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PlotWorkbench.py
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PlotWorkbench.py
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import NeocorticalModuleTraining
import Tools
import NeocorticalMemoryConsolidation
from DataWrapper import training_patterns_associative as training_set
avgs_global = []
avgs_local = []
for set_size in range(2,6):
original_training_set = training_set[:set_size * 5]
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. = ' + \
str(avg_local_g)
print final_result_line
Tools.append_line_to_log(final_result_line)