Created
July 19, 2012 15:21
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Finite difference approach to calculating the Hessian
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#!/usr/bin/env python | |
""" | |
Some Hessian codes | |
""" | |
import numpy as np | |
from scipy.optimize import approx_fprime | |
def hessian ( x0, epsilon=1.e-5, linear_approx=False, *args ): | |
""" | |
A numerical approximation to the Hessian matrix of cost function at | |
location x0 (hopefully, the minimum) | |
""" | |
# ``calculate_cost_function`` is the cost function implementation | |
# The next line calculates an approximation to the first | |
# derivative | |
f1 = approx_fprime( x0, calculate_cost_function, *args) | |
# This is a linear approximation. Obviously much more efficient | |
# if cost function is linear | |
if linear_approx: | |
f1 = np.matrix(f1) | |
return f1.transpose() * f1 | |
# Allocate space for the hessian | |
n = x0.shape[0] | |
hessian = np.zeros ( ( n, n ) ) | |
# The next loop fill in the matrix | |
xx = x0 | |
for j in xrange( n ): | |
xx0 = xx[j] # Store old value | |
xx[j] = xx0 + epsilon # Perturb with finite difference | |
# Recalculate the partial derivatives for this new point | |
f2 = approx_fprime( x0, calculate_cost_function, *args) | |
hessian[:, j] = (f2 - f1)/epsilon # scale... | |
xx[j] = xx0 # Restore initial value of x0 | |
return hessian | |
I tried your version of the hessian and it gives me some bad results if I compare it to the hessian returned by the package numdifftools
. I think that you should have a look and correct your file. But thanks for sharing some code!
I tried your version of the hessian and it gives me some bad results if I compare it to the hessian returned by the package
numdifftools
. I think that you should have a look and correct your file. But thanks for sharing some code!
Hi were you able to get the hessian matrix for a dataset using the numdifftools
Hi there. I second @glederrey. There are huge discrepancies between this result and that of numdifftools
.
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Thank you so much for this code, is saved so much of my time! Just wanted to say thank you for uploading it!