def optimize(cost,_bounds,_constraints):
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration as COG
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor, Monitor
from mystic.tools import random_seed
from mystic.termination import Or, CollapseWeight, CollapsePosition, state
if debug:
random_seed(123) # or 666 to force impose_unweighted reweighting
stepmon = VerboseMonitor(1,1)
else:
stepmon = VerboseMonitor(10) if verbose else Monitor()
stepmon._npts = npts
evalmon = Monitor()
lb,ub = _bounds
ndim = len(lb)
solver = DifferentialEvolutionSolver2(ndim,npop)
solver.SetRandomInitialPoints(min=lb,max=ub)
solver.SetStrictRanges(min=lb,max=ub)
solver.SetEvaluationLimits(maxiter,maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
solver.SetConstraints(_constraints)
tol = convergence_tol
term = Or(COG(tol,ngen), CollapseWeight(), CollapsePosition())
solver.Solve(cost,termination=term,strategy=Best1Exp, disp=verbose, \
CrossProbability=crossover,ScalingFactor=percent_change)
#while collapse and solver.Collapse(verbose): #XXX: total_evaluations?
# if debug: print(state(solver._termination).keys())
# solver.Solve() #XXX: cost, term, strategy, cross, scale ?
# if debug: solver.SaveSolver('debug.pkl')
solved = solver.bestSolution
#print("solved: %s" % solver.Solution())
func_max = MINMAX * solver.bestEnergy #NOTE: -solution assumes -Max
#func_max = 1.0 + MINMAX*solver.bestEnergy #NOTE: 1-sol => 1-success = fail
func_evals = solver.evaluations
from mystic.munge import write_support_file
write_support_file(stepmon, npts=npts)
return solved, func_max, func_evals
def optimize(cost,_bounds,_constraints):
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration as COG
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor, Monitor
from mystic.tools import random_seed
from mystic.termination import Or, CollapseWeight, CollapsePosition, state
if debug:
random_seed(123) # or 666 to force impose_unweighted reweighting
stepmon = VerboseMonitor(1,1)
else:
stepmon = VerboseMonitor(10) if verbose else Monitor()
stepmon._npts = npts
evalmon = Monitor()
lb,ub = _bounds
ndim = len(lb)
solver = DifferentialEvolutionSolver2(ndim,npop)
solver.SetRandomInitialPoints(min=lb,max=ub)
solver.SetStrictRanges(min=lb,max=ub)
solver.SetEvaluationLimits(maxiter,maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
solver.SetConstraints(_constraints)
tol = convergence_tol
term = Or(COG(tol,ngen), CollapseWeight(), CollapsePosition())
solver.Solve(cost,termination=term,strategy=Best1Exp, disp=verbose, \
CrossProbability=crossover,ScalingFactor=percent_change)
#while collapse and solver.Collapse(verbose): #XXX: total_evaluations?
# if debug: print(state(solver._termination).keys())
# solver.Solve() #XXX: cost, term, strategy, cross, scale ?
# if debug: solver.SaveSolver('debug.pkl')
solved = solver.bestSolution
#print("solved: %s" % solver.Solution())
func_max = MINMAX * solver.bestEnergy #NOTE: -solution assumes -Max
#func_max = 1.0 + MINMAX*solver.bestEnergy #NOTE: 1-sol => 1-success = fail
func_evals = solver.evaluations
from mystic.munge import write_support_file
write_support_file(stepmon, npts=npts)
return solved, func_max, func_evals
def optimize(cost, _bounds, _constraints):
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration as COG
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor, Monitor
from mystic.tools import random_seed
#random_seed(123)
stepmon = VerboseMonitor(2)
#stepmon = Monitor()
evalmon = Monitor()
lb, ub = _bounds
ndim = len(lb)
solver = DifferentialEvolutionSolver2(ndim, npop)
solver.SetRandomInitialPoints(min=lb, max=ub)
solver.SetStrictRanges(min=lb, max=ub)
solver.SetEvaluationLimits(maxiter, maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
solver.SetConstraints(_constraints)
tol = convergence_tol
solver.Solve(cost,termination=COG(tol,ngen),strategy=Best1Exp, \
CrossProbability=crossover,ScalingFactor=percent_change)
solved = solver.bestSolution
#print("solved: %s" % solver.Solution())
func_max = MINMAX * solver.bestEnergy #NOTE: -solution assumes -Max
#func_max = 1.0 + MINMAX*solver.bestEnergy #NOTE: 1-sol => 1-success = fail
func_evals = solver.evaluations
from mystic.munge import write_support_file
write_support_file(stepmon, npts=npts)
return solved, func_max, func_evals
def optimize(cost,_bounds,_constraints):
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration as COG
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor, Monitor
from mystic.tools import random_seed
#random_seed(123)
stepmon = VerboseMonitor(2)
