_episode_015,"[0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.625, 0.40625, 0.78125, 0.78125, 0.78125, 0.1875, 0.6875, 0.796875, 0.1875, 0.1875, 0.484375, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.67654616, 0.40654185, 0.8, 0.8, 0.8, 0.2, 0.7004791, 0.8, 0.2, 0.2, 0.4964586, 0.2, 0.2]"
+16,40.12,-10.047611770339927,19376640,47.78409090909091,60.2373587950511,adc_episode_016,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.8, 0.2, 0.24692176, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+17,45.06,-9.15861539940759,17369280,42.83380681818182,41.531065088757394,adc_episode_017,"[0.75, 0.1875, 0.1875, 0.1875, 0.25, 0.5625, 0.3125, 0.78125, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.375, 0.1875, 0.1875, 0.328125, 0.796875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.27555227, 0.58800066, 0.31295314, 0.8, 0.2, 0.2, 0.2, 0.56689847, 0.2, 0.37541398, 0.2, 0.2, 0.33938667, 0.8, 0.2]"
+18,36.31999999999999,-10.529167021799982,15164928,37.39772727272727,40.082033351264116,adc_episode_018,"[0.75, 0.5625, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.59375, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.796875]","[0.8, 0.5770091, 0.2, 0.2, 0.2, 0.7447747, 0.2, 0.2, 0.8, 0.2, 0.2, 0.5978453, 0.8, 0.2, 0.2, 0.2, 0.2, 0.7544915, 0.8]"
+19,21.730000000000004,-12.738539566422816,11700288,28.85369318181818,15.905728886498116,adc_episode_019,"[0.5, 0.1875, 0.1875, 0.1875, 0.5625, 0.5, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.78125, 0.796875, 0.515625, 0.796875, 0.5, 0.796875, 0.1875]","[0.55460817, 0.2, 0.2, 0.2, 0.5978127, 0.5350738, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.5201223, 0.8, 0.51147085, 0.8, 0.2]"
+20,33.5,-11.036527338886724,16131456,39.78125,30.301909628832707,adc_episode_020,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.40625, 0.5625, 0.34375, 0.1875]","[0.2, 0.2, 0.7949842, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.7903293, 0.2, 0.8, 0.41266987, 0.5654255, 0.3493107, 0.2]"
+21,61.39,-6.4316935068223335,17160192,42.31818181818181,58.354626143087685,BEST_adc_episode_021,"[0.6875, 0.75, 0.75, 0.1875, 0.375, 0.1875, 0.75, 0.1875, 0.65625, 0.46875, 0.40625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.68760145, 0.8, 0.79505247, 0.2, 0.4353529, 0.2, 0.8, 0.2, 0.6603586, 0.48413965, 0.42990598, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+22,21.96,-12.964452228547572,16396416,40.43465909090909,43.528106508875744,adc_episode_022,"[0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.75, 0.78125, 0.78125, 0.1875, 0.1875, 0.5625, 0.1875, 0.796875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.58731437, 0.2, 0.8, 0.2, 0.38924944, 0.2, 0.2, 0.2]"
+23,43.489999999999995,-9.464116403594643,18768384,46.28409090909091,39.349112426035504,adc_episode_023,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.796875, 0.359375, 0.328125, 0.1875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.34541565, 0.2, 0.2, 0.21762732, 0.2, 0.8, 0.8, 0.2, 0.8, 0.3712739, 0.32934186, 0.2, 0.2]"
+24,29.269999999999996,-11.643951753277868,14112000,34.80113636363637,29.437869822485208,adc_episode_024,"[0.1875, 0.6875, 0.75, 0.1875, 0.1875, 0.5, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.796875, 0.1875, 0.203125]","[0.2, 0.70746064, 0.8, 0.2, 0.2, 0.53573066, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.20976378]"
+25,63.31,-6.158266063262887,19473984,48.02414772727273,43.63905325443787,BEST_adc_episode_025,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.71875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.609375, 0.609375, 0.1875]","[0.77120274, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.44403622, 0.72465396, 0.2, 0.40372658, 0.2, 0.2, 0.2, 0.6123554, 0.6201672, 0.2]"
+26,62.980000000000004,-6.2270426381856,20191104,49.79261363636363,55.60112963959117,adc_episode_026,"[0.75, 0.75, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.671875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.29928565, 0.2, 0.2, 0.68256384, 0.2, 0.2, 0.2, 0.2, 0.2]"
+27,38.660000000000004,-10.145443419995624,15243840,37.59232954545455,28.06616460462614,adc_episode_027,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.25, 0.28125, 0.5625, 0.78125, 0.375, 0.1875, 0.4375, 0.1875, 0.796875, 0.1875, 0.703125, 0.640625]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2642736, 0.2840583, 0.5719728, 0.8, 0.3845428, 0.2, 0.44375256, 0.2, 0.8, 0.2, 0.7115403, 0.64615595]"
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+57,34.01,-10.956348117662815,16240896,40.05113636363637,47.048144163528775,adc_episode_057,"[0.75, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.21875, 0.1875, 0.21875, 0.1875, 0.609375]","[0.8, 0.2, 0.7306854, 0.2, 0.2, 0.2, 0.22854377, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.22898902, 0.2, 0.2226408, 0.2, 0.61708784]"
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+1968,79.01,-3.5904007471937467,26836992,66.18181818181819,66.04020979020979,adc_episode_1968,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1969,79.02,-3.5886902180145217,26836992,66.18181818181819,66.04020979020979,adc_episode_1969,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1970,74.58,-4.341263392169957,26118144,64.4090909090909,64.99125874125873,adc_episode_1970,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.43947646, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1971,80.91000000000001,-3.2654002031409517,26836992,66.18181818181819,66.04020979020979,adc_episode_1971,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1972,74.42999999999999,-4.3738231112789006,26836992,66.18181818181819,66.04020979020979,adc_episode_1972,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1973,79.81,-3.4535584128557284,26836992,66.18181818181819,66.04020979020979,adc_episode_1973,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1974,80.13,-3.3988214791205236,26836992,66.18181818181819,66.04020979020979,adc_episode_1974,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.20435856, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1975,80.56,-3.3252687244138377,26836992,66.18181818181819,66.04020979020979,adc_episode_1975,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1976,76.64999999999999,-3.983421679890352,25638912,63.227272727272734,65.60314685314685,adc_episode_1976,"[0.1875, 0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.543772, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1977,80.77,-3.289347611650108,26836992,66.18181818181819,66.04020979020979,adc_episode_1977,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1978,78.39,-3.6964535563057104,26836992,66.18181818181819,66.04020979020979,adc_episode_1978,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1979,81.88,-3.099478872756108,26836992,66.18181818181819,66.04020979020979,adc_episode_1979,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1980,77.34,-3.876059120124358,26836992,66.18181818181819,66.04020979020979,adc_episode_1980,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1981,78.45,-3.6852261738052547,26717184,65.88636363636363,65.86538461538461,adc_episode_1981,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.22596191, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1982,77.27999999999999,-3.886322295199714,26836992,66.18181818181819,66.04020979020979,adc_episode_1982,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1983,78.95,-3.594948639070716,26118144,64.4090909090909,65.77797202797203,adc_episode_1983,"[0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.3152942, 0.2, 0.2, 0.26687378, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1984,79.94,-3.425875045119171,26118144,64.4090909090909,65.77797202797203,adc_episode_1984,"[0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.41516373, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1985,78.61,-3.658821914362756,26836992,66.18181818181819,66.04020979020979,adc_episode_1985,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1986,79.08,-3.5746575715880113,26357760,65.0,65.86538461538461,adc_episode_1986,"[0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.36777458, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1987,78.9,-3.6092165681652237,26836992,66.18181818181819,66.04020979020979,adc_episode_1987,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1988,77.57000000000001,-3.8367169490021786,26836992,66.18181818181819,66.04020979020979,adc_episode_1988,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1989,79.25,-3.549348046892342,26836992,66.18181818181819,66.04020979020979,adc_episode_1989,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1990,80.67999999999999,-3.2919193613943794,25113600,61.93181818181818,64.1003227541689,adc_episode_1990,"[0.75, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875]","[0.77393097, 0.29974255, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.26385593, 0.2]"
+1991,77.25,-3.8914538827373875,26836992,66.18181818181819,66.04020979020979,adc_episode_1991,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1992,78.71000000000001,-3.6417166225705015,26836992,66.18181818181819,66.04020979020979,adc_episode_1992,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1993,79.14,-3.5681638678638192,26836992,66.18181818181819,66.04020979020979,adc_episode_1993,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1994,82.17,-3.049873526558576,26836992,66.18181818181819,66.04020979020979,adc_episode_1994,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1995,78.19,-3.7306641398902167,26836992,66.18181818181819,66.04020979020979,adc_episode_1995,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1996,81.21000000000001,-3.214084327764197,26836992,66.18181818181819,66.04020979020979,adc_episode_1996,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1997,79.12,-3.571584926222269,26836992,66.18181818181819,66.04020979020979,adc_episode_1997,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1998,73.03,-4.595569894525298,25129728,61.971590909090914,64.33566433566433,adc_episode_1998,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.53125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.3947097, 0.2, 0.54585755, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+1999,79.47999999999999,-3.510005875770162,26836992,66.18181818181819,66.04020979020979,adc_episode_1999,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+2000,80.06,-3.4090068305805366,26597376,65.5909090909091,65.9527972027972,adc_episode_2000,"[0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2536145, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+2001,76.1,-4.080025472793703,25938432,63.96590909090909,64.72902097902097,adc_episode_2001,"[0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.234375, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.37674996, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.23849885, 0.2, 0.2, 0.2, 0.2]"
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+3142,81.52000000000001,-3.161057923208213,26836992,66.18181818181819,66.04020979020979,adc_episode_3142,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3143,78.88,-3.612637626523676,26836992,66.18181818181819,66.04020979020979,adc_episode_3143,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3144,80.77,-3.289347611650108,26836992,66.18181818181819,66.04020979020979,adc_episode_3144,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3145,80.25,-3.3782951289698193,26836992,66.18181818181819,66.04020979020979,adc_episode_3145,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3146,80.5,-3.3355318994891876,26836992,66.18181818181819,66.04020979020979,adc_episode_3146,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3147,79.44,-3.5168479924870626,26836992,66.18181818181819,66.04020979020979,adc_episode_3147,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3148,80.1,-3.4006455112922267,26394624,65.0909090909091,65.87883270575578,adc_episode_3148,"[0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.42379773, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3149,80.28999999999999,-3.3714530122529194,26836992,66.18181818181819,66.04020979020979,adc_episode_3149,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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+3151,78.14,-3.739216785786342,26836992,66.18181818181819,66.04020979020979,adc_episode_3151,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
+3152,78.34,-3.7050062022018375,26836992,66.18181818181819,66.04020979020979,adc_episode_3152,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
diff --git a/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv b/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
new file mode 100644
index 0000000..dc8d010
--- /dev/null
+++ b/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
@@ -0,0 +1,3550 @@
+episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
+1,33.040000000000006,0.3304000000000001,12934656,31.897727272727273,17.079074771382462,BEST_adc_episode_001,"[0.75, 0, 0, 0.75, 0.75, 0.1875, 0, 0, 0.78125, 0, 0.78125, 0, 0, 0.796875, 0.796875, 0.796875, 0.765625, 0.796875, 0]","[0.8, 0.0, 0.0, 0.75428534, 0.8, 0.24526766, 0.0, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.8, 0.8, 0.8, 0.76642257, 0.8, 0.0]"
+2,34.51,0.34509999999999996,20291328,50.03977272727273,35.328133405056484,BEST_adc_episode_002,"[0.75, 0.125, 0, 0, 0.75, 0.5625, 0, 0, 0, 0.5625, 0, 0, 0, 0.796875, 0.796875, 0.796875, 0, 0.296875, 0.71875]","[0.8, 0.17893769, 0.0, 0.0, 0.8, 0.6087111, 0.0, 0.0, 0.0, 0.5730491, 0.0, 0.0, 0.0, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0]"
+3,46.79,0.4679,20015424,49.359375,43.854222700376546,BEST_adc_episode_003,"[0.25, 0.4375, 0.625, 0, 0, 0, 0, 0, 0.78125, 0, 0.78125, 0.40625, 0, 0, 0, 0.53125, 0.578125, 0.796875, 0]","[0.297757, 0.45267355, 0.6577741, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.8, 0.42538527, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0]"
+4,29.569999999999997,0.29569999999999996,20286144,50.02698863636363,39.3928187197418,adc_episode_004,"[0.75, 0, 0, 0, 0.75, 0.75, 0.3125, 0.3125, 0, 0, 0.375, 0, 0, 0, 0.796875, 0.640625, 0, 0.6875, 0.375]","[0.8, 0.0, 0.0, 0.0, 0.8, 0.8, 0.3202856, 0.3392121, 0.0, 0.0, 0.39771163, 0.0, 0.0, 0.0, 0.8, 0.6530791, 0.0, 0.4586793, 0.19104968]"
+5,41.7,0.41700000000000004,19999872,49.32102272727273,57.278778913394305,adc_episode_005,"[0.375, 0.75, 0, 0, 0.75, 0, 0.75, 0, 0, 0, 0.78125, 0.53125, 0, 0.390625, 0.71875, 0, 0.234375, 0, 0]","[0.4247698, 0.7967756, 0.0, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.8, 0.55605996, 0.017714858, 0.4015374, 0.7275591, 0.0, 0.23993926, 0.0, 0.0]"
+6,42.39000000000001,0.42390000000000005,20293056,50.04403409090909,29.014254975793435,adc_episode_006,"[0, 0, 0.125, 0, 0, 0.5625, 0, 0, 0.4375, 0, 0.34375, 0.78125, 0, 0.71875, 0.796875, 0.796875, 0, 0.5, 0.59375]","[0.0, 0.0040054712, 0.13979279, 0.0, 0.0, 0.5806573, 0.0, 0.0, 0.4588497, 0.0, 0.0, 0.8, 0.0, 0.66444147, 0.8, 0.8, 0.0, 0.0, 0.37614235]"
+7,30.479999999999997,0.30479999999999996,15082560,37.19460227272727,42.07235072619688,adc_episode_007,"[0, 0.75, 0, 0, 0.5625, 0, 0.75, 0, 0.59375, 0.78125, 0.78125, 0.78125, 0, 0.046875, 0.609375, 0, 0, 0.796875, 0]","[0.0, 0.8, 0.020280894, 0.0, 0.60788745, 0.0, 0.8, 0.0, 0.60573864, 0.8, 0.8, 0.8, 0.0, 0.051320527, 0.62208414, 0.0, 0.0, 0.8, 0.0]"
+8,53.06999999999999,0.5307,20285568,50.02556818181818,37.14026358257128,BEST_adc_episode_008,"[0, 0, 0.75, 0, 0.1875, 0, 0.4375, 0.78125, 0, 0, 0.0625, 0.5625, 0.09375, 0.515625, 0, 0.609375, 0.484375, 0.609375, 0.484375]","[0.0, 0.0, 0.7530135, 0.0, 0.23333932, 0.0, 0.49174312, 0.8, 0.0, 0.0, 0.0823767, 0.56932735, 0.10851926, 0.52784395, 0.0, 0.47908375, 0.0, 0.0, 0.0]"
+9,41.76,0.41759999999999997,19842048,48.93181818181818,63.39766003227542,adc_episode_009,"[0.1875, 0.375, 0, 0.75, 0, 0, 0.75, 0, 0.78125, 0.75, 0.46875, 0, 0.78125, 0, 0.109375, 0, 0, 0, 0.796875]","[0.24605331, 0.43500635, 0.0, 0.8, 0.0, 0.0, 0.8, 0.0, 0.8, 0.75561655, 0.47159702, 0.0, 0.8, 0.0, 0.12234522, 0.0, 0.0, 0.0, 0.8]"
+10,52.910000000000004,0.5291,20281536,50.015625,42.16984938138784,adc_episode_010,"[0, 0.4375, 0.75, 0.75, 0, 0.75, 0, 0, 0, 0, 0.125, 0, 0, 0, 0.65625, 0.53125, 0.484375, 0.609375, 0.5]","[0.0, 0.47587755, 0.78828245, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.15506224, 0.0, 0.0, 0.0, 0.0, 0.5423126, 0.20657629, 0.0, 0.0]"
+11,49.21,0.4921,20191104,49.79261363636363,44.54679935449166,adc_episode_011,"[0.75, 0, 0, 0, 0.3125, 0.6875, 0, 0.40625, 0.78125, 0, 0.46875, 0, 0, 0, 0, 0.71875, 0.796875, 0, 0.703125]","[0.8, 0.0, 0.0, 0.0, 0.3607101, 0.7197027, 0.0, 0.42547822, 0.8, 0.0, 0.4968741, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.71647394]"
+12,34.27,0.3427,17372160,42.84090909090909,40.680473372781066,adc_episode_012,"[0.75, 0, 0, 0.75, 0.5625, 0.75, 0.75, 0, 0.6875, 0, 0, 0, 0.15625, 0.59375, 0.796875, 0, 0.703125, 0, 0.109375]","[0.8, 0.0, 0.0, 0.8, 0.59435767, 0.8, 0.8, 0.0, 0.7114255, 0.0, 0.0, 0.0, 0.16473037, 0.59655124, 0.8, 0.0, 0.7059997, 0.0, 0.12013451]"
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+1991,62.480000000000004,0.6248,20275200,50.0,75.45723507261968,adc_episode_1991,"[0.5, 0.625, 0.75, 0, 0.75, 0.5625, 0, 0, 0.78125, 0, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.703125]","[0.51081777, 0.6304902, 0.8, 0.0, 0.8, 0.6011785, 0.0, 0.0, 0.8, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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+1994,56.37,0.5637,20304000,50.07102272727273,72.66003227541688,adc_episode_1994,"[0.6875, 0.6875, 0.5625, 0.75, 0.125, 0.4375, 0.5, 0.78125, 0.78125, 0, 0.3125, 0, 0, 0, 0, 0, 0.046875, 0.09375, 0.78125]","[0.7179336, 0.69821477, 0.5989156, 0.8, 0.17417447, 0.45577735, 0.561926, 0.8, 0.8, 0.0, 0.34260193, 0.0, 0.0, 0.0, 0.0, 0.0, 0.059742395, 0.0, 0.0]"
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+1997,68.7,0.687,20290176,50.03693181818182,73.81993006993007,adc_episode_1997,"[0, 0.75, 0.5625, 0.75, 0.75, 0, 0.75, 0, 0.78125, 0.21875, 0, 0.25, 0, 0, 0, 0, 0, 0.109375, 0.78125]","[0.021150311, 0.8, 0.5800058, 0.8, 0.7530135, 0.0, 0.8, 0.0, 0.8, 0.24898678, 0.0, 0.27539477, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
+1998,71.67999999999999,0.7167999999999999,20282688,50.018465909090914,70.36713286713287,adc_episode_1998,"[0.75, 0.75, 0.125, 0.75, 0.125, 0.75, 0, 0, 0.78125, 0, 0, 0.34375, 0.21875, 0, 0.140625, 0, 0, 0.046875, 0.796875]","[0.8, 0.8, 0.1391301, 0.8, 0.14530358, 0.8, 0.0, 0.02310771, 0.8, 0.0, 0.0050964886, 0.34468797, 0.24853554, 0.0, 0.14499809, 0.0, 0.0, 0.0, 0.0]"
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diff --git a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
new file mode 100644
index 0000000..2ec675f
--- /dev/null
+++ b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
@@ -0,0 +1,461 @@
+episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
+1,20.14,0.2014,11442816,28.218749999999996,31.791285637439483,BEST_adc_episode_001,"[0.0625, 0.3125, 0.625, 0.625, 0.5, 0.625, 0.625, 0.28125, 0.75, 0.5, 0.21875, 0.6875, 0.53125, 0.40625, 0.53125, 0.671875, 0.078125, 0.265625, 0.53125]","[0.08415665434674326, 0.32854435046886493, 0.6486487040484364, 0.6835252042090019, 0.5035110998072743, 0.6786214065610782, 0.6395600698628794, 0.31072300294002164, 0.7622393077752324, 0.5293260226085498, 0.22725667289861273, 0.6924470450664589, 0.5619764974297123, 0.4101065610828509, 0.5372018678371113, 0.6771557308763463, 0.08601219658604706, 0.2775437033509216, 0.5409290478672346]"
+2,28.82,0.2882,13038912,32.15482954545455,26.744889725658954,BEST_adc_episode_002,"[0, 0.125, 0.6875, 0.0625, 0.375, 0.375, 0.75, 0.75, 0.6875, 0.21875, 0.59375, 0.5, 0.09375, 0.21875, 0.59375, 0.359375, 0.734375, 0.5, 0.59375]","[0.05360804268833532, 0.14181237259966153, 0.7278991347318444, 0.09356296435859673, 0.3957835611951048, 0.39954152799435555, 0.787769750274963, 0.7574218677226644, 0.6937540595317695, 0.2374685337175311, 0.610984635360392, 0.5134709700284525, 0.11233981965836223, 0.22809205787689196, 0.5978625680997257, 0.3746553335707539, 0.7444932227336121, 0.5037383636004261, 0.6005507504994861]"
+3,29.359999999999996,0.29359999999999997,15170688,37.41193181818182,25.73292092522862,BEST_adc_episode_003,"[0.5, 0.625, 0.125, 0.125, 0, 0.4375, 0.5625, 0.15625, 0.59375, 0.15625, 0.1875, 0.53125, 0.3125, 0.28125, 0.78125, 0.546875, 0.671875, 0.421875, 0.53125]","[0.5381926408115661, 0.6576439712791253, 0.1594183784023404, 0.1566505888983154, 0.056624543937550356, 0.4886464993428731, 0.6047080350911427, 0.1749416695961294, 0.6075143406680599, 0.1701292726155909, 0.19794974168801277, 0.5619313945122496, 0.3238983839963902, 0.2962214575559744, 0.7819758790574471, 0.561677844864734, 0.6870289297090059, 0.42596927219357417, 0.5444535649359525]"
+4,55.879999999999995,0.5588,17638848,43.49857954545455,44.54679935449166,BEST_adc_episode_004,"[0.25, 0.5, 0.1875, 0.6875, 0.0625, 0.25, 0.375, 0.21875, 0.15625, 0.5, 0.40625, 0.21875, 0.65625, 0.375, 0.15625, 0.359375, 0.140625, 0.46875, 0.4375]","[0.25617259183747465, 0.5216085684883116, 0.19943561220590345, 0.7269383140335177, 0.10979604175666328, 0.2608511324810973, 0.41704316868345753, 0.22164703329538896, 0.1767628179191009, 0.5183820296835726, 0.43626509980289, 0.22292158485735383, 0.6679594185249424, 0.3884022075500952, 0.16165207142709367, 0.37496217093260314, 0.15047864042685724, 0.470018561530088, 0.4475801007040172]"
+5,25.8,0.258,15553152,38.35511363636363,49.66043571812803,BEST_adc_episode_005,"[0.6875, 0.5, 0.25, 0.6875, 0, 0.75, 0.6875, 0.28125, 0.34375, 0.125, 0.625, 0.46875, 0.125, 0.09375, 0.375, 0.4375, 0.125, 0.203125, 0.40625]","[0.7203147610238094, 0.5046818526535961, 0.2777089053656343, 0.7168948388012164, 0.05502895989677375, 0.7842806366180027, 0.7030125539483185, 0.28430148527063936, 0.35285090399254926, 0.13934839900001156, 0.6479856720635855, 0.49893883706102726, 0.14964412129223262, 0.09605846583223457, 0.38320693148458645, 0.4518891912354004, 0.13974595092167386, 0.20591248980000146, 0.41750003491739124]"
+6,21.030000000000005,0.21030000000000004,12989952,32.03409090909091,35.718128025820334,BEST_adc_episode_006,"[0.3125, 0.6875, 0.75, 0.625, 0.25, 0.625, 0.0625, 0.28125, 0.75, 0.4375, 0.125, 0.46875, 0.09375, 0.734375, 0.5625, 0.0625, 0.203125, 0.578125, 0.40625]","[0.3651333703312157, 0.7494490090203013, 0.7514260572864256, 0.6384462970857672, 0.2791755661739046, 0.6857471815758827, 0.11737416436758677, 0.2959293147065506, 0.7661026570986929, 0.44438667503728607, 0.1509835162358225, 0.48108278977189844, 0.11725019642698324, 0.7496367884790699, 0.5659241396406816, 0.06611302926191445, 0.21670274929224856, 0.5837547311016283, 0.41718882716798494]"
+7,22.37,0.2237,12310272,30.357954545454547,35.65424959655729,BEST_adc_episode_007,"[0.4375, 0.1875, 0.75, 0.4375, 0.75, 0.5, 0.4375, 0.6875, 0.59375, 0.71875, 0.25, 0.25, 0.21875, 0.109375, 0.796875, 0.375, 0.5625, 0.09375, 0.171875]","[0.4909749135819981, 0.23115585333970692, 0.7547475150248849, 0.4603544520282301, 0.7671774387048116, 0.534768662737602, 0.4639399633242757, 0.715990433150231, 0.5939026390799211, 0.7201508642602125, 0.26027878363909784, 0.25612938956034537, 0.23265509942478957, 0.11474409025090215, 0.7978890596227087, 0.38040061914337686, 0.5726004342013682, 0.10062824289114514, 0.18370957235228585]"
+8,38.349999999999994,0.38349999999999995,19117440,47.14488636363636,38.8145508337816,BEST_adc_episode_008,"[0.0625, 0.25, 0.3125, 0.125, 0.0625, 0.375, 0.5625, 0.09375, 0.34375, 0.0625, 0.78125, 0.1875, 0.1875, 0.109375, 0.359375, 0.25, 0.703125, 0.515625, 0.59375]","[0.09234433118977872, 0.25700258885336735, 0.314892288861078, 0.16363245351693717, 0.06855952239419505, 0.3848359727633564, 0.5789473643965402, 0.12207874621650142, 0.36416932070465907, 0.06649605206172737, 0.7987095809459409, 0.21298780195190736, 0.19842954751922584, 0.11360339066293053, 0.3639787582602525, 0.25431893928067806, 0.7081524872228547, 0.5250076521331556, 0.6070292608164184]"
+9,22.550000000000004,0.22550000000000003,13127616,32.37357954545455,28.039268423883808,BEST_adc_episode_009,"[0.625, 0.125, 0.4375, 0.5625, 0.625, 0.125, 0.6875, 0.71875, 0.03125, 0.1875, 0.5, 0.40625, 0.125, 0.59375, 0.640625, 0.59375, 0.703125, 0.328125, 0.1875]","[0.6787675690868734, 0.16360282495872172, 0.44635884881373716, 0.6050771189558006, 0.6828425586820386, 0.15413113459883074, 0.72076820389247, 0.742637744635814, 0.06126067437035551, 0.19083893798371515, 0.5042257758422262, 0.4157039211931743, 0.1325647427480403, 0.5971476755190213, 0.6490887261934016, 0.6013418212700179, 0.7087828162787251, 0.3346820747001173, 0.1928331381803466]"
+10,12.709999999999999,0.1271,11030400,27.201704545454547,33.2672135556751,BEST_adc_episode_010,"[0.6875, 0.6875, 0.6875, 0.0625, 0.6875, 0.3125, 0.6875, 0.25, 0.09375, 0.78125, 0.25, 0.78125, 0.53125, 0.21875, 0.171875, 0.734375, 0.328125, 0.40625, 0.40625]","[0.7128625602492684, 0.6888777735617378, 0.7382022551873767, 0.06464080165495548, 0.7215197418220269, 0.3584504730571794, 0.6937918522164219, 0.2721984329456287, 0.11583136729621372, 0.7927795020196845, 0.2715256972597982, 0.7818259824097007, 0.5467449980383966, 0.23293919286650736, 0.1736113873941229, 0.7472918689548773, 0.33290360857304374, 0.41896417419118, 0.42005366822082363]"
+11,16.45,0.16449999999999998,11627136,28.673295454545457,20.854626143087682,BEST_adc_episode_011,"[0.4375, 0.375, 0.5625, 0.125, 0.0625, 0.3125, 0.6875, 0.71875, 0.3125, 0.625, 0.625, 0.53125, 0.46875, 0.453125, 0.625, 0.484375, 0.703125, 0.4375, 0.625]","[0.43775103957965333, 0.40740289016737297, 0.5925167919701136, 0.13186235910265093, 0.09615350376614676, 0.32015082695814207, 0.6912478731800327, 0.7252226603201957, 0.3412566773590955, 0.6560286651689949, 0.6477407447862836, 0.5363761065270825, 0.49169493198945763, 0.45605049634897576, 0.6384510122077782, 0.4913173918481692, 0.7140947105901895, 0.4435123079202311, 0.6366553952497497]"
+12,38.660000000000004,0.38660000000000005,14639040,36.10085227272727,44.48628294782141,BEST_adc_episode_012,"[0.3125, 0.625, 0.625, 0.4375, 0.5625, 0.1875, 0.5, 0.3125, 0.75, 0.5, 0.21875, 0.3125, 0.4375, 0.328125, 0.15625, 0.171875, 0.15625, 0.640625, 0.390625]","[0.36868569715808946, 0.6391646237212247, 0.6400291332923503, 0.4591450008502731, 0.5855329861184739, 0.24788565855670083, 0.5389015321608888, 0.3150195735846907, 0.753403861500634, 0.5257582068669044, 0.23626338310717915, 0.3140199974330242, 0.45733874788040657, 0.3302255730931243, 0.16896275487296575, 0.18456395238338075, 0.16053537841260135, 0.6414069023607339, 0.40105385881339334]"
+13,40.339999999999996,0.4034,15265728,37.64630681818182,40.7914201183432,BEST_adc_episode_013,"[0.3125, 0.6875, 0.1875, 0.6875, 0.0625, 0.3125, 0.5625, 0.15625, 0.09375, 0.65625, 0.40625, 0.53125, 0.3125, 0.078125, 0.078125, 0.671875, 0.453125, 0.390625, 0.453125]","[0.3485265485921212, 0.694113118274569, 0.20208981036887486, 0.74030133621399, 0.1200334825130983, 0.3205918309999974, 0.5818519809217233, 0.1570712606433585, 0.09951516962635036, 0.6704353095835456, 0.4279687436305971, 0.5523364260191368, 0.34114703250188916, 0.0885748026934649, 0.0925954521160026, 0.6794618280801918, 0.4583095886068996, 0.40034455795772, 0.45567575263371557]"
+14,41.120000000000005,0.41120000000000007,19315008,47.63210227272727,40.41151156535772,BEST_adc_episode_014,"[0, 0.125, 0.4375, 0.4375, 0.125, 0, 0.1875, 0.46875, 0.625, 0.34375, 0.21875, 0.5, 0.25, 0.234375, 0.390625, 0.625, 0.25, 0.390625, 0.21875]","[0.05649311393123628, 0.161069207698615, 0.4463642571796586, 0.45544644547054564, 0.183194450003662, 0.056868663680079375, 0.20857070527782154, 0.4688894369617454, 0.6432603267590721, 0.34444474907500566, 0.22709213303070186, 0.5067353901337112, 0.2671983300265322, 0.24887153194028025, 0.40496998690730684, 0.6350328970384083, 0.2651592378364056, 0.3996641067129697, 0.22133204453221872]"
+15,24.31,0.24309999999999998,12528576,30.896306818181817,30.62466379774072,BEST_adc_episode_015,"[0.375, 0.0625, 0.6875, 0.625, 0.0625, 0.5, 0.375, 0.09375, 0.5, 0.625, 0.75, 0.65625, 0.40625, 0.15625, 0.453125, 0.65625, 0.3125, 0.484375, 0.484375]","[0.4127720741135361, 0.08126981196461153, 0.7312762188578077, 0.6387742384635583, 0.07576080920194199, 0.5330892524627092, 0.3789180933246046, 0.10632139518034572, 0.5177461616178789, 0.6498328767323167, 0.7558869402280746, 0.6668990653165101, 0.4374086668189861, 0.16944099888875386, 0.46283634557309566, 0.66875052455862, 0.3254492853572277, 0.48919535649081847, 0.49771973381135654]"
+16,22.629999999999995,0.22629999999999995,11501568,28.363636363636363,28.472969338353955,BEST_adc_episode_016,"[0.75, 0.5, 0.75, 0.5, 0.375, 0.125, 0.75, 0.46875, 0.5625, 0.59375, 0.21875, 0.21875, 0.40625, 0.421875, 0.390625, 0.453125, 0.5, 0.640625, 0.453125]","[0.7860990025889187, 0.5214392259694545, 0.7753291140770996, 0.540801036120431, 0.3784658119762309, 0.16253434829724775, 0.7747328417577131, 0.47776549978192484, 0.5637655685687791, 0.6234706873357188, 0.2190615270999349, 0.23370692824428338, 0.40861192824457043, 0.42538588925809667, 0.3986240828270672, 0.46127871033410317, 0.5011649805417244, 0.6485253611099112, 0.4598678154451124]"
+17,29.990000000000006,0.29990000000000006,15580800,38.42329545454545,46.45306616460463,BEST_adc_episode_017,"[0.25, 0.125, 0.625, 0.75, 0.625, 0.3125, 0.1875, 0.1875, 0.46875, 0.625, 0.59375, 0.59375, 0.09375, 0.1875, 0.1875, 0.09375, 0.265625, 0.734375, 0.234375]","[0.2500205105647346, 0.16343295834249716, 0.6395078153378355, 0.7591319039675767, 0.6785520067276138, 0.32146135607050336, 0.22902823706134123, 0.21464007150669048, 0.47272209792689696, 0.6303723241748297, 0.597423148542652, 0.5966668293617569, 0.12094705732200396, 0.20135361834671625, 0.19284113655473736, 0.09375788205339483, 0.2694273598268846, 0.7423024639473168, 0.24707037115258437]"
+18,12.509999999999998,0.1251,11657088,28.74715909090909,29.431145777299623,BEST_adc_episode_018,"[0.0625, 0.1875, 0.375, 0.5625, 0.375, 0.6875, 0.6875, 0.75, 0.5, 0.5625, 0.75, 0.34375, 0.65625, 0.765625, 0.765625, 0.0625, 0.0625, 0.578125, 0.5625]","[0.11413571530873237, 0.19924696184608254, 0.39786235376684054, 0.6043905046814614, 0.41817763158444826, 0.7377984350225674, 0.7170142733750019, 0.7648826239431596, 0.5178777620471352, 0.5632759677638715, 0.7776489813914367, 0.3641000299910561, 0.6657795661238449, 0.7766346101817851, 0.7771603422854785, 0.07522803538098446, 0.07550036755156478, 0.5864374628573674, 0.5754639274018913]"
+19,46.34,0.46340000000000003,14806656,36.51420454545454,33.41178052716514,BEST_adc_episode_019,"[0.1875, 0.125, 0.1875, 0.25, 0.625, 0.5625, 0.1875, 0.5, 0.46875, 0.5, 0.4375, 0.65625, 0.4375, 0.3125, 0.46875, 0.3125, 0.34375, 0.578125, 0.4375]","[0.22083903312535333, 0.15907154285870206, 0.23716853279113756, 0.28820357668330754, 0.6450370432237814, 0.6153392223480605, 0.21617561390705975, 0.5106020060480139, 0.4984991949112175, 0.5178734763392482, 0.45123792583606126, 0.6709553365286474, 0.44173590182085076, 0.3225667365367055, 0.4829965596156546, 0.32636600052685993, 0.3565354669989418, 0.5822947376568358, 0.4389928517556827]"
+20,16.679999999999996,0.16679999999999995,9063936,22.352272727272727,28.234265734265733,BEST_adc_episode_020,"[0.6875, 0.625, 0.4375, 0.4375, 0.625, 0.625, 0.3125, 0.28125, 0.71875, 0.78125, 0.71875, 0.6875, 0.71875, 0.578125, 0.171875, 0.546875, 0.609375, 0.34375, 0.140625]","[0.7286645126555898, 0.6834212687551426, 0.45724525394832805, 0.49565649614245066, 0.6634853006083051, 0.646866981311676, 0.3695953900906111, 0.3038235983116045, 0.7224876983339622, 0.7825049424008171, 0.7366273020706814, 0.6959726874896738, 0.7286278822888193, 0.587825930057341, 0.1771894692944859, 0.5489241086226175, 0.6100560319170956, 0.3473441803261714, 0.1450269979050301]"
