def test_iris(self): cov = Covariance() cov.fit(self.iris_points) csep = class_separation(cov.transform(), self.iris_labels) # deterministic result self.assertAlmostEqual(csep, 0.72981476)
def test_iris(self): cov = Covariance() cov.fit(self.iris_points) csep = class_separation(cov.transform(), self.iris_labels) # deterministic result self.assertAlmostEqual(csep, 0.73068122)
def test_cov(self): cov = Covariance() cov.fit(self.X) res_1 = cov.transform(self.X) cov = Covariance() res_2 = cov.fit_transform(self.X) # deterministic result assert_array_almost_equal(res_1, res_2)
def train_covariance(X): model = Covariance() model.fit(X) return model.transform(X), model.metric()
#print(TrainData) #print(type(TrainData)) #print(TrainLabels) #print(type(TrainLabels)) if Method == 'LMNN': print("Method: LMNN", '\n') lmnn = LMNN(k=3, learn_rate=1e-6, verbose=False) x = lmnn.fit(FSTrainData, TrainLabels) TFSTestData = x.transform(FSTestData) print('Transformation Done', '\n') elif Method == 'COV': print("Method: COV", '\n') cov = Covariance().fit(FSTrainData) TFSTestData = cov.transform(FSTestData) print('Transformation Done', '\n') elif Method == 'ITML': print("Method: ITML", '\n') itml = ITML_Supervised(num_constraints=200, A0=None) x = itml.fit(FSTrainData, TrainLabels) TFSTestData = x.transform(FSTestData) print('Transformation Done', '\n') elif Method == 'LFDA': print("Method: LFDA", '\n') lfda = LFDA(k=4, dim=1) x = lfda.fit(FSTrainData, TrainLabels) TFSTestData = x.transform(FSTestData) print('Transformation Done', '\n')
def test_covariance(): iris = load_iris()['data'] cov = Covariance().fit(iris) x = cov.transform(iris) print x