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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
#
# Adapted from parallel_desolve.py
__doc__ = """
# Tests MPI version of Storn and Price's Polynomial 'Fitting' Problem,
# using a single solver with the population launched in parallel.
# This can be much slower, due to overhead of repeatedly setting up MPI.
#
# Exact answer: Chebyshev Polynomial of the first kind. T8(x)
# 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.
# To run in parallel: (must install 'pyina')
python ezmap_desolve.py
"""
try:
from pyina.launchers import Mpi as Pool
except:
print(__doc__)
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration, VTR
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor
from mystic.tools import random_seed
from mystic.math import poly1d
#from raw_chebyshev8 import chebyshev8cost as ChebyshevCost # no globals
#from raw_chebyshev8b import chebyshev8cost as ChebyshevCost # use globals
from mystic.models.poly import chebyshev8cost as ChebyshevCost # no helper
ND = 9
NP = 40
MAX_GENERATIONS = NP*NP
NNODES = NP//5
seed = 321
if __name__=='__main__':
def print_solution(func):
print(poly1d(func))
return
psow = VerboseMonitor(10)
ssow = VerboseMonitor(10)
random_seed(seed)
print("first sequential...")
solver = DifferentialEvolutionSolver2(ND,NP) #XXX: sequential
solver.SetRandomInitialPoints(min=[-100.0]*ND, max=[100.0]*ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(ssow)
solver.Solve(ChebyshevCost, VTR(0.01), strategy=Best1Exp, \
CrossProbability=1.0, ScalingFactor=0.9, disp=1)
print("")
print_solution( solver.bestSolution )
#'''
random_seed(seed)
print("\n and now parallel...")
solver2 = DifferentialEvolutionSolver2(ND,NP) #XXX: parallel
solver2.SetMapper(Pool(NNODES).map)
solver2.SetRandomInitialPoints(min=[-100.0]*ND, max=[100.0]*ND)
solver2.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver2.SetGenerationMonitor(psow)
solver2.Solve(ChebyshevCost, VTR(0.01), strategy=Best1Exp, \
CrossProbability=1.0, ScalingFactor=0.9, disp=1)
print("")
print_solution( solver2.bestSolution )
#'''
# end of file
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