def unary_nd(f, x, eps=EPS):
if isinstance(x, array_types):
if np.iscomplexobj(x):
nd_grad = np.zeros(x.shape) + 0j
elif isinstance(x, garray_obj):
nd_grad = np.array(np.zeros(x.shape), dtype=np.gpu_float32)
else:
nd_grad = np.zeros(x.shape)
for dims in it.product(*list(map(range, x.shape))):
nd_grad[dims] = unary_nd(indexed_function(f, x, dims), x[dims])
return nd_grad
elif isinstance(x, tuple):
return tuple([unary_nd(indexed_function(f, tuple(x), i), x[i])
for i in range(len(x))])
elif isinstance(x, dict):
return {k : unary_nd(indexed_function(f, x, k), v) for k, v in iteritems(x)}
elif isinstance(x, list):
return [unary_nd(indexed_function(f, x, i), v) for i, v in enumerate(x)]
elif np.iscomplexobj(x):
result = (f(x + eps/2) - f(x - eps/2)) / eps \
- 1j*(f(x + 1j*eps/2) - f(x - 1j*eps/2)) / eps
return type(safe_type(x))(result)
else:
return type(safe_type(x))((f(x + eps/2) - f(x - eps/2)) / eps)
def _flip(a, trans):
if anp.iscomplexobj(a):
return 'H' if trans in ('N', 0) else 'N'
else:
return 'T' if trans in ('N', 0) else 'N'
