def quick_grad_check(fun, arg0, extra_args=(), kwargs={}, verbose=True,
eps=EPS, rtol=RTOL, atol=ATOL, rs=None):
"""Checks the gradient of a function (w.r.t. to its first arg) in a random direction"""
if verbose:
print("Checking gradient of {0} at {1}".format(fun, arg0))
if rs is None:
rs = np.random.RandomState()
random_dir = rs.standard_normal(np.shape(arg0))
random_dir = random_dir / np.sqrt(np.sum(random_dir * random_dir))
unary_fun = lambda x : fun(arg0 + x * random_dir, *extra_args, **kwargs)
numeric_grad = unary_nd(unary_fun, 0.0, eps=eps)
analytic_grad = np.sum(grad(fun)(arg0, *extra_args, **kwargs) * random_dir)
assert np.allclose(numeric_grad, analytic_grad, rtol=rtol, atol=atol), \
"Check failed! nd={0}, ad={1}".format(numeric_grad, analytic_grad)
if verbose:
print("Gradient projection OK (numeric grad: {0}, analytic grad: {1})".format(
numeric_grad, analytic_grad))
return 0.5 * (np.tril(mat) + np.triu(mat, 1).T)
elif len(mat.shape) == 3:
return 0.5 * (np.tril(mat) + np.swapaxes(np.triu(mat, 1), 1,2))
else:
raise ArithmeticError
def generalized_outer_product(mat):
if len(mat.shape) == 1:
return np.outer(mat, mat)
elif len(mat.shape) == 2:
return np.einsum('ij,ik->ijk', mat, mat)
else:
raise ArithmeticError
def covgrad(x, mean, cov):
# I think once we have Cholesky we can make this nicer.
solved = np.linalg.solve(cov, (x - mean).T).T
return lower_half(np.linalg.inv(cov) - generalized_outer_product(solved))
logpdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, x, lambda g: -np.expand_dims(g, 1) * np.linalg.solve(cov, (x - mean).T).T), argnum=0)
logpdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, mean, lambda g: np.expand_dims(g, 1) * np.linalg.solve(cov, (x - mean).T).T), argnum=1)
logpdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, cov, lambda g: -np.reshape(g, np.shape(g) + (1, 1)) * covgrad(x, mean, cov)), argnum=2)
# Same as log pdf, but multiplied by the pdf (ans).
pdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, x, lambda g: -g * ans * np.linalg.solve(cov, x - mean)), argnum=0)
pdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, mean, lambda g: g * ans * np.linalg.solve(cov, x - mean)), argnum=1)
pdf.defgrad(lambda ans, x, mean, cov: unbroadcast(ans, cov, lambda g: -g * ans * covgrad(x, mean, cov)), argnum=2)
entropy.defgrad_is_zero(argnums=(0,))
entropy.defgrad(lambda ans, mean, cov: unbroadcast(ans, cov, lambda g: 0.5 * g * np.linalg.inv(cov).T), argnum=1)