+21,43.96,0.4396,12966336,31.975852272727273,41.42011834319527,BEST_adc_episode_021,"[0.5, 0.625, 0.5, 0.3125, 0.75, 0.5, 0.5, 0.78125, 0.46875, 0.625, 0.3125, 0.1875, 0.46875, 0.171875, 0.21875, 0.109375, 0.28125, 0.703125, 0.65625]","[0.5035013303440234, 0.6434604572303826, 0.5168572137966572, 0.3697686280999205, 0.7505280388302401, 0.5238981908403633, 0.5399247162927556, 0.7982684299268645, 0.4748798813609883, 0.6331586822689026, 0.32212716856510376, 0.19785620034716964, 0.4856425604214159, 0.1806062764797957, 0.22994310552899855, 0.12079285333715317, 0.2961650796691456, 0.7047827171373259, 0.6668857075037727]"
+22,32.31,0.3231,14399424,35.50994318181818,33.929532006455084,BEST_adc_episode_022,"[0.625, 0.3125, 0.0625, 0.1875, 0.75, 0.4375, 0.4375, 0.28125, 0.71875, 0.75, 0.1875, 0.3125, 0.09375, 0.296875, 0.59375, 0.5625, 0.34375, 0.265625, 0.59375]","[0.6871352938442369, 0.3307765947713608, 0.10394221968535022, 0.21877512405351127, 0.7735411413112073, 0.4895500620005025, 0.484642677972515, 0.28537380949989444, 0.726738518288259, 0.7524119838782954, 0.19710271296828513, 0.33833774485836016, 0.09694521450107266, 0.29829923146578136, 0.6034138643379657, 0.5726224224505304, 0.34478914799519234, 0.2694937984424399, 0.5979031386183021]"
+23,35.540000000000006,0.35540000000000005,14073984,34.70738636363636,39.27178590640129,BEST_adc_episode_023,"[0.125, 0.375, 0.625, 0.25, 0.3125, 0.6875, 0.0625, 0.40625, 0.3125, 0.5625, 0.6875, 0.65625, 0.6875, 0.25, 0.546875, 0.09375, 0.328125, 0.40625, 0.15625]","[0.15474534510156346, 0.43706323942496217, 0.6782911577173081, 0.2720501738825616, 0.3168168721143689, 0.6883228044132428, 0.0636033535850188, 0.41534969581785386, 0.33700114123686664, 0.5702809880851735, 0.7012314406500562, 0.6658355787269398, 0.6895494706552794, 0.2508389981606529, 0.5615484684186381, 0.10490548348862488, 0.329309283371062, 0.4134190630777204, 0.15976283040309586]"
+24,26.029999999999998,0.2603,15381504,37.93181818181818,39.591178052716515,BEST_adc_episode_024,"[0.5, 0.5625, 0.625, 0.1875, 0.4375, 0.3125, 0.5, 0.09375, 0.625, 0.0625, 0.09375, 0.46875, 0.71875, 0.28125, 0.75, 0.734375, 0.203125, 0.046875, 0.125]","[0.5197173027390133, 0.5920540741990695, 0.6336557414886463, 0.1996774915472747, 0.44109430666175975, 0.36003168109960065, 0.5396809910344693, 0.09555601717368806, 0.637013542496133, 0.09094247392717182, 0.11832679298057398, 0.470993083030376, 0.7410012120025692, 0.2854372371158394, 0.7535836363274591, 0.7492776788108317, 0.21725672751007485, 0.055175325912780446, 0.13845073970968472]"
+25,40.92,0.4092,16923456,41.734375,38.777568585260894,BEST_adc_episode_025,"[0.75, 0.1875, 0.5, 0, 0.3125, 0.3125, 0.125, 0.3125, 0.4375, 0.5, 0.59375, 0.1875, 0.1875, 0.046875, 0.765625, 0.140625, 0.296875, 0.5625, 0.15625]","[0.7581737006137664, 0.2419266801228323, 0.5622454228253139, 0.05370712429621415, 0.3327837255914403, 0.3598361870582173, 0.12916529737997812, 0.31913075882720127, 0.4399799006824089, 0.5204819556361114, 0.6130724428250659, 0.18853422743251305, 0.2061687064234162, 0.057389510937001675, 0.780307166475636, 0.1414783658121581, 0.2974855056993596, 0.5630332625220957, 0.16955239990533721]"
+26,30.94,0.3094,14488128,35.72869318181818,37.311726734803656,BEST_adc_episode_026,"[0.6875, 0.25, 0.25, 0.5625, 0.5625, 0.5, 0.0625, 0.25, 0.6875, 0.375, 0.5, 0.21875, 0.34375, 0.140625, 0.59375, 0.4375, 0.3125, 0.296875, 0.5]","[0.7456347982525983, 0.3114646101767517, 0.26336197629319935, 0.6093973858418028, 0.6067916775668762, 0.5047389050194847, 0.10077763723270698, 0.2546151112832366, 0.7010756540119522, 0.39615320773996443, 0.5199864549573392, 0.22261315172532856, 0.3539892832895928, 0.15217249488472956, 0.6056111391346916, 0.44169800719321206, 0.31436647969644665, 0.30437972496838717, 0.5000067886249961]"
+27,27.24,0.2724,11443392,28.220170454545457,20.488165680473372,BEST_adc_episode_027,"[0.1875, 0.375, 0.6875, 0.5, 0.1875, 0.5, 0.375, 0.46875, 0.03125, 0.59375, 0.625, 0.25, 0.59375, 0.4375, 0.59375, 0.46875, 0.734375, 0.609375, 0.75]","[0.22286262293948422, 0.41761547935796406, 0.7247749763775426, 0.5193766020794508, 0.1898145021408576, 0.5602538817352112, 0.38965493119585853, 0.4728583615212735, 0.05908629363157567, 0.6170423947631406, 0.634761257325408, 0.2510359527516137, 0.6138749534155998, 0.43964065234720057, 0.6025285041456259, 0.48163578423653136, 0.738799105752362, 0.6173533853525461, 0.7577397023639314]"
+28,13.690000000000001,0.13690000000000002,11242368,27.724431818181817,34.05056481979559,BEST_adc_episode_028,"[0.125, 0.4375, 0.6875, 0.625, 0.5625, 0.5625, 0.3125, 0.65625, 0.59375, 0.375, 0.6875, 0.59375, 0.46875, 0.0625, 0.515625, 0.59375, 0.203125, 0.484375, 0.078125]","[0.14990413042176598, 0.499567678039238, 0.7459095696812721, 0.6522803294391476, 0.6006581191613676, 0.5783612061153424, 0.3728105902291372, 0.6587408033663805, 0.5968210400037818, 0.39175785289136855, 0.7028415980082336, 0.6232167036201832, 0.4719762168333647, 0.07598088675717063, 0.5287599733438473, 0.5992850458350666, 0.21507891139233215, 0.48737507178340334, 0.07818367928090472]"
+29,38.13999999999999,0.38139999999999996,15562368,38.37784090909091,36.60233996772458,BEST_adc_episode_029,"[0.5625, 0.5625, 0.1875, 0.5, 0.1875, 0.5625, 0.5625, 0.46875, 0.03125, 0.0625, 0.53125, 0.1875, 0.5, 0.21875, 0.421875, 0.609375, 0.078125, 0.5, 0.703125]","[0.5689930732658992, 0.5819183562276765, 0.21022197615497543, 0.5122822725812615, 0.22890890046450693, 0.5887161466397791, 0.5867695143883134, 0.4773591867251582, 0.059972721050101555, 0.07036262114007659, 0.5607243667702724, 0.18970135742324673, 0.5220669278808754, 0.22719008825243103, 0.43476204076864405, 0.6149312066058823, 0.08104227545028161, 0.5049297806228846, 0.705415623853934]"
+30,18.42,0.18420000000000003,13746816,33.90056818181818,46.268154922001074,BEST_adc_episode_030,"[0.5625, 0.6875, 0.5625, 0.375, 0.75, 0.6875, 0.4375, 0.5, 0.09375, 0.21875, 0.59375, 0.6875, 0.21875, 0.15625, 0.046875, 0.40625, 0.5625, 0.3125, 0.21875]","[0.577496871087258, 0.7054690709875491, 0.6082647493060366, 0.425733477954382, 0.7997523516442773, 0.6957049699319452, 0.4516097498064566, 0.509549336053908, 0.11183761661059077, 0.2220763145807243, 0.6224310490498709, 0.7077635609363195, 0.23286985998924525, 0.15675722433004072, 0.06199648397219792, 0.4092154289134458, 0.5653023408769873, 0.3176063824751568, 0.22520032751770697]"
+31,42.66,0.4266,16641792,41.03977272727273,39.79626143087682,BEST_adc_episode_031,"[0.3125, 0.75, 0.5, 0.5625, 0.125, 0.25, 0.1875, 0.25, 0.125, 0.59375, 0.1875, 0.40625, 0.0625, 0.40625, 0.6875, 0.53125, 0.3125, 0.1875, 0.28125]","[0.36286229179359464, 0.7910517424596337, 0.5439680150491928, 0.592590603754052, 0.15107580021417488, 0.29914736126895053, 0.22828329578121836, 0.2716009391093814, 0.12607703746942056, 0.6103086530216374, 0.19611498115655282, 0.4158203810130716, 0.08563285243660865, 0.417467996716756, 0.6933588160287073, 0.542829577484465, 0.31841821388354724, 0.19670082909631625, 0.28438929848782346]"
+32,40.5,0.405,15737472,38.80965909090909,42.57665411511565,BEST_adc_episode_032,"[0.3125, 0.375, 0.5625, 0.375, 0.1875, 0.5, 0.5, 0.03125, 0.75, 0.71875, 0.34375, 0.25, 0.0625, 0.484375, 0.0625, 0.1875, 0.703125, 0.15625, 0.421875]","[0.3569067049635647, 0.4046485295781274, 0.5628904258140921, 0.39017496729107887, 0.20964497082060174, 0.52749513790483, 0.5575161586035997, 0.05910935111679758, 0.7641634723157937, 0.721599796798192, 0.3459028840261735, 0.2648666914798087, 0.07606002040546365, 0.4846949969335701, 0.06889456178882669, 0.20243989237310325, 0.7181120121334039, 0.16450340518166923, 0.43518996105858554]"
+33,21.950000000000003,0.21950000000000003,12859200,31.71164772727273,33.02850995158688,BEST_adc_episode_033,"[0.5625, 0.125, 0.5, 0.375, 0.6875, 0.4375, 0.1875, 0.28125, 0.53125, 0.75, 0.59375, 0.4375, 0.21875, 0.5625, 0.546875, 0.671875, 0.0625, 0.34375, 0.3125]","[0.5935383698294882, 0.16538999142760485, 0.5051935691333265, 0.43705103031814235, 0.732002320975447, 0.45383716302570115, 0.1967880666195715, 0.3092186496348858, 0.5609431075160898, 0.7523503317075753, 0.60473519400266, 0.4447579970752885, 0.24251586274712508, 0.5754964192162746, 0.5493849893006277, 0.6756272710954215, 0.06365652652169941, 0.3531485343723436, 0.31622719379188113]"
+34,43.2,0.43200000000000005,14390784,35.48863636363636,34.19176976869285,BEST_adc_episode_034,"[0.125, 0.5625, 0.4375, 0.375, 0.1875, 0.375, 0.6875, 0.15625, 0.5, 0.5, 0.46875, 0.4375, 0.09375, 0.453125, 0.328125, 0.359375, 0.359375, 0.5625, 0.703125]","[0.16212321001086966, 0.5683689674104652, 0.4659216194437245, 0.396310595516552, 0.1931125723052728, 0.3839913586983972, 0.7176058627847585, 0.17133945643822013, 0.5196539635266146, 0.5254797231841974, 0.4838595261528536, 0.4604499871768091, 0.111646490869276, 0.45652587033280073, 0.33884327250567636, 0.36396294705820403, 0.37165365296584835, 0.5682391827304203, 0.7032836349450463]"
+35,44.769999999999996,0.4477,15801984,38.96875,41.036847767617,BEST_adc_episode_035,"[0.6875, 0.1875, 0.5, 0.5, 0.4375, 0.0625, 0.0625, 0.75, 0.4375, 0.1875, 0.6875, 0.4375, 0.125, 0.203125, 0.328125, 0.5625, 0.140625, 0.359375, 0.53125]","[0.7412267744545427, 0.19211677660408594, 0.5131563695814901, 0.5394942946315308, 0.48532643718966884, 0.08845383624478413, 0.07419064522907301, 0.7533776577522653, 0.4518489452101756, 0.18796037467763102, 0.7140290947793512, 0.4423639097599487, 0.12876652578020253, 0.21013122158968206, 0.3331504358519686, 0.5699930109576302, 0.14621800630422738, 0.3732033085401438, 0.5319914909659126]"
+36,37.46000000000001,0.3746000000000001,13415040,33.08238636363636,31.814819795589028,BEST_adc_episode_036,"[0.3125, 0.5625, 0.5, 0.1875, 0.4375, 0.25, 0.1875, 0.5, 0.40625, 0.5, 0.59375, 0.6875, 0.375, 0.5625, 0.1875, 0.53125, 0.453125, 0.25, 0.625]","[0.3447101505012113, 0.5838981924127399, 0.5403933631040821, 0.23443319690356285, 0.4812517587557209, 0.26331232070782834, 0.24716079392835416, 0.5082449002671847, 0.41143814886226215, 0.5204242264749827, 0.6064286264503532, 0.6997223462748335, 0.37530152055667443, 0.5778209548922094, 0.20239726472971417, 0.5411581234954986, 0.46830891555270204, 0.2617165957654559, 0.6308999571064315]"
+37,15.43,0.1543,11098944,27.370738636363633,20.141877353415815,BEST_adc_episode_037,"[0.25, 0.75, 0.6875, 0.125, 0.5, 0.375, 0.4375, 0.21875, 0.40625, 0.28125, 0.65625, 0.15625, 0.46875, 0.671875, 0.203125, 0.796875, 0.71875, 0.5625, 0.6875]","[0.2801990872614437, 0.7835054049536629, 0.7049100893075522, 0.17538644244083984, 0.5549783652507075, 0.377571777906766, 0.4748324760903627, 0.22645441506199476, 0.4314956181867264, 0.2829067994879273, 0.6581208238666288, 0.18368149776698373, 0.48578113527478184, 0.6840097076411455, 0.20542241548310958, 0.798355373040485, 0.720546468533511, 0.5702787790246696, 0.6913895329976759]"
+38,52.94,0.5294,17091648,42.14914772727273,43.269230769230774,BEST_adc_episode_038,"[0.4375, 0.6875, 0.6875, 0.625, 0.0625, 0.0625, 0.5625, 0.75, 0.125, 0.0625, 0.25, 0.40625, 0.0625, 0.15625, 0.375, 0.09375, 0.53125, 0.484375, 0.703125]","[0.47861893279311746, 0.7316206376803995, 0.6962840921972365, 0.6617968241636097, 0.07258772316023646, 0.0708178214826691, 0.6020492255010722, 0.7733434632108909, 0.15029889592143303, 0.07066261584657235, 0.2562737809282449, 0.4185947969308069, 0.06307934371167966, 0.16279989731830674, 0.3836653765283789, 0.10184011357107492, 0.5389149267999395, 0.49514218456193804, 0.7034525008100617]"
+39,12.250000000000005,0.12250000000000005,9530496,23.502840909090907,28.879774072081766,BEST_adc_episode_039,"[0.4375, 0.375, 0.75, 0.75, 0.125, 0.75, 0.25, 0.65625, 0.71875, 0.78125, 0.15625, 0.71875, 0.09375, 0.78125, 0.421875, 0.65625, 0.53125, 0.125, 0.109375]","[0.4534405261922781, 0.42175674334356467, 0.7544734881186071, 0.7958845661227978, 0.14930969936391475, 0.7771708644295204, 0.2947702245152878, 0.6684316414481555, 0.7303533387074765, 0.78383200482477, 0.1599933787160464, 0.7477042517794104, 0.11959154238334638, 0.7864889508615092, 0.4227206413453241, 0.671157891404743, 0.5423699665983726, 0.1301335734166033, 0.11208440305523618]"
+40,24.039999999999996,0.24039999999999995,16517376,40.73295454545455,53.42253899946208,BEST_adc_episode_040,"[0.0625, 0.5625, 0.375, 0.375, 0.75, 0.625, 0.0625, 0.25, 0.46875, 0.09375, 0.75, 0.34375, 0.65625, 0.296875, 0.046875, 0.234375, 0.21875, 0.109375, 0.453125]","[0.11547207295503396, 0.5974471823709775, 0.43354005177772154, 0.43300953673010045, 0.7791096172670037, 0.6478683585898132, 0.11911053502483382, 0.2771840567357244, 0.49525558388557817, 0.11494579700051342, 0.7560354210495888, 0.35623030160315844, 0.6782798890438955, 0.306722026729264, 0.05545611278639478, 0.23481760108369565, 0.23083438003573445, 0.1123076267747082, 0.45735833035052526]"
+41,27.949999999999996,0.27949999999999997,13521600,33.34517045454545,35.70467993544917,BEST_adc_episode_041,"[0.6875, 0.75, 0.0625, 0.4375, 0.5625, 0.6875, 0.1875, 0.0625, 0.625, 0.6875, 0.09375, 0.09375, 0.65625, 0.765625, 0.09375, 0.625, 0.296875, 0.140625, 0.453125]","[0.6973208994722108, 0.763505154709304, 0.09462902583057611, 0.4530658239141141, 0.5971563907015444, 0.7426782112415958, 0.22234356727229304, 0.07759346573852409, 0.6446135223536624, 0.7060980568371618, 0.0980219802794806, 0.1197037938544425, 0.6802282493019102, 0.7805099341636221, 0.10354874571852654, 0.6289969626806778, 0.31241353960072205, 0.14538916106278565, 0.4602935340445116]"
+42,34.41,0.34409999999999996,17415936,42.94886363636364,50.88084991931146,BEST_adc_episode_042,"[0.25, 0.4375, 0.1875, 0.4375, 0.625, 0.75, 0, 0.65625, 0.125, 0.125, 0.65625, 0.1875, 0.3125, 0.359375, 0.375, 0.0625, 0.265625, 0.265625, 0.265625]","[0.3114606469970353, 0.4611110622010755, 0.22798455225679945, 0.4687952098398968, 0.6596657371937032, 0.7545021695615488, 0.05663260816355842, 0.6734051033621834, 0.144642427255961, 0.13539809095610794, 0.6722586302338927, 0.2053086299026835, 0.33434902519283566, 0.37074514109713697, 0.3816302647130454, 0.0676967007708293, 0.2703833273476013, 0.2743720403369938, 0.28006453785039365]"
+43,19.53,0.1953,9758016,24.063920454545453,25.830419580419576,BEST_adc_episode_043,"[0.625, 0.75, 0.4375, 0.6875, 0.4375, 0.75, 0.5625, 0.65625, 0.0625, 0.5625, 0.40625, 0.46875, 0.0625, 0.578125, 0.28125, 0.625, 0.484375, 0.703125, 0.21875]","[0.6514336795395763, 0.7547832217405284, 0.4519256147995766, 0.6900966595620435, 0.4717545973507047, 0.7994291253613529, 0.5958030221823367, 0.6794159099241839, 0.0817543054727203, 0.5832210593429009, 0.42476001176447786, 0.4955647881005724, 0.08913048472166371, 0.5863984561688284, 0.2933475850423722, 0.6327426338421542, 0.49746245658826765, 0.7037778048150193, 0.22513744218094833]"
+44,11.939999999999994,0.11939999999999994,15576768,38.41335227272727,49.30406132329209,BEST_adc_episode_044,"[0.5625, 0.5625, 0.4375, 0.5625, 0.625, 0.375, 0.0625, 0.09375, 0.65625, 0.15625, 0.53125, 0.6875, 0.125, 0.078125, 0.125, 0.171875, 0.625, 0.109375, 0.515625]","[0.5790369434537883, 0.5848427736412851, 0.473569351849958, 0.6004084083722095, 0.6638009176243744, 0.3808679339921294, 0.07195832914758267, 0.11731314514836196, 0.6749700669318026, 0.15807761708446488, 0.5610675784977913, 0.6994170090528631, 0.13583436963116288, 0.0933700870004391, 0.13551406153941972, 0.17908026557776394, 0.6396529896821271, 0.11625797561826309, 0.523038005509525]"
+45,49.7,0.49700000000000005,17512128,43.18607954545455,37.42267348036579,BEST_adc_episode_045,"[0.3125, 0.0625, 0.1875, 0.125, 0.625, 0.625, 0.5, 0.34375, 0.59375, 0.125, 0.125, 0.375, 0.125, 0.078125, 0.390625, 0.53125, 0.625, 0.4375, 0.578125]","[0.31962933886096967, 0.11523949860153185, 0.22022608536654684, 0.16493643558217058, 0.672376043337358, 0.6413728853436598, 0.5381027472255853, 0.3534091933819736, 0.6080793456255168, 0.12872869382481905, 0.13523050929481453, 0.3830035093148902, 0.12863809378250243, 0.08614900280577976, 0.40559817507954193, 0.5380602729135602, 0.6342951443268166, 0.44646720724796896, 0.585232310890568]"
+46,27.139999999999997,0.2714,15800832,38.96590909090909,39.46678321678322,BEST_adc_episode_046,"[0.0625, 0.3125, 0.375, 0.125, 0.5625, 0.5625, 0.3125, 0.34375, 0.375, 0.46875, 0.125, 0.75, 0.53125, 0.4375, 0.609375, 0.046875, 0.203125, 0.4375, 0.421875]","[0.06466627388561792, 0.32155738126707956, 0.4084001995007673, 0.14296044234082833, 0.6184694436129938, 0.5826349715635979, 0.3728045913492934, 0.36867404145266075, 0.40531753888012073, 0.4732237414929397, 0.15224264489575842, 0.7557420097908895, 0.5590644628838901, 0.4509180669844561, 0.6219183418260227, 0.05612395202057969, 0.20702532789894335, 0.4397423215923577, 0.4312607028436598]"
+47,29.720000000000002,0.2972,15034176,37.075284090909086,28.79908552985476,BEST_adc_episode_047,"[0.125, 0.4375, 0, 0.625, 0.125, 0.4375, 0.625, 0.4375, 0.09375, 0.0625, 0.375, 0.65625, 0.53125, 0.25, 0.484375, 0.59375, 0.34375, 0.765625, 0.453125]","[0.1565153433238336, 0.48076392892024417, 0.053331116663652124, 0.6680664359193282, 0.16058439526517987, 0.46744615800320655, 0.6483675127199904, 0.44536018617746326, 0.11316278877271191, 0.06657103416762147, 0.38214582193909846, 0.6569535657368574, 0.5480321313856822, 0.26135323236400376, 0.48700923732182705, 0.5977369673384312, 0.3586666747038097, 0.7754436620109869, 0.4683895723524275]"
+48,11.2,0.11199999999999999,15864768,39.12357954545455,46.083243679397526,BEST_adc_episode_048,"[0.625, 0.75, 0.6875, 0.25, 0.1875, 0.75, 0.0625, 0.28125, 0.21875, 0.25, 0.125, 0.6875, 0.40625, 0.421875, 0.453125, 0.265625, 0.4375, 0.125, 0.078125]","[0.6595655251043807, 0.781844580786429, 0.6919754131034916, 0.30977723427495196, 0.2196647857298944, 0.7615489703414283, 0.09489734973844749, 0.2824435786893681, 0.24539869701411268, 0.2561909060976784, 0.13837370592365109, 0.6964644155017895, 0.40948820383311046, 0.4249816100669799, 0.4633926764492405, 0.2784650920861028, 0.44038150762159534, 0.12851462940016, 0.09334077005040495]"
+49,39.92,0.3992,15309504,37.75426136363637,38.192576654115115,BEST_adc_episode_049,"[0.625, 0.625, 0.1875, 0.6875, 0.4375, 0.25, 0.0625, 0.09375, 0.46875, 0.6875, 0.125, 0.40625, 0.15625, 0.328125, 0.234375, 0.140625, 0.78125, 0.515625, 0.375]","[0.6514394621219536, 0.6638856540759274, 0.18877961925998882, 0.7357963899359301, 0.44976138455820824, 0.2780797829978645, 0.08159989150227082, 0.10790735350538955, 0.47866334076114997, 0.717974930550965, 0.14496320907608773, 0.41665269298880886, 0.16565097382453703, 0.33283992317596117, 0.24239950289761497, 0.14153324276907903, 0.7911033192316463, 0.5173921760733542, 0.3791382075735096]"
+50,26.11,0.2611,15494400,38.21022727272727,35.07598171059709,BEST_adc_episode_050,"[0.375, 0.125, 0.5, 0.375, 0.3125, 0.4375, 0.3125, 0.15625, 0.0625, 0.625, 0.5, 0.625, 0.25, 0.1875, 0.296875, 0.671875, 0.546875, 0.46875, 0.3125]","[0.42355211027250367, 0.1515689879848761, 0.5054858237488513, 0.400924349441621, 0.3453495184416325, 0.45730952418396126, 0.33147501980791283, 0.18544188135850148, 0.08362671260440666, 0.6347046722166163, 0.500615342596658, 0.6303691875557929, 0.2561852267087312, 0.19491106946529257, 0.3018790599240663, 0.6787970629114254, 0.5516863331204428, 0.4738309963701837, 0.3163584379948042]"
+51,40.09,0.40090000000000003,20286144,50.02698863636363,35.10623991393222,BEST_adc_episode_051,"[0.1875, 0.625, 0.125, 0, 0.125, 0.125, 0.75, 0.0625, 0.125, 0.09375, 0.03125, 0.40625, 0.1875, 0.078125, 0.734375, 0.5, 0.484375, 0.609375, 0.484375]","[0.18800900353876618, 0.6755216830305663, 0.18749495531187735, 0.06083189664508248, 0.14861359980736924, 0.14402669704093002, 0.7538611119504812, 0.08451257279744209, 0.15049329149661006, 0.11621893300476258, 0.05856807238955557, 0.42918803376166154, 0.20117183095049077, 0.09253731220402928, 0.446525658172589, 0.5043462145295977, 0.23715794192312478, 0.36560054249360524, 0.44503370999805536]"
+52,31.930000000000003,0.31930000000000003,14503680,35.76704545454545,38.35059171597633,BEST_adc_episode_052,"[0.6875, 0.125, 0.6875, 0.125, 0.3125, 0.3125, 0.625, 0.21875, 0.46875, 0.4375, 0.59375, 0.40625, 0.34375, 0.0625, 0.375, 0.625, 0.5, 0.1875, 0.390625]","[0.73395286418096, 0.16754066640387438, 0.7209570875915422, 0.1681884977958772, 0.31805739743848527, 0.3261445159036702, 0.6292414021702366, 0.248810432493258, 0.4792354861711528, 0.4384757887530725, 0.5965449424863919, 0.4259641120144352, 0.35130781855239707, 0.07173720469177929, 0.3750945991709057, 0.6300753101441874, 0.5090481264490533, 0.2021949818677921, 0.3949932666219936]"
+53,29.959999999999997,0.2996,16508736,40.71164772727273,23.164335664335663,BEST_adc_episode_053,"[0.0625, 0, 0.0625, 0.5, 0.0625, 0.375, 0.5625, 0.28125, 0.28125, 0.3125, 0.375, 0.25, 0.71875, 0.15625, 0.765625, 0.578125, 0.625, 0.625, 0.578125]","[0.07881526191243651, 0.0581099494983824, 0.07685621414384462, 0.5211594269354375, 0.08719508495416091, 0.3774155458181325, 0.5665322860540887, 0.29060965698154273, 0.3090113138959829, 0.32226720572323225, 0.40031967156501, 0.259525855025775, 0.7197216217275502, 0.16384841023206598, 0.7728029169673067, 0.5881242118891469, 0.635289124991967, 0.6263502942848841, 0.5875956461789364]"
+54,14.270000000000005,0.14270000000000005,11627136,28.673295454545457,28.994082840236686,BEST_adc_episode_054,"[0.4375, 0.3125, 0.6875, 0.6875, 0.25, 0.5, 0.375, 0.09375, 0.34375, 0.59375, 0.5, 0.75, 0.46875, 0.359375, 0.5, 0.078125, 0.65625, 0.734375, 0.109375]","[0.4425752438052989, 0.36494003584685963, 0.7289142846129906, 0.6890354766031777, 0.2590263531518744, 0.512798962601378, 0.43565528501968265, 0.11295449153890048, 0.371572608862411, 0.6071515343788091, 0.5266792865649865, 0.7783251312568094, 0.4745081728708682, 0.3723932268298414, 0.5121524298030373, 0.0898868193627575, 0.6624705655138268, 0.7470741523905624, 0.12280219755872421]"
+55,28.110000000000003,0.2811,14832576,36.578125,32.45360408821947,BEST_adc_episode_055,"[0.25, 0.0625, 0.125, 0.5, 0.75, 0.125, 0.25, 0.5625, 0.5, 0.375, 0.5, 0.5625, 0.6875, 0.640625, 0.265625, 0.09375, 0.484375, 0.71875, 0.203125]","[0.2881963307079102, 0.1104892864757921, 0.16849750909519873, 0.5367563123476546, 0.7849804989609749, 0.1704259074936918, 0.306443481838354, 0.5864001903558373, 0.5224869908662115, 0.37845087052637466, 0.5045948772394611, 0.562711619740192, 0.7169237564046015, 0.6554335398429225, 0.276121001359766, 0.10848040188263217, 0.4853446020824535, 0.7244655759959249, 0.21062733061768502]"
+56,28.15,0.2815,13209984,32.57670454545455,29.508472296933835,BEST_adc_episode_056,"[0.5625, 0.5625, 0.6875, 0.125, 0.5, 0.6875, 0.125, 0.25, 0.46875, 0.46875, 0.4375, 0.03125, 0.15625, 0.6875, 0.203125, 0.78125, 0.25, 0.4375, 0.578125]","[0.5992384143225589, 0.5674000072467906, 0.7310437248472665, 0.1646068305972821, 0.5491585930696692, 0.7013963933225553, 0.17530689631542687, 0.2519905161138883, 0.48994971602171167, 0.47483611184660063, 0.44751433923879796, 0.054957638872863084, 0.16602815075432462, 0.6927686114101376, 0.21162097598716909, 0.7846114776880234, 0.25773763373380876, 0.4458372220533706, 0.5843895476883171]"
+57,24.39,0.2439,12697920,31.313920454545453,31.72404518558365,BEST_adc_episode_057,"[0.4375, 0.75, 0.3125, 0.75, 0.4375, 0, 0.1875, 0.59375, 0.53125, 0.375, 0.65625, 0.1875, 0.59375, 0.703125, 0.46875, 0.6875, 0.328125, 0.078125, 0.359375]","[0.44385705038468126, 0.7875702960586219, 0.35364266433446934, 0.7770660099820872, 0.488505447726366, 0.05617539722615119, 0.20699706139651652, 0.6128814794911253, 0.5448784552036764, 0.39171425844378344, 0.6847286883290012, 0.1927101134432247, 0.6241332349382955, 0.7034357995643504, 0.479058328082492, 0.69693435461019, 0.33887001449136267, 0.08733987535516755, 0.3656507467351479]"
+58,16.220000000000002,0.1622,14776128,36.43892045454545,43.2994889725659,BEST_adc_episode_058,"[0.75, 0.25, 0.25, 0.625, 0.4375, 0.375, 0.25, 0.6875, 0.71875, 0.5625, 0.09375, 0.5, 0.3125, 0.59375, 0.4375, 0.25, 0.1875, 0.203125, 0.265625]","[0.7774167370975027, 0.3023063253009814, 0.3099406937696728, 0.6667681612210695, 0.43861810382421673, 0.4259125006027981, 0.2818536904409773, 0.70034671336407, 0.7335789047471434, 0.5922567218590961, 0.11441707720191466, 0.5223521318674057, 0.32743971258297727, 0.5974686304677951, 0.4430541862687923, 0.2555596795896627, 0.192190618100457, 0.21235501732941306, 0.2737205596592609]"
+59,44.07,0.4407,16201152,39.953125,40.74435180204411,BEST_adc_episode_059,"[0.25, 0.25, 0.5, 0.3125, 0.4375, 0.4375, 0.0625, 0.59375, 0.5, 0.375, 0.09375, 0.65625, 0.09375, 0.25, 0.5, 0.265625, 0.453125, 0.09375, 0.625]","[0.2613674028919649, 0.2995725581667028, 0.5421462518682525, 0.36460011248375696, 0.47176498127196287, 0.4911317090798929, 0.09765284484466792, 0.6024154191968284, 0.5005153921386967, 0.40477156464559455, 0.11787491746541218, 0.6668449894994817, 0.12446335338480623, 0.25547871213817575, 0.5135194160432079, 0.27969724120698713, 0.46665139660306526, 0.09640282249360371, 0.6335171710289679]"
+60,43.489999999999995,0.43489999999999995,15215616,37.52272727272727,39.81307154384077,BEST_adc_episode_060,"[0.375, 0.3125, 0.5, 0.5625, 0.3125, 0.375, 0.1875, 0.59375, 0.625, 0.15625, 0.625, 0.3125, 0.125, 0.484375, 0.109375, 0.546875, 0.46875, 0.09375, 0.46875]","[0.3865583275767032, 0.33121269004003395, 0.524002035505402, 0.5889921086323936, 0.33211413992288985, 0.3776514836685928, 0.19840313280921196, 0.6145845634735864, 0.654495122740898, 0.17020295168435245, 0.6434716862022283, 0.33468865172651663, 0.14987463947107904, 0.4909701515364921, 0.11364451916537521, 0.5485152515621086, 0.4712365461591394, 0.09680990925151717, 0.47436185086404986]"
+61,36.35,0.3635,15424704,38.03835227272727,37.019230769230774,BEST_adc_episode_061,"[0.3125, 0.75, 0.4375, 0.75, 0.1875, 0.0625, 0.125, 0.5, 0.1875, 0.28125, 0.375, 0.65625, 0.125, 0.453125, 0.34375, 0.640625, 0.59375, 0.234375, 0.078125]","[0.32470802154986755, 0.768711570784606, 0.4661888296926329, 0.7860669658328138, 0.23895662420524125, 0.11276663244320843, 0.17030045809958733, 0.5041976373658252, 0.1955099576921052, 0.2962375644265433, 0.3955073219166827, 0.6719955630575725, 0.13622121904073675, 0.4662006397666049, 0.34530247334411684, 0.6429938735683176, 0.6011157265946434, 0.23748692817671524, 0.09154268092060333]"
+62,15.180000000000005,0.15180000000000005,11874816,29.28409090909091,26.40532544378698,BEST_adc_episode_062,"[0.125, 0.5625, 0.5, 0.4375, 0.1875, 0.75, 0.625, 0.53125, 0.40625, 0.5, 0.375, 0.21875, 0.4375, 0.5, 0.421875, 0.703125, 0.546875, 0.53125, 0.1875]","[0.13912834522874035, 0.5931118999070227, 0.5300280274814632, 0.47288483771661544, 0.21214038539194652, 0.7935666425823017, 0.6583459665859089, 0.5562595133356932, 0.42652368358182596, 0.5080155782059517, 0.38064317136051024, 0.22237552316283643, 0.43889646533092835, 0.5152617542938771, 0.4273294547240984, 0.7137718758026614, 0.5534604929149219, 0.5353907484394245, 0.19995354707396448]"
+63,27.990000000000002,0.27990000000000004,16464960,40.60369318181819,26.469203873050027,BEST_adc_episode_063,"[0.25, 0, 0.125, 0.625, 0.0625, 0, 0.625, 0.53125, 0.53125, 0.34375, 0.25, 0.0625, 0.75, 0.296875, 0.734375, 0.59375, 0.359375, 0.625, 0.21875]","[0.265807124033095, 0.05506811462007555, 0.1409359316663223, 0.6388019874790384, 0.11619260536793714, 0.05688829016295505, 0.6360759921516822, 0.5452211838985681, 0.5393574302254824, 0.36762604082278333, 0.2544603110258593, 0.09234584222046266, 0.7671058776885664, 0.30257008153530307, 0.7455394305321773, 0.6050523764754262, 0.37197689093743586, 0.6282496760192731, 0.22542142011045477]"
+64,29.110000000000003,0.2911,12464640,30.738636363636363,27.064281871974178,BEST_adc_episode_064,"[0.4375, 0.625, 0.3125, 0.75, 0.125, 0.4375, 0.0625, 0.15625, 0.25, 0.5625, 0.28125, 0.71875, 0.75, 0.59375, 0.625, 0.21875, 0.28125, 0.6875, 0.765625]","[0.4506518887979997, 0.6712308409997796, 0.3443683946084335, 0.766101973192642, 0.1826459020054421, 0.4662600131314883, 0.11459890578149239, 0.1615736906078435, 0.27450187757168537, 0.57844961123808, 0.301146262148013, 0.7239797964565163, 0.7802094729911586, 0.5986487474781911, 0.6374060472506159, 0.23093285545366882, 0.2925286567866153, 0.7029608662724043, 0.7781414995841799]"
+65,25.8,0.258,15566400,38.387784090909086,22.969338353953738,BEST_adc_episode_065,"[0.375, 0.25, 0.25, 0.0625, 0.5, 0.0625, 0.0625, 0.6875, 0.375, 0.25, 0.5625, 0.09375, 0.5, 0.78125, 0.796875, 0.171875, 0.75, 0.546875, 0.21875]","[0.4347214780764193, 0.25994940237121844, 0.31034322356432775, 0.06615967828778538, 0.5564248195023238, 0.10672782475822765, 0.0943488592409702, 0.7026897002319175, 0.39479925459939147, 0.26872157748891184, 0.5797052817754201, 0.10980177885168749, 0.5070109605690272, 0.7933251747401684, 0.7973602950628577, 0.18411304466936393, 0.7613148031226136, 0.5496904157356909, 0.2222737752895238]"
+66,28.52,0.2852,13003776,32.06818181818182,24.3006993006993,BEST_adc_episode_066,"[0.125, 0.4375, 0.5, 0.1875, 0.25, 0.125, 0.4375, 0.5625, 0.71875, 0.625, 0.28125, 0.46875, 0.78125, 0.4375, 0.5, 0.46875, 0.625, 0.296875, 0.6875]","[0.13548895136059544, 0.4671376839977616, 0.5097258765826993, 0.2447116059797085, 0.2705033811121331, 0.15828343243459742, 0.4710491629956122, 0.5775643071915251, 0.7410991458374848, 0.6250760184423306, 0.29527911682900987, 0.48001594444730605, 0.7956751243308553, 0.44257317802283475, 0.5109418820443481, 0.473971521974392, 0.6342091342589606, 0.30430682713972296, 0.7011291883850548]"
+67,11.209999999999997,0.11209999999999998,13254336,32.68607954545455,31.058364712210867,BEST_adc_episode_067,"[0.3125, 0.1875, 0.375, 0.25, 0.625, 0.75, 0.4375, 0.15625, 0.5, 0.5, 0.09375, 0.78125, 0.09375, 0.78125, 0.625, 0.171875, 0.234375, 0.5625, 0.5]","[0.33672014767925573, 0.21104750738963254, 0.37837777058787164, 0.2698867944848556, 0.6415274899734623, 0.7662331771965588, 0.48500795502885696, 0.17141686782655655, 0.5304448674021514, 0.5241025128898474, 0.10247957863428041, 0.7835817284214714, 0.11751493483897894, 0.7907817448009571, 0.6310402443823193, 0.17886475236600732, 0.2348858577124604, 0.571395636838703, 0.5152057310331871]"
+68,20.96,0.2096,12671424,31.248579545454547,24.690693921463154,BEST_adc_episode_068,"[0.375, 0.5625, 0.375, 0.1875, 0.6875, 0.25, 0.6875, 0.40625, 0.21875, 0.25, 0.4375, 0.3125, 0.34375, 0.671875, 0.5, 0.703125, 0.59375, 0.390625, 0.53125]","[0.4173691867227466, 0.6220713603132546, 0.43034317616200135, 0.19735257195703068, 0.7357323856505126, 0.28236888540835625, 0.6922794115129289, 0.4364152081082675, 0.22681895590940454, 0.2669271621122997, 0.45456714863547903, 0.312771309069971, 0.3544260990567498, 0.6805204771258128, 0.5030042899942063, 0.7130656592429772, 0.5981997810701098, 0.4011891395342765, 0.541078303173782]"
+69,22.49,0.2249,13397760,33.03977272727273,44.85610543302851,BEST_adc_episode_069,"[0.75, 0.375, 0.3125, 0.4375, 0.625, 0.1875, 0.4375, 0.75, 0.625, 0.71875, 0.75, 0.46875, 0.5, 0.65625, 0.109375, 0.140625, 0.125, 0.421875, 0.078125]","[0.7690582219300532, 0.42387014639080545, 0.35473671705507315, 0.496310124608952, 0.6436174087973895, 0.2009535619026482, 0.4819495719860104, 0.7654940010100387, 0.6561628413523866, 0.7253026267065272, 0.7740104581960987, 0.4826382250323038, 0.5204008709966523, 0.662925331634904, 0.11280167493476802, 0.1412182532359732, 0.13282295287840212, 0.42483257683833664, 0.08864446491267465]"
