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ExperimentExecution.py
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ExperimentExecution.py
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import numpy as np
from HPC import HPC
from SimpleNeocorticalNetwork import SimpleNeocorticalNetwork
from Experiments_4_x import experiment_4_x_1, experiment_4_x_2
from data_capital import data_letters_capital
from data_lowercase import data_letters_lowercase
import Tools
io_dim = 49
training_patterns_associative = []
# Setup all training patterns:
for letter_data in data_letters_capital:
io = [[]]
for row in letter_data:
for el in row:
io[0].append(el)
new_array = np.asarray(io, dtype=np.float32)
training_patterns_associative.append([new_array, new_array])
training_patterns_heterogeneous = []
letter_ctr = 0
for letter_data in data_letters_lowercase:
io_lowercase = [[]]
for row in letter_data:
for el in row:
io_lowercase[0].append(el)
lowercase_letter = np.asarray(io_lowercase, dtype=np.float32)
uppercase_letter = training_patterns_associative[letter_ctr][0]
training_patterns_heterogeneous.append([uppercase_letter, lowercase_letter])
for trial in range(1):
for train_set_size_ctr in range(3, 4):
turnover_rate = 0.50
# print "TRIAL #", trial, "turnover rate:", turnover_rate
# dims,
# connection_rate_input_ec, perforant_path, mossy_fibers,
# firing_rate_ec, firing_rate_dg, firing_rate_ca3,
# _gamma, _epsilon, _nu, _turnover_rate, _k_m, _k_r, _a_i, _alpha):
hpc = HPC([io_dim, 240, 1600, 480, io_dim],
0.67, 0.25, 0.04, # connection rates: (in_ec, ec_dg, dg_ca3)
0.10, 0.01, 0.04, # firing rates: (ec, dg, ca3)
0.7, 100.0, 0.1, turnover_rate, # gamma, epsilon, nu, turnover rate
0.10, 0.95, 0.8, 2.0) # k_m, k_r, a_i, alpha. alpha is 2 in 4.1
hipp_chaotic_pats, _ = experiment_4_x_1(hpc, train_set_size_ctr, training_patterns_associative)
Tools.save_experiment_4_1_results(hpc, hipp_chaotic_pats, "train_set_size_"+str(train_set_size_ctr)+"_exp_1"+
"turnover_rate_" + str(turnover_rate))
# ann = SimpleNeocorticalNetwork(io_dim, 30, io_dim, 0.01, 0.9)
# print "Starting experiment 4_2..."
# This also saves the experiment_4_x_1 results!
# information_vector = experiment_4_x_2(hpc, ann, train_set_size_ctr,
# training_patterns_associative[:5 * train_set_size_ctr])
# print "Saving the results."
# Tools.save_experiment_4_2_results(information_vector, "train_set_size_" + str(train_set_size_ctr) +
# "_exp_2_")
# For now, this is the ONLY place where the counter is incremented.
Tools.increment_experiment_counter()