#stepmon = Monitor()
evalmon = Monitor()
lb,ub = _bounds
ndim = len(lb)
solver = DifferentialEvolutionSolver2(ndim,npop)
solver.SetRandomInitialPoints(min=lb,max=ub)
solver.SetStrictRanges(min=lb,max=ub)
solver.SetEvaluationLimits(maxiter,maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
solver.SetConstraints(_constraints)
tol = convergence_tol
solver.Solve(cost,termination=COG(tol,ngen),strategy=Best1Exp, \
CrossProbability=crossover,ScalingFactor=percent_change)
solved = solver.bestSolution
#print("solved: %s" % solver.Solution())
func_max = MINMAX * solver.bestEnergy #NOTE: -solution assumes -Max
#func_max = 1.0 + MINMAX*solver.bestEnergy #NOTE: 1-sol => 1-success = fail
func_evals = solver.evaluations
from mystic.munge import write_support_file
write_support_file(stepmon, npts=npts)
return solved, func_max, func_evals
plot_exact()
# configure monitor
stepmon = VerboseLoggingMonitor(1, 2)
# use buckshot-Powell to solve 8th-order Chebyshev coefficients
solver = BuckshotSolver(ndim, npts)
solver.SetNestedSolver(PowellDirectionalSolver)
solver.SetMapper(Pool().map)
solver.SetGenerationMonitor(stepmon)
solver.SetStrictRanges(min=[-300] * ndim, max=[300] * ndim)
solver.Solve(chebyshev8cost, NCOG(1e-4), disp=1)
solution = solver.Solution()
shutdown() # help multiprocessing shutdown all workers
# write 'convergence' support file
from mystic.munge import write_support_file
write_support_file(solver._stepmon) #XXX: only saves the 'best'
# use pretty print for polynomials
print(poly1d(solution))
# compare solution with actual 8th-order Chebyshev coefficients
print("\nActual Coefficients:\n %s\n" % poly1d(chebyshev8coeffs))
# plot solution versus exact coefficients
plot_solution(solution)
getch()
# end of file
#monitor = Monitor(all=True)
#monitor = Monitor(all=False)
#monitor = VerboseMonitor(1,1)
#monitor = VerboseMonitor(1,1, all=True)
#monitor = VerboseMonitor(1,1, all=False)
#monitor = VerboseMonitor(0,1)
monitor = VerboseMonitor(1, 0)
#monitor = LoggingMonitor(1)
#monitor = LoggingMonitor(1, all=True)
#monitor = LoggingMonitor(1, all=False)
#monitor = VerboseLoggingMonitor(1)
#monitor = VerboseLoggingMonitor(0,1)
#test0(monitor)
#test1(monitor)
test2(monitor) # GenerationMonitor works like test0
#test2(monitor, diffenv=False) # (to make like test1, add enclosing [])
#test2(monitor, diffenv=True)
# these are for "MonitorPlotter(s)"; need to adapt log.py plotters for test1
write_support_file(monitor, 'paramlog1.py') # plot with 'support_*.py'
#write_converge_file(monitor,'paramlog2.py') #XXX: no existing plotters?
#write_raw_file(monitor,'paramlog3.py') #XXX: no existing plotters?
import os
for fname in ('paramlog1.py', ):
if os.path.exists(fname):
os.remove(fname)
# EOF
#monitor = Monitor(all=False)
#monitor = VerboseMonitor(1,1)
#monitor = VerboseMonitor(1,1, all=True)
#monitor = VerboseMonitor(1,1, all=False)
#monitor = VerboseMonitor(0,1)
monitor = VerboseMonitor(1,0)
#monitor = LoggingMonitor(1)
#monitor = LoggingMonitor(1, all=True)
#monitor = LoggingMonitor(1, all=False)
#monitor = VerboseLoggingMonitor(1)
#monitor = VerboseLoggingMonitor(0,1)
#test0(monitor)
#test1(monitor)
test2(monitor) # GenerationMonitor works like test0
#test2(monitor, diffenv=False) # (to make like test1, add enclosing [])
#test2(monitor, diffenv=True)
# these are for "MonitorPlotter(s)"; need to adapt log.py plotters for test1
write_support_file(monitor,'paramlog1.py') # plot with 'support_*.py'
#write_converge_file(monitor,'paramlog2.py') #XXX: no existing plotters?
#write_raw_file(monitor,'paramlog3.py') #XXX: no existing plotters?
import os
for fname in ('paramlog1.py',):
if os.path.exists(fname):
os.remove(fname)
# EOF
# draw frame and exact coefficients
plot_exact()
# configure monitor
stepmon = VerboseLoggingMonitor(1,2)
# use buckshot-Powell to solve 8th-order Chebyshev coefficients
solver = BuckshotSolver(ndim, npts)
solver.SetNestedSolver(PowellDirectionalSolver)
solver.SetMapper(Pool().map)
solver.SetGenerationMonitor(stepmon)
solver.SetStrictRanges(min=[-300]*ndim, max=[300]*ndim)
solver.Solve(chebyshev8cost, NCOG(1e-4), disp=1)
solution = solver.Solution()
# write 'convergence' support file
from mystic.munge import write_support_file
write_support_file(solver._stepmon) #XXX: only saves the 'best'
# use pretty print for polynomials
print poly1d(solution)
# compare solution with actual 8th-order Chebyshev coefficients
print "\nActual Coefficients:\n %s\n" % poly1d(chebyshev8coeffs)
# plot solution versus exact coefficients
plot_solution(solution)
getch()
# end of file
