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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
Testing the polynomial fitting problem of [1] using scipy's Nelder-Mead algorithm.
Reference:
[1] Storn, R. and Price, K. Differential Evolution - A Simple and Efficient
Heuristic for Global Optimization over Continuous Spaces. Journal of Global
Optimization 11: 341-359, 1997.
"""
from test_ffit import Chebyshev8, ChebyshevCost, plot_solution, print_solution
if __name__ == '__main__':
import random
from mystic.solvers import fmin
#from mystic._scipyoptimize import fmin
from mystic.tools import random_seed
random_seed(123)
import pp
import sys
if len(sys.argv) > 1:
tunnelport = sys.argv[1]
ppservers = ("localhost:%s" % tunnelport,)
else:
ppservers = ()
myserver = pp.Server(ppservers=ppservers)
trials = []
for trial in range(8):
x = tuple([random.uniform(-100,100) + Chebyshev8[i] for i in range(9)])
trials.append(x)
results = [myserver.submit(fmin,(ChebyshevCost,x),(),()) for x in trials]
for solution in results:
print_solution(solution())
#plot_solution(solution)
# end of file
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