def test_GP(self): lb = numpy.array([-2.,-2.],float) ub = numpy.array([2.,2.],float) print("Now we start DIRECT, with scaled output for the GP function") rectListOptim,output = directOptim(optimTestFun.gp,lb,ub, iteration=10, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_Rosen(self): lb = numpy.array([-2.,2.],float) ub = numpy.array([2.,2.],float) print("Now we start DIRECT, with scaled output") rectListOptim,output = directOptim(optimTestFun.rosen,lb,ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_Himmelblau(self): lb = numpy.array([-5.,-5.],float) ub = numpy.array([5.,5.],float) print("Now we start DIRECT, using the Himmelblau test function") print("This is a multimodal function") rectListOptim,output = directOptim(optimTestFun.himmelblau,lb,ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_Rosen(self): lb = numpy.array([-2., 2.], float) ub = numpy.array([2., 2.], float) print("Now we start DIRECT, with scaled output") rectListOptim, output = directOptim(optimTestFun.rosen, lb, ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_GP(self): lb = numpy.array([-2., -2.], float) ub = numpy.array([2., 2.], float) print("Now we start DIRECT, with scaled output for the GP function") rectListOptim, output = directOptim(optimTestFun.gp, lb, ub, iteration=10, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_Himmelblau(self): lb = numpy.array([-5., -5.], float) ub = numpy.array([5., 5.], float) print("Now we start DIRECT, using the Himmelblau test function") print("This is a multimodal function") rectListOptim, output = directOptim(optimTestFun.himmelblau, lb, ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True)
def test_mccormick(self): lb = numpy.array([-5.,-5.],float) ub = numpy.array([5.,5.],float) print("Now we start DIRECT, using the Mccormick test function") rectListOptim,output = directOptim(optimTestFun.himmelblau,lb,ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True) # plotDirectBox(rectListOptim,lb,ub,scaleOutput=False) # rectOperation.plotParetoFront(rectListOptim) #####
def test_mccormick(self): lb = numpy.array([-5., -5.], float) ub = numpy.array([5., 5.], float) print("Now we start DIRECT, using the Mccormick test function") rectListOptim, output = directOptim(optimTestFun.himmelblau, lb, ub, iteration=20, numBox=1000, targetMin=0, scaleOutput=False, full_output=True) # plotDirectBox(rectListOptim,lb,ub,scaleOutput=False) # rectOperation.plotParetoFront(rectListOptim) #####
from pygotools.optutils import optimTestFun, consMani import pygotools.direct import numpy import scipy.spatial import matplotlib.pyplot as plt boundSize = 2 lb = -numpy.ones(2) * boundSize ub = numpy.ones(2) * boundSize func = optimTestFun.rosen rectListOptim, output = directOptim(func, lb, ub, iteration=50, numBox=1000, targetMin=0, scaleOutput=False, full_output=True) pygotools.direct.plotDirectBox(rectListOptim, lb, ub, scaleOutput=False) # class object directObj = direct(func, lb, ub) rectListOptim, output = directObj.divide(50, numBox=10000, full_output=True) potentialIndex = pygotools.direct.identifyPotentialOptimalObjectPareto( rectListOptim) pygotools.direct.plotParetoFrontRect(rectListOptim, potentialIndex) pygotools.direct.plotDirectBox(rectListOptim, lb, ub, scaleOutput=False)
from pygotools.direct import direct, directOptim from pygotools.optutils import optimTestFun, consMani import pygotools.direct import numpy import scipy.spatial import matplotlib.pyplot as plt boundSize = 2 lb = -numpy.ones(2) * boundSize ub = numpy.ones(2) * boundSize func = optimTestFun.rosen rectListOptim,output = directOptim(func,lb,ub, iteration=50, numBox=1000, targetMin=0, scaleOutput=False, full_output=True) pygotools.direct.plotDirectBox(rectListOptim,lb,ub,scaleOutput=False) # class object directObj = direct(func,lb,ub) rectListOptim,output = directObj.divide(50,numBox=10000,full_output=True) potentialIndex = pygotools.direct.identifyPotentialOptimalObjectPareto(rectListOptim) pygotools.direct.plotParetoFrontRect(rectListOptim,potentialIndex) pygotools.direct.plotDirectBox(rectListOptim,lb,ub,scaleOutput=False) # in terms of inequalities