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"""!
@brief Unit-tests for Phase Oscillatory Neural Network for Pattern Recognition based on Kuramoto model.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import unittest;
import matplotlib;
matplotlib.use('Agg');
from pyclustering.nnet.tests.syncpr_templates import SyncprTestTemplates;
from pyclustering.nnet import solve_type;
from pyclustering.nnet.syncpr import syncpr;
class SyncprUnitTest(unittest.TestCase):
def testCreateTenOscillatorsNetwork(self):
net = syncpr(10, 0.1, 0.1, ccore=False);
assert len(net) == 10;
def testCreateHundredOscillatorsNetwork(self):
net = syncpr(100, 0.1, 0.1, ccore=False);
assert len(net) == 100;
def testOutputDynamicFastSolver(self):
SyncprTestTemplates.templateOutputDynamic(solve_type.FAST, False);
def testOutputDynamicRK4Solver(self):
SyncprTestTemplates.templateOutputDynamic(solve_type.RK4, False);
def testOutputDinamicLengthSimulation(self):
net = syncpr(5, 0.1, 0.1, ccore=False);
output_dynamic = net.simulate(10, 10, [-1, 1, -1, 1, -1], solution = solve_type.FAST, collect_dynamic = True);
assert len(output_dynamic) == 11; # 10 steps without initial values.
def testOutputDynamicLengthStaticSimulation(self):
SyncprTestTemplates.templateOutputDynamicLengthStaticSimulation(True, False);
def testOutputDynamicLengthStaticSimulationWithouCollecting(self):
SyncprTestTemplates.templateOutputDynamicLengthStaticSimulation(False, False);
def testOutputDynamicLengthDynamicSimulation(self):
SyncprTestTemplates.templateOutputDynamicLengthDynamicSimulation(True, False);
def testOutputDynamicLengthDynamicSimulationWithoutCollecting(self):
SyncprTestTemplates.templateOutputDynamicLengthDynamicSimulation(False, False);
def testTrainNetworkAndRecognizePattern(self):
SyncprTestTemplates.templateTrainNetworkAndRecognizePattern(False);
def testIncorrectPatternValues(self):
SyncprTestTemplates.templateIncorrectPatternValues(False);
def testIncorrectSmallPatternSize(self):
patterns = [ [1, 1, 1, 1, 1, -1] ];
SyncprTestTemplates.templateIncorrectPatternForTraining(patterns, False);
def testIncorrectLargePatternSize(self):
patterns = [ [1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1] ];
SyncprTestTemplates.templateIncorrectPatternForTraining(patterns, False);
def testIncorrectSmallPatternSizeSimulation(self):
pattern = [1, 1, 1, 1, 1, -1];
SyncprTestTemplates.templateIncorrectPatternForSimulation(pattern, False);
def testIncorrectLargePatternSizeSimulation(self):
pattern = [1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1];
SyncprTestTemplates.templateIncorrectPatternForSimulation(pattern, False);
def testPatternVisualizerCollectDynamic(self):
SyncprTestTemplates.templatePatternVisualizer(True);
def testPatternVisualizerWithoutCollectDynamic(self):
SyncprTestTemplates.templatePatternVisualizer(False);
def testMemoryOrder(self):
SyncprTestTemplates.templateMemoryOrder(False);
def testStaticSimulation(self):
SyncprTestTemplates.templateStaticSimulation(False);
def testDynamicSimulation(self):
SyncprTestTemplates.templateDynamicSimulation(False);
def testGlobalSyncOrder(self):
SyncprTestTemplates.templateGlobalSyncOrder(False);
def testLocalSyncOrder(self):
SyncprTestTemplates.templateLocalSyncOrder(False);
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