if __name__ == '__main__': args = dict( loglikelihood=like, transform=transform, prior=prior, parameter_names = ['c%d' % i for i in range(len(centers))], nsteps=2000, seed = 0, ) #knowledge(classical(method='neldermead', **args), **args) #knowledge(classical(method='cobyla', **args), **args) for method in 'neldermead', 'cobyla', 'ralg', 'mma', 'auglag', 'minuit': print 'next method:', method knowledge(classical(method=method, **args), **args) ret = knowledge(onebyone(**args), **args) knowledge(onebyone(parallel=True, find_uncertainties=True, **args), **args) knowledge(de(output_basename='test_all_de', **args), **args) knowledge(mcmc(output_basename='test_all_mcmc', **args), **args) knowledge(ensemble(output_basename='test_all_mcmc', **args), **args) knowledge(multinest(output_basename='test_all_mn', **args), **args)
logfile.write('# num. of eval : %s\n' % ret['neval'])
logfile.flush()
return ret
if __name__ == '__main__':
args = dict(
loglikelihood=like, transform=transform, prior=prior,
parameter_names = ['c%d' % i for i in range(n_params)],
nsteps=2000,
seed = 0,
)
for method in 'neldermead', 'cobyla', 'bobyqa', 'ralg', 'mma', 'auglag':
print 'next method:', method
knowledge(classical(method=method, **args), **args)
ret = knowledge(onebyone(**args), **args)
knowledge(onebyone(parallel=True, find_uncertainties=True, **args), **args)
knowledge(de(output_basename='test_rosenbrock_de', **args), **args)
knowledge(mcmc(output_basename='test_rosenbrock_mcmc', **args), **args)
knowledge(ensemble(output_basename='test_rosenbrock_mcmc', **args), **args)
knowledge(multinest(output_basename='test_rosenbrock_mn', **args), **args)
