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#!/usr/bin/env python
#
# Author: Patrick Hung (patrickh @caltech)
# 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
"""
test_getCost.py
Example to demonstrate use of CostFactory
"""
from mystic.termination import *
from mystic.strategy import *
from forward_model import *
from mystic.math import poly1d as ForwardPolyFactory
from mystic.models import poly; PolyCostFactory = poly.CostFactory
from mystic.solvers import DifferentialEvolutionSolver
from mystic.monitors import VerboseMonitor
from mystic.tools import getch
ND = 3
NP = 80
MAX_GENERATIONS = ND*NP
from numpy import array
def data(params):
fwd = ForwardPolyFactory(params)
x = (array([list(range(101))])-50.)[0]
return x,fwd(x)
def de_solve(CF):
solver = DifferentialEvolutionSolver(ND, NP)
solver.enable_signal_handler()
stepmon = VerboseMonitor(10,50)
minrange = [-100., -100., -100.];
maxrange = [100., 100., 100.];
solver.SetRandomInitialPoints(min = minrange, max = maxrange)
solver.SetStrictRanges(min = minrange, max = maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(stepmon)
solver.Solve(CF, termination=ChangeOverGeneration(generations=300),\
CrossProbability=0.5, ScalingFactor=0.5,\
sigint_callback=plot_sol)
solution = solver.Solution()
return solution, stepmon
def plot_sol(params,linestyle='b-'):
d = data(params)
plt.plot(d[0],d[1],'%s'%linestyle,linewidth=2.0)
plt.axis(plotview)
plt.draw()
plt.pause(0.001)
return
from numpy import sum as numpysum
def cost_function(params):
x = data(params)[1] - datapts
return numpysum(real((conjugate(x)*x)))
if __name__ == '__main__':
plotview = [-60,60, 0,2500]
target = [1., 2., 1.]
x,datapts = data(target)
#myCost = cost_function
##myCost = PolyCostFactory(target,x)
F = CostFactory()
F.addModel(ForwardPolyFactory,len(target),'poly')
myCost = F.getCostFunction(evalpts=x, observations=datapts)
import matplotlib.pyplot as plt
plt.ion()
print("target: %s" % target)
plot_sol(target,'r-')
solution, stepmon = de_solve(myCost)
print("solution: %s" % solution)
plot_sol(solution,'g-')
print("")
# print("at step 10: %s" % stepmon.x[10])
getch()
# End of file
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