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################################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
import numpy as np
from scipy import optimize
_epsilon = np.sqrt(np.finfo(float).eps)
def minimize(f, x0, maxiter=None, verbose=False):
"""
Simple wrapper for SciPy's optimize.
The given function must return a tuple: (value, gradient).
"""
options = {'disp': verbose}
if maxiter is not None:
options['maxiter'] = maxiter
opt = optimize.minimize(f, x0, jac=True, method='CG', options=options)
return opt.x
def check_gradient(f, x0, verbose=True, epsilon=_epsilon, return_abserr=False):
"""
Simple wrapper for SciPy's gradient checker.
The given function must return a tuple: (value, gradient).
Returns absolute and relative errors
"""
df = f(x0)[1]
df_num = optimize.approx_fprime(x0,
lambda x: f(x)[0],
epsilon)
abserr = np.linalg.norm(df-df_num)
norm_num = np.linalg.norm(df_num)
if abserr == 0 and norm_num == 0:
err = 0
else:
err = abserr / norm_num
if verbose:
print("Norm of numerical gradient: %g" % np.linalg.norm(df_num))
print("Norm of function gradient: %g" % np.linalg.norm(df))
print("Gradient relative error = %g and absolute error = %g" %
(err,
abserr))
return (abserr, err)
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