hpc.reset_hpc_module() tar_patts = [] for p in training_patterns_associative[:5*train_set_size_ctr]: tar_patts.append(p[1]) print "Starting experiment 4.1, HPC chaotic recall i iters 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_global_exposure( hpc, train_set_size_ctr, training_patterns_associative[:5 * train_set_size_ctr], train_iters=50) # For now, this is the ONLY place where the counter is incremented. Tools.increment_experiment_counter() hpc._ASYNC_FLAG = False # ============ Config. X: ============ for i in range(20): for train_set_size_ctr in range(2, 6): hpc.reset_hpc_module() tar_patts = [] for p in training_patterns_heterogeneous[:5*train_set_size_ctr]: tar_patts.append(p[1]) print "Starting experiment 4.1, HPC chaotic recall i iters 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_heterogeneous[:5 * train_set_size_ctr], train_iters=15)