lb,
ub,
iteration=50,
numBox=1000,
targetMin=0,
scaleOutput=False,
full_output=True)
index = directUtil.findLowestObjIndex(rectListOptim)
rectListOptim[index].getFx()
rectListOptim[index].getLocation()
pyOptimUtil.direct.plotDirectBox(rectListOptim, lb, ub, scaleOutput=False)
# class object
directObj = directAlg.direct(optimTestFun.rosen, lb, ub)
rectListOptim, output = directObj.divide(50, numBox=10000, full_output=True)
potentialIndex = directUtil.identifyPotentialOptimalObjectPareto(rectListOptim)
pyOptimUtil.direct.directUtil.plotParetoFrontRect(rectListOptim,
potentialIndex)
pyOptimUtil.direct.plotDirectBox(rectListOptim, lb, ub, scaleOutput=False)
# in terms of inequalities
A, b = polyOperation.addBoxToInequalityLBUB(lb, ub)
directObj = directAlg.direct(optimTestFun.gp, lb, ub, A, b)
polyListOptim, output = directObj.divide(10, numBox=2000, full_output=True)
potentialIndex = polyOperation.identifyPotentialOptimalPolygonPareto(
print newPolyList[i].getMaxDistanceToVertices()
pyOptimUtil.direct.plotDirectPolygon(newPolyList, potentialIndex)
x, sol, G, h = polyOperation.findAnalyticCenter(A, b, full_output=True)
import pyOptimUtil.direct
rectListOptim, output = directAlg.directOptim(
optimTestFun.rosen, lb, ub, iteration=50, numBox=1000, targetMin=0, scaleOutput=False, full_output=True
)
pyOptimUtil.direct.plotDirectBox(rectListOptim, lb, ub, scaleOutput=False)
# class object
directObj = directAlg.direct(optimTestFun.rosen, lb, ub)
rectListOptim = directObj.divide(10)
directObj = directAlg.direct(optimTestFun.rosen, lb, ub, A, b)
polyListOptim = directObj.divide(5)
potentialIndex = polyOperation.identifyPotentialOptimalPolygonPareto(polyListOptim)
pyOptimUtil.direct.plotDirectPolygon(polyListOptim, potentialIndex)
polyListOptim = directObj.divide(1)
potentialIndex = polyOperation.identifyPotentialOptimalPolygonPareto(polyListOptim)
pyOptimUtil.direct.plotDirectPolygon(polyListOptim, potentialIndex)
scaleOutput=False,
full_output=True)
directUtil.plotDirectBox(rectListOptim,lb,ub,scaleOutput=False)
from pyOptimUtil.direct import optimTestFun, directAlg, directUtil
import pyOptimUtil.direct
optimalListLipschitz = pyOptimUtil.direct.identifyPotentialOptimalRectangleLipschitz(rectListOptim)
optimalList = pyOptimUtil.direct.identifyPotentialOptimalRectanglePareto(rectListOptim)
directUtil.plotParetoFront(rectListOptim,optimalList)
# can also use the class instead
directClass = directAlg.direct(optimTestFun.rosen,lb,ub)
rectListOptim = directClass.divide()
optimalListLipschitz = pyOptimUtil.direct.identifyPotentialOptimalRectangleLipschitz(rectListOptim)
optimalListPareto = pyOptimUtil.direct.identifyPotentialOptimalRectanglePareto(rectListOptim)
directUtil.plotDirectBox(rectListOptim,lb,ub,False,optimalListPareto)
directUtil.plotParetoFront(rectListOptim,optimalListPareto)
#####
#
# SIR model with two parameters
#
#####
pro = GLP(f=objLegrand.cost,x0=theta,df=objLegrand.sensitivity,lb=lb,ub=ub)
pro.plot = True
rGalileo = pro.solve('galileo')
pro = GLP(f=objLegrand.cost,x0=theta,df=objLegrand.sensitivity,lb=lb,ub=ub)
pro.plot = True
rDe = pro.solve('de')
#
# DIRECT!
#
from pyOptimUtil.direct import polyOperation, directAlg
directObj = directAlg.direct(objLegrand.cost,lb,ub)
rectListOptim,output = directObj.divide(1000,numBox=10000,full_output=True)
A,b = polyOperation.addBoxToInequalityLBUB(lb,ub)
directObj = directAlg.direct(objLegrand.cost,lb,ub,A,b)
polyListOptim,output = directObj.divide(50,numBox=5000,full_output=True)
#
# initial values
#
thetaIV = resQPRefine['x'].tolist() + x0
