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)
