Gradient of a multivariate function numpy I'm trying to calculate the gradient of multivariate function g using NumPy.
g = lambda w: -np.sin(np.pi*np.sum(w**2)) + np.log(np.sum(w**2))
gradient = lambda w: ...
the parameter w is a vector, for example, w = np.array([0.5,0.5]). I calculated the gradient like this;
gradient = lambda w: np.pi*np.sum(2*w)*-np.cos(np.pi*np.sum(w**2)) + np.sum(2*w)*(1/np.sum(w**2))
It does not give meaningful results. Is this formula correct or not?

If we write it more explicitly,
$$f(w)=-\sin\left(\pi\sum w_i^2\right)+\ln \left(\sum w_i^2\right)$$
$${\partial f \over \partial w_i}=-\cos\left(\pi\sum w_i^2\right)\pi2w_i+\frac{2w_i}{\sum w_i^2}$$
So, the derivative is a vector; which means your expression will look like
gradient = lambda w: -np.pi*2*w*np.cos(np.pi*np.sum(w**2)) + 2*w*(1/np.sum(w**2))

when used denominator layout.
