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
