numericgrad.py

import numpy as np


def num_grad(f, x, verbose=False, h=1e-5):
    """
    Evaluates gradient df/dx via finite differences:
    df/dx ~ (f(x+h) - f(x-h)) / 2h
    Adopted from https://github.com/ddtm/dl-course/
    """
    fx = f(x) # evaluate function value at original point
    grad = np.zeros_like(x)
    # iterate over all indexes in x
    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
    while not it.finished:

        # evaluate function at x+h
        ix = it.multi_index
        oldval = x[ix]
        x[ix] = oldval + h # increment by h
        fxph = f(x) # evalute f(x + h)
        x[ix] = oldval - h
        fxmh = f(x) # evaluate f(x - h)
        x[ix] = oldval # restore

        # compute the partial derivative with centered formulasum
        grad[ix] = (fxph - fxmh) / (2 * h) # the slope
        if verbose:
            print (ix, grad[ix])
        it.iternext() # step to next dimension

    return grad