def test_square():
X = mf.DenseSymmMatrix(10)
Xsq = X.msquare()
assert (len(Xsq) == 10)
D = [1, 2, 3, 4, 5]
X.set_matrix(D)
Xsq = X.msquare()
assert (len(Xsq) == 5)
def test_trace():
""" Compute the matrix trace """
D = [1, 2, 3, 4, 5]
X = mf.DenseSymmMatrix()
X.set_matrix(D)
assert (len(X) == 5)
assert (X.mtrace() == 15)
Xsq = X.msquare()
assert (Xsq.mtrace() == 55)
def test_set_matrix():
A = np.array([[1, 2], [4, 5]])
X = mf.DenseSymmMatrix()
X.set_matrix(A)
assert (X.size == 2)
Acp = X.get_matrix()
assert (np.array_equal(A, Acp))
D = [1, 2, 3, 4, 5]
X.set_matrix(D)
assert (len(X) == 5)
def test_create_matrix_given_eig():
X = mf.DenseSymmMatrix()
D = [1, 2, 3, 4, 5]
X.rand_symm_matrix_given_eig(D)
assert (len(X) == 5)
# check eigenvalues of X
Xarr = X.get_matrix()
w, v = la.eigh(Xarr)
assert (
np.allclose(w, D)
) # allclose: true if two arrays are element-wise equal within a tolerance.
def test_create_empty():
""" Create a zero matrix """
X = mf.DenseSymmMatrix()
assert (len(X) == 0)