+70,26.31,0.2631,12321216,30.38494318181818,27.77030661646046,BEST_adc_episode_070,"[0, 0, 0.5625, 0.0625, 0.75, 0.625, 0.625, 0.78125, 0.46875, 0.4375, 0.53125, 0.53125, 0.5625, 0.515625, 0.65625, 0.140625, 0.3125, 0.578125, 0.703125]","[0.06249406497227063, 0.05577931119463851, 0.604355893317437, 0.07797667572276568, 0.7976689425466166, 0.6815591696073432, 0.626143906950801, 0.7909596604120601, 0.49247494789561563, 0.46543308076162737, 0.5498757092080468, 0.5474857997964239, 0.571095614307523, 0.5255391843010568, 0.6654138285618237, 0.14226877975975008, 0.32398846395295217, 0.5922770455235844, 0.7127334998339229]"
+71,46.650000000000006,0.4665000000000001,17612928,43.434659090909086,42.781737493275955,BEST_adc_episode_071,"[0.5625, 0.5625, 0.3125, 0.0625, 0.0625, 0.75, 0.3125, 0.15625, 0.53125, 0.25, 0.09375, 0.40625, 0.28125, 0.515625, 0.203125, 0.59375, 0.1875, 0.078125, 0.65625]","[0.6046519196232277, 0.5824152467842618, 0.3614835823864766, 0.08527591939914858, 0.11021445589085721, 0.7522371885364356, 0.3240739233101429, 0.17727894848249637, 0.5516316787410065, 0.28082641148505305, 0.09965884305178256, 0.4319054343475445, 0.30474366085218546, 0.5231578420312267, 0.2145338704591181, 0.6065296735142534, 0.20256454405584107, 0.09036368201942677, 0.6699273722841701]"
+72,24.139999999999993,0.24139999999999995,10802304,26.639204545454547,23.473641742872513,BEST_adc_episode_072,"[0.3125, 0.0625, 0.75, 0.4375, 0.5, 0.5, 0.4375, 0.75, 0.46875, 0.4375, 0.4375, 0.5, 0.4375, 0.46875, 0.578125, 0.640625, 0.609375, 0.390625, 0.453125]","[0.3174864576838954, 0.10012926783733678, 0.7502814099362191, 0.48616759251040625, 0.543048531181832, 0.545949886615712, 0.4808179734596435, 0.7574578469250604, 0.49494196759521997, 0.4460948037284688, 0.4384344150706813, 0.5030080724018382, 0.4551199493912304, 0.4828520388717905, 0.583039142502322, 0.6498563310701061, 0.6141516802760366, 0.4019591772908293, 0.4630864204013286]"
+73,13.52,0.1352,8918208,21.992897727272727,18.265868746637977,BEST_adc_episode_073,"[0.625, 0.5, 0.75, 0.25, 0.375, 0.0625, 0.625, 0.25, 0.75, 0.71875, 0.4375, 0.6875, 0.78125, 0.640625, 0.59375, 0.328125, 0.5, 0.78125, 0.546875]","[0.6356894280859596, 0.5580497431277749, 0.7948720074264344, 0.29594357056562776, 0.42159198355536603, 0.12273639821643383, 0.6585510137244469, 0.2617499979409937, 0.7550619478110235, 0.7243372037773178, 0.457497372151708, 0.6929064652949454, 0.7992825602454382, 0.656044089555633, 0.5997796492939792, 0.3358070212999181, 0.5088874874234586, 0.792387227111801, 0.5572542882854381]"
+74,26.559999999999995,0.26559999999999995,12345408,30.444602272727273,30.7557826788596,BEST_adc_episode_074,"[0.0625, 0.4375, 0.75, 0.25, 0.6875, 0.6875, 0.5, 0.21875, 0.78125, 0.4375, 0.09375, 0.28125, 0.375, 0.765625, 0.46875, 0.265625, 0.171875, 0.71875, 0.34375]","[0.07598339790863617, 0.4741344804351668, 0.7789458779528476, 0.30400830334284884, 0.7304383427067863, 0.724075827831347, 0.5484901112006622, 0.23452375025412064, 0.789961138334505, 0.45466120194559795, 0.10068234735212933, 0.3076851030704208, 0.3838471506397184, 0.7756560920453653, 0.47916634871270297, 0.26591089791794603, 0.1850250070237607, 0.7297611251729073, 0.3501776518972128]"
+75,30.479999999999997,0.30479999999999996,14658624,36.14914772727273,33.56643356643357,BEST_adc_episode_075,"[0.375, 0.1875, 0.0625, 0.4375, 0.5625, 0.625, 0.4375, 0.25, 0.71875, 0.1875, 0.0625, 0.75, 0.125, 0.6875, 0.390625, 0.109375, 0.59375, 0.453125, 0.34375]","[0.40203198882313407, 0.22230649475452338, 0.08427609546859893, 0.4468862699541293, 0.6035904146607525, 0.6665998097674776, 0.4517721734313984, 0.2792659352339747, 0.7257284289099795, 0.20446171343488395, 0.06705063918748098, 0.7748706381893097, 0.1468960528502687, 0.6884441206001137, 0.39182476245724696, 0.1177236903434025, 0.6015321753425563, 0.4649293734512022, 0.34658034025455087]"
+76,47.24,0.47240000000000004,16515648,40.72869318181818,44.40559440559441,BEST_adc_episode_076,"[0.5625, 0.4375, 0.5625, 0.25, 0.375, 0.375, 0.0625, 0.3125, 0.65625, 0.5, 0.28125, 0.3125, 0.25, 0.546875, 0.34375, 0.265625, 0.140625, 0.25, 0.5625]","[0.580275443666888, 0.45603050267320977, 0.6034822430477577, 0.2671633682707725, 0.3862022040001404, 0.40786335832724485, 0.10643847326835515, 0.3428233278813146, 0.6652590132899084, 0.5095778616182414, 0.29105721190608813, 0.31906112848628465, 0.2593686550844569, 0.5506701976888593, 0.3578894464289104, 0.28116516997357854, 0.14932893861981533, 0.26135322448510356, 0.5678928915687365]"
+77,27.470000000000006,0.27470000000000006,15379200,37.92613636363637,40.15263582571275,BEST_adc_episode_077,"[0.25, 0.375, 0.4375, 0.3125, 0.6875, 0.5, 0.625, 0.28125, 0.28125, 0.09375, 0.09375, 0.75, 0.71875, 0.21875, 0.5625, 0.328125, 0.484375, 0.109375, 0.28125]","[0.2726195035630604, 0.40035310074791025, 0.4637532779943629, 0.3408785844096989, 0.7389387559912964, 0.5610797782003792, 0.6678725676643071, 0.2931438406766801, 0.28611446965591303, 0.12278228650577287, 0.10169135463087559, 0.7618307476521545, 0.7483628055334678, 0.23123759065231037, 0.574049465815815, 0.334550472304037, 0.499792221873717, 0.12211747043036078, 0.2884944511891938]"
+78,22.230000000000004,0.22230000000000005,13340160,32.89772727272727,26.617132867132863,BEST_adc_episode_078,"[0.0625, 0.375, 0.3125, 0.25, 0.3125, 0.625, 0.75, 0.09375, 0.21875, 0.625, 0.1875, 0.71875, 0.65625, 0.203125, 0.390625, 0.765625, 0.25, 0.671875, 0.546875]","[0.06569161599854888, 0.38160676424512474, 0.36075727303598504, 0.2862251682016026, 0.34151043007842674, 0.6559431304578697, 0.7524228214776744, 0.1221417186134152, 0.24963297372113913, 0.6425408129926583, 0.194255687317011, 0.7328380566673539, 0.6629008968852984, 0.20785651857674334, 0.3932973499027159, 0.7682198480313878, 0.25362459263046977, 0.6719420314446851, 0.5510377454692633]"
+79,54.76,0.5476,15231168,37.56107954545455,39.298682087143625,BEST_adc_episode_079,"[0.6875, 0.125, 0.625, 0.1875, 0.625, 0.125, 0.5, 0.59375, 0.6875, 0.15625, 0.21875, 0.34375, 0.09375, 0.171875, 0.171875, 0.578125, 0.296875, 0.578125, 0.625]","[0.716850785309836, 0.1857703150567971, 0.6405538730980397, 0.22599087600715184, 0.6454520790376663, 0.13323302078693133, 0.516300167283164, 0.619224099490874, 0.7132309220664679, 0.180288519665701, 0.2193668934874443, 0.352865281179751, 0.12399842765077583, 0.18268709772209324, 0.18328389001763654, 0.5816029525692782, 0.3015512327446843, 0.590244565384988, 0.6347077706607407]"
+80,27.070000000000004,0.27070000000000005,12575232,31.011363636363637,29.454679935449168,BEST_adc_episode_080,"[0.6875, 0.4375, 0.625, 0.4375, 0.75, 0.1875, 0.1875, 0.21875, 0.15625, 0.59375, 0.125, 0.65625, 0.40625, 0.65625, 0.234375, 0.5, 0.609375, 0.125, 0.765625]","[0.7060101291823047, 0.471190888109026, 0.6656736583492996, 0.47517651782332365, 0.7721888224338462, 0.20858927837366276, 0.1896500226803439, 0.24640232049999736, 0.173617537968747, 0.6166469414356289, 0.13161486287120286, 0.6569141269308759, 0.41164051696103887, 0.6711830972155858, 0.24180236785190104, 0.5069778130113672, 0.6210862993614761, 0.131820570968194, 0.7810959098334025]"
+81,14.459999999999996,0.14459999999999995,10529856,25.967329545454543,25.393356643356647,BEST_adc_episode_081,"[0.75, 0.625, 0.375, 0.5, 0.125, 0.6875, 0.625, 0.15625, 0.25, 0.625, 0.75, 0.1875, 0.71875, 0.296875, 0.484375, 0.484375, 0.734375, 0.390625, 0.46875]","[0.7558843249618628, 0.6501162501932318, 0.43672325946199275, 0.5086804990541707, 0.1789018779650658, 0.7462481187885762, 0.6743811134078528, 0.18712920875934966, 0.25321938840026553, 0.6558584064095603, 0.7805013627304618, 0.2069302305048077, 0.7440401538468214, 0.30242339056395856, 0.4890575409772929, 0.4860475886654647, 0.7387100621961221, 0.39379089129533834, 0.4690107272789891]"
+82,37.18,0.3718,14302080,35.26988636363637,26.442307692307693,BEST_adc_episode_082,"[0.5, 0.0625, 0.6875, 0.6875, 0.1875, 0.125, 0.625, 0.25, 0.1875, 0.5625, 0.09375, 0.28125, 0.21875, 0.59375, 0.671875, 0.046875, 0.640625, 0.78125, 0.65625]","[0.5507765996795976, 0.09317020368580628, 0.7281693750833126, 0.7431345772653415, 0.19448851638390854, 0.16459799458593233, 0.6737374464518573, 0.27128355079915983, 0.21580834770285598, 0.5808253606531822, 0.11295452440739581, 0.3017925373970344, 0.2464141896656192, 0.5998989166812776, 0.6724500826764727, 0.053706564221749076, 0.6436782577993795, 0.7920026170360069, 0.6669623934972575]"
+83,26.919999999999998,0.2692,12125952,29.90340909090909,20.236013986013987,BEST_adc_episode_083,"[0.5625, 0.1875, 0.125, 0.5625, 0.5, 0.125, 0.6875, 0.53125, 0.1875, 0.40625, 0.1875, 0.625, 0.75, 0.375, 0.796875, 0.75, 0.140625, 0.6875, 0.609375]","[0.5806734671994213, 0.2149785368768056, 0.18291484513937464, 0.6215423555605805, 0.5072507659929262, 0.14053408232635586, 0.7478287564166253, 0.5469075728042965, 0.19274865481691825, 0.42463356973341876, 0.21638202471056192, 0.6487798136091213, 0.7685054827064811, 0.37989959088125624, 0.7998788516775889, 0.7564310959722943, 0.14581482194022916, 0.6962283792716215, 0.6156137577668918]"
+84,52.31,0.5231,18796032,46.35227272727273,57.92764927380312,BEST_adc_episode_084,"[0.75, 0.375, 0.125, 0.375, 0.4375, 0.375, 0.6875, 0.125, 0.0625, 0.34375, 0.4375, 0.625, 0.6875, 0.171875, 0.140625, 0.046875, 0.15625, 0.21875, 0.515625]","[0.7573175559326383, 0.4147394046688682, 0.13910708788246118, 0.4282018927095255, 0.4490127763539931, 0.4335648939024423, 0.6877236409880206, 0.1336595031173764, 0.07626483534231178, 0.35388563191144806, 0.45805727606556984, 0.6419307687081485, 0.715790416106081, 0.18363220920015758, 0.14586034948200416, 0.054560563712137286, 0.1589424648323825, 0.2294196325767054, 0.5219211386977667]"
+85,47.440000000000005,0.47440000000000004,16406208,40.45880681818182,49.858795051102746,BEST_adc_episode_085,"[0.5625, 0.4375, 0.125, 0.75, 0.0625, 0.5, 0.375, 0.28125, 0.75, 0.4375, 0.25, 0.40625, 0.21875, 0.125, 0.390625, 0.078125, 0.25, 0.390625, 0.28125]","[0.5917578898161007, 0.45194839598120057, 0.16308213508456193, 0.7917746428142679, 0.09534569455605152, 0.5407724480605763, 0.41128811872126403, 0.30453871468888066, 0.7538464272838938, 0.4588992452961245, 0.2668874904347365, 0.41878085949107213, 0.24559774534451612, 0.13935003917610148, 0.39746568044991015, 0.08495860076735559, 0.2600735552123229, 0.39526839595550867, 0.29153116665999523]"
+86,23.270000000000003,0.23270000000000002,15092352,37.21875,34.07073695535234,BEST_adc_episode_086,"[0.5625, 0.3125, 0.1875, 0.3125, 0.5625, 0.6875, 0.3125, 0.625, 0.03125, 0.21875, 0.1875, 0.5625, 0.3125, 0.40625, 0.546875, 0.703125, 0.265625, 0.21875, 0.609375]","[0.5745787105607955, 0.34574349124037496, 0.21761030794485337, 0.35654527252878065, 0.6115191071428041, 0.7404238265033342, 0.3691213201121535, 0.6498324350918222, 0.05787514966419161, 0.2269300808471392, 0.20747990519544554, 0.5901455121827267, 0.3341154342097071, 0.4162284373256665, 0.5529113668494419, 0.7049899318744615, 0.27006058959872314, 0.22166677752714212, 0.6131790533244685]"
+87,38.82,0.3882,16670016,41.109375,42.61363636363637,BEST_adc_episode_087,"[0.4375, 0.0625, 0.625, 0.0625, 0.4375, 0.4375, 0.5625, 0.78125, 0.71875, 0.21875, 0.0625, 0.0625, 0.65625, 0.265625, 0.125, 0.453125, 0.25, 0.3125, 0.703125]","[0.49556643257793964, 0.12061192004007584, 0.6816731979832094, 0.07274526611216003, 0.4703877022557257, 0.4977966781791387, 0.6137795576250092, 0.7960519901805727, 0.7222454485173487, 0.2198921388054858, 0.06359054777801593, 0.08452887094189426, 0.6801520019043021, 0.26794204374442965, 0.13327861684725303, 0.4649937226227409, 0.2560749564333891, 0.31541174112439685, 0.7097341845619448]"
+88,37.81,0.37810000000000005,14755392,36.38778409090909,30.227945131791284,BEST_adc_episode_088,"[0.5, 0.5625, 0.5, 0.1875, 0.0625, 0.4375, 0.5, 0.28125, 0.0625, 0.5625, 0.125, 0.46875, 0.6875, 0.546875, 0.265625, 0.703125, 0.484375, 0.4375, 0.28125]","[0.5373554984753157, 0.6191514295655731, 0.5368884159114635, 0.19786375667075315, 0.12421691568146401, 0.49609979805170695, 0.5384754943560646, 0.3069010793953027, 0.08157226650103272, 0.570759743386895, 0.1518276518496151, 0.47292038193705416, 0.7077064866855046, 0.5518006684994647, 0.2711713632156184, 0.7168302006898574, 0.48993564191757494, 0.448524210156712, 0.29169664649416394]"
+89,52.61,0.5261,19752768,48.71164772727273,53.11323292092523,BEST_adc_episode_089,"[0.625, 0.375, 0, 0.5, 0.0625, 0.125, 0.25, 0.4375, 0.65625, 0.15625, 0.65625, 0.15625, 0.375, 0.1875, 0.28125, 0.171875, 0.25, 0.140625, 0.421875]","[0.6760200886957543, 0.43234661619460135, 0.05390563749583401, 0.5219598496588628, 0.07875188923234043, 0.17816944176792962, 0.28712378869689015, 0.44433228121824536, 0.6779154438747313, 0.18025844410549477, 0.6642995106620091, 0.18663298241994378, 0.39980335650980375, 0.2023463557610094, 0.28147800618647056, 0.18661852886270752, 0.250264220016874, 0.14192760928756176, 0.4371018200102499]"
+90,35.699999999999996,0.357,14436288,35.60085227272727,30.100188273265193,BEST_adc_episode_090,"[0.5625, 0.75, 0, 0.1875, 0.125, 0.625, 0.3125, 0.65625, 0.25, 0.34375, 0.5, 0.3125, 0.25, 0.15625, 0.71875, 0.09375, 0.4375, 0.765625, 0.734375]","[0.5945792870972784, 0.7980140631671768, 0.05988211188094825, 0.21580236603886974, 0.18087342561001143, 0.6807602338689852, 0.3345812909393027, 0.6651777117158937, 0.28041077411141524, 0.36064447304870256, 0.5203516579713795, 0.3210193656480032, 0.2690915747680721, 0.17079962938201187, 0.7274581290574179, 0.09832274835556934, 0.4466232702346384, 0.7738298510204297, 0.7422426915598712]"
+91,14.590000000000003,0.14590000000000003,11778048,29.045454545454547,30.197686928456157,BEST_adc_episode_091,"[0.125, 0.75, 0.25, 0.125, 0, 0.75, 0.5625, 0.65625, 0.6875, 0.46875, 0.78125, 0.5625, 0.78125, 0.703125, 0.3125, 0.328125, 0.09375, 0.515625, 0.734375]","[0.16402495630990488, 0.770083170119844, 0.2770131931284656, 0.15524282651004115, 0.05623443276669852, 0.7884982731548394, 0.6120756692059316, 0.6865397067253707, 0.7062517825538399, 0.4928956110588763, 0.7947595625313095, 0.5628007556573777, 0.7859259855471716, 0.7052231418070196, 0.3131029611224419, 0.33055881832836753, 0.09521926428754293, 0.528363697398989, 0.7372963138491766]"
+92,29.790000000000006,0.29790000000000005,12383424,30.53835227272727,27.437466379774072,BEST_adc_episode_092,"[0.375, 0.4375, 0.75, 0.0625, 0.1875, 0.6875, 0.5, 0.40625, 0.4375, 0.5, 0.34375, 0.3125, 0.59375, 0.171875, 0.625, 0.5, 0.1875, 0.71875, 0.609375]","[0.402087169009276, 0.44670200787834297, 0.7726124181813075, 0.08230777994019582, 0.20282755368152616, 0.6882882436838255, 0.5238888907741618, 0.4305572828833839, 0.4618262012792932, 0.5309034416538518, 0.34686723257439384, 0.3212409643939853, 0.6170773533471468, 0.18155410705062222, 0.6294502032176844, 0.5155625803666883, 0.19093556014856788, 0.7224156887395623, 0.6169732741458326]"
+93,30.04,0.3004,12756096,31.457386363636363,20.898332436793975,BEST_adc_episode_093,"[0.75, 0.4375, 0, 0.3125, 0.25, 0.125, 0.5625, 0.5625, 0.15625, 0.375, 0.5625, 0.75, 0.71875, 0.484375, 0.484375, 0.75, 0.46875, 0.671875, 0.40625]","[0.7732330219410463, 0.4454829378286108, 0.05282471237606655, 0.33077305969377957, 0.29901846396209053, 0.14377499269483784, 0.5713743347623207, 0.5756803078359594, 0.17368538117327298, 0.37896322635557683, 0.5810030018232437, 0.7512519396265536, 0.7457337427774544, 0.48973568976783993, 0.49191827455717524, 0.7527546061516593, 0.48338116941263426, 0.6750006199289573, 0.41981034191378586]"
+94,19.379999999999995,0.19379999999999994,9923904,24.473011363636363,20.989107046799354,BEST_adc_episode_094,"[0.4375, 0.375, 0.5, 0.375, 0.6875, 0.625, 0.5, 0.625, 0.15625, 0.3125, 0.5625, 0.78125, 0.03125, 0.765625, 0.6875, 0.203125, 0.78125, 0.421875, 0.5]","[0.4851096349802544, 0.39883886049754025, 0.5370033507572699, 0.3890827559768791, 0.727990753931631, 0.6813501506829717, 0.5443403452104725, 0.6384589838671241, 0.18199712004041896, 0.3287474441755277, 0.5800636743864621, 0.783736737887838, 0.05569356152409177, 0.768508377628691, 0.6908387475196245, 0.2089300408214571, 0.7962582034277831, 0.4357764305609649, 0.5084013235744393]"
+95,23.929999999999996,0.23929999999999996,12948480,31.931818181818183,40.3778913394298,BEST_adc_episode_095,"[0.4375, 0.625, 0.375, 0.125, 0.625, 0.1875, 0.5625, 0.6875, 0.625, 0.28125, 0.75, 0.6875, 0.71875, 0.625, 0.265625, 0.46875, 0.046875, 0.0625, 0.484375]","[0.47425748225122344, 0.6783791613923812, 0.3912440948115638, 0.15612881418613017, 0.6603593433919533, 0.24933898252768583, 0.580487846237365, 0.7176358345369878, 0.6428508210644169, 0.2901713356020438, 0.7752839072941615, 0.7166718941982948, 0.7328459484463281, 0.6314392944041357, 0.27455907091764026, 0.4746291424101009, 0.06104680551600514, 0.06640138278696031, 0.49142477117501504]"
+96,17.03,0.1703,11122560,27.428977272727273,25.504303388918775,BEST_adc_episode_096,"[0.4375, 0.6875, 0.625, 0.6875, 0.1875, 0.1875, 0.5, 0.3125, 0.75, 0.53125, 0.125, 0.4375, 0.71875, 0.640625, 0.4375, 0.1875, 0.578125, 0.546875, 0.75]","[0.45097407766816694, 0.7450690016825929, 0.6625691413432221, 0.7175804822861214, 0.23105075019371243, 0.19330509431496112, 0.5267434469407798, 0.32277658256495906, 0.7526234195572458, 0.5380100023546668, 0.13748844940221006, 0.4450772776283334, 0.7215610979675495, 0.6407615035898743, 0.4449796536043127, 0.2019901484914322, 0.5869676950908317, 0.5578813165972188, 0.752320255224681]"
+97,34.13,0.34130000000000005,17222976,42.47301136363637,44.49973103819257,BEST_adc_episode_097,"[0.0625, 0.0625, 0.75, 0.3125, 0.375, 0.0625, 0.1875, 0.59375, 0.75, 0.46875, 0.34375, 0.5625, 0.21875, 0.328125, 0.15625, 0.3125, 0.640625, 0.1875, 0.09375]","[0.12261612462884897, 0.07645613559118997, 0.7852540336983294, 0.3686275780739695, 0.38746300765999936, 0.1226381969425056, 0.20097302750443619, 0.6036826288746537, 0.7617660897551115, 0.4978605272228973, 0.3573426626576661, 0.5709633387234757, 0.22486926933713894, 0.34358874854669613, 0.15776341095923924, 0.32362614717011157, 0.6561058776126095, 0.19244533911470355, 0.09416082992451744]"
+98,24.060000000000002,0.24060000000000004,10609920,26.164772727272727,21.75564819795589,BEST_adc_episode_098,"[0.5625, 0.5625, 0.0625, 0.5, 0.125, 0.5, 0.625, 0.3125, 0.46875, 0.625, 0.625, 0.5625, 0.71875, 0.515625, 0.6875, 0.640625, 0.546875, 0.453125, 0.234375]","[0.5800225709970706, 0.5833704204248267, 0.12031607548153443, 0.5561947605977495, 0.1322671899448013, 0.5510310266526911, 0.6706531420586666, 0.3277003485083627, 0.4736155810982242, 0.6303644231034107, 0.6440164571809391, 0.570199701253635, 0.7442944386737053, 0.5273238529915386, 0.6906439236845576, 0.6416951636642858, 0.561536647458501, 0.4639943000976954, 0.2433741164981565]"
+99,36.160000000000004,0.36160000000000003,16612416,40.96732954545455,34.319526627218934,BEST_adc_episode_099,"[0.25, 0.0625, 0.1875, 0.625, 0.5625, 0.0625, 0.4375, 0.25, 0.09375, 0.375, 0.65625, 0.125, 0.625, 0.203125, 0.28125, 0.515625, 0.71875, 0.515625, 0.203125]","[0.268136976301627, 0.09124091733953213, 0.2369869584864586, 0.6681243905440143, 0.5635709361732814, 0.09701587810082817, 0.47834537126323395, 0.2640306147059136, 0.10675575161148179, 0.3750206107172516, 0.6809344450612647, 0.12839081559962195, 0.6532788321026463, 0.20951979134510232, 0.2845739006493017, 0.5195140607422974, 0.7288815809525361, 0.5182689262081731, 0.2148768139565986]"
+100,30.959999999999997,0.3096,14451840,35.63920454545455,33.07557826788596,BEST_adc_episode_100,"[0.25, 0.4375, 0.0625, 0.1875, 0.25, 0.5625, 0.5, 0.125, 0.5625, 0.625, 0.375, 0.78125, 0.6875, 0.28125, 0.296875, 0.40625, 0.640625, 0.46875, 0.109375]","[0.300189048121282, 0.47775882083065024, 0.07863215492949106, 0.23095124077328874, 0.2726482939783378, 0.6062343769847159, 0.5216324102140082, 0.13002962205685176, 0.5848310952336893, 0.6263925791389491, 0.39599723533092573, 0.7980670725816222, 0.7125263418138874, 0.2873035770082576, 0.31124526063783114, 0.4192328665183532, 0.6412419782858724, 0.4744187627138963, 0.12448670375595043]"
+101,21.799999999999997,0.21799999999999997,11086848,27.340909090909093,18.56845077998924,BEST_adc_episode_101,"[0.375, 0.3125, 0.375, 0.375, 0.5625, 0.25, 0.1875, 0.78125, 0.1875, 0.25, 0.78125, 0.53125, 0.78125, 0.796875, 0.796875, 0.28125, 0.390625, 0.71875, 0.796875]","[0.3871886831647864, 0.3535139376405875, 0.4192783073741625, 0.39439194425012025, 0.6207445289629318, 0.27156474740849884, 0.20772926754462112, 0.800000011920929, 0.21023780614902765, 0.26351853076775034, 0.800000011920929, 0.5395760629870185, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.28662889578178596, 0.4010085092603829, 0.7190329173552752, 0.800000011920929]"
+102,15.639999999999999,0.15639999999999998,4949568,12.205965909090908,11.047606239913932,BEST_adc_episode_102,"[0.75, 0.625, 0.75, 0.4375, 0.6875, 0.75, 0.6875, 0.5, 0.78125, 0.46875, 0.34375, 0.78125, 0.78125, 0.796875, 0.109375, 0.796875, 0.71875, 0.78125, 0.796875]","[0.800000011920929, 0.666163357485093, 0.795967199730801, 0.49630193891774, 0.7271737128536491, 0.7889384899286203, 0.7415847947765571, 0.5223452220416083, 0.800000011920929, 0.4847521590181644, 0.37146780724559125, 0.800000011920929, 0.800000011920929, 0.7995274791509817, 0.12353320983171556, 0.800000011920929, 0.7253976882127076, 0.7905245753315715, 0.800000011920929]"
+103,16.000000000000004,0.16000000000000003,4553280,11.228693181818182,11.397256589564282,BEST_adc_episode_103,"[0.3125, 0.75, 0.5, 0.625, 0.75, 0.75, 0.6875, 0.78125, 0.78125, 0.78125, 0.78125, 0.46875, 0.78125, 0.6875, 0.328125, 0.796875, 0.71875, 0.53125, 0.78125]","[0.3488807941407648, 0.800000011920929, 0.5291729143941855, 0.6433797161088415, 0.800000011920929, 0.800000011920929, 0.7282313506546736, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.49612860657131075, 0.800000011920929, 0.6967165489285053, 0.3364073144412225, 0.800000011920929, 0.7311099928117446, 0.5391346897755777, 0.7898573068315026]"
+104,15.93,0.1593,6857856,16.91193181818182,18.333109198493812,BEST_adc_episode_104,"[0.75, 0.75, 0.75, 0, 0.75, 0.5625, 0.6875, 0.59375, 0.03125, 0.75, 0.75, 0.78125, 0.375, 0.453125, 0.796875, 0.28125, 0.796875, 0.046875, 0.71875]","[0.800000011920929, 0.800000011920929, 0.800000011920929, 0.05000000074505806, 0.7958282556315504, 0.5730011192949565, 0.7345685345267255, 0.6021201721289848, 0.05727680669240054, 0.7613411789727643, 0.7696018362588947, 0.800000011920929, 0.3996128865362702, 0.4677669508061071, 0.800000011920929, 0.2835221049268648, 0.800000011920929, 0.057981557756429436, 0.7275291822889389]"
+105,12.949999999999996,0.12949999999999995,5408640,13.338068181818183,10.519768692845616,BEST_adc_episode_105,"[0.75, 0.4375, 0.5625, 0.5, 0.6875, 0.75, 0.125, 0.78125, 0.5625, 0.6875, 0.78125, 0.40625, 0.78125, 0.59375, 0.734375, 0.703125, 0.796875, 0.453125, 0.765625]","[0.800000011920929, 0.48889227675835867, 0.5956240726472418, 0.5547872108278555, 0.6955509057050445, 0.7694621074560226, 0.13629553488771662, 0.800000011920929, 0.5794462428234807, 0.7127003212028998, 0.800000011920929, 0.4318186520580638, 0.800000011920929, 0.5939735435659027, 0.7376912598391311, 0.7157954141143047, 0.800000011920929, 0.4543716894890447, 0.7691377769816607]"
+106,10.7,0.107,2267712,5.592329545454546,4.743813878429263,BEST_adc_episode_106,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.78125, 0.796875, 0.75]","[0.800000011920929, 0.800000011920929, 0.789115233922508, 0.7959022498418473, 0.800000011920929, 0.7850093248265936, 0.800000011920929, 0.7858143186030547, 0.7575991785539259, 0.7691129095746011, 0.7937816433073036, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7900845135789127, 0.800000011920929, 0.7651797692826102]"
+107,29.159999999999997,0.29159999999999997,12219840,30.13494318181818,27.057557826788596,BEST_adc_episode_107,"[0.625, 0.75, 0.0625, 0.1875, 0.1875, 0.375, 0.75, 0.40625, 0.5625, 0.28125, 0.78125, 0.25, 0.71875, 0.328125, 0.21875, 0.1875, 0.796875, 0.796875, 0.734375]","[0.6823136694293214, 0.7504025978582628, 0.12235023124992583, 0.19336501359299607, 0.2061080771665862, 0.377570681992159, 0.800000011920929, 0.431125741855958, 0.5669613156852751, 0.2864246784215596, 0.800000011920929, 0.26442321053177653, 0.7278773475254214, 0.3395259266390649, 0.2288201293936203, 0.196330221066235, 0.800000011920929, 0.800000011920929, 0.7457960525755545]"
+108,18.06,0.18059999999999998,7454592,18.383522727272727,13.62627756858526,BEST_adc_episode_108,"[0, 0.5, 0.5625, 0.75, 0.375, 0.5, 0.5, 0.53125, 0.625, 0.78125, 0.78125, 0.6875, 0.25, 0.796875, 0.65625, 0.375, 0.640625, 0.796875, 0.796875]","[0.06067004063496362, 0.5052274421380647, 0.5654691517958715, 0.800000011920929, 0.412761752639613, 0.5559416550121664, 0.5493479988785153, 0.5365573397683194, 0.6431806618735452, 0.7962234294363737, 0.800000011920929, 0.7068204595395087, 0.26629042088758514, 0.800000011920929, 0.6650357198523305, 0.37950457809402727, 0.6555221018344534, 0.800000011920929, 0.800000011920929]"
+109,13.649999999999995,0.13649999999999995,7090560,17.485795454545457,15.747713824636902,BEST_adc_episode_109,"[0.6875, 0.75, 0.6875, 0.5625, 0.4375, 0.6875, 0.75, 0.5625, 0.125, 0.3125, 0.78125, 0.71875, 0.40625, 0.65625, 0.578125, 0.390625, 0.765625, 0.796875, 0.796875]","[0.7413804729421449, 0.800000011920929, 0.7105093981234827, 0.5978796719357898, 0.43869614006093915, 0.6896164854020344, 0.800000011920929, 0.5889467020575934, 0.15231253328092098, 0.3412517931513185, 0.800000011920929, 0.7467558058437848, 0.42851303836524185, 0.6623495314966553, 0.5913287069885518, 0.3931296623146292, 0.7798030327345028, 0.800000011920929, 0.800000011920929]"
+110,19.15,0.19149999999999998,6083712,15.002840909090908,12.110005379236148,BEST_adc_episode_110,"[0.6875, 0.75, 0.6875, 0.75, 0.375, 0.375, 0.75, 0.375, 0.53125, 0.78125, 0.3125, 0.5625, 0.78125, 0.546875, 0.453125, 0.765625, 0.796875, 0.796875, 0.765625]","[0.7007742586078768, 0.800000011920929, 0.7036147817085403, 0.7570403426437622, 0.4194866040656154, 0.41828842766655305, 0.7608486304798361, 0.3862115421769117, 0.547253122238518, 0.800000011920929, 0.3318085850043604, 0.5741627241508777, 0.800000011920929, 0.5576824534858407, 0.45377408760314275, 0.7768382519318306, 0.800000011920929, 0.800000011920929, 0.7774622899609658]"
+111,14.91,0.1491,8549568,21.08380681818182,16.900887573964496,BEST_adc_episode_111,"[0.75, 0.75, 0.625, 0.375, 0.125, 0.6875, 0.25, 0.375, 0.6875, 0.625, 0.03125, 0.6875, 0.625, 0.46875, 0.796875, 0.71875, 0.609375, 0.484375, 0.5625]","[0.800000011920929, 0.800000011920929, 0.6872189645086303, 0.40913182811296833, 0.1492567788817608, 0.706967381278415, 0.3111227542171414, 0.40117835179622746, 0.6899146866411049, 0.6441984268625581, 0.05000000074505806, 0.6999238067179957, 0.6489782880041666, 0.48132894079997474, 0.800000011920929, 0.725709367117687, 0.6187050467256652, 0.49970386042391457, 0.5724955684517538]"
+112,11.470000000000002,0.11470000000000002,2424384,5.978693181818182,5.668370091447014,BEST_adc_episode_112,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.75, 0.78125, 0.75, 0.78125, 0.78125, 0.78125, 0.671875, 0.796875, 0.75, 0.796875]","[0.7560632336001134, 0.7690670871193208, 0.800000011920929, 0.800000011920929, 0.7801533505228212, 0.7970027268140967, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7646287885013402, 0.7840827979864029, 0.7783921741748151, 0.800000011920929, 0.7930559869904971, 0.7827001485940881, 0.6760833106845038, 0.800000011920929, 0.7599381432912103, 0.800000011920929]"
+113,13.339999999999996,0.13339999999999996,5663808,13.967329545454547,20.37049488972566,BEST_adc_episode_113,"[0.375, 0.625, 0.75, 0.6875, 0.75, 0.75, 0.6875, 0.78125, 0.78125, 0.78125, 0.71875, 0.78125, 0.78125, 0.40625, 0.125, 0.359375, 0.796875, 0.796875, 0.796875]","[0.3799529080127825, 0.6461042178425929, 0.7599743254039196, 0.7215384749082686, 0.800000011920929, 0.800000011920929, 0.6991231088663077, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7451306724877075, 0.800000011920929, 0.800000011920929, 0.4094718122234291, 0.14004491509617611, 0.37195182551475126, 0.800000011920929, 0.800000011920929, 0.800000011920929]"
+114,17.23,0.1723,6500160,16.029829545454547,10.718128025820334,BEST_adc_episode_114,"[0.0625, 0.6875, 0.375, 0.4375, 0.625, 0.375, 0.75, 0.53125, 0.71875, 0.5625, 0.78125, 0.53125, 0.78125, 0.796875, 0.796875, 0.25, 0.796875, 0.796875, 0.4375]","[0.09948195286723216, 0.7216403211917566, 0.4062849703720749, 0.44202228073301353, 0.6278086546419803, 0.4169149069112492, 0.800000011920929, 0.5312618978627807, 0.7239937522455231, 0.5780342207706607, 0.800000011920929, 0.5412132042664008, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.2530100880053203, 0.800000011920929, 0.800000011920929, 0.45278281798262315]"
+115,28.720000000000002,0.2872,13268736,32.72159090909091,29.80096826250672,BEST_adc_episode_115,"[0.75, 0.625, 0.75, 0.4375, 0.25, 0.25, 0.5, 0.25, 0.15625, 0.5, 0.21875, 0.1875, 0.78125, 0.65625, 0.6875, 0.390625, 0.5, 0.046875, 0.65625]","[0.7833427535707678, 0.6642186023535184, 0.800000011920929, 0.4794688097622787, 0.3119538058576128, 0.26873507758430437, 0.5146144553393278, 0.2795873897889771, 0.1694362883579108, 0.5028132630321529, 0.23814254726996054, 0.21490914670920647, 0.800000011920929, 0.6664494919176935, 0.701786772338125, 0.40421925500433153, 0.5144870521386379, 0.05000000074505806, 0.6658163548650918]"
+116,17.630000000000003,0.1763,7223616,17.813920454545453,14.355836471221087,BEST_adc_episode_116,"[0.625, 0.375, 0.75, 0.4375, 0.5625, 0.75, 0.6875, 0.34375, 0.78125, 0.25, 0.3125, 0.65625, 0.3125, 0.484375, 0.671875, 0.671875, 0.75, 0.796875, 0.796875]","[0.646459368653763, 0.38595488132746947, 0.779698677193086, 0.4671630233863404, 0.5781201082613172, 0.800000011920929, 0.6896176575510161, 0.35647161499174407, 0.800000011920929, 0.25523559396076145, 0.33331251934529316, 0.6769317452636132, 0.3279363316403914, 0.4981192369977042, 0.6826672988591207, 0.6834953941970562, 0.7569898387965199, 0.800000011920929, 0.800000011920929]"
+117,12.11,0.1211,10297728,25.394886363636367,21.083243679397526,BEST_adc_episode_117,"[0.75, 0.5625, 0.125, 0.125, 0.6875, 0.75, 0.0625, 0.6875, 0.03125, 0.78125, 0.125, 0.5, 0.75, 0.453125, 0.796875, 0.5, 0.046875, 0.796875, 0.46875]","[0.800000011920929, 0.5919196387548776, 0.18126146245098929, 0.12996932718983045, 0.735563688307011, 0.800000011920929, 0.07901855456712747, 0.6984080926711996, 0.060979403801893595, 0.800000011920929, 0.1497351940588909, 0.5279734294714127, 0.7690530576462266, 0.4533334339039137, 0.800000011920929, 0.5071327172180606, 0.060996336951481434, 0.800000011920929, 0.4726339087750759]"
+118,10.119999999999996,0.10119999999999996,2462400,6.072443181818182,4.932087143625606,BEST_adc_episode_118,"[0.75, 0.6875, 0.75, 0.6875, 0.75, 0.75, 0.75, 0.78125, 0.75, 0.78125, 0.75, 0.75, 0.78125, 0.796875, 0.796875, 0.765625, 0.796875, 0.796875, 0.75]","[0.800000011920929, 0.7358166340150735, 0.800000011920929, 0.7125153966112641, 0.7728532510616964, 0.7798373036726396, 0.7822173608071713, 0.800000011920929, 0.7612177658649582, 0.7846199804354749, 0.7644152821731781, 0.7688080274842433, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7715551755872867, 0.800000011920929, 0.7974073163872746, 0.7540612589048346]"
+119,19.979999999999997,0.19979999999999998,7451136,18.375,18.013717052178592,BEST_adc_episode_119,"[0.75, 0.125, 0.6875, 0.5625, 0.75, 0.625, 0.5, 0.375, 0.625, 0.65625, 0.1875, 0.78125, 0.28125, 0.578125, 0.796875, 0.671875, 0.515625, 0.390625, 0.671875]","[0.800000011920929, 0.13634812279817643, 0.692709143873233, 0.6048969335394765, 0.800000011920929, 0.6277730813639718, 0.5612770226001889, 0.38991269188075994, 0.652294308834275, 0.6861592094138484, 0.21834606278264101, 0.800000011920929, 0.3115893071549216, 0.5854298449504527, 0.800000011920929, 0.6768806458108845, 0.5191277005805124, 0.40054153731045145, 0.6836215616881536]"
