def PolyCostFactory(evalpts,datapts,ndim):
"""generate a cost function instance from evaluation points and data"""
from numpy import sum
F = CostFactory()
F.addModel(ForwardPolyFactory,ndim,"noisy_polynomial")
return F.getCostFunction(evalpts=evalpts,observations=datapts, \
metric=lambda x: sum(x*x),sigma=1000.)
minrange = [-1000., -1000., -100., -10.]
maxrange = [1000., 1000., 100., 10.]
solver.SetRandomInitialPoints(min=minrange, max=maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(stepmon)
solver.Solve(CF, termination = ChangeOverGeneration(generations=100), \
CrossProbability=0.5, ScalingFactor=0.5)
solution = solver.Solution()
return solution, stepmon
if __name__ == '__main__':
F = CostFactory()
F.addModel(ForwardMogiFactory, 4, 'mogi1', outputFilter=component(2))
myCostFunction = F.getCostFunction(evalpts=stations, observations=data_z)
print(F)
rp = F.getRandomParams()
print("new Cost Function : %s " % myCostFunction(rp))
print("orig Cost Function: %s " % cost_function(rp))
f1 = ForwardMogiFactory(rp)
f2 = ForwardMogiFactory(rp)
print('start cf')
for i in range(3000):
xx = cost_function(rp)
print('end cf')
