Пример #1
0
def de_solve(CF):
    solver = DifferentialEvolutionSolver(ND, NP)
    solver.enable_signal_handler()
    stepmon = Monitor()
    solver.SetRandomInitialPoints(min=minrange, max=maxrange)
    solver.SetStrictRanges(min=minrange, max=maxrange)
    solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
    solver.SetGenerationMonitor(stepmon)
    termination = ChangeOverGeneration(generations=generations)
    solver.Solve(CF, termination=termination, strategy=Rand1Exp, sigint_callback=plot_sol(solver))
    solution = solver.Solution()
    return solution, stepmon
Пример #2
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def de_solve(CF):
    solver = DifferentialEvolutionSolver(ND, NP)
    solver.enable_signal_handler()
    stepmon = Monitor()
    solver.SetRandomInitialPoints(min=minrange, max=maxrange)
    solver.SetStrictRanges(min=minrange, max=maxrange)
    solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
    solver.SetGenerationMonitor(stepmon)
    termination = ChangeOverGeneration(generations=generations)
    solver.Solve(CF, termination=termination, strategy=Rand1Exp, \
                 sigint_callback = plot_sol(solver))
    solution = solver.Solution()
    return solution, stepmon
Пример #3
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def main():
    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
Пример #4
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def main():
    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
Пример #5
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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
Пример #6
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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
Пример #7
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def test_griewangk():
    """Test Griewangk's function, which has many local minima.

Testing Griewangk:
Expected: x=[0.]*10 and f=0

Using DifferentialEvolutionSolver:
Solution:  [  8.87516194e-09   7.26058147e-09   1.02076001e-08   1.54219038e-08
  -1.54328461e-08   2.34589663e-08   2.02809360e-08  -1.36385836e-08
   1.38670373e-08   1.59668900e-08]
f value:  0.0
Iterations:  4120
Function evaluations:  205669
Time elapsed:  34.4936850071  seconds

Using DifferentialEvolutionSolver2:
Solution:  [ -2.02709316e-09   3.22017968e-09   1.55275472e-08   5.26739541e-09
  -2.18490470e-08   3.73725584e-09  -1.02315312e-09   1.24680355e-08
  -9.47898116e-09   2.22243557e-08]
f value:  0.0
Iterations:  4011
Function evaluations:  200215
Time elapsed:  32.8412370682  seconds
"""

    print "Testing Griewangk:"
    print "Expected: x=[0.]*10 and f=0"
    from mystic.models import griewangk as costfunc
    ndim = 10
    lb = [-400.]*ndim
    ub = [400.]*ndim
    maxiter = 10000
    seed = 123 # Re-seed for each solver to have them all start at same x0
    
    # DifferentialEvolutionSolver
    print "\nUsing DifferentialEvolutionSolver:"
    npop = 50
    random_seed(seed)
    from mystic.solvers import DifferentialEvolutionSolver
    from mystic.termination import ChangeOverGeneration as COG
    from mystic.termination import CandidateRelativeTolerance as CRT
    from mystic.termination import VTR
    from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp
    esow = Monitor()
    ssow = Monitor() 
    solver = DifferentialEvolutionSolver(ndim, npop)
    solver.SetRandomInitialPoints(lb, ub)
    solver.SetStrictRanges(lb, ub)
    solver.SetEvaluationLimits(generations=maxiter)
    solver.SetEvaluationMonitor(esow)
    solver.SetGenerationMonitor(ssow)
    solver.enable_signal_handler()
    #term = COG(1e-10)
    #term = CRT()
    term = VTR(0.)
    time1 = time.time() # Is this an ok way of timing?
    solver.Solve(costfunc, term, strategy=Rand1Exp, \
                 CrossProbability=0.3, ScalingFactor=1.0)
    sol = solver.Solution()
    time_elapsed = time.time() - time1
    fx = solver.bestEnergy
    print "Solution: ", sol
    print "f value: ", fx
    print "Iterations: ", solver.generations
    print "Function evaluations: ", len(esow.x)
    print "Time elapsed: ", time_elapsed, " seconds"
    assert almostEqual(fx, 0.0, tol=3e-3)

    # DifferentialEvolutionSolver2
    print "\nUsing DifferentialEvolutionSolver2:"
    npop = 50
    random_seed(seed)
    from mystic.solvers import DifferentialEvolutionSolver2
    from mystic.termination import ChangeOverGeneration as COG
    from mystic.termination import CandidateRelativeTolerance as CRT
    from mystic.termination import VTR
    from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp
    esow = Monitor()
    ssow = Monitor() 
    solver = DifferentialEvolutionSolver2(ndim, npop)
    solver.SetRandomInitialPoints(lb, ub)
    solver.SetStrictRanges(lb, ub)
    solver.SetEvaluationLimits(generations=maxiter)
    solver.SetEvaluationMonitor(esow)
    solver.SetGenerationMonitor(ssow)
    #term = COG(1e-10)
    #term = CRT()
    term = VTR(0.)
    time1 = time.time() # Is this an ok way of timing?
    solver.Solve(costfunc, term, strategy=Rand1Exp, \
                 CrossProbability=0.3, ScalingFactor=1.0)
    sol = solver.Solution()
    time_elapsed = time.time() - time1
    fx = solver.bestEnergy
    print "Solution: ", sol
    print "f value: ", fx
    print "Iterations: ", solver.generations
    print "Function evaluations: ", len(esow.x)
    print "Time elapsed: ", time_elapsed, " seconds"
    assert almostEqual(fx, 0.0, tol=3e-3)