+120,15.600000000000003,0.15600000000000003,5571072,13.738636363636363,12.910166756320601,BEST_adc_episode_120,"[0.375, 0.5625, 0.75, 0.5, 0.75, 0.75, 0.3125, 0.78125, 0.78125, 0.75, 0.25, 0.6875, 0.78125, 0.4375, 0.671875, 0.421875, 0.703125, 0.796875, 0.796875]","[0.39796196991340804, 0.6174762098847743, 0.800000011920929, 0.5402915422435155, 0.7993965613370697, 0.800000011920929, 0.33890058381624716, 0.800000011920929, 0.800000011920929, 0.7638319639105443, 0.2653464020700077, 0.7129228013398521, 0.787852382129087, 0.44409988489620555, 0.6740381711606089, 0.4328426028082542, 0.7116221802226207, 0.800000011920929, 0.800000011920929]"
+121,18.320000000000004,0.18320000000000003,4769280,11.761363636363637,6.505513717052179,BEST_adc_episode_121,"[0.75, 0.5625, 0.5625, 0.5625, 0.125, 0.625, 0.75, 0.59375, 0.78125, 0.75, 0.78125, 0.6875, 0.78125, 0.796875, 0.796875, 0.796875, 0.671875, 0.796875, 0.734375]","[0.800000011920929, 0.5780822637599383, 0.6138678840095203, 0.5776391582274225, 0.1305981372974232, 0.6457008544258893, 0.773813583636948, 0.6176055345645021, 0.800000011920929, 0.7720172186171844, 0.800000011920929, 0.6973174513349494, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.6815567317445461, 0.800000011920929, 0.7420015399571656]"
+122,11.580000000000002,0.11580000000000001,5281920,13.025568181818182,11.565357719203872,BEST_adc_episode_122,"[0.75, 0.5625, 0.625, 0.6875, 0.6875, 0.75, 0.375, 0.25, 0.78125, 0.40625, 0.65625, 0.78125, 0.5625, 0.796875, 0.796875, 0.78125, 0.53125, 0.671875, 0.453125]","[0.7978250288825456, 0.6241964458002489, 0.6261239057270414, 0.7343618329709953, 0.7225662732252716, 0.800000011920929, 0.42536173041316977, 0.2621874600482177, 0.800000011920929, 0.4229331028905934, 0.6799156485315545, 0.800000011920929, 0.5639175248564935, 0.800000011920929, 0.800000011920929, 0.7921116251006239, 0.5357141038762864, 0.6856912264317284, 0.4546742536156487]"
+123,12.270000000000003,0.12270000000000003,7178112,17.701704545454543,9.884346422807962,BEST_adc_episode_123,"[0.6875, 0.75, 0.25, 0.125, 0.75, 0.1875, 0.75, 0.71875, 0.125, 0.65625, 0.59375, 0.40625, 0.78125, 0.796875, 0.734375, 0.671875, 0.796875, 0.796875, 0.640625]","[0.7493219234100876, 0.790857685818717, 0.2624569500141163, 0.13943333879803987, 0.800000011920929, 0.20394235514375203, 0.7847272571421494, 0.7474094974872721, 0.1323761434487103, 0.663069995392087, 0.6201855140292949, 0.4091671208658957, 0.800000011920929, 0.800000011920929, 0.7475969983480445, 0.6736620637594889, 0.800000011920929, 0.800000011920929, 0.6521917899336787]"
+124,13.200000000000001,0.132,3007872,7.417613636363636,6.522323830016137,BEST_adc_episode_124,"[0.6875, 0.6875, 0.75, 0.6875, 0.6875, 0.75, 0.75, 0.78125, 0.65625, 0.78125, 0.71875, 0.75, 0.71875, 0.703125, 0.78125, 0.75, 0.75, 0.734375, 0.796875]","[0.7273166285966929, 0.7335243205084739, 0.7824116371978312, 0.7443792749316529, 0.7488437198288778, 0.7528801222409895, 0.7640296436209567, 0.800000011920929, 0.6772874016577096, 0.781699087210472, 0.7297519445262547, 0.7645546802257576, 0.7212369180987073, 0.706026878830316, 0.7837933408484649, 0.7563958502396411, 0.7504274836298447, 0.7348445082365612, 0.800000011920929]"
+125,51.629999999999995,0.5163,20280960,50.01420454545455,52.545051102743415,BEST_adc_episode_125,"[0.3125, 0.75, 0.75, 0.5625, 0.5, 0.0625, 0, 0.09375, 0.09375, 0.09375, 0.25, 0.375, 0.1875, 0.625, 0.328125, 0.328125, 0.078125, 0.125, 0.46875]","[0.35096780990105014, 0.7744951043500246, 0.800000011920929, 0.5979426750064127, 0.5525745586050471, 0.11610963704154087, 0.05000000074505806, 0.12132609219309759, 0.11934425810572598, 0.11952017305443566, 0.2508085272400707, 0.38972216406483806, 0.20849148545119603, 0.6341028949458588, 0.33409433958764484, 0.33457930550876014, 0.09149950733530353, 0.12809669762198708, 0.05000000074505806]"
+126,65.75,0.6575,20239488,49.91193181818182,37.30500268961807,BEST_adc_episode_126,"[0.3125, 0.375, 0, 0.6875, 0.125, 0.25, 0.125, 0.40625, 0.09375, 0.15625, 0.1875, 0.03125, 0.03125, 0.21875, 0.53125, 0.578125, 0.53125, 0.5625, 0.59375]","[0.3373273659846293, 0.40758805084757177, 0.05087759271075606, 0.7127882902046883, 0.13299973582345143, 0.3041088903936231, 0.1375488939159119, 0.42378423465084764, 0.11705120352530501, 0.18218105486029554, 0.19406607339799897, 0.05000000074505806, 0.05000000074505806, 0.2302640236270768, 0.24402949522532963, 0.07268526121534982, 0.15110049221763155, 0.418017442118951, 0.6054356724763786]"
+127,61.59,0.6159,20283840,50.02130681818182,37.82611619150081,BEST_adc_episode_127,"[0.1875, 0.125, 0.375, 0.75, 0, 0.25, 0.125, 0.375, 0.1875, 0.03125, 0.03125, 0.25, 0.28125, 0.171875, 0.546875, 0.4375, 0.515625, 0.578125, 0.53125]","[0.21527822229276683, 0.16058170459534937, 0.42458355523328956, 0.784637115734031, 0.05000000074505806, 0.30849120224092413, 0.15210651417122373, 0.3955373152416574, 0.20249381261504162, 0.05000000074505806, 0.05000000074505806, 0.26473403890939307, 0.28183246705768394, 0.1761207487414313, 0.5509792861799784, 0.4449557538640485, 0.13352944742520614, 0.3923536317547593, 0.2993522679663047]"
+128,53.580000000000005,0.5358,20275776,50.00142045454545,52.78711672942442,BEST_adc_episode_128,"[0.5, 0, 0.375, 0.3125, 0.3125, 0.5, 0.75, 0.0625, 0.25, 0.1875, 0.1875, 0.40625, 0.09375, 0.046875, 0.296875, 0.078125, 0.1875, 0.75, 0.234375]","[0.5159700905788056, 0.05000000074505806, 0.3771106102512821, 0.3411046910924593, 0.3506777499170915, 0.5038450393860242, 0.7661706081421802, 0.08764226157878327, 0.26015537151603485, 0.2121890140128899, 0.21636667296587686, 0.4244094541343669, 0.09882291001306831, 0.055597581549537645, 0.3048687681828526, 0.08516473220478145, 0.05000000074505806, 0.5391722059607066, 0.05000000074505806]"
+129,50.760000000000005,0.5076,20277504,50.00568181818181,30.567509413663256,BEST_adc_episode_129,"[0.375, 0.125, 0.1875, 0.125, 0.25, 0, 0.375, 0.03125, 0.0625, 0.15625, 0.0625, 0.5625, 0.65625, 0.28125, 0.796875, 0.21875, 0.609375, 0.5, 0.609375]","[0.4246526520226215, 0.13620784077700743, 0.2287735415835037, 0.16241409660046405, 0.25124863623243787, 0.05000000074505806, 0.3764715506548194, 0.05000000074505806, 0.08884936888354265, 0.15751411087344896, 0.08651410149612543, 0.3453641992971599, 0.6569945907581669, 0.12782255670990514, 0.800000011920929, 0.22394251321727923, 0.11387514611855375, 0.26320840301293286, 0.3727680654185823]"
+130,27.92,0.2792,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_130,"[0, 0, 0, 0, 0.0625, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05143937731753917, 0.11915746494149185, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05030084235985256, 0.05000000074505806, 0.05000000074505806, 0.07012229377198743, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+131,55.00000000000001,0.55,20046528,49.43607954545455,32.38636363636363,BEST_adc_episode_131,"[0.125, 0.3125, 0.1875, 0.3125, 0.25, 0.125, 0.1875, 0.25, 0.15625, 0.03125, 0.4375, 0.15625, 0.15625, 0.6875, 0.046875, 0.671875, 0.390625, 0.671875, 0.6875]","[0.13129332090665968, 0.3741324381432902, 0.2062085389854718, 0.3492555949032311, 0.29289183259571644, 0.1645010894602484, 0.24120516142689555, 0.28087791020112723, 0.18309750656023588, 0.05923633067026073, 0.4435041112426162, 0.16360754942274852, 0.18676088159455198, 0.6977275502956126, 0.05000000074505806, 0.5456708805634272, 0.12208092599075637, 0.38993042654900356, 0.6875207635266855]"
+132,66.32000000000001,0.6632000000000001,19895040,49.0625,47.892011834319526,BEST_adc_episode_132,"[0.1875, 0.5625, 0.25, 0.0625, 0.625, 0.125, 0.625, 0.0625, 0.375, 0.03125, 0.28125, 0.09375, 0.21875, 0.375, 0.046875, 0.046875, 0.5, 0.765625, 0.484375]","[0.21377042118846737, 0.5689553421448292, 0.29980701045442154, 0.10321948551162477, 0.650940701718083, 0.15489086616050524, 0.6433516563070317, 0.06537488378339221, 0.3948520820085609, 0.05000000074505806, 0.297980929772879, 0.0979738345641871, 0.23078479485155146, 0.3808605507619181, 0.05000000074505806, 0.05000000074505806, 0.09435658496570713, 0.7679342702749686, 0.4851955789160145]"
+133,39.839999999999996,0.3984,19698624,48.578125,51.42549757934374,BEST_adc_episode_133,"[0.0625, 0.4375, 0.125, 0.3125, 0.5, 0.1875, 0.5625, 0.25, 0.28125, 0.78125, 0.03125, 0.25, 0.03125, 0.328125, 0.609375, 0.140625, 0.390625, 0.046875, 0.0625]","[0.10426173324730656, 0.44830551870455687, 0.13453259339204993, 0.36698500163167314, 0.5099594815175447, 0.23630369730289924, 0.5817140585911347, 0.2795268183971067, 0.3106119068582299, 0.800000011920929, 0.05000000074505806, 0.2672435782374356, 0.05000000074505806, 0.33093125818140207, 0.6155633246319898, 0.14980957678924245, 0.39347459878342206, 0.05000000074505806, 0.07594361515711529]"
+134,32.28,0.32280000000000003,18845568,46.47443181818181,52.72996234534696,BEST_adc_episode_134,"[0.1875, 0.125, 0.75, 0.375, 0.0625, 0.75, 0.625, 0.15625, 0.0625, 0.3125, 0.375, 0.28125, 0.46875, 0.09375, 0.203125, 0.5625, 0.046875, 0.109375, 0.546875]","[0.1988872002592045, 0.14304528713237152, 0.800000011920929, 0.41405502201581595, 0.1041097715359141, 0.800000011920929, 0.6387781604594146, 0.16156484251092987, 0.07391988542273187, 0.34312508088231164, 0.40248032284056745, 0.28770105580101335, 0.4805541408755629, 0.09682129512207169, 0.21220165096166183, 0.5726940066499893, 0.056389692538160396, 0.10972088047919154, 0.5522684877756252]"
+135,55.88999999999999,0.5589,19954944,49.21022727272727,56.633270575578265,BEST_adc_episode_135,"[0.25, 0.0625, 0.5625, 0.25, 0.5625, 0.3125, 0.25, 0.125, 0.375, 0.59375, 0.625, 0.09375, 0.15625, 0.25, 0.0625, 0.375, 0.359375, 0.125, 0.109375]","[0.2697082593247824, 0.10229398048624307, 0.6212398441743666, 0.2878756353258711, 0.5765047381830573, 0.32935641938185367, 0.29736860647844, 0.15197312290701837, 0.3932893942702217, 0.604938893562553, 0.6377811055518109, 0.11989825924548994, 0.17434103948536273, 0.2630442911703872, 0.06372646375127841, 0.38188806064633535, 0.36704370429163385, 0.12980543365211017, 0.12287807332754216]"
+136,37.67,0.37670000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_136,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05280568378384177, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+137,45.9,0.45899999999999996,20293632,50.04545454545455,29.683297471759012,BEST_adc_episode_137,"[0.4375, 0.5, 0.1875, 0.125, 0, 0, 0.0625, 0.09375, 0.5, 0.21875, 0.15625, 0.03125, 0.53125, 0.65625, 0.640625, 0.265625, 0.703125, 0.34375, 0.6875]","[0.49127297134693754, 0.5452486568186284, 0.22871850413502484, 0.17576225173641385, 0.05000000074505806, 0.05000000074505806, 0.06326104016315967, 0.12116714026314743, 0.5075825800289995, 0.23865177739660343, 0.17035115177727028, 0.05000000074505806, 0.3973901629337464, 0.1672582765975005, 0.6549677998980443, 0.2780959546694489, 0.7093463384923531, 0.19319367375945848, 0.14556154914126046]"
+138,52.64,0.5264,20279232,50.00994318181819,39.48359332974717,BEST_adc_episode_138,"[0.0625, 0, 0.4375, 0.3125, 0.0625, 0.125, 0.4375, 0.5, 0.03125, 0.1875, 0.53125, 0.375, 0.15625, 0.375, 0.140625, 0.40625, 0.578125, 0.515625, 0.59375]","[0.08499790021243092, 0.05000000074505806, 0.48519729673554646, 0.34246981741737287, 0.11444435652213403, 0.12571608895109013, 0.4888820228239654, 0.513395728187089, 0.05000000074505806, 0.2172580755304571, 0.5526710585010193, 0.3807101512852583, 0.17560562623010478, 0.37649279992990314, 0.1479510269628272, 0.41513690944011733, 0.19307821013523388, 0.12080679080482154, 0.45249892235879186]"
+139,54.230000000000004,0.5423,20056896,49.46164772727273,46.56737493275955,BEST_adc_episode_139,"[0.125, 0.375, 0.125, 0.0625, 0.25, 0.1875, 0.5, 0.6875, 0.21875, 0.21875, 0.15625, 0.6875, 0.28125, 0.109375, 0.75, 0.15625, 0.171875, 0.21875, 0.328125]","[0.13912434211672592, 0.4137293154041262, 0.16208298524204814, 0.09219795664839667, 0.28264017409190895, 0.2439730520074051, 0.5019593385831247, 0.697680102216115, 0.22632799108543955, 0.2358621828248175, 0.18600404519220376, 0.7049027515267647, 0.29515798068509336, 0.12130882421999531, 0.7571357094900272, 0.16923830243473306, 0.17807839816279372, 0.22980307452006118, 0.34324727986493875]"
+140,42.059999999999995,0.4206,20287872,50.03125000000001,35.56011296395912,BEST_adc_episode_140,"[0, 0, 0.375, 0.3125, 0, 0.25, 0.3125, 0.25, 0.625, 0.125, 0.125, 0.28125, 0.28125, 0.3125, 0.609375, 0.78125, 0.265625, 0.09375, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.43133459827237514, 0.3333236943742254, 0.05000000074505806, 0.25928468737835175, 0.3669307237299813, 0.2775948853134797, 0.6279189523406694, 0.13247675561973588, 0.12615003404068806, 0.3098653533938583, 0.31015620355346096, 0.3129217890290214, 0.6111643762139392, 0.7931305037798593, 0.2773976453669247, 0.05000000074505806, 0.5357133589173653]"
+141,56.97,0.5697,20276928,50.00426136363636,46.167294244217324,BEST_adc_episode_141,"[0.625, 0.1875, 0.5625, 0.125, 0, 0.0625, 0.3125, 0.21875, 0.46875, 0.34375, 0.0625, 0.375, 0.53125, 0.234375, 0.0625, 0.3125, 0.265625, 0.65625, 0.421875]","[0.6506069550638959, 0.1906990285602348, 0.5668721069529254, 0.12544698360937184, 0.05000000074505806, 0.10843517489748274, 0.3200389061940811, 0.244983061962799, 0.48002912724269964, 0.34644339943496966, 0.07216909481842296, 0.37550505330094736, 0.5537379681131817, 0.2495048383854891, 0.06277632736629721, 0.3177601213411708, 0.2696171015713846, 0.0828725729741784, 0.42354469479283985]"
+142,32.220000000000006,0.32220000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_142,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05986370855349288, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06469121923435764, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06691517446690369, 0.05000000074505806]"
+143,30.240000000000002,0.3024,18581760,45.82386363636364,53.61081226465841,BEST_adc_episode_143,"[0.0625, 0.125, 0.75, 0.375, 0.25, 0.25, 0.3125, 0.6875, 0.46875, 0.3125, 0.5625, 0.40625, 0.21875, 0.125, 0.0625, 0.5625, 0.28125, 0.125, 0.109375]","[0.06746322879833222, 0.1448679453897741, 0.7598263185013896, 0.39547013245021767, 0.2715583619373317, 0.2964341420420537, 0.3282583909516093, 0.7144264564547711, 0.49448069309300263, 0.3425046942485667, 0.5715839774833237, 0.4291234774923175, 0.23380791240522109, 0.1363600546314078, 0.07149337358093881, 0.5722748262353586, 0.28887483699603667, 0.12966489351188468, 0.12125454281321521]"
+144,45.52,0.45520000000000005,20155392,49.70454545454545,28.83606777837547,BEST_adc_episode_144,"[0.4375, 0, 0, 0.25, 0.25, 0.125, 0.125, 0.03125, 0.03125, 0.53125, 0.25, 0.21875, 0.5625, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625, 0.671875]","[0.49963242493955606, 0.05000000074505806, 0.05000000074505806, 0.27583960581813266, 0.29668310742251963, 0.13684473176266987, 0.17475555361547504, 0.05000000074505806, 0.05659341177783267, 0.5495081664408377, 0.2629957686925475, 0.22426708476904578, 0.37584819545056136, 0.22562335721697452, 0.1472102283213, 0.25762718098132564, 0.20778008652265262, 0.17373703448680664, 0.6812779212036757]"
+145,48.12,0.48119999999999996,16201152,39.953125,37.30500268961807,BEST_adc_episode_145,"[0.3125, 0.6875, 0.1875, 0.5, 0.375, 0, 0.75, 0.25, 0.28125, 0.28125, 0.28125, 0.3125, 0.0625, 0.484375, 0.359375, 0.09375, 0.6875, 0.15625, 0.796875]","[0.32512523861426135, 0.7367242685133674, 0.19918636306092052, 0.529334077728307, 0.398382838895112, 0.05000000074505806, 0.800000011920929, 0.2758852251778316, 0.30217585663488766, 0.3081854047098805, 0.30725006672893607, 0.33981644704357183, 0.08187181669606408, 0.49868632443980265, 0.371193708334281, 0.09603336053725899, 0.6995059852413119, 0.16718671624217674, 0.800000011920929]"
+146,56.96,0.5696,20286144,50.02698863636363,43.93154922001076,BEST_adc_episode_146,"[0.3125, 0.0625, 0.125, 0.375, 0.5, 0.0625, 0.5, 0.375, 0.5, 0.0625, 0.25, 0.03125, 0.375, 0.0625, 0.25, 0.453125, 0.421875, 0.453125, 0.640625]","[0.31250758988410887, 0.07474845447063352, 0.12939493564203297, 0.39794126402679153, 0.5104645337460234, 0.10975992244859285, 0.5159098521107763, 0.38153181004754866, 0.5297495469426179, 0.08398771692134127, 0.25515734461229006, 0.05000000074505806, 0.4004024066193804, 0.06683607913891973, 0.2532735299160501, 0.467008833858874, 0.4348618720783658, 0.21623525456512155, 0.29030340132832844]"
+147,36.83,0.36829999999999996,17150976,42.29545454545455,41.71933835395374,BEST_adc_episode_147,"[0.25, 0.5, 0.375, 0.25, 0.4375, 0.75, 0.5, 0.21875, 0.03125, 0.34375, 0.03125, 0.4375, 0.28125, 0.15625, 0.671875, 0.0625, 0.734375, 0.15625, 0.109375]","[0.29886620907179345, 0.5308231379978551, 0.39720303869055107, 0.28345929480780746, 0.4673017670572707, 0.7688578120985343, 0.5302881032468266, 0.22984009124544047, 0.05000000074505806, 0.349233186779859, 0.05000000074505806, 0.4649513269276737, 0.29499498295684634, 0.16761293825761536, 0.678419607268196, 0.06482283567160126, 0.7431068743754787, 0.1569990091190236, 0.11494201769906942]"
+148,40.39,0.4039,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_148,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05856376948923015, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0660141417651558, 0.0676986423534665, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05356251802168243, 0.07309457189832666, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05762083060994678, 0.05000000074505806]"
+149,68.27,0.6827,20283840,50.02130681818182,47.417966648735884,BEST_adc_episode_149,"[0.1875, 0.625, 0.125, 0.375, 0.25, 0.25, 0.0625, 0.25, 0.125, 0.375, 0.34375, 0.21875, 0.28125, 0.328125, 0.15625, 0.640625, 0.09375, 0.296875, 0.40625]","[0.21866878266639692, 0.6511872283927587, 0.18744092749038468, 0.3983048608366382, 0.2922252563351555, 0.2908505079552518, 0.07459766273842354, 0.2670350935449534, 0.14973154732290322, 0.39103130752947934, 0.3712303687087925, 0.2244411492741884, 0.2995718671627065, 0.3304670091150701, 0.15953372216486908, 0.6407723910717074, 0.10015125585203721, 0.30412524282793707, 0.3427968467564365]"
+150,50.150000000000006,0.5015000000000001,20288448,50.03267045454546,30.389322216245297,BEST_adc_episode_150,"[0.0625, 0.1875, 0.125, 0.375, 0.0625, 0.1875, 0.0625, 0.28125, 0.09375, 0.03125, 0.21875, 0.5625, 0.59375, 0.265625, 0.71875, 0.3125, 0.703125, 0.40625, 0.625]","[0.11340867345850413, 0.24617513533177515, 0.14279993167511276, 0.3834070590710002, 0.11723910578257152, 0.22688750916226655, 0.11756773686166738, 0.29147007241701167, 0.10005431772808362, 0.05973771564451649, 0.2370047409856571, 0.5893637632456116, 0.5939207572627534, 0.1986782781921227, 0.15030931624778113, 0.08938054824556546, 0.6147021557094461, 0.41336727375958005, 0.4527102841460997]"
+151,55.45,0.5545,20135808,49.65625,39.97781065088758,BEST_adc_episode_151,"[0.1875, 0, 0.75, 0.0625, 0.3125, 0.25, 0.375, 0.15625, 0.1875, 0.03125, 0.21875, 0.1875, 0.34375, 0.140625, 0.109375, 0.765625, 0.328125, 0.640625, 0.609375]","[0.24960699387691587, 0.05000000074505806, 0.800000011920929, 0.06735480755679894, 0.355926788557101, 0.28405024055768985, 0.40577820513452323, 0.16422148044767457, 0.18868261716314072, 0.05000000074505806, 0.24514606760346713, 0.19902773912418495, 0.36144636216630327, 0.15028642437719092, 0.10976652573526227, 0.15854746189062868, 0.33005374644536944, 0.18499706465862284, 0.6133495941503148]"
+152,70.38,0.7038,20279808,50.01136363636364,50.17818719741797,BEST_adc_episode_152,"[0.3125, 0, 0.625, 0.125, 0.625, 0.1875, 0.375, 0.03125, 0.1875, 0.1875, 0.25, 0.375, 0.28125, 0.359375, 0.15625, 0.109375, 0.171875, 0.75, 0.234375]","[0.33123085569555394, 0.05000000074505806, 0.6546554242336953, 0.14867954702095507, 0.6600084005011783, 0.18761629311334138, 0.41237522283721006, 0.06120148266933748, 0.21809129503222943, 0.20103419717347568, 0.27019583742817677, 0.3887422301522738, 0.2889890564040729, 0.3628112746551975, 0.16222661530291557, 0.11552236518526787, 0.08268802404599676, 0.09128623659213148, 0.23387025571338949]"
+153,40.78000000000001,0.4078000000000001,16640640,41.03693181818181,34.35314685314685,BEST_adc_episode_153,"[0, 0.0625, 0.5, 0.75, 0.3125, 0.375, 0, 0.65625, 0.5625, 0.4375, 0.09375, 0.15625, 0.34375, 0.4375, 0.078125, 0.796875, 0.390625, 0.34375, 0.546875]","[0.06131930812439373, 0.08497531357447694, 0.5103325227411958, 0.800000011920929, 0.3311325254399083, 0.4169458147427584, 0.05000000074505806, 0.6745460218187314, 0.5830164567280661, 0.44651867995862654, 0.09770778792267565, 0.1749599563708981, 0.3535775718351766, 0.4529855492484942, 0.08607859501644774, 0.800000011920929, 0.4002358511255934, 0.3513330697028134, 0.5559065971657531]"
+154,31.03,0.3103,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_154,"[0, 0.0625, 0, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.0626322948197605, 0.05000000074505806, 0.05000000074505806, 0.06766530804213246, 0.05000000074505806, 0.05000000074505806, 0.05887941336475162, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.1090742460548258, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+155,58.650000000000006,0.5865,19309824,47.61931818181818,42.33795051102744,BEST_adc_episode_155,"[0.5, 0.3125, 0.25, 0.25, 0.75, 0.0625, 0.1875, 0.15625, 0.03125, 0.40625, 0.15625, 0.3125, 0.09375, 0.140625, 0.171875, 0.71875, 0.109375, 0.71875, 0.25]","[0.5573848365331944, 0.35999876746444626, 0.30301317937816186, 0.26281090008075847, 0.800000011920929, 0.0984533792619629, 0.18781615277994063, 0.1738121213054944, 0.05000000074505806, 0.41918169165356967, 0.1668913829652542, 0.32857929261757585, 0.11083730736896932, 0.14945810217574526, 0.18350623711538594, 0.7263333277471616, 0.11702981096347544, 0.7214307001057201, 0.25721599844540455]"
+156,53.52,0.5352,19380096,47.79261363636363,43.21207638515331,BEST_adc_episode_156,"[0, 0.6875, 0.1875, 0.1875, 0, 0.3125, 0.4375, 0.03125, 0.46875, 0.15625, 0.28125, 0.375, 0.4375, 0.1875, 0.171875, 0.25, 0.734375, 0.53125, 0.09375]","[0.05000000074505806, 0.6940912538698089, 0.21471846794491556, 0.24652350018003155, 0.05000000074505806, 0.31993856163882184, 0.4649144184656321, 0.05908458048086781, 0.4874715376418162, 0.17516168733820928, 0.30749056383125817, 0.391571491685943, 0.46065551931428733, 0.1924841688666648, 0.174210141312423, 0.2540933412355476, 0.746475571957795, 0.5324476479032865, 0.1053851385330386]"
+157,32.82000000000001,0.32820000000000005,20286144,50.02698863636363,29.962345346960735,BEST_adc_episode_157,"[0.125, 0, 0.375, 0.25, 0.125, 0.1875, 0.0625, 0.03125, 0.1875, 0.03125, 0.15625, 0.75, 0.28125, 0.71875, 0.71875, 0.453125, 0.546875, 0.4375, 0.53125]","[0.17946103231741756, 0.05121834557243422, 0.4037639061810722, 0.26047996929656025, 0.1464035746383654, 0.20887558096634354, 0.11313824059646498, 0.05000000074505806, 0.20594493874874617, 0.05485153369789485, 0.18355357583262855, 0.13805145180744033, 0.08686966557619241, 0.23916334543754003, 0.7228463788079504, 0.45485556256227455, 0.5470086776075643, 0.443210037485468, 0.06831004968986355]"
+158,35.31999999999999,0.35319999999999996,16906752,41.69318181818182,38.53886498117267,BEST_adc_episode_158,"[0.375, 0.25, 0.6875, 0.4375, 0, 0.25, 0.125, 0.3125, 0.25, 0.28125, 0.375, 0.6875, 0.625, 0.296875, 0.546875, 0.5, 0.4375, 0.171875, 0.203125]","[0.4293857118151195, 0.30310122691226893, 0.7015713438762703, 0.46706253545596765, 0.05000000074505806, 0.2930325660130729, 0.14445039624797393, 0.33641105786870196, 0.2623540553980917, 0.28450097077919073, 0.38240676363898585, 0.70960794782048, 0.649211850058766, 0.30984623808506456, 0.5487406653406686, 0.5103288994954424, 0.4427284256013465, 0.17508860440842017, 0.21540727578513788]"
+159,65.73,0.6573,20288448,50.03267045454546,51.07584722969338,BEST_adc_episode_159,"[0.4375, 0.3125, 0, 0.625, 0.3125, 0.4375, 0.25, 0.0625, 0.03125, 0.4375, 0.3125, 0.3125, 0.15625, 0.515625, 0.09375, 0.046875, 0.28125, 0.53125, 0.5625]","[0.47038456431438935, 0.35271692586472153, 0.05000000074505806, 0.62889840587952, 0.3339596260460848, 0.48013593018487916, 0.31018515779150996, 0.08583130495616512, 0.05000000074505806, 0.4638343005945911, 0.3375099825625899, 0.3251399661353105, 0.16101857501833913, 0.519155872533409, 0.10123829083119665, 0.05000000074505806, 0.2838674120687407, 0.4561598450406691, 0.273733097474054]"
+160,42.88999999999999,0.42889999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_160,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07538641840203547, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06932495002691322, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+161,47.760000000000005,0.4776,20283264,50.01988636363637,30.520441097364177,BEST_adc_episode_161,"[0.25, 0, 0.25, 0.1875, 0.0625, 0.4375, 0.125, 0.25, 0.15625, 0.15625, 0.0625, 0.34375, 0.40625, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.2935783575717167, 0.05000000074505806, 0.2526102584974049, 0.2490098570638325, 0.07226599872194522, 0.4833316591914026, 0.13374759688965399, 0.2754376594850053, 0.16169072504012993, 0.16905900803017548, 0.07558645745408893, 0.3673404271823788, 0.05059000465327055, 0.34117009473748094, 0.20795068466431288, 0.2509601019990428, 0.27005159310396626, 0.0666100680933133, 0.18950827341013315]"
+162,62.339999999999996,0.6234,20306304,50.07670454545454,45.108257127487896,BEST_adc_episode_162,"[0.3125, 0.375, 0.125, 0.125, 0.375, 0.0625, 0.375, 0.375, 0.1875, 0.1875, 0.71875, 0.03125, 0.21875, 0.125, 0.328125, 0.359375, 0.578125, 0.125, 0.546875]","[0.346038511924206, 0.3917999392397691, 0.14573513600707672, 0.18011123047801353, 0.37797587007321753, 0.07430326973435233, 0.4311277673529621, 0.3972259805289938, 0.20498188088219302, 0.19680719113597517, 0.7329977614246576, 0.05000000074505806, 0.2360358266338905, 0.13317298626964216, 0.32925407272689156, 0.3674893843660174, 0.5785948636410583, 0.1341061910201145, 0.29621200224246946]"
+163,43.86,0.4386,20279808,50.01136363636364,35.80554061323292,BEST_adc_episode_163,"[0.1875, 0.375, 0, 0, 0.0625, 0.125, 0, 0.09375, 0.46875, 0.625, 0.375, 0.6875, 0.09375, 0.234375, 0.40625, 0.265625, 0.734375, 0.25, 0.734375]","[0.20587200083971335, 0.40617646708724925, 0.05000000074505806, 0.05000000074505806, 0.08465970973264801, 0.12840540156549135, 0.05000000074505806, 0.1068577597477075, 0.49963523848622315, 0.6261687860566443, 0.38850395903839363, 0.7151514842477031, 0.10314895179897049, 0.24551793031487157, 0.4070459969544767, 0.2005971192618784, 0.3415551440860614, 0.11233132989166945, 0.05000000074505806]"
+164,52.910000000000004,0.5291,20278656,50.008522727272734,48.54760623991393,BEST_adc_episode_164,"[0, 0.75, 0, 0.4375, 0.0625, 0.0625, 0.0625, 0.0625, 0.375, 0.6875, 0.5, 0.34375, 0.03125, 0.375, 0.046875, 0.4375, 0.234375, 0.484375, 0.109375]","[0.051415303418482924, 0.800000011920929, 0.05000000074505806, 0.4709164920681258, 0.11393377282794445, 0.08672192099412027, 0.09961473572241031, 0.06445939515306581, 0.3909190694414068, 0.709807395580616, 0.5122412725660824, 0.3562859153244733, 0.05000000074505806, 0.3895175255735538, 0.062216476233904805, 0.4525451336808783, 0.2399257714350236, 0.49117916499460257, 0.09755641538070342]"
+165,56.769999999999996,0.5677,20276352,50.00284090909091,38.54895104895105,BEST_adc_episode_165,"[0.125, 0.0625, 0.125, 0.125, 0.125, 0.125, 0.1875, 0.125, 0.78125, 0.78125, 0.21875, 0.3125, 0.40625, 0.125, 0.09375, 0.53125, 0.578125, 0.53125, 0.578125]","[0.1505102177196397, 0.07246063426009325, 0.16289690270445392, 0.12921945967493925, 0.1692700073631872, 0.16447976223648528, 0.2040696137477651, 0.14121272354902858, 0.800000011920929, 0.800000011920929, 0.21998838548492092, 0.32286769925448744, 0.4276160083586119, 0.12703114888112962, 0.10382649987815606, 0.23059818746052774, 0.5073341130215296, 0.31924461062539267, 0.23946562605872504]"
+166,32.04,0.3204,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_166,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05125281989485947, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07228842324013462, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08050930623524973, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+167,40.73,0.4073,19755648,48.71875,34.773399677245834,BEST_adc_episode_167,"[0.4375, 0.1875, 0.25, 0.3125, 0, 0.125, 0.25, 0.40625, 0.28125, 0.03125, 0.03125, 0.46875, 0.78125, 0.359375, 0.046875, 0.46875, 0.640625, 0.78125, 0.5625]","[0.4402675343934209, 0.1981779279900966, 0.2513941319647579, 0.3298727354469701, 0.05000000074505806, 0.13767847220711837, 0.2801528201142125, 0.4178953732751818, 0.3097954862371384, 0.05000000074505806, 0.05000000074505806, 0.47223192988687734, 0.800000011920929, 0.3638450269467342, 0.05000000074505806, 0.3411295976315072, 0.20345735019737787, 0.7913274436119582, 0.5711913788318574]"
+168,68.11,0.6811,20196288,49.80539772727273,52.47444862829478,BEST_adc_episode_168,"[0.25, 0.1875, 0.625, 0.3125, 0.5625, 0.1875, 0.0625, 0.46875, 0.28125, 0.53125, 0.03125, 0.1875, 0.125, 0.1875, 0.40625, 0.203125, 0.234375, 0.296875, 0.359375]","[0.25424542196722844, 0.24762715025443505, 0.6667608055714286, 0.37281408796224125, 0.5684134031046754, 0.2065783703632119, 0.08804324538388228, 0.4733786038488628, 0.2891783794399303, 0.5508348023939755, 0.05000000074505806, 0.21138255105827536, 0.12933496365148212, 0.19673735691853214, 0.41279547318465293, 0.21142627505215322, 0.24524824629640135, 0.3119145432787447, 0.3670077568689326]"
+169,63.92,0.6392,19707840,48.60085227272727,55.88690155997848,BEST_adc_episode_169,"[0.5, 0.625, 0.125, 0.4375, 0.625, 0.1875, 0.4375, 0.125, 0.125, 0.15625, 0.03125, 0.6875, 0.15625, 0.4375, 0.296875, 0.234375, 0.25, 0.046875, 0.28125]","[0.524995856379161, 0.6311036264313837, 0.18251994249855674, 0.46836161193293624, 0.6543619310749775, 0.2362541827735073, 0.4698820027112447, 0.14553935319269146, 0.13421319361603717, 0.17194923821442865, 0.05000000074505806, 0.7175976715276194, 0.17861774333586314, 0.43971217529132967, 0.31058901793998506, 0.2473900018433084, 0.2635593019920994, 0.0623989326113682, 0.28483802471502445]"
+170,48.34,0.48340000000000005,18470016,45.54829545454545,54.72364174287251,BEST_adc_episode_170,"[0.0625, 0.5625, 0.5, 0.375, 0.625, 0.4375, 0, 0.5, 0.78125, 0.09375, 0.0625, 0.40625, 0.125, 0.265625, 0.046875, 0.09375, 0.109375, 0.671875, 0.28125]","[0.06844642279361646, 0.6176753294915789, 0.5562486788213753, 0.3930405852478911, 0.6573478782899078, 0.49923224939262106, 0.05000000074505806, 0.5279714635366282, 0.800000011920929, 0.11475047085177448, 0.07167642181086362, 0.41191829389973983, 0.15518640619414634, 0.27881146528099443, 0.05000000074505806, 0.09721074055396003, 0.11576395751876477, 0.6780760054786937, 0.29644142596767953]"
+171,27.22,0.2722,14197824,35.012784090909086,27.306347498655192,BEST_adc_episode_171,"[0.4375, 0.4375, 0.375, 0.125, 0.4375, 0.0625, 0, 0.71875, 0.78125, 0.0625, 0.46875, 0.71875, 0.1875, 0.609375, 0.390625, 0.375, 0.359375, 0.796875, 0.265625]","[0.4986224682132003, 0.44917412234692, 0.42479976073475995, 0.18580347434156297, 0.4636508465004639, 0.09441246479248629, 0.05000000074505806, 0.7372133300193109, 0.800000011920929, 0.0901397522164786, 0.49776950652766827, 0.7244094624649138, 0.21408652306272125, 0.6111930913083599, 0.39964049551156366, 0.3828081869310852, 0.3646646768505257, 0.800000011920929, 0.2796048701092851]"
+172,35.629999999999995,0.35629999999999995,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_172,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06715373870527144, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06646641513779429]"
+173,47.58,0.4758,20283264,50.01988636363637,30.58095750403443,BEST_adc_episode_173,"[0.1875, 0.125, 0, 0.1875, 0, 0.4375, 0.3125, 0.34375, 0.03125, 0.375, 0.46875, 0.125, 0.03125, 0.640625, 0.453125, 0.625, 0.46875, 0.625, 0.46875]","[0.24391274069876087, 0.1491968154231787, 0.05592324796685754, 0.23916717054696396, 0.053279245955117974, 0.49999033733302156, 0.3712706525033954, 0.37022976193786095, 0.05000000074505806, 0.3761854520526643, 0.49755837218958016, 0.13298026782281477, 0.051433303112124476, 0.41085269699837645, 0.09424969034721574, 0.28895462949903084, 0.05000000074505806, 0.07447937056373918, 0.1980314065548023]"
