def _generate_metrics(rng, size, eigval_scale=0.1):
eigval, eigvec = _generate_rand_eigval_eigvec(rng, size, eigval_scale)
return [
matrices.IdentityMatrix(),
matrices.IdentityMatrix(size),
matrices.PositiveDiagonalMatrix(eigval),
matrices.DensePositiveDefiniteMatrix((eigvec * eigval) @ eigvec.T),
matrices.EigendecomposedPositiveDefiniteMatrix(eigvec, eigval)
]
def metric_list(rng, size):
eigval = np.exp(0.1 * rng.standard_normal(size))
eigvec = np.linalg.qr(rng.standard_normal((size, size)))[0]
return [
matrices.IdentityMatrix(),
matrices.IdentityMatrix(size),
matrices.PositiveDiagonalMatrix(eigval),
matrices.DensePositiveDefiniteMatrix((eigvec * eigval) @ eigvec.T),
matrices.EigendecomposedPositiveDefiniteMatrix(eigvec, eigval),
]
def __init__(self):
super().__init__()
for sz in SIZES:
self.matrices[sz] = matrices.IdentityMatrix(sz)
self.np_matrices[sz] = np.identity(sz)
def __init__(self):
super().__init__({
sz: (matrices.IdentityMatrix(sz), np.identity(sz))
for sz in SIZES
})
def matrix_pair(self, size):
return matrices.IdentityMatrix(size), np.identity(size)
def test_invalid_shape_matmul(self, matrix):
with pytest.raises(ValueError, match="Inconsistent dimensions"):
matrix @ matrices.IdentityMatrix(matrix.shape[1] + 1)
with pytest.raises(ValueError, match="Inconsistent dimensions"):
matrices.IdentityMatrix(matrix.shape[0] + 1) @ matrix
def test_matrix_inequality_different_shapes(self, matrix):
different_shape_matrix = matrices.IdentityMatrix(matrix.shape[0] + 1)
assert matrix != different_shape_matrix
assert hash(matrix) != hash(different_shape_matrix)
