def test_norm_logpdf(): x = npr.randn() l = npr.randn() scale = npr.rand()**2 + 1.1 fun = autograd.scipy.stats.norm.logpdf d_fun = grad(fun) check_grads(fun, x, l, scale) check_grads(d_fun, x, l, scale)
def test_logsumexp6(): x = npr.randn(1, 5) def f(a): return autograd.scipy.misc.logsumexp(a, axis=1, keepdims=True) check_grads(f, x) check_grads(lambda a: to_scalar(grad(f)(a)), x)
def test_norm_logpdf(): x = npr.randn() l = npr.randn() scale=npr.rand()**2 + 1.1 fun = autograd.scipy.stats.norm.logpdf d_fun = grad(fun) check_grads(fun, x, l, scale) check_grads(d_fun, x, l, scale)
def test_yn(): x = npr.randn()**2 + 0.3 fun = lambda x: to_scalar(autograd.scipy.special.yn(2, x)) d_fun = grad(fun) check_grads(fun, x) check_grads(d_fun, x)
def test_polygamma(): x = npr.randn() fun = lambda x: to_scalar(autograd.scipy.special.polygamma(0, x)) d_fun = grad(fun) check_grads(fun, x) check_grads(d_fun, x)