def test_matrix_inner(fn):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
sparse_mat = _sparse_matrix(fn)
sparse_mat_as_dense = np.asarray(sparse_mat.todense())
dense_mat = _dense_matrix(fn)
true_inner_sparse = np.vdot(yarr, np.dot(sparse_mat_as_dense, xarr))
true_inner_dense = np.vdot(yarr, np.dot(dense_mat, xarr))
w_sparse = NumpyFnMatrixWeighting(sparse_mat)
w_dense = NumpyFnMatrixWeighting(dense_mat)
assert almost_equal(w_sparse.inner(x, y), true_inner_sparse)
assert almost_equal(w_dense.inner(x, y), true_inner_dense)
# With free functions
w_sparse_inner = npy_weighted_inner(sparse_mat)
w_dense_inner = npy_weighted_inner(dense_mat)
assert almost_equal(w_sparse_inner(x, y), true_inner_sparse)
assert almost_equal(w_dense_inner(x, y), true_inner_dense)
# Exponent != 2 -> no inner
w_dense = NumpyFnMatrixWeighting(dense_mat, exponent=1)
with pytest.raises(NotImplementedError):
w_dense.inner(x, y)
def test_array_inner(fn):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
weight_arr = _pos_array(fn)
weighting_arr = NumpyFnArrayWeighting(weight_arr)
true_inner = np.vdot(yarr, xarr * weight_arr)
assert almost_equal(weighting_arr.inner(x, y), true_inner)
# With free function
inner_vec = npy_weighted_inner(weight_arr)
assert almost_equal(inner_vec(x, y), true_inner)
# Exponent != 2 -> no inner product, should raise
with pytest.raises(NotImplementedError):
NumpyFnArrayWeighting(weight_arr, exponent=1.0).inner(x, y)
