def main():
solver = DifferentialEvolutionSolver(ND, NP)
solver.SetRandomInitialPoints(min = [-100.0]*ND, max = [100.0]*ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.enable_signal_handler()
strategy = Best1Bin
stepmon = VerboseMonitor(1)
solver.SetGenerationMonitor(stepmon)
solver.Solve(wavy, ChangeOverGeneration(generations=50), \
strategy=strategy, CrossProbability=1.0, ScalingFactor=0.9, \
sigint_callback = plot_solution)
solution = solver.Solution()
return solution, solver
def main():
solver = DifferentialEvolutionSolver(ND, NP)
solver.SetRandomInitialPoints(min=[-100.0] * ND, max=[100.0] * ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.enable_signal_handler()
strategy = Best1Bin
stepmon = VerboseMonitor(1)
solver.SetGenerationMonitor(stepmon)
solver.Solve(wavy, ChangeOverGeneration(generations=50), \
strategy=strategy, CrossProbability=1.0, ScalingFactor=0.9, \
sigint_callback = plot_solution)
solution = solver.Solution()
return solution, solver
def main():
from mystic.solvers import DifferentialEvolutionSolver2 as DifferentialEvolutionSolver
solver = DifferentialEvolutionSolver(ND, NP)
solver.SetRandomInitialPoints(min = [-100.0]*ND, max = [100.0]*ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(VerboseMonitor(30))
solver.enable_signal_handler()
strategy = Best1Exp
#strategy = Best1Bin
solver.Solve(ChebyshevCost, termination=VTR(0.01), strategy=strategy, \
CrossProbability=1.0, ScalingFactor=0.9, \
sigint_callback=plot_solution)
solution = solver.Solution()
return solution
def main():
from mystic.solvers import DifferentialEvolutionSolver2 as DifferentialEvolutionSolver
solver = DifferentialEvolutionSolver(ND, NP)
solver.SetRandomInitialPoints(min=[-100.0] * ND, max=[100.0] * ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(VerboseMonitor(30))
solver.enable_signal_handler()
strategy = Best1Exp
#strategy = Best1Bin
solver.Solve(ChebyshevCost, termination=VTR(0.01), strategy=strategy, \
CrossProbability=1.0, ScalingFactor=0.9, \
sigint_callback=plot_solution)
solution = solver.Solution()
return solution
def de_solve(CF, a4=None, a5=None):
"""solve with DE for given cost funciton; fix a4 and/or a5 if provided"""
minrange = [0, 100, 100, 1, 1]
maxrange = [100, 2000, 200, 200, 200]
interval = 10
if a5 != None:
minrange[4] = maxrange[4] = a5
interval = 20
if a4 != None:
minrange[3] = maxrange[3] = a4
if interval == 20: interval = 1000
solver = DifferentialEvolutionSolver(ND, NP)
solver.enable_signal_handler()
stepmon = MyMonitor(interval)
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=50))
solution = solver.Solution()
return solution, stepmon
def de_solve(CF, a4=None, a5=None):
"""solve with DE for given cost funciton; fix a4 and/or a5 if provided"""
minrange = [0, 100, 100, 1, 1]
maxrange = [100, 2000, 200, 200, 200]
interval = 10
if a5 != None:
minrange[4] = maxrange[4] = a5
interval = 20
if a4 != None:
minrange[3] = maxrange[3] = a4
if interval == 20: interval = 1000
solver = DifferentialEvolutionSolver(ND, NP)
solver.enable_signal_handler()
stepmon = MyMonitor(interval)
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=50))
solution = solver.Solution()
return solution, stepmon
if abs((xx-x)*(xx-x)+(yy-y)*(yy-y) - rr*rr) < 0.01:
svl.append(i)
return svl
# DEsolver inputs
MAX_GENERATIONS = 2000
ND, NP = 3, 30 # dimension, population size
minrange = [0., 0., 0.]
maxrange = [50., 50., 10.]
# prepare DESolver
from mystic.solvers import DifferentialEvolutionSolver2 \
as DifferentialEvolutionSolver
from mystic.termination import ChangeOverGeneration, VTR
solver = DifferentialEvolutionSolver(ND, NP)
solver.enable_signal_handler()
solver.SetRandomInitialPoints(min=minrange,max=maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.Solve(cost, termination=ChangeOverGeneration(generations=100))
if __name__ == '__main__':
# x0, y0, R0
#guess = [1,1,1] # bad initial guess
#guess = [5,5,1] # ok guess
guess = [10,15,5] # good initial guess
# plot training set & training set boundary
pylab.plot(xy[:,0],xy[:,1],'k+',markersize=6)
c = circle(x0, y0, R0)
if abs((xx-x)*(xx-x)+(yy-y)*(yy-y) - rr*rr) < 0.01:
svl.append(i)
return svl
# DEsolver inputs
MAX_GENERATIONS = 2000
ND, NP = 3, 30 # dimension, population size
minrange = [0., 0., 0.]
maxrange = [50., 50., 10.]
# prepare DESolver
from mystic.solvers import DifferentialEvolutionSolver2 \
as DifferentialEvolutionSolver
from mystic.termination import ChangeOverGeneration, VTR
solver = DifferentialEvolutionSolver(ND, NP)
solver.enable_signal_handler()
solver.SetRandomInitialPoints(min=minrange,max=maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.Solve(cost, termination=ChangeOverGeneration(generations=100))
if __name__ == '__main__':
# x0, y0, R0
#guess = [1,1,1] # bad initial guess
#guess = [5,5,1] # ok guess
guess = [10,15,5] # good initial guess
# plot training set & training set boundary
pylab.plot(xy[:,0],xy[:,1],'k+',markersize=6)
c = circle(x0, y0, R0)
