def iterate_over_experiments_suite_span_output_demo_local(start_index, stop_index): ann = NeocorticalNetwork(49, 30, 49, 0.01, 0.9) for exp_index in range(start_index, stop_index): current_chaotic_patterns, current_pseudopatterns = \ Tools.retrieve_patterns_for_consolidation(exp_index, exp_index%4 + 2) # 2-5 looped training_set = [] t0 = time.time() ann.reset() for cp_subset in current_chaotic_patterns: training_subset = [] for cp in cp_subset: training_subset.append([cp[1], cp[1]]) for i in range(15): ann.train(training_subset) results_line = 'Neocortical module consolidation. Output as IO. Exp#'+str(exp_index)+\ '\n'+str(i+1)+' iters: g='+str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index%4+2))) ann.reset() for cp_subset in current_chaotic_patterns: training_subset = [] for cp in cp_subset: training_subset.append([cp[1], cp[1]]) for i in range(200): ann.train(training_subset) results_line += '\n'+str(i+1)+' iters: g=' + str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index % 4 + 2))) t1 = time.time() print 'Trained and evaluated performance in '+'{:8.3f}'.format(t1-t0), 'seconds' print results_line Tools.append_line_to_log(results_line) return ann
def iterate_over_experiments_suite_halved_pseudopattern_size(start_index, stop_index, scheme_num): for exp_index in range(start_index, stop_index): current_chaotic_patterns, current_pseudopatterns = \ Tools.retrieve_patterns_for_consolidation(exp_index, exp_index%4 + 2) # 2-5 looped training_set = get_training_set_from_patterns_in_scheme_half_pseudopatterns(current_chaotic_patterns, current_pseudopatterns, scheme_num) t0 = time.time() ann = get_ann_trained_on_patterns(training_patterns=training_set, training_iterations=15) results_line = 'Neocortical module consolidation. Halved pseudopattern set size. Scheme: '+str(scheme_num)+\ '. Exp#'+str(exp_index)+'\n15 iters: g='+\ str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index%4+2))) for i in range(200): ann.train(training_set) results_line += '\n1k iters: g=' + str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index % 4 + 2))) t1 = time.time() print 'Trained and evaluated performance in '+'{:8.3f}'.format(t1-t0), 'seconds' print results_line Tools.append_line_to_log(results_line)
def iterate_over_experiments_suite(start_index, stop_index, scheme_num): ann = NeocorticalNetwork(49, 30, 49, 0.01, 0.9) for exp_index in range(start_index, stop_index): current_chaotic_patterns, current_pseudopatterns = \ Tools.retrieve_patterns_for_consolidation(exp_index, exp_index%4 + 2) # 2-5 looped training_set = get_training_set_from_patterns_in_scheme_full_set(current_chaotic_patterns, current_pseudopatterns, scheme_num) t0 = time.time() ann.reset() for i in range(15): ann.train(training_set) results_line = 'Neocortical module consolidation. Scheme: '+str(scheme_num)+'. Exp#'+str(exp_index)+ \ '\n'+str(i+1)+' iters: g='+str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index%4+2))) ann.reset() for i in range(200): ann.train(training_set) results_line += '\n'+str(i+1)+' iters: g=' + str(evaluate_goodness_of_fit(ann, get_target_patterns(exp_index % 4 + 2))) t1 = time.time() print 'Trained and evaluated performance in '+'{:8.3f}'.format(t1-t0), 'seconds' print results_line Tools.append_line_to_log(results_line)