def value_and_grad(fun, argnum=0):
"""Returns a function that returns both value and gradient. Suitable for use
in scipy.optimize"""
def double_val_fun(*args, **kwargs):
val = fun(*args, **kwargs)
return make_tuple(val, unbox_if_possible(val))
gradval_and_val = grad_and_aux(double_val_fun, argnum)
flip = lambda x, y: make_tuple(y, x)
return lambda *args, **kwargs: flip(*gradval_and_val(*args, **kwargs))
def value_and_grad(fun, argnum=0):
"""Returns a function that returns both value and gradient. Suitable for use
in scipy.optimize"""
def double_val_fun(*args, **kwargs):
val = fun(*args, **kwargs)
return make_tuple(val, unbox_if_possible(val))
gradval_and_val = grad_and_aux(double_val_fun, argnum)
flip = lambda x, y: make_tuple(y, x)
return lambda *args, **kwargs: flip(*gradval_and_val(*args, **kwargs))
def double_val_fun(*args, **kwargs):
val = fun(*args, **kwargs)
return make_tuple(val, unbox_if_possible(val))
def gradfun_rearranged(*args, **kwargs):
multi_arg = make_tuple(*[args[i] for i in argnums])
return gradfun(multi_arg, *args, **kwargs)
def double_val_fun(*args, **kwargs):
val = fun(*args, **kwargs)
return make_tuple(val, unbox_if_possible(val))
def outer_fun(x):
def inner_fun(y):
return y[0] * y[1]
return np.sum(np.sin(np.array(grad(inner_fun)(make_tuple(x, x)))))
def gradfun_rearranged(*args, **kwargs):
multi_arg = make_tuple(*[args[i] for i in argnums])
return gradfun(multi_arg, *args, **kwargs)
def outer_fun(x):
def inner_fun(y):
return y[0] * y[1]
return np.sum(np.sin(np.array(grad(inner_fun)(make_tuple(x,x)))))