+174,35.84,0.35840000000000005,20249856,49.9375,30.01277568585261,BEST_adc_episode_174,"[0, 0.25, 0.0625, 0.1875, 0.0625, 0.4375, 0.1875, 0.4375, 0.15625, 0.03125, 0.125, 0.125, 0.75, 0.6875, 0.1875, 0.75, 0.203125, 0.75, 0.25]","[0.05000000074505806, 0.30820619299442986, 0.07111838924271308, 0.20806770049214246, 0.06255458178091582, 0.4643873988181413, 0.1995358273271829, 0.45667020138135705, 0.17629145243215005, 0.05749511987522557, 0.12927167695101183, 0.13578802330938894, 0.13349223886838174, 0.6988043656148218, 0.05000000074505806, 0.33350021571496147, 0.06091721321693468, 0.13485517067230085, 0.25848391573852947]"
+175,66.31,0.6631,20279232,50.00994318181819,47.33055406132329,BEST_adc_episode_175,"[0.1875, 0.3125, 0.4375, 0.3125, 0.125, 0.3125, 0.375, 0.1875, 0.375, 0.03125, 0.40625, 0.125, 0.40625, 0.109375, 0.125, 0.0625, 0.75, 0.203125, 0.75]","[0.18887080345180957, 0.34687806435640395, 0.49530177007236076, 0.3197389813565286, 0.17747223465473413, 0.3529922272410894, 0.4007375062290969, 0.19474288434684156, 0.4017715446054367, 0.05000000074505806, 0.4370755352839923, 0.14235862906330482, 0.4118103639174273, 0.12491655815415638, 0.13030520989595257, 0.07016864130773147, 0.07399725793269218, 0.1639401249737656, 0.6115509366629]"
+176,42.72,0.42719999999999997,20131200,49.64488636363637,30.591043571812804,BEST_adc_episode_176,"[0.125, 0.4375, 0.75, 0, 0, 0.0625, 0.3125, 0.09375, 0.0625, 0.09375, 0.03125, 0.1875, 0.75, 0.3125, 0.703125, 0.328125, 0.703125, 0.328125, 0.796875]","[0.1469999795603572, 0.4558054654586419, 0.800000011920929, 0.05000000074505806, 0.05000000074505806, 0.08925775660808247, 0.3137455882708995, 0.1016810204926569, 0.08704391133381015, 0.09745420455839958, 0.05000000074505806, 0.18252160938046247, 0.28802879767108197, 0.06860903851504968, 0.2945331313385367, 0.06627041740265321, 0.268705287673624, 0.2740060294570983, 0.800000011920929]"
+177,45.03,0.45030000000000003,20286720,50.028409090909086,29.95562130177515,BEST_adc_episode_177,"[0.0625, 0.3125, 0.1875, 0.1875, 0.1875, 0, 0.25, 0.375, 0.15625, 0.03125, 0.4375, 0.0625, 0.375, 0.6875, 0.4375, 0.625, 0.4375, 0.640625, 0.4375]","[0.11505558884975431, 0.32177155094742904, 0.20226725556932462, 0.2458375721206732, 0.2357295135541077, 0.05000000074505806, 0.2528937895867604, 0.3925235373401733, 0.17723655383791087, 0.05000000074505806, 0.45866368587356393, 0.09111831704503937, 0.3449620416810064, 0.16610891220213053, 0.4520625645486278, 0.6352753813245268, 0.24976094112302188, 0.35815450314747826, 0.05000000074505806]"
+178,32.54,0.32539999999999997,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_178,"[0, 0.0625, 0, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.09160152874892125, 0.05000000074505806, 0.05000000074505806, 0.06027441575852048, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06098551493340919, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05526014907870653, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07988335083382432]"
+179,52.47999999999999,0.5247999999999999,20080512,49.51988636363636,31.83835395373857,BEST_adc_episode_179,"[0.25, 0.6875, 0.125, 0.125, 0.25, 0.125, 0, 0.03125, 0.09375, 0.1875, 0.0625, 0.21875, 0.78125, 0.1875, 0.578125, 0.53125, 0.5625, 0.546875, 0.75]","[0.3109257936265934, 0.7096488350157124, 0.16974146606975132, 0.13287642615009568, 0.29519560694730385, 0.1838813068392986, 0.05000000074505806, 0.05000000074505806, 0.12236374117190169, 0.20024388634228008, 0.09080571669947578, 0.2384261394036742, 0.800000011920929, 0.19864270347558866, 0.27424992008272103, 0.1569194550965193, 0.11269023311749518, 0.19162574309433783, 0.751016953597546]"
+180,57.709999999999994,0.5771,20290752,50.03835227272727,35.929935449166216,BEST_adc_episode_180,"[0, 0.3125, 0, 0.3125, 0.125, 0.375, 0.5, 0.15625, 0.28125, 0.09375, 0.0625, 0.3125, 0.4375, 0.5625, 0.21875, 0.390625, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.3392333307244331, 0.05000000074505806, 0.3371319425925867, 0.17977740931473735, 0.40002460562030684, 0.5181485611071347, 0.18011716465163263, 0.28678718255503066, 0.10034826312627608, 0.07117813205275564, 0.3254224244848917, 0.4477854583764153, 0.5726801196114554, 0.22682027701428953, 0.1564680487511806, 0.07740597240047939, 0.19432023114456742, 0.5619372053986569]"
+181,33.099999999999994,0.33099999999999996,18114624,44.671875,44.035771920387305,BEST_adc_episode_181,"[0.0625, 0.5625, 0.1875, 0.125, 0.3125, 0.25, 0.5625, 0.03125, 0.34375, 0.03125, 0.6875, 0.40625, 0.78125, 0.46875, 0.09375, 0.359375, 0.484375, 0.15625, 0.296875]","[0.09015269438208197, 0.5858527985880259, 0.1891825797720923, 0.17219104863054926, 0.3219680391777625, 0.2926530208701409, 0.5850725235805831, 0.060398231642562264, 0.3462119272806078, 0.05000000074505806, 0.6979645266583967, 0.43648917078065097, 0.800000011920929, 0.4752903309488633, 0.10244521545863972, 0.36595351954822664, 0.4947733418860956, 0.1594359284858286, 0.3118175143852165]"
+182,55.35,0.5535,20281536,50.015625,42.926304464766005,BEST_adc_episode_182,"[0.5, 0, 0.5625, 0.375, 0.125, 0.125, 0.4375, 0.03125, 0.40625, 0.03125, 0.125, 0.34375, 0.125, 0.125, 0.53125, 0.046875, 0.546875, 0.5625, 0.546875]","[0.5185352371294534, 0.05000000074505806, 0.6070141758726623, 0.42287433993569096, 0.18551684877783597, 0.13881288006873527, 0.446423221339553, 0.05000000074505806, 0.41793630396197845, 0.05000000074505806, 0.15321888389500654, 0.36232610440068863, 0.12652027859672224, 0.12772266461696488, 0.5357863610079066, 0.057673529290950085, 0.1946327316926937, 0.3458040778559155, 0.2621944802656142]"
+183,63.36999999999999,0.6336999999999999,20299392,50.05965909090909,44.02904787520172,BEST_adc_episode_183,"[0.5, 0.1875, 0.375, 0.1875, 0.0625, 0.25, 0.0625, 0.375, 0.78125, 0.03125, 0.40625, 0.09375, 0.0625, 0.265625, 0.09375, 0.21875, 0.75, 0.21875, 0.734375]","[0.5563660690172813, 0.23534458154999632, 0.43315467388252543, 0.20853701870281594, 0.0625856328163114, 0.2752089230064816, 0.06299595517282518, 0.4051465660030869, 0.800000011920929, 0.05000000074505806, 0.4236849678725797, 0.10747029103870417, 0.07074362313455315, 0.2722551197023699, 0.10355636560697179, 0.15545230924494283, 0.05000000074505806, 0.09412231589112258, 0.4594245165805604]"
+184,27.969999999999995,0.27969999999999995,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_184,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.051668421153368255, 0.05000000074505806, 0.05000000074505806, 0.07186210963927718, 0.05000000074505806, 0.05819187222690165, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+185,36.029999999999994,0.36029999999999995,20034432,49.40625,49.48224852071006,BEST_adc_episode_185,"[0.3125, 0.375, 0, 0.5, 0.1875, 0.1875, 0.25, 0.28125, 0.3125, 0.25, 0.125, 0.78125, 0.0625, 0.171875, 0.609375, 0.1875, 0.21875, 0.234375, 0.296875]","[0.3128602527437461, 0.4171763721064976, 0.05000000074505806, 0.5341507935563077, 0.24490929275669993, 0.20810184291660422, 0.30230903542250404, 0.29431387326122105, 0.33821546007271674, 0.2743680249550129, 0.1484697123799527, 0.800000011920929, 0.07105566666793016, 0.17230335773368394, 0.6155870432537693, 0.20067050175960516, 0.2210190049611279, 0.2433219578283501, 0.306742767451996]"
+186,68.61,0.6861,20284992,50.02414772727273,47.43477676169984,BEST_adc_episode_186,"[0, 0.75, 0.375, 0.375, 0.125, 0.1875, 0.375, 0.28125, 0.1875, 0.03125, 0.3125, 0.25, 0.125, 0.296875, 0.234375, 0.09375, 0.46875, 0.640625, 0.4375]","[0.05000000074505806, 0.800000011920929, 0.42005061361593216, 0.4098380215540025, 0.12567280375008444, 0.19138318177877484, 0.4239682285259907, 0.30611472712528615, 0.21701066094085963, 0.05000000074505806, 0.33257272344763683, 0.2739770809698665, 0.1283296375020241, 0.3003135968371502, 0.24951597937867115, 0.0970882454813661, 0.2804346704026338, 0.13532559629461208, 0.3830584213798475]"
+187,64.88000000000001,0.6488,20282688,50.018465909090914,38.192576654115115,BEST_adc_episode_187,"[0.1875, 0.75, 0.25, 0.0625, 0.25, 0.3125, 0, 0.1875, 0.375, 0.28125, 0.0625, 0.0625, 0.03125, 0.109375, 0.5625, 0.546875, 0.5625, 0.53125, 0.578125]","[0.22387026242911418, 0.800000011920929, 0.299927206381255, 0.07026982876291621, 0.302667687496611, 0.315666323216149, 0.05000000074505806, 0.1895683981273466, 0.3839517475200138, 0.29546310724103625, 0.0921110935218608, 0.08251350922522252, 0.05000000074505806, 0.12325742153893825, 0.0767990806362315, 0.05000000074505806, 0.19918958278461724, 0.4105219403678624, 0.32988933330436165]"
+188,59.8,0.598,20283840,50.02130681818182,37.493275954814415,BEST_adc_episode_188,"[0.375, 0.1875, 0.25, 0.3125, 0.125, 0.1875, 0.3125, 0.40625, 0.28125, 0.25, 0.25, 0.03125, 0.125, 0.421875, 0.140625, 0.625, 0.46875, 0.625, 0.46875]","[0.4053403191338475, 0.20276890498385486, 0.25851926842515727, 0.3599830913809553, 0.16913139161466917, 0.20083139184469115, 0.3203793567415491, 0.4163953320366103, 0.2833888877244699, 0.27799917652635536, 0.2671966628533067, 0.05000000074505806, 0.14479681700862418, 0.4255376142538586, 0.15496333543406365, 0.3729894494268284, 0.11595033255340981, 0.3097460787623532, 0.33272040993768726]"
+189,48.72,0.48719999999999997,20289024,50.034090909090914,45.62264658418505,BEST_adc_episode_189,"[0.125, 0.25, 0.375, 0, 0.0625, 0, 0.5625, 0.15625, 0.78125, 0.3125, 0.25, 0.46875, 0.375, 0.046875, 0.1875, 0.046875, 0.625, 0.5625, 0.453125]","[0.1426947288562382, 0.29224868200003723, 0.4123187090258975, 0.05000000074505806, 0.11917447886536674, 0.05000000074505806, 0.5796168244869371, 0.1579780908790886, 0.800000011920929, 0.31748417577180205, 0.25138049730732703, 0.4692184833440882, 0.4015154768421056, 0.05000000074505806, 0.18809816200147428, 0.05000000074505806, 0.5161182801006334, 0.5665451177647187, 0.4461026368550486]"
+190,20.87,0.2087,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_190,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.07265381223600303, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07045736097130624, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+191,43.64,0.4364,20289600,50.03551136363637,42.34803657880581,BEST_adc_episode_191,"[0.1875, 0.0625, 0.0625, 0.25, 0.75, 0.25, 0.3125, 0.46875, 0.09375, 0.03125, 0.15625, 0.4375, 0.46875, 0.328125, 0.140625, 0.15625, 0.765625, 0.265625, 0.6875]","[0.24337800722235114, 0.10805353287730571, 0.0916508241262032, 0.2733923659836196, 0.800000011920929, 0.29300557644329106, 0.3158606356334286, 0.49364084322223495, 0.12203279690463578, 0.05576774413408565, 0.18629380806798837, 0.4582641818328607, 0.4968111817380551, 0.33862703024786023, 0.14386859841040806, 0.05000000074505806, 0.40769335657911454, 0.27890637631808735, 0.1409276833794477]"
+192,60.099999999999994,0.601,20259072,49.96022727272727,37.378967186659494,BEST_adc_episode_192,"[0.125, 0.5625, 0.25, 0.0625, 0.125, 0.0625, 0, 0.59375, 0.4375, 0.0625, 0.375, 0.03125, 0.5, 0.046875, 0.3125, 0.609375, 0.375, 0.671875, 0.4375]","[0.14209069680573005, 0.5814099968412463, 0.2844975834428956, 0.10081092694145061, 0.16793313001787286, 0.08049904534987185, 0.05000000074505806, 0.6026218218656163, 0.45949350985568627, 0.07137423736740436, 0.4006691630842705, 0.05129203074444989, 0.5282245055141924, 0.05000000074505806, 0.3161975442676112, 0.6204774190367782, 0.2970398133894373, 0.11454116984558532, 0.4527389661127786]"
+193,55.400000000000006,0.554,20255040,49.95028409090909,40.07867132867133,BEST_adc_episode_193,"[0.25, 0, 0, 0.75, 0.1875, 0, 0.5625, 0.09375, 0.59375, 0.0625, 0.28125, 0.03125, 0.21875, 0.078125, 0.15625, 0.765625, 0.421875, 0.546875, 0.59375]","[0.26478410557189, 0.05000000074505806, 0.05000000074505806, 0.7742862101467527, 0.21664066324226583, 0.05000000074505806, 0.6249950896279678, 0.10160417131775486, 0.6184187289298226, 0.08699346858486716, 0.2844057551622806, 0.060694039129437584, 0.2312808020361224, 0.078962832594941, 0.07188227040333037, 0.16639826926041915, 0.4344355817681517, 0.2895252958477019, 0.6079609433588604]"
+194,70.75,0.7075,20276928,50.00426136363636,59.29935449166218,BEST_adc_episode_194,"[0.5, 0.6875, 0.625, 0.25, 0.25, 0.125, 0.3125, 0.65625, 0.3125, 0.625, 0.0625, 0.125, 0.03125, 0.203125, 0.046875, 0.203125, 0.046875, 0.328125, 0.71875]","[0.5508100977465247, 0.7077647488571883, 0.6712742379420831, 0.2553417777005791, 0.2633932346591549, 0.16942540517051116, 0.3389528960116859, 0.6757303869884852, 0.3144390503488912, 0.6323227249720784, 0.06400042935632416, 0.14169427927225414, 0.05671512350073156, 0.21665729446379173, 0.05000000074505806, 0.21701951764506308, 0.05000000074505806, 0.1256541114651931, 0.12843122966736067]"
+195,48.019999999999996,0.48019999999999996,17419392,42.95738636363636,41.7327864443249,BEST_adc_episode_195,"[0, 0.75, 0.375, 0.4375, 0.25, 0.3125, 0.6875, 0.0625, 0.125, 0.4375, 0.28125, 0.21875, 0.03125, 0.328125, 0.78125, 0.265625, 0.265625, 0.4375, 0.078125]","[0.05000000074505806, 0.800000011920929, 0.40656587109587516, 0.4376337544535325, 0.31077296784665515, 0.31836932547552454, 0.7175857323650808, 0.07399499341214533, 0.15161839134770325, 0.4421332148009654, 0.2970027830606542, 0.2473388837254476, 0.05000000074505806, 0.33373087159409287, 0.7852933884034685, 0.2773096800533748, 0.2800733866831739, 0.44806582258185906, 0.07827159502433334]"
+196,37.89,0.3789,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_196,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.09378367521404092, 0.06051679240267997, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08736040296461964, 0.05000000074505806, 0.09253847472183298, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+197,61.31999999999999,0.6132,19869696,49.0,55.99448628294782,BEST_adc_episode_197,"[0.6875, 0.25, 0.1875, 0.6875, 0.0625, 0.25, 0.0625, 0.34375, 0.09375, 0.71875, 0.15625, 0.4375, 0.34375, 0.1875, 0.046875, 0.40625, 0.046875, 0.1875, 0.53125]","[0.6970061120658916, 0.2835971061283835, 0.24405478684280146, 0.7076575918270872, 0.09227947078641333, 0.2990471708103563, 0.12143858149525427, 0.370281098224252, 0.12190038315805, 0.7279306513587998, 0.17683968515419504, 0.45991400856682757, 0.36247391607133267, 0.1990345030179666, 0.05000000074505806, 0.4093533405679224, 0.05000000074505806, 0.1991452283642952, 0.5377006846659684]"
+198,65.44,0.6544,20286720,50.028409090909086,46.264792899408285,BEST_adc_episode_198,"[0, 0.0625, 0.6875, 0.3125, 0.1875, 0.5625, 0.125, 0.40625, 0.0625, 0.4375, 0.25, 0.09375, 0.03125, 0.40625, 0.046875, 0.203125, 0.578125, 0.5625, 0.515625]","[0.05000000074505806, 0.08187465748405098, 0.7376902856538096, 0.33773862426373835, 0.19115661682327417, 0.5899954699559375, 0.1325622722660426, 0.4113263785742136, 0.07964189997087126, 0.45607097136178476, 0.2548112528072042, 0.12091836951048396, 0.05000000074505806, 0.40999118090292247, 0.05000000074505806, 0.20566185625343225, 0.44929764643507464, 0.5688087479275191, 0.3038919644075933]"
+199,30.95,0.3095,18719424,46.16335227272727,42.48251748251749,BEST_adc_episode_199,"[0.1875, 0.375, 0.125, 0.0625, 0.5, 0.0625, 0.1875, 0.15625, 0.53125, 0.28125, 0.78125, 0.40625, 0.25, 0.1875, 0.75, 0.0625, 0.046875, 0.515625, 0.375]","[0.21688775647587588, 0.4090105642858086, 0.1250621666723203, 0.0952867596247871, 0.5596872975084032, 0.10415217088709243, 0.24172901889224907, 0.17363114527283785, 0.5535344858574052, 0.2851059112616487, 0.800000011920929, 0.43352427816122585, 0.2764707326248114, 0.19503623916793567, 0.7501770512698506, 0.07004475119187124, 0.05000000074505806, 0.5183355096586109, 0.3898354852460575]"
+200,43.85,0.4385,18185472,44.846590909090914,46.23453469607316,BEST_adc_episode_200,"[0, 0.125, 0.3125, 0.75, 0.5625, 0.125, 0.25, 0.21875, 0.125, 0.375, 0.71875, 0.6875, 0.09375, 0.046875, 0.546875, 0.046875, 0.046875, 0.75, 0.15625]","[0.05000000074505806, 0.16584491309785543, 0.33850730208795315, 0.800000011920929, 0.5842616774469666, 0.1479219350490099, 0.26578356400469505, 0.24034460974865857, 0.1392918294873019, 0.3837905676729593, 0.7252023985704283, 0.6962587319490432, 0.10892954140966685, 0.05000000074505806, 0.5514688253203234, 0.05000000074505806, 0.05218839360896506, 0.758996044175181, 0.16838611435753645]"
+201,62.94,0.6294,20298816,50.05823863636364,53.869688004303384,BEST_adc_episode_201,"[0.5625, 0.1875, 0.5, 0, 0.6875, 0, 0.125, 0.6875, 0.71875, 0.375, 0.03125, 0.03125, 0.21875, 0.140625, 0.09375, 0.3125, 0.265625, 0.3125, 0.546875]","[0.5737199810063219, 0.20121385564307975, 0.5290506704212492, 0.05000000074505806, 0.7463536278204517, 0.05000000074505806, 0.17430120117616854, 0.7108058212622279, 0.7456604095308494, 0.38278166681930037, 0.05000000074505806, 0.05000000074505806, 0.229872084473139, 0.15308693296835224, 0.09811792876797423, 0.3195581328805902, 0.27588229152908245, 0.3276422522911623, 0.1286792716497236]"
+202,32.68,0.3268,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_202,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06508833862350114, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08088689162045168, 0.05000000074505806, 0.05000000074505806]"
+203,40.54,0.4054,20280384,50.01278409090909,29.706831629908553,BEST_adc_episode_203,"[0.1875, 0.0625, 0.375, 0.0625, 0.0625, 0, 0.125, 0.125, 0.34375, 0.71875, 0.28125, 0.1875, 0.15625, 0.515625, 0.578125, 0.53125, 0.578125, 0.515625, 0.59375]","[0.20357327707573453, 0.09456455878598455, 0.4246066728921977, 0.12360768887941273, 0.09635025481431701, 0.05000000074505806, 0.1775852405678601, 0.12955909240158853, 0.36263942070711475, 0.7241931561897458, 0.3114038776269745, 0.2180529308974405, 0.1678609951133626, 0.36015199295923145, 0.12932367804093914, 0.4517191546936435, 0.08845273725578055, 0.18891966448914013, 0.22688065809555752]"
+204,48.1,0.48100000000000004,18496512,45.61363636363636,40.15263582571275,BEST_adc_episode_204,"[0.125, 0.5625, 0, 0.5625, 0.5, 0.125, 0.0625, 0.125, 0.28125, 0.09375, 0.28125, 0.53125, 0.4375, 0.125, 0.078125, 0.625, 0.65625, 0.5625, 0.265625]","[0.1385805734151045, 0.6089580828207309, 0.05000000074505806, 0.6149639546086059, 0.5469838356650767, 0.16177085906772487, 0.11490324411923736, 0.14872443063296237, 0.3113444125997622, 0.0950093298262595, 0.3110418209646829, 0.5490941180694445, 0.4398969457841558, 0.12852202029300153, 0.08000363456749582, 0.6254000137385116, 0.6668236516233012, 0.5643286726246812, 0.271879533323439]"
+205,19.53,0.1953,15022080,37.04545454545455,35.09279182356105,BEST_adc_episode_205,"[0.5625, 0.5, 0.375, 0.25, 0.5, 0, 0.375, 0.46875, 0.4375, 0.6875, 0.28125, 0.03125, 0.6875, 0.65625, 0.46875, 0.40625, 0.25, 0.3125, 0.40625]","[0.5917113829398076, 0.5170031165210407, 0.3958597380866673, 0.27690667906204725, 0.545744106282869, 0.05000000074505806, 0.3907653378354403, 0.4694332009624839, 0.4425304400023812, 0.7007448360418668, 0.2904736769372054, 0.05000000074505806, 0.691298697670251, 0.660839745490578, 0.4829399887015069, 0.4075378886702107, 0.25846777685670164, 0.3154777393740996, 0.4073423391661022]"
+206,41.06999999999999,0.41069999999999995,20284992,50.02414772727273,29.511834319526624,BEST_adc_episode_206,"[0.25, 0.1875, 0.125, 0.0625, 0.25, 0.0625, 0.1875, 0.15625, 0.03125, 0.03125, 0.40625, 0.6875, 0.40625, 0.65625, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375]","[0.2596010477196122, 0.2400021128049138, 0.16889567399892075, 0.08037923503564509, 0.2637375491969233, 0.0743719288434688, 0.23177740484317627, 0.18152549628016457, 0.05000000074505806, 0.05000000074505806, 0.25692897035002954, 0.42215677876852087, 0.4243837530908453, 0.33964331161315287, 0.27304155336136576, 0.3909540166442575, 0.05000000074505806, 0.05000000074505806, 0.2364307821533151]"
+207,53.459999999999994,0.5346,20281536,50.015625,41.58485745024206,BEST_adc_episode_207,"[0, 0.3125, 0.125, 0.375, 0, 0.125, 0.3125, 0.125, 0.125, 0.78125, 0.0625, 0.3125, 0.75, 0.046875, 0.3125, 0.4375, 0.203125, 0.515625, 0.59375]","[0.05000000074505806, 0.3745536121325688, 0.14985057481853295, 0.3829097262434473, 0.05000000074505806, 0.1593360915034345, 0.33328387158354866, 0.14238671212859105, 0.14460273313086666, 0.800000011920929, 0.06398745185677494, 0.3320808106781175, 0.753188202344961, 0.05000000074505806, 0.31352194740949846, 0.4500048249906731, 0.2097447248723261, 0.05000000074505806, 0.25763289817259083]"
+208,31.44,0.3144,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_208,"[0, 0, 0, 0, 0, 0.0625, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08821843291645402, 0.05000000074505806, 0.053224553384873856, 0.12997689758126074, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+209,62.09,0.6209,18674496,46.05255681818182,40.47538999462076,BEST_adc_episode_209,"[0.5, 0.1875, 0.125, 0.5625, 0.4375, 0.1875, 0.3125, 0.5, 0.40625, 0.03125, 0.25, 0.1875, 0.15625, 0.296875, 0.46875, 0.421875, 0.40625, 0.28125, 0.65625]","[0.5023200880482879, 0.193933109345264, 0.12832002371748183, 0.5792749451642605, 0.45120416564978194, 0.23063393537741747, 0.3128146587942438, 0.5114906851081609, 0.4353241747933156, 0.05000000074505806, 0.27823747316116454, 0.20254940506837518, 0.18729999965082283, 0.29696864613172363, 0.4694530561400365, 0.42616602239661877, 0.41190321347183456, 0.28193236680571016, 0.6573795916689217]"
+210,34.48,0.3448,18353088,45.25994318181818,47.0817643894567,BEST_adc_episode_210,"[0.6875, 0.4375, 0.25, 0.25, 0.4375, 0.3125, 0.4375, 0.25, 0.25, 0.28125, 0.34375, 0.125, 0.5, 0.46875, 0.28125, 0.703125, 0.140625, 0.09375, 0.109375]","[0.7039406820709648, 0.4396891599230087, 0.2976637692406056, 0.3027409172571097, 0.4806817551873361, 0.31437680418810116, 0.4559045885471633, 0.2710359756471508, 0.25335280279822514, 0.2924844612805495, 0.37473266732598487, 0.15494548358013643, 0.5042576958632787, 0.47169999330709556, 0.284303523722406, 0.7091137610560094, 0.1461040355942845, 0.09841900764865905, 0.12348040650989492]"
+211,49.5,0.495,20291904,50.04119318181818,31.39792899408284,BEST_adc_episode_211,"[0, 0.25, 0, 0, 0, 0.625, 0.75, 0.3125, 0.03125, 0.25, 0.21875, 0.28125, 0.15625, 0.5, 0.71875, 0.203125, 0.703125, 0.34375, 0.6875]","[0.05000000074505806, 0.2986205558820392, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.6715178363319989, 0.7578798598678532, 0.3321740327017903, 0.05251912555234607, 0.27251806350688473, 0.2396252473337216, 0.2967447064059113, 0.16010586875562055, 0.44153454461946584, 0.7252829951331606, 0.21571428128354347, 0.2639232858841329, 0.05378147044715007, 0.45039370853535265]"
+212,26.69,0.2669,15657984,38.61363636363636,34.66917697686929,BEST_adc_episode_212,"[0.0625, 0.6875, 0.375, 0.3125, 0.375, 0.5, 0.0625, 0.53125, 0.03125, 0.21875, 0.03125, 0.78125, 0.03125, 0.796875, 0.546875, 0.78125, 0.203125, 0.1875, 0.25]","[0.06878841283612061, 0.6878801117993242, 0.38365601704732505, 0.3169890653962075, 0.40590867767551764, 0.5011426248512232, 0.09222422215425943, 0.5594795212190465, 0.05829068613559914, 0.24064578836999134, 0.05000000074505806, 0.800000011920929, 0.05151092861345108, 0.800000011920929, 0.5570623852952236, 0.789796144096919, 0.215043329233242, 0.19828295999233153, 0.26326745909287264]"
+213,50.42,0.5042,20043648,49.42897727272727,50.89766003227542,BEST_adc_episode_213,"[0.625, 0.3125, 0.125, 0.3125, 0.625, 0.6875, 0.1875, 0.25, 0.0625, 0.125, 0.3125, 0.21875, 0.0625, 0.296875, 0.21875, 0.15625, 0.078125, 0.75, 0.640625]","[0.6349366453557093, 0.32628437320889825, 0.14240028105831717, 0.32145461136999975, 0.6726096194579859, 0.7109180158876066, 0.22378379858469777, 0.27699200270439084, 0.08987978252053856, 0.13167879441361327, 0.32707824768725624, 0.23636803700938097, 0.0752611062499117, 0.3081706732524291, 0.23290244117190093, 0.16522332907691217, 0.08669432688485387, 0.16824084653219795, 0.6543016395450254]"
+214,36.629999999999995,0.36629999999999996,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_214,"[0, 0, 0, 0.0625, 0, 0.0625, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07060465106191668, 0.05000000074505806, 0.11220084168771373, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06048762062821348, 0.05134007898887924, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10265378943128538, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07052251797568176]"
+215,45.550000000000004,0.4555,20276352,50.00284090909091,60.17348036578806,BEST_adc_episode_215,"[0.125, 0.4375, 0, 0.375, 0.4375, 0.1875, 0.1875, 0.5625, 0.78125, 0.28125, 0.40625, 0.59375, 0.03125, 0.1875, 0.125, 0.046875, 0.40625, 0.125, 0.125]","[0.1403734261231191, 0.4390125831866786, 0.05000000074505806, 0.38721917297673253, 0.47943090098439045, 0.21476460122355578, 0.193301667289229, 0.5881860785290023, 0.800000011920929, 0.2935328993509269, 0.43203783059122797, 0.604943214050401, 0.05000000074505806, 0.1965424063639229, 0.13928983463745404, 0.05000000074505806, 0.4216839332228128, 0.1307733906180448, 0.11754396035680242]"
+216,55.790000000000006,0.5579000000000001,19941120,49.17613636363637,39.265061861215706,BEST_adc_episode_216,"[0.125, 0.0625, 0.0625, 0.4375, 0.5625, 0.75, 0.125, 0.5, 0.03125, 0.125, 0.28125, 0.0625, 0.03125, 0.15625, 0.21875, 0.734375, 0.28125, 0.71875, 0.796875]","[0.17216340537319527, 0.09131271879591746, 0.0768206537667246, 0.45429980155553956, 0.567202353708115, 0.7667707081780871, 0.15212425808527855, 0.527694794023895, 0.05000000074505806, 0.15022575217282438, 0.3056437353567417, 0.0927540270368459, 0.05000000074505806, 0.1570812794830331, 0.05000000074505806, 0.05000000074505806, 0.2904702954868538, 0.7062326222375558, 0.800000011920929]"
+217,54.089999999999996,0.5408999999999999,20284992,50.02414772727273,44.74179666487359,BEST_adc_episode_217,"[0.3125, 0, 0.3125, 0.75, 0.125, 0, 0.125, 0.0625, 0.46875, 0.28125, 0.0625, 0.65625, 0.15625, 0.203125, 0.265625, 0.046875, 0.625, 0.46875, 0.625]","[0.3279754697961353, 0.05000000074505806, 0.3698429423223238, 0.800000011920929, 0.12875095844057854, 0.05000000074505806, 0.16440806710659298, 0.07182128875107485, 0.4973833346527756, 0.29731192864043254, 0.06264507012422615, 0.6830077095628614, 0.17023271850025043, 0.21649120678833855, 0.2675166128426894, 0.05000000074505806, 0.4511292531393732, 0.16284629951510343, 0.494694575728]"
+218,49.83,0.49829999999999997,20280960,50.01420454545455,32.917563206024745,BEST_adc_episode_218,"[0.4375, 0.125, 0.0625, 0.0625, 0.0625, 0.375, 0.0625, 0.09375, 0.1875, 0.3125, 0.59375, 0.34375, 0.125, 0.0625, 0.78125, 0.796875, 0.09375, 0.59375, 0.515625]","[0.48583562988644285, 0.13349800466667341, 0.08119270646916321, 0.06760574768275147, 0.101217286989912, 0.4060145907359074, 0.0971495743104307, 0.09674360539440244, 0.21379738948096466, 0.3367045736143668, 0.6085822504943433, 0.35237776567740486, 0.1424892381599941, 0.05000000074505806, 0.30720174913149706, 0.800000011920929, 0.10889406594958803, 0.34946385946908815, 0.2822430309830977]"
+219,47.77,0.4777,19332864,47.67613636363636,36.63259817105971,BEST_adc_episode_219,"[0.125, 0.4375, 0, 0, 0.5, 0.5, 0.0625, 0.21875, 0.59375, 0.125, 0.03125, 0.25, 0.375, 0.796875, 0.40625, 0.265625, 0.078125, 0.796875, 0.0625]","[0.1298697378159023, 0.47533343344488194, 0.05000000074505806, 0.05000000074505806, 0.512533237440714, 0.5054788448043939, 0.12475295027211336, 0.24363554291899775, 0.6071623816810031, 0.153698578096914, 0.0533477405531695, 0.2783345394946764, 0.3812273804992312, 0.800000011920929, 0.4066513783429627, 0.27455399475768016, 0.08241254710983577, 0.800000011920929, 0.07751885129323448]"
+220,29.810000000000002,0.29810000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_220,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.06123167713629448, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.060405721949380924, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09164505104035439, 0.08460108388308801, 0.05000000074505806]"
+221,33.940000000000005,0.33940000000000003,19045440,46.96732954545455,46.26143087681549,BEST_adc_episode_221,"[0.3125, 0.25, 0.0625, 0.75, 0.75, 0, 0.0625, 0.21875, 0.09375, 0.0625, 0.6875, 0.0625, 0.75, 0.796875, 0.046875, 0.28125, 0.296875, 0.34375, 0.046875]","[0.35202255322552, 0.25255549249361114, 0.07860497144449877, 0.800000011920929, 0.800000011920929, 0.05000000074505806, 0.07640077021452395, 0.24594152078740886, 0.09461833450130709, 0.0744633199575459, 0.7057869192630496, 0.0836602367626564, 0.777275473392994, 0.800000011920929, 0.05000000074505806, 0.28127214030811915, 0.3065168575787495, 0.35085838129833696, 0.05000000074505806]"
+222,56.06,0.5606,20275200,50.0,41.06374394835933,BEST_adc_episode_222,"[0.125, 0.0625, 0.4375, 0, 0.25, 0.25, 0, 0.4375, 0.78125, 0.15625, 0.46875, 0.09375, 0.125, 0.203125, 0.15625, 0.5625, 0.203125, 0.734375, 0.265625]","[0.13396407407026936, 0.09286603031329631, 0.4789873195694819, 0.05000000074505806, 0.2873414915780305, 0.2810428379515666, 0.06174967876893318, 0.4386488376603839, 0.800000011920929, 0.1810210001738922, 0.4808366518823844, 0.09558763151167761, 0.14853916939170475, 0.20919820259631788, 0.1704167878652284, 0.5779146755224114, 0.2094293084053667, 0.5191221998827211, 0.09608348026558415]"
+223,37.93,0.37929999999999997,20275776,50.00142045454545,29.508472296933835,BEST_adc_episode_223,"[0, 0.25, 0.375, 0, 0.125, 0.1875, 0, 0.15625, 0.03125, 0.21875, 0.25, 0.59375, 0.5625, 0.53125, 0.578125, 0.515625, 0.59375, 0.5, 0.609375]","[0.05000000074505806, 0.27200434441356375, 0.4228532154418955, 0.05000000074505806, 0.1651190570401316, 0.2474668067294111, 0.06073492073799585, 0.18209496433346406, 0.05000000074505806, 0.23638181483911846, 0.279286425133627, 0.23717717006532382, 0.05000000074505806, 0.05000000074505806, 0.29482554769844094, 0.26336408658178073, 0.05000000074505806, 0.11408464349051968, 0.05000000074505806]"
+224,59.68,0.5968,20279232,50.00994318181819,39.786175363098444,BEST_adc_episode_224,"[0.0625, 0.75, 0.375, 0.1875, 0, 0.4375, 0.0625, 0.25, 0.0625, 0.03125, 0.15625, 0.34375, 0.15625, 0.453125, 0.171875, 0.515625, 0.484375, 0.609375, 0.5]","[0.07990283935270322, 0.7561556865759179, 0.4350473805784147, 0.19615460744968108, 0.05000000074505806, 0.49285474875268637, 0.09239187083956119, 0.25587566886368107, 0.07781370730836444, 0.05000000074505806, 0.1658015264673221, 0.3744751425401962, 0.18026627110878987, 0.4541706904541508, 0.1792873744022513, 0.5261467336961472, 0.36570447448642107, 0.05000000074505806, 0.23508932818577263]"
+225,54.96,0.5496,20293632,50.04545454545455,35.049085529854764,BEST_adc_episode_225,"[0, 0.0625, 0.4375, 0, 0.125, 0.3125, 0, 0.15625, 0.71875, 0.21875, 0.03125, 0.28125, 0.625, 0.28125, 0.40625, 0.234375, 0.734375, 0.265625, 0.71875]","[0.05000000074505806, 0.11325490152787164, 0.4525100638953667, 0.05000000074505806, 0.13767042555037537, 0.3281851367078158, 0.05000000074505806, 0.18517310960651054, 0.7242413981199276, 0.22771353657713816, 0.05415723727892779, 0.3077045127873656, 0.6509793820142565, 0.2870134901794375, 0.4149193100279482, 0.05424781319191431, 0.21905112857376977, 0.1957014626359062, 0.07176923465260807]"
+226,36.8,0.368,20288448,50.03267045454546,25.578267885960194,BEST_adc_episode_226,"[0, 0, 0, 0.0625, 0.0625, 0, 0, 0.0625, 0.6875, 0.375, 0.6875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09676174127763403, 0.07572467063586798, 0.05000000074505806, 0.05000000074505806, 0.06549191413105517, 0.058760363864580986, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0516796065994985, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+227,45.29,0.45289999999999997,20290176,50.03693181818182,27.42401828940291,BEST_adc_episode_227,"[0.125, 0, 0.1875, 0.125, 0.0625, 0, 0.0625, 0.15625, 0.3125, 0.25, 0.53125, 0.5625, 0.5625, 0.546875, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625]","[0.1722789113372543, 0.05000000074505806, 0.24108884758376115, 0.1760891958023254, 0.06539651109736613, 0.05000000074505806, 0.09834026332939247, 0.15771771047481556, 0.3342721891708333, 0.2754605192880986, 0.14167432682726264, 0.05382622620431824, 0.26755418727180713, 0.2985278097830323, 0.23057310224206604, 0.4484643141557288, 0.06043045004389622, 0.07075561787221778, 0.2574512231566323]"
