def test_pinv(self):
test_cases = self.pinv_test_cases
for in_mat, expected_out in test_cases:
out_mat = Linalg.pinv(DenseMatrix(in_mat))
np.testing.assert_array_almost_equal(out_mat.mat, expected_out, 7)
out_mat = Linalg.pinv(SparseMatrix(in_mat))
np.testing.assert_array_almost_equal(out_mat.mat.todense(),
expected_out, 7)
def test_pinv(self):
test_cases = self.pinv_test_cases
for in_mat, expected_out in test_cases:
out_mat = Linalg.pinv(DenseMatrix(in_mat))
np.testing.assert_array_almost_equal(out_mat.mat, expected_out, 7)
out_mat = Linalg.pinv(SparseMatrix(in_mat))
np.testing.assert_array_almost_equal(out_mat.mat.todense(),
expected_out, 7)
def apply(self, matrix_):
matrix_.assert_positive()
#w_init, h_init = self.nndsvd_init(matrix_)
w_init, h_init = self.v_col_init(matrix_)
#w_init, h_init = self.random_init(matrix_)
w, h = Linalg.nmf(matrix_, w_init, h_init)
return w, Linalg.pinv(h)
def apply(self, matrix_):
matrix_.assert_positive()
#w_init, h_init = self.nndsvd_init(matrix_)
w_init, h_init = self.v_col_init(matrix_)
#w_init, h_init = self.random_init(matrix_)
w, h = Linalg.nmf(matrix_, w_init, h_init)
return w, Linalg.pinv(h)