0.7, 100.0, 0.1, turnover_rate, # gamma, epsilon, nu, turnover rate
0.10, 0.95, 0.8, 2.0, weighting_dg, # k_m, k_r, a_i, alpha. alpha is 2 in 4.1
_ASYNC_FLAG=_ASYNC_FLAG, _TURNOVER_MODE=_TURNOVER_MODE)
# ============ Config. X: ============
for i in range(1):
for train_set_size_ctr in range(2, 3):
hpc.reset_hpc_module()
tar_patts = []
for p in training_patterns_associative[:5*train_set_size_ctr]:
tar_patts.append(p[1])
ann = NeocorticalNetwork(io_dim, 30, io_dim, 0.01, 0.9)
print "Starting experiment; HPC chaotic recall i iterations and HPC pseudopatterns..."
# This also saves the experiment results:
# relative frequency as in successful 2x5 goodness of fit.
Experiments_4_x.experiment_4_2_hpc_recall_every_i_iters(
hpc, train_set_size_ctr, training_patterns_associative[:5 * train_set_size_ctr], train_iters=15)
# For now, this is the ONLY place where the counter is incremented.
Tools.increment_experiment_counter()
print "Performing memory consolidation.."
# This is rather hard-coded for demo-purposes.
NeocorticalMemoryConsolidation.iterate_over_experiments_suite_span_output_demo_local(Tools.get_experiment_counter()-1,
Tools.get_experiment_counter())
print "Please see the saved_data/ folder for the associated experiment output."
global_gs = []
local_gs = []
for i in range(40):
ann_global = NeocorticalModuleTraining.global_sequential_FFBP_training(ss=set_size, training_iterations=200)
ann_local = NeocorticalModuleTraining.traditional_training_with_catastrophic_interference(
ss=set_size, training_iterations=200)
# global_io_results = Tools.generate_recall_attempt_results_for_ann(ann_global, original_training_set)
# local_io_results = Tools.generate_recall_attempt_results_for_ann(ann_local, original_training_set)
#
# Tools.save_aggregate_image_from_ios(global_io_results, 'global_aggregate_im', 0)
# Tools.save_aggregate_image_from_ios(local_io_results, 'local_aggregate_im', 1)
global_goodness = NeocorticalMemoryConsolidation.evaluate_goodness_of_fit(ann_global, original_training_set)
local_goodness = NeocorticalMemoryConsolidation.evaluate_goodness_of_fit(ann_local, original_training_set)
global_gs.append(global_goodness)
local_gs.append(local_goodness)
log_line = 'EVALUATED baseline. g\'s - ' + 'global: ' + str(global_goodness) + ', local: ' + str(local_goodness)
print log_line
Tools.append_line_to_log(log_line)
avg_global_g = Tools.get_avg(global_gs)
avg_local_g = Tools.get_avg(local_gs)
avgs_global.append(avg_global_g)
avgs_local.append(avg_local_g)
final_result_line = 'Final results for current set size: global avg. = ' + str(avg_global_g) + ', local avg. = ' + \
