def templateSyncEnsembleAllocation(self, stimulus, steps, connection, amount_neighbors, analysed_iterations, expected_length_ensembles): testing_result = True for _ in range(3): network_instance = cnn_network(len(stimulus), connection, amount_neighbors) assert len(stimulus) == len(network_instance) output_dynamic = network_instance.simulate(steps, stimulus) ensembles = output_dynamic.allocate_sync_ensembles( analysed_iterations) obtained_ensemble_sizes = [ len(ensemble) for ensemble in ensembles ] # critical checks - always determined assert len(stimulus) == len(network_instance) assert len(stimulus) == sum(obtained_ensemble_sizes) if (expected_length_ensembles != None): obtained_ensemble_sizes.sort() expected_length_ensembles.sort() if (obtained_ensemble_sizes != expected_length_ensembles): continue assert testing_result == True
def testChaoticNeuralNetwork3DVisualization(self): stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE11) network_instance = cnn_network(len(stimulus)) output_dynamic = network_instance.simulate(10, stimulus) network_instance.show_network() cnn_visualizer.show_dynamic_matrix(output_dynamic) cnn_visualizer.show_observation_matrix(output_dynamic) cnn_visualizer.show_output_dynamic(output_dynamic)
def testChaoticNeuralNetwork3DVisualization(self): stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE11); network_instance = cnn_network(len(stimulus)); output_dynamic = network_instance.simulate(10, stimulus); network_instance.show_network(); cnn_visualizer.show_dynamic_matrix(output_dynamic); cnn_visualizer.show_observation_matrix(output_dynamic); cnn_visualizer.show_output_dynamic(output_dynamic);
def template_dynamic_cnn(num_osc, steps, stimulus, neighbors, connection, show_network = False): network_instance = cnn_network(num_osc, connection, amount_neighbors = neighbors); output_dynamic = network_instance.simulate(steps, stimulus); print(output_dynamic.allocate_sync_ensembles(10)); if (show_network is True): network_instance.show_network(); cnn_visualizer.show_output_dynamic(output_dynamic); cnn_visualizer.show_dynamic_matrix(output_dynamic); cnn_visualizer.show_observation_matrix(output_dynamic);
def templateSyncEnsembleAllocation(self, stimulus, steps, connection, amount_neighbors, analysed_iterations, expected_length_ensembles): testing_result = True; for _ in range(3): network_instance = cnn_network(len(stimulus), connection, amount_neighbors); assert len(stimulus) == len(network_instance); output_dynamic = network_instance.simulate(steps, stimulus); ensembles = output_dynamic.allocate_sync_ensembles(analysed_iterations); obtained_ensemble_sizes = [len(ensemble) for ensemble in ensembles]; # critical checks - always determined assert len(stimulus) == len(network_instance); assert len(stimulus) == sum(obtained_ensemble_sizes); if (expected_length_ensembles != None): obtained_ensemble_sizes.sort(); expected_length_ensembles.sort(); if (obtained_ensemble_sizes != expected_length_ensembles): continue; assert testing_result == True;