def validate(self, input_mat):
x = aesara.tensor.matrix()
f = aesara.function([x], self.op(x))
out = f(input_mat)
svd_diag = np.linalg.svd(input_mat, compute_uv=False)
numpy_out = np.sum(np.log(np.abs(svd_diag)))
# Compare the result computed to the expected value.
np.allclose(numpy_out, out)
# Test gradient:
verify_grad(self.op, [input_mat])
def test_grad(self):
np.random.seed(42)
def func(chol_vec, delta):
chol = at.stack([
at.stack([at.exp(0.1 * chol_vec[0]), 0]),
at.stack([chol_vec[1], 2 * at.exp(chol_vec[2])]),
])
cov = at.dot(chol, chol.T)
return MvNormalLogp()(cov, delta)
chol_vec_val = floatX(np.array([0.5, 1.0, -0.1]))
delta_val = floatX(np.random.randn(1, 2))
verify_grad(func, [chol_vec_val, delta_val])
delta_val = floatX(np.random.randn(5, 2))
verify_grad(func, [chol_vec_val, delta_val])
def test_grad(self):
verify_grad(i0e, [0.5])
verify_grad(i0e, [-2.0])
verify_grad(i0e, [[0.5, -2.0]])
verify_grad(i0e, [[[0.5, -2.0]]])
def test_grad(self):
x = np.linspace(0, 1, 100)
y = x * x
spline = SplineWrapper(
interpolate.InterpolatedUnivariateSpline(x, y, k=1))
verify_grad(spline, [0.5])
