Source code for bayespy.utils.optimize

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# Copyright (C) 2011-2013 Jaakko Luttinen
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# This file is licensed under the MIT License.
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import numpy as np
from scipy import optimize

[docs]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
[docs]def check_gradient(f, x0, verbose=True, epsilon=optimize.optimize._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)