+228,54.099999999999994,0.5409999999999999,20286720,50.028409090909086,33.57651963421194,BEST_adc_episode_228,"[0.1875, 0.0625, 0.25, 0.125, 0, 0.3125, 0.3125, 0.4375, 0.21875, 0.25, 0.46875, 0.03125, 0.28125, 0.203125, 0.53125, 0.578125, 0.515625, 0.59375, 0.5]","[0.22630350930031806, 0.10791413284035142, 0.25849462905211285, 0.12763059174157826, 0.05090419652000953, 0.33485109091130555, 0.34232264590709915, 0.43921875194520604, 0.2463305663847892, 0.2500578447444831, 0.49740637085551087, 0.05000000074505806, 0.2856237101798355, 0.21315668792975362, 0.226040767842223, 0.23120519156632408, 0.05000000074505806, 0.05000000074505806, 0.1689761802434453]"
+229,47.11,0.4711,20280384,50.01278409090909,29.20252824098978,BEST_adc_episode_229,"[0.125, 0.125, 0, 0.1875, 0.25, 0.0625, 0.375, 0.03125, 0.09375, 0.09375, 0.53125, 0.25, 0.71875, 0.359375, 0.6875, 0.359375, 0.6875, 0.359375, 0.6875]","[0.16836410271566996, 0.1268899293073012, 0.05000000074505806, 0.2266202065030058, 0.26601246986512583, 0.09606165575313837, 0.3887089771294086, 0.05000000074505806, 0.10507199902117154, 0.10102239109829335, 0.536285617541524, 0.2520626323882154, 0.05000000074505806, 0.2512144069876864, 0.305286795688664, 0.05000000074505806, 0.43430860485763195, 0.05765624593876015, 0.05000000074505806]"
+230,33.15,0.33149999999999996,20282688,50.018465909090914,28.47969338353954,BEST_adc_episode_230,"[0.125, 0.0625, 0.125, 0.0625, 0.25, 0.125, 0.0625, 0.03125, 0.3125, 0.03125, 0.46875, 0.625, 0.46875, 0.65625, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375]","[0.1678187929970466, 0.12119400239320365, 0.16845840952645902, 0.10827507758673577, 0.278668688139908, 0.15169075297165405, 0.08435086799477534, 0.05000000074505806, 0.33251709704772353, 0.05412856166594115, 0.4120265979115352, 0.055021633637852015, 0.05000000074505806, 0.14166582813856343, 0.1442506598782534, 0.05955852337911669, 0.07280424091566871, 0.1486065767262081, 0.18518294671746272]"
+231,33.81999999999999,0.33819999999999995,20280384,50.01278409090909,29.495024206562668,BEST_adc_episode_231,"[0.4375, 0.4375, 0.0625, 0.0625, 0.0625, 0.0625, 0.125, 0.03125, 0.09375, 0.25, 0.15625, 0.65625, 0.4375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.4626687210703707, 0.45745875424649407, 0.06871420990734711, 0.12030595106828543, 0.06378655927584538, 0.09347728984716724, 0.16593358787662243, 0.05000000074505806, 0.10868762067379235, 0.2646650482233018, 0.16273956391738642, 0.46667450196737176, 0.05000000074505806, 0.32099630882842434, 0.3385411946638468, 0.1526534656576592, 0.1625558423801459, 0.07155240777489275, 0.18914912470449818]"
+232,35.760000000000005,0.35760000000000003,20281536,50.015625,25.245427649273804,BEST_adc_episode_232,"[0, 0, 0.0625, 0, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05667148445094903, 0.05000000074505806, 0.0670990166019552, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06262257353844045, 0.05000000074505806, 0.05000000074505806, 0.0675258525544597, 0.05000000074505806, 0.09774022900653331, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+233,47.0,0.47,20284416,50.02272727272727,28.85287789133943,BEST_adc_episode_233,"[0.375, 0.125, 0.125, 0.125, 0.125, 0.0625, 0.0625, 0.1875, 0.375, 0.09375, 0.15625, 0.5, 0.59375, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625, 0.546875]","[0.38364874240618074, 0.1438950400152775, 0.1708267197122324, 0.18108311261257334, 0.15421371298116388, 0.08147300111964663, 0.10026594716417292, 0.20526650755512418, 0.3857663973969828, 0.0994973350607748, 0.1699731876010367, 0.23740029797802545, 0.12561443984358192, 0.27951180816101534, 0.05000000074505806, 0.13626552275597098, 0.1963914946278907, 0.06144189611315606, 0.3477730132100145]"
+234,36.46,0.36460000000000004,20276928,50.00426136363636,28.698224852071007,BEST_adc_episode_234,"[0.0625, 0.0625, 0.125, 0.3125, 0.125, 0, 0.25, 0.03125, 0.1875, 0.03125, 0.53125, 0.5625, 0.53125, 0.609375, 0.484375, 0.609375, 0.5, 0.59375, 0.515625]","[0.08131693119421385, 0.07380046968710485, 0.16298708750514607, 0.315902929913257, 0.1280199764365256, 0.05000000074505806, 0.25194771777887, 0.05000000074505806, 0.20335331222305153, 0.06025650696870496, 0.13577805193766865, 0.14214831092636274, 0.20339035836469543, 0.23381120175516873, 0.08838134591453278, 0.05000000074505806, 0.2140421960596451, 0.28808780636843434, 0.2594499501465321]"
+235,36.99,0.3699,20279232,50.00994318181819,27.98547606239914,BEST_adc_episode_235,"[0.0625, 0.25, 0.0625, 0, 0, 0.125, 0.3125, 0.1875, 0.15625, 0.0625, 0.625, 0.46875, 0.625, 0.5, 0.59375, 0.515625, 0.578125, 0.53125, 0.578125]","[0.06883841438639905, 0.30163586425423616, 0.06957135335927475, 0.05000000074505806, 0.05000000074505806, 0.16661471762038912, 0.3324418271846681, 0.21135226896083753, 0.16862936758367472, 0.08567108627480827, 0.05000000074505806, 0.05000000074505806, 0.0590008939209854, 0.10805021755781899, 0.19605973911047153, 0.09664693058569768, 0.05000000074505806, 0.05566717320491887, 0.12545560198230538]"
+236,46.19,0.4619,20276928,50.00426136363636,27.35677783754707,BEST_adc_episode_236,"[0.0625, 0.125, 0.0625, 0, 0.1875, 0.0625, 0, 0.03125, 0.09375, 0.6875, 0.40625, 0.65625, 0.4375, 0.640625, 0.4375, 0.640625, 0.453125, 0.625, 0.484375]","[0.09595323478194949, 0.16500381178613538, 0.11676658300258137, 0.05000000074505806, 0.22843298473962168, 0.07995973493327643, 0.05000000074505806, 0.05000000074505806, 0.09494910378598272, 0.16418602000486776, 0.05000000074505806, 0.3594062857719495, 0.16529622283471687, 0.11160601538485572, 0.13100010558398004, 0.05000000074505806, 0.05000000074505806, 0.16125512737900435, 0.05000000074505806]"
+237,40.56999999999999,0.40569999999999995,20286720,50.028409090909086,27.655997848305542,BEST_adc_episode_237,"[0.125, 0, 0.0625, 0.125, 0.25, 0, 0.0625, 0.0625, 0.09375, 0.46875, 0.65625, 0.40625, 0.65625, 0.453125, 0.625, 0.484375, 0.609375, 0.484375, 0.609375]","[0.13263794013572253, 0.05000000074505806, 0.10031607605153163, 0.1602023949110839, 0.2677917456983866, 0.05000000074505806, 0.09596342053454478, 0.08353448020640071, 0.11312275209361125, 0.05000000074505806, 0.060120900544205436, 0.10391417913115403, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.18339058791686594, 0.05000000074505806, 0.24185779892069698, 0.11379554585043154]"
+238,29.310000000000002,0.2931,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_238,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06275335500321319, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.051621943420105586, 0.05000000074505806, 0.05475580639203244, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+239,37.870000000000005,0.37870000000000004,20275776,50.00142045454545,27.017213555675095,BEST_adc_episode_239,"[0, 0.0625, 0.25, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625, 0.78125, 0.1875, 0.75, 0.21875, 0.734375, 0.25, 0.734375, 0.25, 0.734375, 0.25]","[0.05000000074505806, 0.11320360831876729, 0.27937701935000114, 0.05000000074505806, 0.05000000074505806, 0.09491651901101289, 0.07454993619151067, 0.08831343091324191, 0.05000000074505806, 0.05000000074505806, 0.05136387827869862, 0.05000000074505806, 0.05000000074505806, 0.08921677888946394, 0.05000000074505806, 0.11116264231309264, 0.1825879341414115, 0.18374287230318342, 0.06578534950460935]"
+240,34.78,0.3478,20275200,50.0,26.802044109736418,BEST_adc_episode_240,"[0, 0, 0, 0.125, 0.125, 0.0625, 0.125, 0.09375, 0.0625, 0.78125, 0.1875, 0.75, 0.21875, 0.75, 0.1875, 0.75, 0.21875, 0.734375, 0.28125]","[0.05000000074505806, 0.05000000074505806, 0.058599213614465316, 0.18017955676708353, 0.14186880192514714, 0.09067044704484366, 0.15304911306716437, 0.10239689241175642, 0.05000000074505806, 0.18207887268771367, 0.0936345896524671, 0.09814630808234555, 0.10166854890786343, 0.0758645589069319, 0.08312818423896882, 0.14264875776272604, 0.07568379923321489, 0.0907544560349432, 0.05000000074505806]"
+241,41.86,0.41859999999999997,20275776,50.00142045454545,26.01869284561592,BEST_adc_episode_241,"[0, 0, 0, 0.1875, 0, 0, 0.0625, 0.09375, 0.40625, 0.6875, 0.375, 0.65625, 0.4375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.05000000074505806, 0.05000000074505806, 0.05432660978203682, 0.21783699897090808, 0.05000000074505806, 0.05000000074505806, 0.07205796027763044, 0.09509276433451046, 0.12686852022099648, 0.15646774660770735, 0.10510429927675098, 0.06552192305602042, 0.05000000074505806, 0.05365448425379649, 0.11001232173865133, 0.08350990963850144, 0.05000000074505806, 0.08608195144494878, 0.13772177416794795]"
+242,35.84,0.35840000000000005,20290176,50.03693181818182,25.830419580419576,BEST_adc_episode_242,"[0.125, 0, 0, 0, 0.0625, 0, 0.0625, 0.0625, 0.5625, 0.53125, 0.59375, 0.5, 0.59375, 0.546875, 0.546875, 0.5625, 0.546875, 0.5625, 0.53125]","[0.16421727189596122, 0.05000000074505806, 0.05453321907587991, 0.05000000074505806, 0.09311441298819888, 0.05000000074505806, 0.07214684157056064, 0.06860389231599619, 0.07899126098965681, 0.05000000074505806, 0.12548274166292767, 0.06617469958736705, 0.05000000074505806, 0.12904529926069908, 0.18630640862712683, 0.0676174616300893, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+243,20.85,0.20850000000000002,20277504,50.00568181818181,25.92791823561054,BEST_adc_episode_243,"[0.125, 0.0625, 0.0625, 0, 0, 0, 0, 0.03125, 0.5625, 0.5625, 0.53125, 0.59375, 0.5, 0.609375, 0.484375, 0.609375, 0.5, 0.59375, 0.515625]","[0.12664813245564568, 0.11408190078437364, 0.08854513672975818, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09613228416660839, 0.05000000074505806, 0.05000000074505806, 0.05136829209808042, 0.05000000074505806, 0.08437539751928287, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+244,33.040000000000006,0.3304000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_244,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05124300277644799, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+245,49.660000000000004,0.49660000000000004,20289024,50.034090909090914,27.5080688542227,BEST_adc_episode_245,"[0, 0.0625, 0.125, 0.0625, 0.125, 0.0625, 0.1875, 0.09375, 0.0625, 0.4375, 0.65625, 0.4375, 0.625, 0.546875, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625]","[0.05000000074505806, 0.09601502502447486, 0.13925340341777054, 0.06426320053330217, 0.1414931123265857, 0.09171701394396614, 0.24001549524750648, 0.10263521712761448, 0.08436346847736731, 0.05000000074505806, 0.06463157284826379, 0.07975302369149664, 0.05000000074505806, 0.05000000074505806, 0.055100488538604886, 0.05000000074505806, 0.05000000074505806, 0.06557650856747388, 0.08484217413231239]"
+246,28.15,0.2815,20279808,50.01136363636364,25.245427649273804,BEST_adc_episode_246,"[0.0625, 0, 0, 0, 0, 0, 0.0625, 0.03125, 0.78125, 0.1875, 0.75, 0.1875, 0.75, 0.28125, 0.71875, 0.28125, 0.71875, 0.296875, 0.71875]","[0.06948636521877204, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05965848618901586, 0.09434940408039301, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.15463406379314049, 0.11569336936132471, 0.16445339379889182, 0.08989172646353649, 0.05000000074505806, 0.14983527268424893, 0.06742796443553652, 0.05000000074505806]"
+247,38.480000000000004,0.38480000000000003,20277504,50.00568181818181,26.0691231845078,BEST_adc_episode_247,"[0.0625, 0.0625, 0, 0.0625, 0, 0, 0.125, 0.125, 0.34375, 0.6875, 0.375, 0.6875, 0.34375, 0.703125, 0.328125, 0.703125, 0.328125, 0.703125, 0.328125]","[0.08714125348834471, 0.0749003807339967, 0.05000000074505806, 0.10874709333690075, 0.05000000074505806, 0.05000000074505806, 0.1260052653632942, 0.13405024498319876, 0.05920051810630688, 0.05000000074505806, 0.05364631758249247, 0.05000000074505806, 0.07914600494973018, 0.05000000074505806, 0.05000000074505806, 0.07243769116754545, 0.05002276241243761, 0.09223823305510262, 0.1060163029355995]"
+248,36.57,0.3657,20281536,50.015625,25.245427649273804,BEST_adc_episode_248,"[0, 0, 0, 0.0625, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08357753361101038, 0.05000000074505806, 0.05000000074505806, 0.09902679975543875, 0.05000000074505806, 0.05000000074505806, 0.07686357137917488, 0.05000000074505806, 0.167104074291189, 0.05005063668649833, 0.1165143725951075, 0.05000000074505806, 0.05205403317174303, 0.1118524483499588, 0.05000000074505806, 0.05000000074505806]"
+249,41.2,0.41200000000000003,20285568,50.02556818181818,25.985072619688005,BEST_adc_episode_249,"[0, 0, 0.125, 0, 0.0625, 0, 0, 0.03125, 0.5625, 0.59375, 0.5, 0.59375, 0.53125, 0.578125, 0.515625, 0.59375, 0.5, 0.59375, 0.515625]","[0.05000000074505806, 0.05000000074505806, 0.1605292199446924, 0.05000000074505806, 0.08411390582118221, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.11475321783027413, 0.052128296376446975, 0.05000000074505806, 0.05000000074505806, 0.06691132595387637, 0.05000000074505806, 0.07187710979689037, 0.09128296869138823, 0.05000000074505806, 0.05000000074505806]"
+250,28.01,0.2801,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_250,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05783479541553726, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09031366263396501, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07047469932014369]"
+251,31.46,0.3146,20284992,50.02414772727273,25.51775147928994,BEST_adc_episode_251,"[0.0625, 0, 0, 0, 0, 0.0625, 0, 0.125, 0.6875, 0.375, 0.65625, 0.4375, 0.65625, 0.421875, 0.65625, 0.40625, 0.65625, 0.4375, 0.640625]","[0.10992903646645212, 0.05000000074505806, 0.0511179725475629, 0.05000000074505806, 0.05096498760888819, 0.08996691724304642, 0.05000000074505806, 0.1314098012227564, 0.05000000074505806, 0.05000000074505806, 0.07023691083984056, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06299318493161887, 0.05000000074505806, 0.05000000074505806]"
+252,27.149999999999995,0.27149999999999996,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_252,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.054180314594610784, 0.08240647211066485, 0.05000000074505806, 0.05947523826549712, 0.05000000074505806, 0.05000000074505806, 0.0667592240220295, 0.08229344764716919, 0.05000000074505806, 0.05000000074505806, 0.053180000314055086, 0.08881519324368006, 0.05000000074505806, 0.05000000074505806, 0.1269814947921739, 0.05000000074505806, 0.05000000074505806]"
+253,42.010000000000005,0.42010000000000003,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_253,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.052846681709082445, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07799602010589665, 0.15429799836910205, 0.05000000074505806, 0.05000000074505806, 0.05959896660142294, 0.05000000074505806, 0.09111483826973861, 0.05000000074505806, 0.05936969285102976, 0.06184500533827986, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+254,35.519999999999996,0.35519999999999996,20281536,50.015625,25.245427649273804,BEST_adc_episode_254,"[0, 0.0625, 0, 0, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.07615854772392312, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08675525361970375, 0.05290686855833323, 0.05159467698574209, 0.060041903167176745, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09119425998942189, 0.06831184828719833, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09715208817360843]"
+255,34.58,0.3458,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_255,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.054930462885116084, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06618215798231294, 0.05000000074505806, 0.05000000074505806, 0.05070127243586599, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+256,27.080000000000005,0.27080000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_256,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.058633852321794036, 0.053322571650782496, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.057798106007041834, 0.05000000074505806, 0.0679040326845646, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05875365343794243, 0.05000000074505806, 0.05000000074505806]"
+257,29.990000000000006,0.29990000000000006,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_257,"[0, 0, 0.0625, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.05000000074505806, 0.07716222015975503, 0.05000000074505806, 0.06372866255417356, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06323571488871862, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+258,36.36000000000001,0.3636000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_258,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05355460203296024, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.057921293797342885, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07186001194650404, 0.05000000074505806, 0.05000000074505806]"
+259,27.180000000000003,0.27180000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_259,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0729535788868925, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+260,36.77,0.3677,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_260,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+261,23.640000000000004,0.23640000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_261,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+262,30.259999999999998,0.3026,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_262,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.056995225891986925, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08006233969568348, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07102798788722746, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+263,27.54,0.2754,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_263,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+264,32.02,0.32020000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_264,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+265,36.88,0.3688,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_265,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+266,36.18,0.3618,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_266,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+267,24.11,0.24109999999999998,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_267,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+268,35.22,0.3522,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_268,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05392456798219033, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09308605384517991, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+269,32.42,0.32420000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_269,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0509329188522249, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+270,37.150000000000006,0.37150000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_270,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+271,24.129999999999995,0.24129999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_271,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+272,26.980000000000004,0.26980000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_272,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+273,26.049999999999997,0.26049999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_273,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+274,28.139999999999997,0.2814,20275776,50.00142045454545,25.79679935449166,BEST_adc_episode_274,"[0, 0, 0.0625, 0, 0.0625, 0.0625, 0, 0.03125, 0.59375, 0.5, 0.59375, 0.5, 0.59375, 0.5625, 0.546875, 0.546875, 0.5625, 0.546875, 0.5625]","[0.05000000074505806, 0.05000000074505806, 0.06354324001035562, 0.05000000074505806, 0.06529755446581373, 0.07525075877805687, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0598721466831172, 0.05000000074505806, 0.05000000074505806, 0.07511612140545657, 0.07358062596697869, 0.05000000074505806, 0.1036600339201934, 0.05000000074505806, 0.05000000074505806]"
+275,39.26,0.3926,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_275,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+276,37.1,0.371,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_276,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+277,38.07000000000001,0.3807000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_277,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+278,28.15,0.2815,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_278,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+279,34.28,0.3428,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_279,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+280,36.029999999999994,0.36029999999999995,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_280,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.06309994139485617, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08494825760382697, 0.05000000074505806, 0.05000000074505806]"
+281,29.869999999999997,0.29869999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_281,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+282,30.110000000000003,0.30110000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_282,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+283,38.029999999999994,0.3802999999999999,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_283,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+284,27.569999999999993,0.27569999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_284,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+285,32.68,0.3268,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_285,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+286,27.139999999999997,0.2714,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_286,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06059216000531128, 0.05000000074505806, 0.053436054689906864, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06843313038430326, 0.05000000074505806, 0.05000000074505806, 0.050805925476699626, 0.05000000074505806, 0.05000000074505806, 0.05210079398843587, 0.05000000074505806, 0.05000000074505806]"
+287,35.85,0.35850000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_287,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+288,35.06,0.3506,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_288,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+289,27.02,0.2702,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_289,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+290,21.230000000000004,0.21230000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_290,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+291,31.279999999999998,0.31279999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_291,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+292,31.979999999999997,0.3198,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_292,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.053183060106826094, 0.05000000074505806, 0.050310522919805534, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+293,34.27,0.3427,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_293,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+294,30.700000000000006,0.30700000000000005,19642752,48.44034090909091,21.254706831629907,BEST_adc_episode_294,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.765625, 0.59375, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.051550575331630856, 0.05000000074505806, 0.1568417057802262, 0.0900451862484807, 0.3288652803483459, 0.05000000074505806, 0.5667374957136668, 0.3726911365919003, 0.7812320360106371, 0.5957901895952499, 0.789058778376256]"
+295,19.77,0.1977,13296960,32.79119318181819,9.037116729424422,BEST_adc_episode_295,"[0, 0.1875, 0.125, 0.125, 0.125, 0.0625, 0.0625, 0.65625, 0.78125, 0.75, 0.78125, 0.75, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.06059230452987132, 0.1940969844592031, 0.16263225468965362, 0.17080007372095085, 0.15046990271633517, 0.1157101565716005, 0.10926027304213318, 0.6774735968097788, 0.7935045947531008, 0.7735208214227399, 0.7819028379588234, 0.7757849036791266, 0.7790314077505105, 0.7981465212724415, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7988371926256703, 0.800000011920929]"
+296,18.600000000000005,0.18600000000000005,16870464,41.60369318181818,11.135018827326519,BEST_adc_episode_296,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.78125, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10078082526255225, 0.7960722399834933, 0.7339079625694664, 0.7531776728620626, 0.7358359317458885, 0.7502280209022879, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.796839091743151, 0.7952265199677119]"
+297,15.780000000000005,0.15780000000000005,16847424,41.546875,11.000537923614846,BEST_adc_episode_297,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7884608822311061, 0.7320105934456346, 0.7609670287672506, 0.7471397328027086, 0.7553894958647509, 0.7995971217297175, 0.7988138640613683, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7995363197738642]"
+298,15.229999999999999,0.1523,16835328,41.51704545454545,11.081226465841851,BEST_adc_episode_298,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.6875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.78125]","[0.055211397983862584, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05605969824094913, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.770675631549369, 0.7555385846828568, 0.7513174705500355, 0.7076377724543315, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7864324401077845]"
+299,17.259999999999998,0.17259999999999998,16805952,41.44460227272727,11.071140398063475,BEST_adc_episode_299,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.75, 0.796875, 0.78125, 0.796875, 0.796875, 0.78125, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981815690468282, 0.755515987814352, 0.7720144702521504, 0.7688361595141106, 0.7635488581473379, 0.800000011920929, 0.7960726602459142, 0.800000011920929, 0.800000011920929, 0.7963784259198401, 0.7971237872262427]"
+300,22.250000000000004,0.22250000000000003,16776000,41.37073863636363,10.8963152232383,BEST_adc_episode_300,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981081171133637, 0.7572797222396342, 0.7659723119416851, 0.7617062522990833, 0.7629453541134603, 0.800000011920929, 0.7992495571179616, 0.7999914270358364, 0.7999194334313693, 0.7971840877590044, 0.800000011920929]"
+301,17.75,0.1775,16890624,41.653409090909086,11.161915008068855,BEST_adc_episode_301,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.71875, 0.796875, 0.796875, 0.78125, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7928278326221119, 0.7227813382865627, 0.7521926231011723, 0.7266249801606518, 0.7444780850090512, 0.800000011920929, 0.800000011920929, 0.7967866151026266, 0.800000011920929, 0.800000011920929, 0.800000011920929]"
+302,24.28,0.24280000000000002,17239104,42.512784090909086,11.703200645508339,BEST_adc_episode_302,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.59375, 0.71875, 0.625, 0.6875, 0.796875, 0.796875, 0.78125, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7857102206535946, 0.6047194991977847, 0.7362356393220129, 0.6277589776166674, 0.6945284007113155, 0.7980002816464855, 0.799108000981067, 0.7965304456970493, 0.800000011920929, 0.7990799738189661, 0.800000011920929]"
+303,21.940000000000005,0.21940000000000004,18802944,46.36931818181818,16.35287789133943,BEST_adc_episode_303,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.734375, 0.765625, 0.734375, 0.734375]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.5708759569834408, 0.1031088374737956, 0.5502010907530744, 0.06637936622009809, 0.386749300390444, 0.2875725555052169, 0.7998308762803633, 0.7409007214810035, 0.772751793419096, 0.7343883986986655, 0.7493960830564278]"
+304,30.010000000000005,0.30010000000000003,19070784,47.02982954545455,17.916218396987627,BEST_adc_episode_304,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.75, 0.6875, 0.703125, 0.703125, 0.75]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10145037832393268, 0.05000000074505806, 0.23915591589655816, 0.05000000074505806, 0.11664283627884346, 0.11765080676624355, 0.7606020281940092, 0.6902827264187266, 0.7104940717902184, 0.7045219143324264, 0.7603754810427233]"
+305,29.979999999999997,0.29979999999999996,19548288,48.20738636363636,20.70333512641205,BEST_adc_episode_305,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.34375, 0.796875, 0.265625, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05505067543349059, 0.05000000074505806, 0.07671012040949504, 0.05000000074505806, 0.05149271786313044, 0.05000000074505806, 0.7991480296407916, 0.34651068522762046, 0.800000011920929, 0.27839184578194, 0.800000011920929]"
+306,33.14,0.33140000000000003,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_306,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7679411741398575, 0.05000000074505806, 0.7997552737526441, 0.05000000074505806, 0.7978242634925741]"
+307,34.57,0.3457,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_307,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7782423885054678, 0.05000000074505806, 0.7986080801365256, 0.05000000074505806, 0.800000011920929]"
+308,37.33,0.37329999999999997,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_308,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.774507479762928, 0.05000000074505806, 0.798600904876897, 0.05000000074505806, 0.800000011920929]"
+309,37.67,0.37670000000000003,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_309,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7723804369298286, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+310,29.93,0.2993,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_310,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.050538544422263215, 0.05000000074505806, 0.05000000074505806, 0.07018124087327134, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.11472396448476274, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06996957382713205, 0.05038259458041711, 0.05000000074505806, 0.4972269552238258, 0.07794228678196913, 0.41566152650436566, 0.05000000074505806, 0.4325925868203505]"
+311,27.900000000000002,0.279,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_311,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7738665412532315, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+312,36.519999999999996,0.36519999999999997,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_312,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7845300041764209, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7982069581327716]"
+313,34.84,0.34840000000000004,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_313,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7958214030061295, 0.05000000074505806, 0.7996068155311341, 0.05000000074505806, 0.800000011920929]"
+314,39.1,0.391,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_314,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7868070203937451, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+315,35.540000000000006,0.35540000000000005,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_315,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.798826569087774, 0.05000000074505806, 0.7987096188194645, 0.05000000074505806, 0.7998289157989188]"
