decompositions: Adapted Incompletecholesky and IncompleteLUT tests

Author: Lucas-Haubert

tests/test_incomplete_cholesky.py

@@ -19,13 +19,15 @@
 B = A.dot(X)
 X_est = ichol.solve(B)
 assert isinstance(X_est, np.ndarray)
-assert nanoeigenpy.is_approx(X, X_est)
+residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
+assert residual < 0.1
 
 x = rng.random(dim)
 b = A.dot(x)
 x_est = ichol.solve(b)
 assert isinstance(x_est, np.ndarray)
-assert nanoeigenpy.is_approx(x, x_est)
+residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
+assert residual < 0.1
 
 X_sparse = csc_matrix(rng.random((dim, 10)))
 B_sparse = A.dot(X_sparse).tocsc()

tests/test_incomplete_lut.py

@@ -1,51 +1,49 @@
-# import numpy as np
-# from scipy.sparse import csc_matrix
-# import nanoeigenpy
-
-# dim = 5
-# rng = np.random.default_rng(42)
-
-# A = rng.random((dim, dim))
-# A += np.diag(10.0 + rng.random(dim))
-# A = csc_matrix(A)
-
-# ilut = nanoeigenpy.IncompleteLUT(A)
-# assert ilut.info() == nanoeigenpy.ComputationInfo.Success
-# assert ilut.rows() == dim
-# assert ilut.cols() == dim
-
-# X = rng.random((dim, 5))
-# B = A.dot(X)
-# X_est = ilut.solve(B)
-# assert isinstance(X_est, np.ndarray)
-# print("X")
-# print(X)
-# print("X_est")
-# print(X_est)
-# assert nanoeigenpy.is_approx(X, X_est)
-
-# x = rng.random(dim)
-# b = A.dot(x)
-# x_est = ilut.solve(b)
-# assert isinstance(x_est, np.ndarray)
-# assert nanoeigenpy.is_approx(x, x_est)
-
-# X_sparse = csc_matrix(rng.random((dim, 10)))
-# B_sparse = A.dot(X_sparse).tocsc()
-# if not B_sparse.has_sorted_indices:
-#     B_sparse.sort_indices()
-# X_est_sparse = ilut.solve(B_sparse)
-# assert isinstance(X_est_sparse, csc_matrix)
-
-# ilut.analyzePattern(A)
-# ilut.factorize(A)
-# assert ilut.info() == nanoeigenpy.ComputationInfo.Success
-
-# ilut_params = nanoeigenpy.IncompleteLUT(A, 1e-4, 15)
-# assert ilut_params.info() == nanoeigenpy.ComputationInfo.Success
-
-# ilut_set = nanoeigenpy.IncompleteLUT()
-# ilut_set.setDroptol(1e-3)
-# ilut_set.setFillfactor(20)
-# ilut_set.compute(A)
-# assert ilut_set.info() == nanoeigenpy.ComputationInfo.Success
+import numpy as np
+from scipy.sparse import csc_matrix
+import nanoeigenpy
+
+dim = 100
+rng = np.random.default_rng()
+
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(5.0 + rng.random(dim))
+A = csc_matrix(A)
+
+ilut = nanoeigenpy.IncompleteLUT(A)
+assert ilut.info() == nanoeigenpy.ComputationInfo.Success
+assert ilut.rows() == dim
+assert ilut.cols() == dim
+
+X = rng.random((dim, 100))
+B = A.dot(X)
+X_est = ilut.solve(B)
+assert isinstance(X_est, np.ndarray)
+residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
+assert residual < 0.1
+
+x = rng.random(dim)
+b = A.dot(x)
+x_est = ilut.solve(b)
+assert isinstance(x_est, np.ndarray)
+residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
+assert residual < 0.1
+
+X_sparse = csc_matrix(rng.random((dim, 10)))
+B_sparse = A.dot(X_sparse).tocsc()
+if not B_sparse.has_sorted_indices:
+    B_sparse.sort_indices()
+X_est_sparse = ilut.solve(B_sparse)
+assert isinstance(X_est_sparse, csc_matrix)
+
+ilut.analyzePattern(A)
+ilut.factorize(A)
+assert ilut.info() == nanoeigenpy.ComputationInfo.Success
+
+ilut_params = nanoeigenpy.IncompleteLUT(A, 1e-4, 15)
+assert ilut_params.info() == nanoeigenpy.ComputationInfo.Success
+
+ilut_set = nanoeigenpy.IncompleteLUT()
+ilut_set.setDroptol(1e-3)
+ilut_set.setFillfactor(20)
+ilut_set.compute(A)
+assert ilut_set.info() == nanoeigenpy.ComputationInfo.Success