def modify_data(self, X):
sentinel = sparse_rand(X.shape[0],
1,
density=self.sentinel_density,
format='csr',
random_state=self.random_state)
X = hstack([X, sentinel], format='csr')
return X
def test_sparse_input():
X = sparse_rand(100, 3, density=0.1, format='csr')
# Should not error
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_components=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
def test_sparse_modified_ldl(m, n):
A = sparse_rand(m, n, density=0.025).toarray()
A = np.dot(A, A.T)
# Perform decompositions
np_chlsky = cholesky(A)
D, L = modified_ldl(A)
py_chlsky = L*np.sqrt(D)
L = tril(csr_matrix(L), format='csr')
# Check L is actually sparse!
assert len(L.data) < np.product(A.shape)
assert issparse(L)
spD, spL = sparse_ldl.modified_ldl(A, L.indptr, L.indices)
# Check implementation of Cholesky above is the same as numpy
np.testing.assert_allclose(spD, D)
np.testing.assert_allclose(spL, L.data, atol=1e-7)
def A(m, n):
"""A random positive definite matrix"""
A = sparse_rand(m, n, density=0.1).toarray()
return np.dot(A, A.T) + np.eye(m)*m