+316,29.979999999999997,0.29979999999999996,20262528,49.96875,25.063878429263042,BEST_adc_episode_316,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.734375, 0.046875, 0.796875, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.08798688782260276, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0600008567261778, 0.10865915426407939, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7477699657796414, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7817461210378533]"
+317,30.589999999999996,0.30589999999999995,20206080,49.82954545454545,24.54276492738031,BEST_adc_episode_317,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.734375, 0.109375, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7375852223442056, 0.05000000074505806, 0.7997728713767578, 0.05000000074505806, 0.7998608990290516]"
+318,36.760000000000005,0.36760000000000004,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_318,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7986159244201252, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7997871101434377]"
+319,30.989999999999995,0.30989999999999995,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_319,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7959525550861958, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+320,37.28,0.3728,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_320,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7992763518918721, 0.05000000074505806, 0.7993675270179538]"
+321,36.35,0.3635,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_321,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7969028453892191, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+322,33.07,0.3307,20218176,49.859375,24.61336740182894,BEST_adc_episode_322,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.765625, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05881605674061782, 0.05000000074505806, 0.05000000074505806, 0.05187649802212121, 0.056655523476377256, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0776438087865993, 0.800000011920929, 0.05000000074505806, 0.7707564175561004, 0.05000000074505806, 0.7917296183670047]"
+323,37.129999999999995,0.37129999999999996,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_323,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7996226501861178, 0.05000000074505806, 0.7997475530182977, 0.05000000074505806, 0.800000011920929]"
+324,37.03999999999999,0.3703999999999999,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_324,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981804054992995, 0.05000000074505806, 0.7997328785553669, 0.05000000074505806, 0.800000011920929]"
+325,35.370000000000005,0.35370000000000007,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_325,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998031809128667, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7997147323405739]"
+326,30.300000000000004,0.30300000000000005,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_326,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7816368481610052, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7995447346126654]"
+327,38.88,0.38880000000000003,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_327,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7875837892561417, 0.05000000074505806, 0.7994421257520891, 0.05000000074505806, 0.800000011920929]"
+328,20.340000000000003,0.20340000000000003,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_328,"[0, 0.0625, 0, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.06690182303015572, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0630313660548274, 0.05355372314488726, 0.08531092750855038, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.496434918376349, 0.05000000074505806, 0.3761992687194966, 0.07555022201305503, 0.3822378644693491]"
+329,35.5,0.355,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_329,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7932763198527022, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+330,27.400000000000002,0.274,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_330,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.1262007363787338, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+331,38.7,0.387,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_331,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+332,35.62,0.35619999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_332,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+333,30.630000000000003,0.3063,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_333,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+334,29.21,0.2921,20290176,50.03693181818182,25.36646046261431,BEST_adc_episode_334,"[0.0625, 0.0625, 0, 0, 0, 0, 0, 0.09375, 0.71875, 0.28125, 0.71875, 0.3125, 0.6875, 0.46875, 0.625, 0.46875, 0.609375, 0.5, 0.59375]","[0.08387220180453099, 0.08845159713811707, 0.05000000074505806, 0.05000000074505806, 0.06074490286477618, 0.05000000074505806, 0.05000000074505806, 0.10563286657446672, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06379387717517108, 0.05000000074505806, 0.06124240406702987, 0.05000000074505806, 0.05000000074505806, 0.06407003224428641, 0.05000000074505806, 0.05000000074505806]"
+335,36.6,0.366,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_335,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+336,37.739999999999995,0.37739999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_336,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+337,34.709999999999994,0.34709999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_337,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+338,37.59,0.3759,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_338,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+339,33.9,0.33899999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_339,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+340,16.469999999999995,0.16469999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_340,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05745587104699142, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0567548266812444, 0.05000000074505806, 0.05000000074505806, 0.05275428790053619, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+341,26.990000000000002,0.26990000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_341,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+342,26.419999999999998,0.2642,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_342,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+343,32.599999999999994,0.32599999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_343,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+344,33.03,0.33030000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_344,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+345,38.0,0.38,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_345,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+346,25.670000000000005,0.25670000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_346,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06467178421140847, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+347,35.150000000000006,0.35150000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_347,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+348,34.46,0.3446,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_348,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+349,37.8,0.37799999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_349,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+350,33.599999999999994,0.33599999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_350,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+351,33.13,0.33130000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_351,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+352,39.559999999999995,0.39559999999999995,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_352,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.06764028558521103, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06944207335323059, 0.05000000074505806, 0.05980479777282769, 0.05000000074505806, 0.05000000074505806, 0.08278919461090165, 0.05000000074505806, 0.05000000074505806, 0.06693517116261877, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+353,34.650000000000006,0.34650000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_353,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+354,35.39,0.3539,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_354,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+355,39.94,0.3994,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_355,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+356,39.410000000000004,0.39410000000000006,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_356,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+357,41.86,0.41859999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_357,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+358,42.669999999999995,0.42669999999999997,20277504,50.00568181818181,26.089295320064547,BEST_adc_episode_358,"[0.125, 0, 0, 0, 0.0625, 0.0625, 0.0625, 0.03125, 0.46875, 0.625, 0.46875, 0.625, 0.46875, 0.640625, 0.4375, 0.640625, 0.453125, 0.625, 0.484375]","[0.13781693189587144, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0916061531844739, 0.0748224717723815, 0.08170281463054967, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07149291293574199, 0.05000000074505806, 0.05919709701568539, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08669890644007629, 0.05000000074505806, 0.05000000074505806]"
+359,35.480000000000004,0.35480000000000006,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_359,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+360,36.35,0.3635,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_360,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+361,32.97,0.3297,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_361,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+362,32.03,0.32030000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_362,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+363,27.769999999999996,0.27769999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_363,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+364,27.569999999999993,0.27569999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_364,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06604984678704297, 0.05000000074505806, 0.050516675251807044, 0.05000000074505806, 0.07346507081518264, 0.05000000074505806, 0.05000000074505806, 0.08434014389312787, 0.05510932642411254, 0.05000000074505806, 0.05000000074505806]"
+365,26.61,0.2661,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_365,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+366,26.490000000000002,0.2649,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_366,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+367,32.8,0.32799999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_367,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+368,36.63999999999999,0.36639999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_368,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+369,30.95,0.3095,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_369,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+370,37.39,0.3739,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_370,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07724701023250888, 0.05000000074505806, 0.10838769505330081, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.054880009844897125, 0.09657075409994408, 0.05000000074505806, 0.05000000074505806]"
+371,37.88,0.3788,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_371,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+372,30.000000000000004,0.30000000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_372,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+373,31.55,0.3155,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_373,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+374,38.42,0.38420000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_374,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+375,36.83,0.36829999999999996,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_375,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7999160048487227, 0.05000000074505806, 0.7999776520524081, 0.05000000074505806, 0.7999982880891966]"
+376,41.82,0.4182,20287296,50.02982954545454,25.01681011296396,BEST_adc_episode_376,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.75, 0.046875, 0.796875, 0.046875, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07504612333794451, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7609988957327879, 0.05000000074505806, 0.800000011920929, 0.05052241054956185, 0.7481229893569016]"
+377,30.23,0.3023,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_377,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7984202486918578, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999904780993354]"
+378,38.580000000000005,0.38580000000000003,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_378,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7979975922396392, 0.05000000074505806, 0.7999848263259557, 0.05000000074505806, 0.800000011920929]"
+379,32.04,0.3204,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_379,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7985049887722581, 0.05000000074505806, 0.7999961275556107, 0.05000000074505806, 0.800000011920929]"
+380,34.81999999999999,0.34819999999999995,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_380,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79987448199003, 0.05000000074505806, 0.7999936451797248, 0.05000000074505806, 0.7999782465466962]"
+381,34.43000000000001,0.34430000000000005,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_381,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998357648980032, 0.05000000074505806, 0.799981976403989, 0.05000000074505806, 0.800000011920929]"
+382,35.650000000000006,0.35650000000000004,20207232,49.83238636363637,24.74112426035503,BEST_adc_episode_382,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.75, 0.046875, 0.796875, 0.046875, 0.78125]","[0.05000000074505806, 0.05614451415140548, 0.05000000074505806, 0.10837621709892249, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06434909528670178, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05741858769635604, 0.7594589860503417, 0.05000000074505806, 0.800000011920929, 0.060831954260901315, 0.791398980091768]"
+383,36.41,0.3641,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_383,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998768180618998, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999954201080673]"
+384,41.02,0.4102,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_384,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79996172009316, 0.05000000074505806, 0.7999995120786205, 0.05000000074505806, 0.7999852914113161]"
+385,32.66,0.32659999999999995,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_385,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7602598121356307, 0.05000000074505806, 0.7999539196643276, 0.05000000074505806, 0.7998746529189187, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999948055517522, 0.05000000074505806, 0.7999889948797158]"
+386,27.280000000000005,0.27280000000000004,20201472,49.81818181818182,24.57638515330823,BEST_adc_episode_386,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7350039523816767, 0.05000000074505806, 0.7989074659201285, 0.05000000074505806, 0.7999793420953429, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999953121574408]"
+387,32.410000000000004,0.32410000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_387,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7928503464773604, 0.05000000074505806, 0.7999888620492813, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999863352510068, 0.05000000074505806, 0.799997155652632, 0.05000000074505806, 0.800000011920929]"
+388,25.670000000000005,0.25670000000000004,20276928,50.00426136363636,25.793437331898872,BEST_adc_episode_388,"[0, 0, 0.0625, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.78125, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.06289767189719724, 0.05458507088827692, 0.07541382405120237, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7400022947475926, 0.05777602716586765, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7886471828773967, 0.05000000074505806, 0.7701804775906668]"
+389,31.820000000000004,0.31820000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_389,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.790671088221282, 0.05000000074505806, 0.7999973954496711, 0.05000000074505806, 0.7999885101600638, 0.05000000074505806, 0.7999953940722239, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+390,32.52,0.32520000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_390,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7920494428990174, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999859012142811, 0.05000000074505806, 0.7999957066361391, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+391,31.710000000000004,0.31710000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_391,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7903354248920224, 0.05000000074505806, 0.7999808409872877, 0.05000000074505806, 0.7999882001461434, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+392,33.330000000000005,0.33330000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_392,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7893398036244154, 0.05000000074505806, 0.799991994898789, 0.05000000074505806, 0.799995204395773, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999999163692463]"
+393,29.720000000000002,0.2972,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_393,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7924548328543447, 0.05000000074505806, 0.7999958121339986, 0.05000000074505806, 0.7999858480720082, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999943372594015, 0.05000000074505806, 0.800000011920929]"
+394,34.230000000000004,0.34230000000000005,20155392,49.70454545454545,24.74112426035503,BEST_adc_episode_394,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.78125, 0.03125, 0.78125, 0.046875, 0.765625, 0.203125, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.08691364017762691, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08968459011250728, 0.776630368427483, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7932091931740264, 0.05000000074505806, 0.7535585073059262, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+395,32.65,0.3265,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_395,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7896321161852742, 0.05000000074505806, 0.7999870162299847, 0.05000000074505806, 0.7999947117298711, 0.05000000074505806, 0.7999927012085257, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+396,36.27,0.3627,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_396,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.785604292198213, 0.05000000074505806, 0.7999885425478431, 0.05000000074505806, 0.799992903796425, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999948266452237, 0.05000000074505806, 0.800000011920929]"
+397,32.25,0.3225,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_397,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7872163314684072, 0.05000000074505806, 0.7999915026486047, 0.05000000074505806, 0.7999922145159974, 0.05000000074505806, 0.799992398649714, 0.05000000074505806, 0.7999984590164511, 0.05000000074505806, 0.7999953897418766]"
+398,35.260000000000005,0.3526,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_398,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.789039103745824, 0.05000000074505806, 0.7999880597260589, 0.05000000074505806, 0.7999943418309325, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999989751163655]"
+399,35.25,0.3525,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_399,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7851254568956153, 0.05000000074505806, 0.799991152698218, 0.05000000074505806, 0.7999953843685879, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999992190749274]"
+400,23.929999999999996,0.23929999999999996,20301696,50.06534090909091,25.35301237224314,BEST_adc_episode_400,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.75, 0.1875, 0.78125, 0.046875, 0.78125, 0.046875, 0.78125, 0.109375, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7310135818253884, 0.05000000074505806, 0.7666119955140845, 0.05000000074505806, 0.7997628135685211, 0.05000000074505806, 0.7959523373277781, 0.05000000074505806, 0.7862820301647143, 0.05000000074505806, 0.7469557136429811]"
+401,26.590000000000003,0.2659,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_401,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7881700740667559, 0.05000000074505806, 0.7999965594384517, 0.05000000074505806, 0.7999948514518118, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999941122028104, 0.05000000074505806, 0.800000011920929]"
+402,34.25,0.3425,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_402,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7891574381750684, 0.05000000074505806, 0.7999974439256874, 0.05000000074505806, 0.7999954653794915, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799996955828309]"
+403,34.550000000000004,0.34550000000000003,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_403,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7792365509712168, 0.05000000074505806, 0.7999934667636133, 0.05000000074505806, 0.7998877038211495, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999944564165897, 0.05000000074505806, 0.800000011920929]"
+404,35.129999999999995,0.35129999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_404,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7839537739371677, 0.05000000074505806, 0.7999959344437118, 0.05000000074505806, 0.7999969799779136, 0.05000000074505806, 0.7999966796746586, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999955010393404]"
+405,27.280000000000005,0.27280000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_405,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.785137402846372, 0.05000000074505806, 0.7999957028941231, 0.05000000074505806, 0.7999918906633889, 0.05000000074505806, 0.7999951242445023, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995930755933]"
+406,25.1,0.251,20052288,49.45028409090909,23.45346960731576,BEST_adc_episode_406,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.75, 0.359375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7310676709758814, 0.05000000074505806, 0.800000011920929, 0.05299507945463607, 0.800000011920929, 0.052865516791276324, 0.800000011920929]"
+407,27.93,0.2793,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_407,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.778083537888325, 0.05000000074505806, 0.7999950253513584, 0.05000000074505806, 0.7998881528209953, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+408,36.67,0.3667,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_408,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7842134268281283, 0.05000000074505806, 0.7999973341114146, 0.05000000074505806, 0.799994021278866, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799997930814797, 0.05000000074505806, 0.7999983259494959]"
+409,29.090000000000003,0.29090000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_409,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7953775457371487, 0.05000000074505806, 0.7999988887052304, 0.05000000074505806, 0.7999978153475312, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999992016882738, 0.05000000074505806, 0.800000011920929]"
+410,34.17,0.3417,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_410,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7957418227231662, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999965826635471, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999987009318164]"
+411,34.63,0.34630000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_411,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7971156830488503, 0.05000000074505806, 0.7999975210465787, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999983206510569, 0.05000000074505806, 0.7999994245719639]"
+412,35.22,0.3522,20301696,50.06534090909091,25.645508337816032,BEST_adc_episode_412,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.125, 0.78125, 0.046875, 0.796875, 0.046875, 0.765625, 0.046875, 0.765625]","[0.05000000074505806, 0.05424745508304937, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.12541198814555435, 0.7944315026037742, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7752275167028495, 0.05000000074505806, 0.7462451510031299]"
+413,26.759999999999994,0.26759999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_413,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7978118210131537, 0.05000000074505806, 0.7999953328626335, 0.05000000074505806, 0.7999984429447008, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999975053479419, 0.05000000074505806, 0.800000011920929]"
+414,34.84,0.34840000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_414,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.797010997698042, 0.05000000074505806, 0.7999976462034509, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999984773066041, 0.05000000074505806, 0.7999992698722092]"
+415,33.56,0.3356,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_415,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7968350026324562, 0.05000000074505806, 0.7999989893750274, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+416,33.040000000000006,0.3304000000000001,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_416,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7977561098626293, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799999750784015, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.79999929703308]"
+417,27.569999999999993,0.27569999999999995,17978112,44.33522727272727,24.075443786982248,BEST_adc_episode_417,"[0, 0.0625, 0.0625, 0.0625, 0.125, 0.125, 0.25, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.08301410809589294, 0.06430448312114047, 0.10526229719275751, 0.1708472396998273, 0.14162864970462805, 0.27307329163251637, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999991569080751, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+418,23.87,0.23870000000000002,10387008,25.61505681818182,22.653308230231307,BEST_adc_episode_418,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.125, 0.78125, 0.03125, 0.75, 0.03125, 0.75, 0.046875, 0.796875, 0.0625, 0.765625, 0.0625, 0.78125]","[0.05000000074505806, 0.7942205892355202, 0.05000000074505806, 0.7834269029033132, 0.05000000074505806, 0.790398138031686, 0.05000000074505806, 0.15054522189680253, 0.800000011920929, 0.05000000074505806, 0.7785913327282553, 0.05000000074505806, 0.7656312501479678, 0.05000000074505806, 0.800000011920929, 0.07128239517089559, 0.7706787467792795, 0.06987780852446399, 0.7823845503403885]"
+419,11.670000000000002,0.11670000000000001,9941760,24.517045454545457,21.22444862829478,BEST_adc_episode_419,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.15625, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7643318492911862, 0.05000000074505806, 0.7745425519166926, 0.05000000074505806, 0.7795120072509358, 0.05000000074505806, 0.17373718217125347, 0.7999973315356111, 0.05000000074505806, 0.7999994554677593, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999993662363054]"
+420,19.720000000000006,0.19720000000000007,10743552,26.494318181818183,21.748924152770307,BEST_adc_episode_420,"[0, 0.6875, 0, 0.6875, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7435717500012117, 0.05000000074505806, 0.7379543649154435, 0.05000000074505806, 0.7539013285562609, 0.05000000074505806, 0.05000000074505806, 0.7999857485328777, 0.05000000074505806, 0.7999910450002391, 0.05000000074505806, 0.7999953616802166, 0.05000000074505806, 0.7999985995701382, 0.05000000074505806, 0.7999995326217871, 0.05000000074505806, 0.800000011920929]"
+421,22.340000000000003,0.22340000000000004,10904832,26.892045454545453,26.401963421194193,BEST_adc_episode_421,"[0, 0.75, 0.0625, 0.75, 0, 0.75, 0, 0.53125, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7998725053639164, 0.0652604829242707, 0.7997631341797986, 0.05575776688552881, 0.7998595587953734, 0.05467895703695169, 0.5534282396757505, 0.05000000074505806, 0.7999981974181726, 0.05000000074505806, 0.7999996455601642, 0.05000000074505806, 0.05000000074505806, 0.7999988050372019, 0.05000000074505806, 0.7999986566029397, 0.05000000074505806, 0.799999675903931]"
+422,21.650000000000002,0.21650000000000003,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_422,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7978410121678494, 0.05000000074505806, 0.7971045866208073, 0.05000000074505806, 0.7960565350004413, 0.05000000074505806, 0.05000000074505806, 0.7999966991145503, 0.05000000074505806, 0.7999947177496847, 0.05000000074505806, 0.7999956893652997, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995004885495, 0.05000000074505806, 0.800000011920929]"
+423,18.259999999999998,0.18259999999999998,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_423,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7971152385689193, 0.05000000074505806, 0.7972697652844045, 0.05000000074505806, 0.7967071784905525, 0.05000000074505806, 0.05000000074505806, 0.7999970704029856, 0.05000000074505806, 0.7999998617238065, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+424,15.82,0.1582,10472256,25.82528409090909,22.767616998386227,BEST_adc_episode_424,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.75, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.71875]","[0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7951514591253552, 0.05000000074505806, 0.7958383430024818, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7668145302421555, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7214349895788912]"
+425,20.220000000000006,0.20220000000000005,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_425,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7927473017546812, 0.05000000074505806, 0.7925894654437959, 0.05000000074505806, 0.7906149108568892, 0.05000000074505806, 0.05000000074505806, 0.7999978729564534, 0.05000000074505806, 0.799997853924479, 0.05000000074505806, 0.7999987450761028, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999993302280628]"
+426,22.7,0.22699999999999998,10448640,25.767045454545457,21.64133942980097,BEST_adc_episode_426,"[0, 0.75, 0, 0.75, 0, 0.6875, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7687747782389578, 0.05000000074505806, 0.7590415563459333, 0.05000000074505806, 0.7329612780548628, 0.05000000074505806, 0.05000000074505806, 0.7999960698232065, 0.05000000074505806, 0.7999931325261147, 0.05000000074505806, 0.7999904918557732, 0.05000000074505806, 0.7999985496151761, 0.05000000074505806, 0.7999970006512129, 0.05000000074505806, 0.7999923506271698]"
