solver.Solve(CF, termination = ChangeOverGeneration(generations=300), \
CrossProbability=0.5, ScalingFactor=0.5, \
sigint_callback = plot_sol)
solution = solver.Solution()
return solution, stepmon
if __name__ == '__main__':
F = CostFactory()
F.addModel(ForwardMogiFactory,
'mogi1',
4,
outputFilter=PickComponent(2, -1))
F.addModel(ForwardMogiFactory,
'mogi2',
4,
outputFilter=PickComponent(2, -1))
myCostFunction = F.getCostFunction(evalpts=stations, observations=data_z)
print F
def C2(x):
"This is the new version"
return 100000 * myCostFunction(x)
def C3(x):
"Cost function constructed by hand"
from test_mogi2 import cost_function
return cost_function(x)
solver.SetRandomInitialPoints(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
if __name__ == '__main__':
F = CostFactory()
F.addModel(ForwardMogiFactory, 4, 'mogi1', outputFilter = PickComponent(2, -1))
F.addModel(ForwardMogiFactory, 4, 'mogi2', outputFilter = PickComponent(2, -1))
myCostFunction = F.getCostFunction(evalpts = stations, observations = data_z)
print F
def C2(x):
"This is the new version"
return 100000 * myCostFunction(x)
def C3(x):
"Cost function constructed by hand"
from test_mogi2 import cost_function
return cost_function(x)
def test():
"call me to see if the functions return the same thing"
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, 'mogi1', 4, outputFilter=PickComponent(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'
print 'start cf2'