def test_sparse_lstsq_regression(self): test_cases = self.pinv_test_cases for m, m_inv in test_cases: m1 = SparseMatrix(m) id_ = SparseMatrix.identity(m1.shape[0]) res = Linalg.lstsq_regression(m1, id_) np.testing.assert_array_almost_equal(res.mat.todense(), m_inv, 7) approx1 = (m1 * res).mat.todense() res2 = Linalg.lstsq_regression(m1, id_, intercept=True) new_a = m1.hstack(SparseMatrix(np.ones((m1.shape[0], 1)))) approx2 = (new_a * res2).mat.todense()
def test_sparse_ridge_regression(self): test_cases = self.pinv_test_cases for m, m_inv in test_cases: m1 = SparseMatrix(m) id_ = SparseMatrix.identity(m1.shape[0]) res1 = Linalg.lstsq_regression(m1, id_) np.testing.assert_array_almost_equal(res1.mat.todense(), m_inv, 7) res2 = Linalg.ridge_regression(m1, id_, 1)[0] error1 = (m1 * res1 - SparseMatrix(m_inv)).norm() error2 = (m1 * res2 - SparseMatrix(m_inv)).norm() #print "err", error1, error2 norm1 = error1 + res1.norm() norm2 = error2 + res2.norm() #print "norm", norm1, norm2 #THIS SHOULD HOLD, MAYBE ROUNDIGN ERROR? #self.assertGreaterEqual(error2, error1) self.assertGreaterEqual(norm1, norm2)