def test_train2(self):
dim_ = 2
dim_1 = 3
dim_2 = 5
for dim in [dim_1 + dim_2, dim_1 + dim_2 + 2]:
expected_a = np.mat(np.random.random((dim_,dim_1)))
expected_b = np.mat(np.random.random((dim_,dim_2)))
m1 = np.mat(np.random.random((dim,dim_1)))
m2 = np.mat(np.random.random((dim,dim_2)))
ph = np.mat(expected_a*m1.T + expected_b*m2.T)
comp_model = FullAdditive(learner=LstsqRegressionLearner(intercept=False))
comp_model._solve(DenseMatrix(m1),DenseMatrix(m2),
DenseMatrix(ph).transpose())
np.testing.assert_array_almost_equal(comp_model._mat_a_t.transpose().mat,
expected_a, 10)
np.testing.assert_array_almost_equal(comp_model._mat_b_t.transpose().mat,
expected_b, 10)
for dim in [dim_1 + dim_2 + 6, dim_1 + dim_2 + 20]:
expected_a = np.mat(np.random.random((dim_,dim_1)))
expected_b = np.mat(np.random.random((dim_,dim_2)))
m1 = np.mat(np.random.random((dim,dim_1)))
m2 = np.mat(np.random.random((dim,dim_2)))
ph = np.mat(expected_a*m1.T + expected_b*m2.T)
comp_model = FullAdditive(learner=LstsqRegressionLearner(intercept=True))
comp_model._solve(DenseMatrix(m1),DenseMatrix(m2),
DenseMatrix(ph).transpose())
np.testing.assert_array_almost_equal(comp_model._mat_a_t.transpose().mat,
expected_a, 10)
np.testing.assert_array_almost_equal(comp_model._mat_b_t[:-1,:].transpose().mat,
expected_b, 10)
def test_train1(self):
test_cases = [(self.m11, self.m21, self.ph1, np.mat([[2]]), np.mat([[3]])),
(self.m11, self.m21, DenseMatrix(np.mat([[0],[0]])),
np.mat([[0]]), np.mat([[0]]))]
for m1, m2, ph, expected_a, expected_b in test_cases:
comp_model = FullAdditive(learner=LstsqRegressionLearner(intercept=False))
comp_model._solve(m1, m2, ph)
np.testing.assert_array_almost_equal(comp_model._mat_a_t.transpose().mat,
expected_a, 10)
np.testing.assert_array_almost_equal(comp_model._mat_b_t.transpose().mat,
expected_b, 10)
def test_train1(self):
test_cases = [(self.m11, self.m21, self.ph1, np.mat([[2]]),
np.mat([[3]])),
(self.m11, self.m21, DenseMatrix(np.mat([[0], [0]])),
np.mat([[0]]), np.mat([[0]]))]
for m1, m2, ph, expected_a, expected_b in test_cases:
comp_model = FullAdditive(learner=LstsqRegressionLearner(
intercept=False))
comp_model._solve(m1, m2, ph)
np.testing.assert_array_almost_equal(
comp_model._mat_a_t.transpose().mat, expected_a, 10)
np.testing.assert_array_almost_equal(
comp_model._mat_b_t.transpose().mat, expected_b, 10)
def test_train2(self):
dim_ = 2
dim_1 = 3
dim_2 = 5
for dim in [dim_1 + dim_2, dim_1 + dim_2 + 2]:
expected_a = np.mat(np.random.random((dim_, dim_1)))
expected_b = np.mat(np.random.random((dim_, dim_2)))
m1 = np.mat(np.random.random((dim, dim_1)))
m2 = np.mat(np.random.random((dim, dim_2)))
ph = np.mat(expected_a * m1.T + expected_b * m2.T)
comp_model = FullAdditive(learner=LstsqRegressionLearner(
intercept=False))
comp_model._solve(DenseMatrix(m1), DenseMatrix(m2),
DenseMatrix(ph).transpose())
np.testing.assert_array_almost_equal(
comp_model._mat_a_t.transpose().mat, expected_a, 10)
np.testing.assert_array_almost_equal(
comp_model._mat_b_t.transpose().mat, expected_b, 10)
for dim in [dim_1 + dim_2 + 6, dim_1 + dim_2 + 20]:
expected_a = np.mat(np.random.random((dim_, dim_1)))
expected_b = np.mat(np.random.random((dim_, dim_2)))
m1 = np.mat(np.random.random((dim, dim_1)))
m2 = np.mat(np.random.random((dim, dim_2)))
ph = np.mat(expected_a * m1.T + expected_b * m2.T)
comp_model = FullAdditive(learner=LstsqRegressionLearner(
intercept=True))
comp_model._solve(DenseMatrix(m1), DenseMatrix(m2),
DenseMatrix(ph).transpose())
np.testing.assert_array_almost_equal(
comp_model._mat_a_t.transpose().mat, expected_a, 10)
np.testing.assert_array_almost_equal(
comp_model._mat_b_t[:-1, :].transpose().mat, expected_b, 10)
