def g(w):
W = w.reshape(W0.shape)
y = [softmax.regression_ll_grad(x, y, W) for x, y in zip(X, P)]
y = -sum(y)
return y.ravel()
def g(w):
W = w.reshape(W0.shape)
y = [softmax.regression_ll_grad(x, y, W, c) for x, y, c in zip(X, P, C)]
y = -sum(y)
y[~M] = 0.0
return y.ravel()
