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"""!
@brief Unit-tests for chaotic neural network (CNN).
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import unittest
# Generate images without having a window appear.
import matplotlib
matplotlib.use('Agg')
from pyclustering.nnet.cnn import type_conn, cnn_network, cnn_visualizer
from pyclustering.samples.definitions import SIMPLE_SAMPLES
from pyclustering.utils import read_sample
class CnnUnitTest(unittest.TestCase):
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 testClusteringPhenomenonSimpleSample01(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 3, 10, [5, 5]);
def testGlobalSynchronizationSimpleSample01(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 9, 10, [10]);
def testDelaunayTriangulationSimpleSample01(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.TRIANGULATION_DELAUNAY, 3, 10, None);
def testClusteringPhenomenonSimpleSample02(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 3, 10, [10, 5, 8]);
def testGlobalSynchronizationSimpleSample02(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 22, 10, [10, 5, 8]);
def testDelaunayTriangulationSimpleSample02(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.TRIANGULATION_DELAUNAY, 22, 10, None);
def testClusteringPhenomenonSimpleSample03(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 3, 10, [10, 10, 10, 30]);
def testClusteringPhenomenonSimpleSample04(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4);
self.templateSyncEnsembleAllocation(stimulus, 200, type_conn.ALL_TO_ALL, 10, 10, [15, 15, 15, 15, 15]);
def testClusteringPhenomenonSimpleSample05(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE5);
self.templateSyncEnsembleAllocation(stimulus, 100, type_conn.ALL_TO_ALL, 5, 10, [15, 15, 15, 15]);
def testChaoticNeuralNetwork2DVisualization(self):
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
network_instance = cnn_network(len(stimulus));
output_dynamic = network_instance.simulate(100, 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);
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