#!/usr/bin/env python
#
# Problem definition:
# A-R Hedar and M Fukushima, "Derivative-Free Filter Simulated Annealing
# Method for Constrained Continuous Global Optimization", Journal of
# Global Optimization, 35(4), 521-549 (2006).
# 
# Original Matlab code written by A. Hedar (Nov. 23, 2005)
# http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/go.htm
# and ported to Python by Mike McKerns (December 2014)
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2021 The Uncertainty Quantification Foundation.
# License: 3-clause BSD.  The full license text is available at:
#  - https://github.com/uqfoundation/mystic/blob/master/LICENSE

from g02 import objective, bounds, xs, ys
from g02_alt import penalty1, penalty2, quadratic_inequality

from mystic.math.measures import impose_product, impose_sum

def constraint1(x): # impose exactly
    return impose_product(0.75, x)

def constraint2(x): # impose exactly
    return impose_sum(7.5*len(x), x)

def penalty(x): return 0.0
penalty2 = quadratic_inequality(penalty2)(penalty)
penalty1 = quadratic_inequality(penalty1)(penalty)



if __name__ == '__main__':
    bounds = bounds(len(xs))

    from mystic.solvers import diffev2
    from mystic.math import almostEqual

    result = diffev2(objective, x0=bounds, bounds=bounds, constraint=constraint2, penalty=penalty1, npop=40, disp=False, full_output=True)

    assert almostEqual(result[0], xs, rel=1e-2)
    assert almostEqual(result[1], ys, rel=1e-2)



# EOF
