unittest: Simplify test_GeneralizedEigenSolver to reduce computation time

Author: Lucas-Haubert

unittest/python/test_GeneralizedEigenSolver.py

@@ -8,27 +8,14 @@
 B = rng.random((dim, dim))
 B = (B + B.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
-ges = eigenpy.GeneralizedEigenSolver()
-ges_size = eigenpy.GeneralizedEigenSolver(dim)
 ges_matrices = eigenpy.GeneralizedEigenSolver(A, B)
 assert ges_matrices.info() == eigenpy.ComputationInfo.Success
 
-ges_with_vectors = eigenpy.GeneralizedEigenSolver(A, B, True)
-assert ges_with_vectors.info() == eigenpy.ComputationInfo.Success
-
-ges_without_vectors = eigenpy.GeneralizedEigenSolver(A, B, False)
-assert ges_without_vectors.info() == eigenpy.ComputationInfo.Success
-
 alphas = ges_matrices.alphas()
 betas = ges_matrices.betas()
 eigenvectors = ges_matrices.eigenvectors()
 eigenvalues = ges_matrices.eigenvalues()
 
-assert alphas.shape == (dim,)
-assert betas.shape == (dim,)
-assert eigenvectors.shape == (dim, dim)
-assert eigenvalues.shape == (dim,)
-
 for k in range(dim):
     v = eigenvectors[:, k]
     lambda_k = eigenvalues[k]
@@ -52,66 +39,3 @@
     if abs(betas[k]) > 1e-12:
         expected_eigenvalue = alphas[k] / betas[k]
         assert abs(eigenvalues[k] - expected_eigenvalue) < 1e-12
-
-ges_compute = eigenpy.GeneralizedEigenSolver()
-result = ges_compute.compute(A, B, False)
-assert result.info() == eigenpy.ComputationInfo.Success
-alphas_only = ges_compute.alphas()
-betas_only = ges_compute.betas()
-eigenvalues_only = ges_compute.eigenvalues()
-
-result_with_vectors = ges_compute.compute(A, B, True)
-assert result_with_vectors.info() == eigenpy.ComputationInfo.Success
-eigenvectors_computed = ges_compute.eigenvectors()
-
-ges_default = eigenpy.GeneralizedEigenSolver()
-result_default = ges_default.compute(A, B)
-assert result_default.info() == eigenpy.ComputationInfo.Success
-
-ges_iter = eigenpy.GeneralizedEigenSolver(A, B)
-ges_iter.setMaxIterations(100)
-ges_iter.setMaxIterations(200)
-
-A1 = rng.random((dim, dim))
-B1 = rng.random((dim, dim))
-spdA = A.T @ A + A1.T @ A1
-spdB = B.T @ B + B1.T @ B1
-
-ges_spd = eigenpy.GeneralizedEigenSolver(spdA, spdB)
-assert ges_spd.info() == eigenpy.ComputationInfo.Success
-
-spd_eigenvalues = ges_spd.eigenvalues()
-max_imag = np.max(np.abs(spd_eigenvalues.imag))
-assert max_imag < 1e-10
-
-ges1 = eigenpy.GeneralizedEigenSolver()
-ges2 = eigenpy.GeneralizedEigenSolver()
-id1 = ges1.id()
-id2 = ges2.id()
-assert id1 != id2
-assert id1 == ges1.id()
-assert id2 == ges2.id()
-
-ges3 = eigenpy.GeneralizedEigenSolver(dim)
-ges4 = eigenpy.GeneralizedEigenSolver(dim)
-id3 = ges3.id()
-id4 = ges4.id()
-assert id3 != id4
-assert id3 == ges3.id()
-assert id4 == ges4.id()
-
-ges5 = eigenpy.GeneralizedEigenSolver(A, B)
-ges6 = eigenpy.GeneralizedEigenSolver(A, B)
-id5 = ges5.id()
-id6 = ges6.id()
-assert id5 != id6
-assert id5 == ges5.id()
-assert id6 == ges6.id()
-
-ges7 = eigenpy.GeneralizedEigenSolver(A, B, True)
-ges8 = eigenpy.GeneralizedEigenSolver(A, B, False)
-id7 = ges7.id()
-id8 = ges8.id()
-assert id7 != id8
-assert id7 == ges7.id()
-assert id8 == ges8.id()