def calculate_cost_using_analyzer(self, parameters): config = {} for i in range(self.parameters_count): config[self.parameters_descriptors[i].name] = parameters[i] config['analyse.ica.num'] = config['analyse.sfa.num'] print "calculating cost for: ", config analyzer = Analyzer(config, self.input_dimensions) analyzer.train(self.training_data) #training_features = analyzer.execute(self.training_data) test_features = analyzer.execute(self.test_data) activation_locations = get_activation_mean_locations(self.coordinate_matrix, test_features) cost = average_cost(self.coordinate_matrix, test_features, activation_locations) return cost
def calculate_cost_using_analyzer(self, parameters, parameters_descriptors): config = {} id = random.randint(0,100000) print "trial id:",id for i in range(len(parameters)): config[parameters_descriptors[i].name] = parameters[i] config['analyse.ica.num'] = config['analyse.sfa.num'] print "[{0}] calculating cost for: {1}".format(id,config) start = time.time() print "[{0}] creating analyser".format(id) analyzer = Analyzer(config, self.input_dimensions) print "[{0}] training analyser".format(id) analyzer.train(self.training_data) #training_features = analyzer.execute(self.training_data) print "[{0}] testing features".format(id) test_features = analyzer.execute(self.test_data) print "[{0}] activating locations".format(id) activation_locations = get_activation_mean_locations(self.coordinate_matrix, test_features) print "[{0}] getting average_cost".format(id) cost = average_cost(self.coordinate_matrix, test_features, activation_locations) end = time.time() print "calculating cost done, took ", (end - start), ", config: ", config return cost
testSize = len(sm) - trainingSize testDataOffset = trainingSize trainingData = sm[0:trainingSize] testData = sm[testDataOffset:(testSize + trainingSize)] else: testDataOffset = 0 trainingData = sm testData = sm else: testDataOffset = 0 trainingData = sm testData = sm analyzer.train(trainingData) trainingFeatures = analyzer.execute(trainingData) testFeatures = analyzer.execute(testData) #testFeatures = trainingFeatures analyzer.reset_states(True) testFeatures2 = [] for timeIndex in range(0, len(testData), 1): testFeatures2.append(analyzer.execute([testData[timeIndex]])[0]) analyzer.reset_states(False) #testFeatures2 = analyzer.execute(testData) testFeaturesCombined = np.append(testFeatures, testFeatures2, 1) print "testFeaturesCombined: " + str(len(testFeaturesCombined.T)) + " x " + str(len(testFeaturesCombined.T[0])) #plotter.plot_features_graphs("dummy", coordm, testFeaturesCombined, 8, sm, 0) title = analyzer.description + ", data: " + str(len(sm)) + ", source: " + sourceDescription activation_locations = place_cell_reliability.get_activation_mean_locations(coordm, trainingFeatures)