#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 20 00:50:31 2017

@author: mjb
"""

# Parameters for DeepStack.
# @module arguments

import torch

torch.set_default_tensor_type('torch.FloatTensor')


# whether to run on GPU
gpu = torch.cuda.is_available()
# list of pot-scaled bet sizes to use in tree
# @field bet_sizing
bet_sizing = [1]
# server running the ACPC dealer
acpc_server = "localhost"
# server port running the ACPC dealer
acpc_server_port = 20000
# the number of betting rounds in the game
streets_count = 2
# the tensor datatype used for storing DeepStack's internal data
Tensor = torch.FloatTensor
LongTensor = torch.LongTensor
# the directory for data files
data_directory = 'Data/'
# the size of the game's ante, in chips
ante = 100
# the size of each player's stack, in chips
stack = 1200
# the number of iterations that DeepStack runs CFR for
cfr_iters = 1000
# the number of preliminary CFR iterations which DeepStack doesn't factor into the average strategy (included in cfr_iters)
cfr_skip_iters = 500
# how many poker situations are solved simultaneously during data generation
gen_batch_size = 10
# how many poker situations are used in each neural net training batch
train_batch_size = 100
# path to the solved poker situation data used to train the neural net
data_path = '../Data/TrainSamples/PotBet/'
# path to the neural net model
model_path = '../Data/Models/PotBet/'
# the name of the neural net file
value_net_name = 'final'
# the neural net architecture
net = '{nn.Linear(input_size, 50), nn.PReLU(), nn.Linear(50, output_size)}'
# how often to save the model during training
save_epoch = 1000
# how many epochs to train for
epoch_count = 1000
# how many solved poker situations are generated for use as training examples
train_data_count = 100
# how many solved poker situations are generated for use as validation examples
valid_data_count = 100
# learning rate for neural net training
learning_rate = 0.001
#
eta = 0.1

assert(cfr_iters > cfr_skip_iters)
if gpu:
  Tensor = torch.cuda.FloatTensor
  LongTensor = torch.cuda.LongTensor



9203b4c3011d070016bc0c95a7d3b370d5dd7f6e
<mask token>

<mask token>
torch.set_default_tensor_type('torch.FloatTensor')
<mask token>
assert cfr_iters > cfr_skip_iters
if gpu:
    Tensor = torch.cuda.FloatTensor
    LongTensor = torch.cuda.LongTensor

<mask token>
torch.set_default_tensor_type('torch.FloatTensor')
gpu = torch.cuda.is_available()
bet_sizing = [1]
acpc_server = 'localhost'
acpc_server_port = 20000
streets_count = 2
Tensor = torch.FloatTensor
LongTensor = torch.LongTensor
data_directory = 'Data/'
ante = 100
stack = 1200
cfr_iters = 1000
cfr_skip_iters = 500
gen_batch_size = 10
train_batch_size = 100
data_path = '../Data/TrainSamples/PotBet/'
model_path = '../Data/Models/PotBet/'
value_net_name = 'final'
net = '{nn.Linear(input_size, 50), nn.PReLU(), nn.Linear(50, output_size)}'
save_epoch = 1000
epoch_count = 1000
train_data_count = 100
valid_data_count = 100
learning_rate = 0.001
eta = 0.1
assert cfr_iters > cfr_skip_iters
if gpu:
    Tensor = torch.cuda.FloatTensor
    LongTensor = torch.cuda.LongTensor

<mask token>
import torch
torch.set_default_tensor_type('torch.FloatTensor')
gpu = torch.cuda.is_available()
bet_sizing = [1]
acpc_server = 'localhost'
acpc_server_port = 20000
streets_count = 2
Tensor = torch.FloatTensor
LongTensor = torch.LongTensor
data_directory = 'Data/'
ante = 100
stack = 1200
cfr_iters = 1000
cfr_skip_iters = 500
gen_batch_size = 10
train_batch_size = 100
data_path = '../Data/TrainSamples/PotBet/'
model_path = '../Data/Models/PotBet/'
value_net_name = 'final'
net = '{nn.Linear(input_size, 50), nn.PReLU(), nn.Linear(50, output_size)}'
save_epoch = 1000
epoch_count = 1000
train_data_count = 100
valid_data_count = 100
learning_rate = 0.001
eta = 0.1
assert cfr_iters > cfr_skip_iters
if gpu:
    Tensor = torch.cuda.FloatTensor
    LongTensor = torch.cuda.LongTensor

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 20 00:50:31 2017

@author: mjb
"""

# Parameters for DeepStack.
# @module arguments

import torch

torch.set_default_tensor_type('torch.FloatTensor')


# whether to run on GPU
gpu = torch.cuda.is_available()
# list of pot-scaled bet sizes to use in tree
# @field bet_sizing
bet_sizing = [1]
# server running the ACPC dealer
acpc_server = "localhost"
# server port running the ACPC dealer
acpc_server_port = 20000
# the number of betting rounds in the game
streets_count = 2
# the tensor datatype used for storing DeepStack's internal data
Tensor = torch.FloatTensor
LongTensor = torch.LongTensor
# the directory for data files
data_directory = 'Data/'
# the size of the game's ante, in chips
ante = 100
# the size of each player's stack, in chips
stack = 1200
# the number of iterations that DeepStack runs CFR for
cfr_iters = 1000
# the number of preliminary CFR iterations which DeepStack doesn't factor into the average strategy (included in cfr_iters)
cfr_skip_iters = 500
# how many poker situations are solved simultaneously during data generation
gen_batch_size = 10
# how many poker situations are used in each neural net training batch
train_batch_size = 100
# path to the solved poker situation data used to train the neural net
data_path = '../Data/TrainSamples/PotBet/'
# path to the neural net model
model_path = '../Data/Models/PotBet/'
# the name of the neural net file
value_net_name = 'final'
# the neural net architecture
net = '{nn.Linear(input_size, 50), nn.PReLU(), nn.Linear(50, output_size)}'
# how often to save the model during training
save_epoch = 1000
# how many epochs to train for
epoch_count = 1000
# how many solved poker situations are generated for use as training examples
train_data_count = 100
# how many solved poker situations are generated for use as validation examples
valid_data_count = 100
# learning rate for neural net training
learning_rate = 0.001
#
eta = 0.1

assert(cfr_iters > cfr_skip_iters)
if gpu:
  Tensor = torch.cuda.FloatTensor
  LongTensor = torch.cuda.LongTensor