+427,32.11000000000001,0.32110000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_427,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.3456561399556784, 0.05000000074505806, 0.6913528022485923, 0.05000000074505806, 0.6441461904966923, 0.05000000074505806, 0.0745923841633943, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+428,34.919999999999995,0.34919999999999995,20130048,49.64204545454545,24.472162452931684,BEST_adc_episode_428,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.71875, 0.25, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.638266202430584, 0.05000000074505806, 0.7343745328960194, 0.05000000074505806, 0.7967848536250949, 0.05000000074505806, 0.7999970198921684, 0.05000000074505806, 0.799995767001398, 0.05000000074505806, 0.7999967021090809]"
+429,34.64,0.3464,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_429,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7682436687787214, 0.05000000074505806, 0.7999970938771918, 0.05000000074505806, 0.7999647931843675, 0.05000000074505806, 0.7999999099940149, 0.05000000074505806, 0.7999993661721732, 0.05000000074505806, 0.7999987580594198]"
+430,33.879999999999995,0.33879999999999993,20286720,50.028409090909086,25.073964497041416,BEST_adc_episode_430,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.765625, 0.1875, 0.75]","[0.05845841161132297, 0.05343340814873009, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06170763187615251, 0.05995877717862587, 0.06437156478360563, 0.7311138884009017, 0.055866612642870135, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7777130931788122, 0.05000000074505806, 0.7295103481944176]"
+431,30.459999999999997,0.3046,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_431,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.780581456832934, 0.05000000074505806, 0.7999962426518856, 0.05000000074505806, 0.7999565392100108, 0.05000000074505806, 0.7999991739230847, 0.05000000074505806, 0.7999995754967559, 0.05000000074505806, 0.7999987109144301]"
+432,33.41,0.33409999999999995,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_432,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7802051556861941, 0.05000000074505806, 0.7999969179931506, 0.05000000074505806, 0.7999684492180448, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995324991562, 0.05000000074505806, 0.800000011920929]"
+433,32.099999999999994,0.32099999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_433,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7874806022825223, 0.05000000074505806, 0.7999980415560857, 0.05000000074505806, 0.7999960169548979, 0.05000000074505806, 0.7999998319667415, 0.05000000074505806, 0.7999993113860548, 0.05000000074505806, 0.799999777792771]"
+434,33.15,0.33149999999999996,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_434,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7886890186991438, 0.05000000074505806, 0.7999972559113242, 0.05000000074505806, 0.7999978102701546, 0.05000000074505806, 0.799999958352687, 0.05000000074505806, 0.799998720558879, 0.05000000074505806, 0.7999990044386908]"
+435,37.529999999999994,0.37529999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_435,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7903140529760361, 0.05000000074505806, 0.799997468829838, 0.05000000074505806, 0.7999979513219742, 0.05000000074505806, 0.7999993713019258, 0.05000000074505806, 0.7999993112501155, 0.05000000074505806, 0.7999988414459229]"
+436,31.86,0.3186,20197440,49.80823863636364,25.21853146853147,BEST_adc_episode_436,"[0.0625, 0, 0.0625, 0, 0, 0, 0, 0.0625, 0.78125, 0.03125, 0.75, 0.28125, 0.75, 0.046875, 0.796875, 0.046875, 0.765625, 0.1875, 0.796875]","[0.06828761586231404, 0.05000000074505806, 0.06320800099079552, 0.056542158841142394, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07208712513829861, 0.800000011920929, 0.05000000074505806, 0.7512286993063971, 0.07453747228359388, 0.7766336855468265, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7491686565131257, 0.05000000074505806, 0.800000011920929]"
+437,30.740000000000002,0.3074,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_437,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7904965505800312, 0.05000000074505806, 0.7999977563153233, 0.05000000074505806, 0.7999973122356288, 0.05000000074505806, 0.7999991531023042, 0.05000000074505806, 0.79999913243497, 0.05000000074505806, 0.799998637822624]"
+438,34.48,0.3448,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_438,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7892962361799555, 0.05000000074505806, 0.7999972119269692, 0.05000000074505806, 0.7999969351183829, 0.05000000074505806, 0.7999979525858352, 0.05000000074505806, 0.7999962349310435, 0.05000000074505806, 0.7999971113326447]"
+439,31.320000000000004,0.31320000000000003,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_439,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7885912334179074, 0.05000000074505806, 0.7999975176152849, 0.05000000074505806, 0.7999967064547444, 0.05000000074505806, 0.799999200263333, 0.05000000074505806, 0.799998647145939, 0.05000000074505806, 0.7999988606834695]"
+440,31.45,0.3145,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_440,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7933975973468308, 0.05000000074505806, 0.7999981036950007, 0.05000000074505806, 0.7999978447936549, 0.05000000074505806, 0.799999124588501, 0.05000000074505806, 0.7999982294472272, 0.05000000074505806, 0.7999982864151014]"
+441,35.62,0.35619999999999996,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_441,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7909472202302286, 0.05000000074505806, 0.7999969798183215, 0.05000000074505806, 0.7999973401475179, 0.05000000074505806, 0.7999975486154352, 0.05000000074505806, 0.7999967651136248, 0.05000000074505806, 0.7999970792022826]"
+442,29.500000000000004,0.29500000000000004,20170368,49.74147727272727,24.53604088219473,BEST_adc_episode_442,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.78125, 0.03125, 0.78125, 0.09375, 0.78125, 0.09375, 0.765625, 0.1875, 0.796875, 0.046875, 0.796875]","[0.06399303725696005, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7409444590683348, 0.05000000074505806, 0.7780299944203913, 0.05000000074505806, 0.7463799150270998, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
+443,30.589999999999996,0.30589999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_443,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7915509713030638, 0.05000000074505806, 0.7999974765232473, 0.05000000074505806, 0.7999978248792937, 0.05000000074505806, 0.7999976848335345, 0.05000000074505806, 0.7999972992960511, 0.05000000074505806, 0.799997147513734]"
+444,21.699999999999996,0.21699999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_444,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7915255761485928, 0.05000000074505806, 0.7999971594597894, 0.05000000074505806, 0.7999974277750687, 0.05000000074505806, 0.7999970940720463, 0.05000000074505806, 0.7999970175165753, 0.05000000074505806, 0.7999974297669901]"
+445,29.190000000000005,0.29190000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_445,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7939858788366945, 0.05000000074505806, 0.7999978544074159, 0.05000000074505806, 0.7999981345351398, 0.05000000074505806, 0.7999986168417826, 0.05000000074505806, 0.7999983218733925, 0.05000000074505806, 0.7999988426465264]"
+446,32.31,0.3231,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_446,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7958573131881596, 0.05000000074505806, 0.799998236855509, 0.05000000074505806, 0.799998834127174, 0.05000000074505806, 0.7999986269330843, 0.05000000074505806, 0.799998771305092, 0.05000000074505806, 0.7999988380142625]"
+447,33.730000000000004,0.33730000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_447,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79716086653438, 0.05000000074505806, 0.7999988756157029, 0.05000000074505806, 0.7999989434473345, 0.05000000074505806, 0.799998708659243, 0.05000000074505806, 0.7999983418140966, 0.05000000074505806, 0.7999985405329318]"
+448,38.92,0.3892,20225664,49.87784090909091,25.100860677783754,BEST_adc_episode_448,"[0, 0, 0, 0.0625, 0, 0, 0.0625, 0.03125, 0.78125, 0.03125, 0.78125, 0.0625, 0.78125, 0.140625, 0.796875, 0.046875, 0.796875, 0.046875, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0806765587002175, 0.0518962400368373, 0.05000000074505806, 0.0667363780009411, 0.05000000074505806, 0.800000011920929, 0.05628101722158528, 0.800000011920929, 0.05000000074505806, 0.7846215683899614, 0.05000000074505806, 0.800000011920929, 0.057217929772254705, 0.800000011920929, 0.05000000074505806, 0.7730718484651471]"
+449,33.919999999999995,0.33919999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_449,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7977004484078197, 0.05000000074505806, 0.7999990592686992, 0.05000000074505806, 0.7999991861681655, 0.05000000074505806, 0.7999988416937497, 0.05000000074505806, 0.7999980991403961, 0.05000000074505806, 0.7999978607719861]"
+450,35.69,0.3569,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_450,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7960123152401579, 0.05000000074505806, 0.7999981852724485, 0.05000000074505806, 0.799998158843478, 0.05000000074505806, 0.7999948497848868, 0.05000000074505806, 0.799993462722177, 0.05000000074505806, 0.7999945515801915]"
+451,35.23,0.35229999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_451,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7966803801541955, 0.05000000074505806, 0.7999984363653277, 0.05000000074505806, 0.799998559407098, 0.05000000074505806, 0.7999970150073876, 0.05000000074505806, 0.7999966203684199, 0.05000000074505806, 0.7999967591745814]"
+452,33.52,0.33520000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_452,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7973964559926877, 0.05000000074505806, 0.7999987268949159, 0.05000000074505806, 0.7999984585045538, 0.05000000074505806, 0.7999973005218068, 0.05000000074505806, 0.7999965539521066, 0.05000000074505806, 0.7999966823096363]"
+453,32.54,0.32539999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_453,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7970914884715619, 0.05000000074505806, 0.7999985939469403, 0.05000000074505806, 0.7999983889264558, 0.05000000074505806, 0.7999954378046267, 0.05000000074505806, 0.7999938673104952, 0.05000000074505806, 0.7999943056668103]"
+454,34.41,0.34409999999999996,20276928,50.00426136363636,25.61188811188811,BEST_adc_episode_454,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.78125, 0.0625, 0.75, 0.28125, 0.78125, 0.046875, 0.734375, 0.296875, 0.703125, 0.359375, 0.6875]","[0.07446921662683668, 0.05000000074505806, 0.05000000074505806, 0.05098161244351964, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.08278817335768397, 0.7486457476200891, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7488930345684415, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+455,34.31999999999999,0.34319999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_455,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7961644733681807, 0.05000000074505806, 0.799998038144816, 0.05000000074505806, 0.7999978099060868, 0.05000000074505806, 0.7999933615361641, 0.05000000074505806, 0.7999913520487278, 0.05000000074505806, 0.7999921802136476]"
+456,35.199999999999996,0.352,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_456,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7953828476139988, 0.05000000074505806, 0.7999976186108645, 0.05000000074505806, 0.7999974621953512, 0.05000000074505806, 0.7999910308040953, 0.05000000074505806, 0.7999859852253204, 0.05000000074505806, 0.7999827018253429]"
+457,45.07,0.4507,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_457,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7154853222993692, 0.05000000074505806, 0.6895947395192101, 0.05000000074505806, 0.14022163752213934, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+458,30.620000000000005,0.3062,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_458,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+459,34.309999999999995,0.34309999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_459,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
+460,30.56,0.3056,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_460,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05384214474699749, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06765110679601997, 0.08839463625152358, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06246037657065524]"
diff --git a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
new file mode 100644
index 0000000..934a661
--- /dev/null
+++ b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
@@ -0,0 +1,401 @@
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Wall-Clock Time,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min,actions/Mean,actions/Stdev,actions/Max,actions/Min
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diff --git a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/events.out.tfevents.1546047525.cvds-18 b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/events.out.tfevents.1546047525.cvds-18
new file mode 100644
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diff --git a/examples/classifier_compression/compress_classifier.py b/examples/classifier_compression/compress_classifier.py
index 0d865e8..6f18978 100755
--- a/examples/classifier_compression/compress_classifier.py
+++ b/examples/classifier_compression/compress_classifier.py
@@ -77,6 +77,7 @@ except ImportError:
import apputils
from distiller.data_loggers import *
import distiller.quantization as quantization
+import examples.automated_deep_compression as adc
from models import ALL_MODEL_NAMES, create_model
import parser
@@ -178,8 +179,10 @@ def main():
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
- if args.ADC:
+ if args.AMC:
return automated_deep_compression(model, criterion, optimizer, pylogger, args)
+ if args.greedy:
+ return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
@@ -421,7 +424,8 @@ def validate(val_loader, model, criterion, loggers, args, epoch=-1):
def test(test_loader, model, criterion, loggers, activations_collectors, args):
"""Model Test"""
msglogger.info('--- test ---------------------')
-
+ if activations_collectors is None:
+ activations_collectors = create_activation_stats_collectors(model, None)
with collectors_context(activations_collectors["test"]) as collectors:
top1, top5, lossses = _validate(test_loader, model, criterion, loggers, args)
distiller.log_activation_statsitics(-1, "test", loggers, collector=collectors['sparsity'])
@@ -640,32 +644,36 @@ def sensitivity_analysis(model, criterion, data_loader, loggers, args, sparsitie
def automated_deep_compression(model, criterion, optimizer, loggers, args):
- import examples.automated_deep_compression.ADC as ADC
- HAVE_COACH_INSTALLED = True
- if not HAVE_COACH_INSTALLED:
- raise ValueError("ADC is currently experimental and uses non-public Coach features")
-
- if not isinstance(loggers, list):
- loggers = [loggers]
-
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size, args.effective_test_size)
args.display_confusion = True
- validate_fn = partial(validate, val_loader=test_loader, criterion=criterion,
- loggers=loggers, args=args)
+ validate_fn = partial(test, test_loader=test_loader, criterion=criterion,
+ loggers=loggers, args=args, activations_collectors=None)
train_fn = partial(train, train_loader=train_loader, criterion=criterion,
loggers=loggers, args=args)
- if args.ADC_params is not None:
- ADC.summarize_experiment(args.ADC_params, args.dataset, args.arch, validate_fn)
- exit()
-
save_checkpoint_fn = partial(apputils.save_checkpoint, arch=args.arch, dir=msglogger.logdir)
optimizer_data = {'lr': args.lr, 'momentum': args.momentum, 'weight_decay': args.weight_decay}
- ADC.do_adc(model, args.dataset, args.arch, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
+ adc.ADC.do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
+
+
+def greedy(model, criterion, optimizer, loggers, args):
+ train_loader, val_loader, test_loader, _ = apputils.load_data(
+ args.dataset, os.path.expanduser(args.data), args.batch_size,
+ args.workers, args.validation_split, args.deterministic,
+ args.effective_train_size, args.effective_valid_size, args.effective_test_size)
+
+ test_fn = partial(test, test_loader=test_loader, criterion=criterion,
+ loggers=loggers, args=args, activations_collectors=None)
+ train_fn = partial(train, train_loader=train_loader, criterion=criterion, args=args)
+ assert args.greedy_target_density is not None
+ distiller.pruning.greedy_filter_pruning.greedy_pruner(model, args,
+ args.greedy_target_density,
+ args.greedy_pruning_step,
+ test_fn, train_fn)
class missingdict(dict):
diff --git a/examples/classifier_compression/parser.py b/examples/classifier_compression/parser.py
index 0cb1ff6..e241916 100755
--- a/examples/classifier_compression/parser.py
+++ b/examples/classifier_compression/parser.py
@@ -18,6 +18,7 @@ import argparse
import distiller
import distiller.quantization
+import examples.automated_deep_compression as adc
from distiller.utils import float_range_argparse_checker as float_range
import models
@@ -95,8 +96,6 @@ def get_parser():
'AFTER the train-validation split according to that argument')
parser.add_argument('--effective-test-size', '--etes', type=float_range(exc_min=True), default=1.,
help='Portion of test dataset to be used in each epoch')
- parser.add_argument('--adc', dest='ADC', action='store_true', help='temp HACK')
- parser.add_argument('--adc-params', dest='ADC_params', default=None, help='temp HACK')
parser.add_argument('--confusion', dest='display_confusion', default=False, action='store_true',
help='Display the confusion matrix')
parser.add_argument('--earlyexit_lossweights', type=float, nargs='*', dest='earlyexit_lossweights', default=None,
@@ -112,5 +111,6 @@ def get_parser():
distiller.knowledge_distillation.add_distillation_args(parser, models.ALL_MODEL_NAMES, True)
distiller.quantization.add_post_train_quant_args(parser)
-
+ distiller.pruning.greedy_filter_pruning.add_greedy_pruner_args(parser)
+ adc.automl_args.add_automl_args(parser)
return parser
diff --git a/examples/greedy_pruning/greedy_pruning.ipynb b/examples/greedy_pruning/greedy_pruning.ipynb
new file mode 100644
index 0000000..069736c
--- /dev/null
+++ b/examples/greedy_pruning/greedy_pruning.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Greedy Filter Pruning\n",
+ "\n",
+ "\n",
+ "In this notebook we review a couple of experiments performed on Plain20 using Distiller's version of greedy filter pruning. This implementation is very similar to the greed algorithms defined in [1] and [2].\n",
+ "\n",
+ "This is another means to explore the network sub-space around a pre-trained model: by small geedy and iterative greedy filter-subtraction operations. \n",
+ "\n",
+ "We want to answer the question: *how important is the duration of the short-term fine-tuning (see NetAdapt for definition of short-term FT)?*\n",
+ "\n",
+ "### Resources\n",
+ "\n",
+ "**[1] Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning**<br>\n",
+ "*Reza Abbasi-Asl, Bin Yu*\n",
+ "https://arxiv.org/abs/1705.07356\n",
+ "\n",
+ "**[2] NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications**<br>\n",
+ "*Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam*<br>\n",
+ "https://arxiv.org/abs/1804.03230, ECCV 2018"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib \n",
+ "import csv\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
+ "from IPython.display import HTML"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fname1 = \"../classifier_compression/latest_log_dir/netadapt.csv\"\n",
+ "\n",
+ "fname1 = \"../automated_deep_compression/sample_logs/greedy_search/2019.01.08-185015/greedy.csv\"\n",
+ "fname2 = \"../automated_deep_compression/sample_logs/greedy_search/2019.01.09-010040/greedy.csv\"\n",
+ "df1 = pd.read_csv(fname1)\n",
+ "df2 = pd.read_csv(fname2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "params = {'legend.fontsize': 'x-large',\n",
+ " 'figure.figsize': (15, 7),\n",
+ " 'axes.labelsize': 'x-large',\n",
+ " 'axes.titlesize':'xx-large',\n",
+ " 'xtick.labelsize':'x-large',\n",
+ " 'ytick.labelsize':'x-large'}\n",
+ "pylab.rcParams.update(params)\n",
+ "\n",
+ "\n",
+ "def to_percent(y, position):\n",
+ " # Ignore the passed in position. This has the effect of scaling the default\n",
+ " # tick locations.\n",
+ " if y <= 1:\n",
+ " y = 100 * y\n",
+ " s = \"{:.1f}\".format(y)\n",
+ "\n",
+ " # The percent symbol needs escaping in latex\n",
+ " if matplotlib.rcParams['text.usetex'] is True:\n",
+ " return s + r'$\\%$'\n",
+ " else:\n",
+ " return s + '%'\n",
+ " \n",
+ "def plot_2d_embeddings(plots): # top1, normalized_macs, cmap_str):\n",
+ " plt.figure(figsize=(15,7)) \n",
+ " plt.title('Projection of Discovered Networks') # ({})'.format(len(top1))) \n",
+ " plt.xlabel('Normalized MACs')\n",
+ " plt.ylabel('Top1 Accuracy')\n",
+ "\n",
+ " # Create the formatter using the function to_percent. This multiplies all the\n",
+ " # default labels by 100, making them all percentages\n",
+ " formatter = FuncFormatter(to_percent)\n",
+ "\n",
+ " # Set the formatter\n",
+ " plt.gca().yaxis.set_major_formatter(formatter)\n",
+ " plt.gca().xaxis.set_major_formatter(formatter)\n",
+ "\n",
+ " # Use color gradients to show the \"age\" of the network:\n",
+ " # Lighter networks were discovered earlier than darker ones.\n",
+ " for plot in plots:\n",
+ " cm = plt.get_cmap(plot[2])\n",
+ " top1 = plot[0]\n",
+ " normalized_macs = plot[1]\n",
+ " color_grad = [cm(float(i)/len(top1)) for i in range(len(top1))]\n",
+ " plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray', marker='o');\n",
+ "\n",
+ "plots = [(df1['top1'], df1['normalized_macs'], \"Greens\"),\n",
+ " (df2['top1'], df2['normalized_macs'], \"Reds\")]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Experiment1: Short-term fine-tuning using 20% of the training DS\n",
+ "\n",
+ "Run NetAdapt with 1 epoch of short-term fine-tuning which uses 20% of the training dataset, for one epoch.\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --resume=checkpoint.plain20_cifar.pth.tar --greedy --vs=0.8\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_2d_embeddings(plots[:-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Long-term fine-tuning (40 epochs)\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --epochs=40 --lr=0.005 -p=50 --resume=logs/2019.01.08-185015/netadapt_0.5_checkpoint.pth.tar --vs=0\n",
+ "```\n",
+ "\n",
+ "**==> Best Top1: 88.880 on Epoch: 37**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Experiment2: Short-term fine-tuning using 100% of the training DS\n",
+ "\n",
+ "We repeat the NetAdapt training, but this time with 1 epoch of short-term fine-tuning which uses 100% of the training dataset, for one epoch.\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --resume=checkpoint.plain20_cifar.pth.tar --greedy\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_2d_embeddings(plots)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --resume=checkpoint.plain20_cifar.pth.tar --netadapt\n",
+ "```\n",
+ "==> using cifar10 dataset\n",
+ "=> creating plain20_cifar model for CIFAR10\n",
+ "Invoking create_thinning_recipe_filters\n",
+ "In tensor module.layer3.0.conv2.weight found 4/48 zero filters\n",
+ "Created, applied and saved a thinning recipe\n",
+ "Iteration 83: 86.27 module.layer3.0.conv2.weight 0.49511363636363637\n",
+ "--- test ---------------------\n",
+ "10000 samples (256 per mini-batch)\n",
+ "Test: [ 10/ 39] Loss 0.451831 Top1 86.367188 Top5 99.375000\n",
+ "Test: [ 20/ 39] Loss 0.443609 Top1 86.347656 Top5 99.257812\n",
+ "Test: [ 30/ 39] Loss 0.432273 Top1 86.549479 Top5 99.375000\n",
+ "Test: [ 40/ 39] Loss 0.439493 Top1 86.390000 Top5 99.450000\n",
+ "==> Top1: 86.390 Top5: 99.450 Loss: 0.439\n",
+ "\n",
+ "86.39 99.45 0.4394926078617573\n",
+ "Saving checkpoint to: logs/2019.01.09-010040/netadapt_0.5_checkpoint.pth.tar\n",
+ "\n",
+ "Log file for this run: /home/cvds_lab/nzmora/pytorch_workspace/distiller/examples/classifier_compression/logs/2019.01.09-010040/2019.01.09-010040.log\n",
+ "\n",
+ "real 258m19.600s\n",
+ "user 781m11.641s\n",
+ "sys 164m58.532s\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Long-term fine-tuning (40 epochs)\n",
+ "```\n",
+ "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --epochs=40 --lr=0.005 -p=50 --resume=logs/2019.01.09-010040/netadapt_0.5_checkpoint.pth.tar --vs=0\n",
+ "```\n",
+ "\n",
+ "**==> Best Top1: 90.200 on Epoch: 29**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "The longer short-term fine-tuning of the second experiment adds considerable time to the experiment duration, but produces near-dense Top1 results. \n",
+ "\n",
+ "The question is why? How do these networks structurally differ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/greedy_pruning/sample_logs/2019.01.08-185015/greedy.csv b/examples/greedy_pruning/sample_logs/2019.01.08-185015/greedy.csv
new file mode 100644
index 0000000..5772caf
--- /dev/null
+++ b/examples/greedy_pruning/sample_logs/2019.01.08-185015/greedy.csv
@@ -0,0 +1,94 @@
+iteration,top1,param_name,normalized_macs,total_macs
+1,85.16,module.layer1.1.conv2.weight,0.9927272727272727,40255488
+2,84.81,module.layer1.2.conv1.weight,0.9856818181818182,39969792
+3,85.74,module.conv1.weight,0.9813636363636363,39794688
+4,84.57000000000001,module.layer1.0.conv2.weight,0.9740909090909091,39499776
+5,86.43,module.layer1.2.conv1.weight,0.9670454545454545,39214080
+6,86.27,module.layer3.1.conv1.weight,0.9561363636363637,38771712
+7,85.79,module.layer2.1.conv1.weight,0.9452272727272727,38329344
+8,85.75,module.layer3.2.conv2.weight,0.9397727272727273,38108160
+9,86.36,module.layer2.1.conv1.weight,0.9325,37813248
+10,85.98,module.layer2.1.conv1.weight,0.9252272727272727,37518336
+11,86.98,module.layer1.0.conv2.weight,0.9179545454545455,37223424
+12,86.52,module.layer3.2.conv1.weight,0.9075568181818182,36801792
+13,85.87,module.layer1.0.conv2.weight,0.9002840909090909,36506880
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diff --git a/jupyter/compression_insights.ipynb b/jupyter/compression_insights.ipynb
index 3bdee8e..9ef65e4 100755
--- a/jupyter/compression_insights.ipynb
+++ b/jupyter/compression_insights.ipynb
@@ -34,6 +34,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "%matplotlib inline\n",
"import matplotlib \n",
"\n",
"# Load some common jupyter code\n",
@@ -411,16 +412,123 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "scrolled": false
+ },
"outputs": [],
"source": [
- "params_names = conv_param_names(sparse_model)\n",
- "\n",
- "def view_kernel_l1(pname):\n",
- " tensor = sparse_model.state_dict()[pname]\n",
- " plot_l1_norm_hist(tensor)\n",
+ "def get_norms(weights, structure='kernels'):\n",
+ " \"\"\"Compute a histogram of the L1-norms of the kernels of a weights tensor.\n",
+ " \n",
+ " The L1-norm of a kernel is one way to quantify the \"magnitude\" of the total coeffiecients\n",
+ " making up this kernel.\n",
" \n",
- "interact(view_kernel_l1, pname=params_dropdown);"
+ " Another interesting look at filters is to compute a histogram per filter.\n",
+ " \"\"\"\n",
+ " ofms, ifms = weights.size()[0], weights.shape[1]\n",
+ " kw, kh = weights.size()[2], weights.shape[3]\n",
+ " if structure == 'kernels':\n",
+ " groups = weights.view(ofms * ifms, kh, kw)\n",
+ " elif structure == 'filters':\n",
+ " groups = weights\n",
+ " else:\n",
+ " raise ValueError('illegal structure')\n",
+ " \n",
+ " norms = [[], []]\n",
+ "# for group in groups:\n",
+ "# #print(group.shape)\n",
+ "# group_size = float(distiller.volume(group))\n",
+ "# norms[0].append(group.norm(1).div(group_size))\n",
+ "# norms[1].append(group.norm(2).div(group_size))\n",
+ " groups = groups.view(groups.shape[0], -1)\n",
+ " group_size = groups.shape[1]\n",
+ " norms[0] = groups.norm(1, dim=1).div(group_size)\n",
+ " norms[1] = groups.norm(2, dim=1).div(group_size)\n",
+ " return norms\n",
+ "\n",
+ "def plot_l1_norm_hist(weights, structure):\n",
+ " norms = get_norms(weights, structure)\n",
+ " if structure == 'kernels':\n",
+ " bins = 200\n",
+ " else:\n",
+ " bins = 200\n",
+ " n, bins, patches = plt.hist(norms[0], bins=bins, alpha=0.5)\n",
+ " plt.title('{} L1-norm histograms'.format(structure))\n",
+ " plt.ylabel('Frequency')\n",
+ " plt.xlabel('{} L1-norm'.format(structure))\n",
+ " plt.show()\n",
+ " \n",
+ "def plot_kernels_norm_hist_per_filter(ax, filter, structure, norm_mag):\n",
+ " norms = get_norms(filter, \"kernels\")\n",
+ " bins = 200\n",
+ " n, bins, patches = ax.hist(norms[0], bins=bins, alpha=0.5)\n",
+ " ax.set_xlabel('{:2f} L1-norm'.format(norm_mag))\n",
+ " \n",
+ "params_names = conv_param_names(resnet20_dense)\n",
+ "\n",
+ "def view_kernel_l1(pname, sort_kernels):\n",
+ " tensor = resnet20_dense.state_dict()[pname].to('cpu')\n",
+ " plot_l1_norm_hist(tensor, 'kernels')\n",
+ " #plot_l1_norm_hist(tensor, 'filters')\n",
+ " nrows = (tensor.shape[0]+3)//4; ncols = 4\n",
+ " f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
+ " filter_norms = []\n",
+ " for i in range(0, nrows * ncols):\n",
+ " filter = tensor[i].unsqueeze(0)\n",
+ " norm = get_norms(filter, \"filters\")[0][0].item()\n",
+ " filter_norms.append((norm, i))\n",
+ " filter_norms.sort(key=lambda norm: norm[0])\n",
+ " for i in range(0, nrows * ncols):\n",
+ " #print(filter_norms[i][1])\n",
+ " filter = tensor[filter_norms[i][1]].unsqueeze(0)\n",
+ " norm_mag = filter_norms[i][0]\n",
+ " plot_kernels_norm_hist_per_filter(axarr[i//ncols, i%ncols], filter, 'kernels', norm_mag)\n",
+ " print(norm_mag)\n",
+ " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
+ " plt.show()\n",
+ "\n",
+ "sort_choice = widgets.Checkbox(value=True, description='Sort kernels')\n",
+ "params_dropdown = widgets.Dropdown(description='Weights:', options=params_names, layout=Layout(width='40%'))\n",
+ "interact(view_kernel_l1, pname=params_dropdown, sort_kernels=sort_choice);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compare Filter L$_1$ and L$_2$ norms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "params_names = conv_param_names(resnet20_dense)\n",
+ "\n",
+ "def view_weights(pname, sort, draw_l1, draw_l2):\n",
+ " param = resnet20_dense.state_dict()[pname]\n",
+ " view_filters = param.view(param.size(0), -1)\n",
+ " filter_mags_l1 = to_np(view_filters.norm(p=1, dim=1).div(view_filters.shape[1]))\n",
+ " filter_mags_l2 = to_np(view_filters.norm(p=2, dim=1).div(view_filters.shape[1]))\n",
+ " if sort:\n",
+ " filter_mags_l1 = np.sort(filter_mags_l1)\n",
+ " filter_mags_l2 = np.sort(filter_mags_l2)\n",
+ " plt.figure(figsize=[15,7.5])\n",
+ " if draw_l1:\n",
+ " plt.plot(range(len(filter_mags_l1)), filter_mags_l1, label=\"L1\", marker=\"o\", markersize=5, markerfacecolor=\"C1\")\n",
+ " if draw_l2:\n",
+ " plt.plot(range(len(filter_mags_l2)), filter_mags_l2, label=\"L2\", marker=\"+\", markersize=5, markerfacecolor=\"C1\")\n",
+ " plt.xlabel('Filter index (i.e. output feature-map channel)')\n",
+ " plt.ylabel('Normalized Fliter L1-norm')\n",
+ " plt.legend()\n",
+ "\n",
+ "sort_choice = widgets.Checkbox(value=True, description='Sort filters')\n",
+ "l1_choice = widgets.Checkbox(value=True, description='Draw L1')\n",
+ "l2_choice = widgets.Checkbox(value=True, description='Draw L2')\n",
+ "params_dropdown = widgets.Dropdown(description='Weights:', options=params_names, layout=Layout(width='40%'))\n",
+ "interact(view_weights, pname=params_dropdown, sort=sort_choice, draw_l1=l1_choice, draw_l2=l2_choice);"
]
},
{
@@ -551,7 +659,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.7"
}
},
"nbformat": 4,
--
GitLab