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 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
