def process_nca(self, **option):
'''Metric Learning algorithm: NCA'''
GeneExp = self.GeneExp_train
Label = self.Label_train
nca = NCA(**option)
nca.fit(GeneExp, Label)
self.Trans['NCA'] = nca.transformer()
def test_iris(self):
n = self.iris_points.shape[0]
nca = NCA(max_iter=(100000//n), learning_rate=0.01)
nca.fit(self.iris_points, self.iris_labels)
# Result copied from Iris example at
# https://github.com/vomjom/nca/blob/master/README.mkd
expected = [[-0.09935, -0.2215, 0.3383, 0.443],
[+0.2532, 0.5835, -0.8461, -0.8915],
[-0.729, -0.6386, 1.767, 1.832],
[-0.9405, -0.8461, 2.281, 2.794]]
assert_array_almost_equal(expected, nca.transformer(), decimal=3)
def test_iris(self):
n = self.iris_points.shape[0]
nca = NCA(max_iter=(100000 // n), learning_rate=0.01)
nca.fit(self.iris_points, self.iris_labels)
# Result copied from Iris example at
# https://github.com/vomjom/nca/blob/master/README.mkd
expected = [[-0.09935, -0.2215, 0.3383, 0.443],
[+0.2532, 0.5835, -0.8461, -0.8915],
[-0.729, -0.6386, 1.767, 1.832],
[-0.9405, -0.8461, 2.281, 2.794]]
assert_array_almost_equal(expected, nca.transformer(), decimal=3)
def test_nca(self): n = self.X.shape[0] nca = NCA(max_iter=(100000//n), learning_rate=0.01) nca.fit(self.X, self.y) L = nca.transformer() assert_array_almost_equal(L.T.dot(L), nca.metric())
def test_nca(self): n = self.X.shape[0] nca = NCA(max_iter=(100000//n), learning_rate=0.01) nca.fit(self.X, self.y) L = nca.transformer() assert_array_almost_equal(L.T.dot(L), nca.metric())
