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#!/usr/bin/python3
# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import unittest
import random
import math
import StOptGrids
import StOptReg
# unit test for continuation values
##################################
class testContValues(unittest.TestCase):
# unit test for continuation cuts
def testSimpleGridsAndRegressor(self):
# low value for the meshes
lowValues =np.array([1.,2.,3.],dtype=float)
# size of the meshes
step = np.array([0.7,2.3,1.9],dtype=float)
# number of steps
nbStep = np.array([3,2,4], dtype=np.int32)
# create the regular grid
#########################
grid = StOptGrids.RegularSpaceGrid(lowValues,step,nbStep)
# simulation
nbSimul =10000
np.random.seed(1000)
x = np.random.uniform(-1.,1.,size=(1,nbSimul));
# mesh
nbMesh = np.array([16],dtype=np.int32)
# Create the regressor
#####################
regressor = StOptReg.LocalLinearRegression(False,x,nbMesh)
# regressed values
toReal = (2+x[0,:]+(1+x[0,:])*(1+x[0,:]))
# function to regress
toRegress = toReal + 4*np.random.normal(0.,1,nbSimul)
# fictitous cuts with 0 sensibility (1 dimension)
toRegressCuts = np.zeros(shape=(4*len(toRegress),grid.getNbPoints()))
for i in range(toRegressCuts.shape[1]):
toRegressCuts[:len(toRegress),i] = toRegress
# Now create the continuation cut object
########################################
contOb = StOptReg.ContinuationCut(grid,regressor,toRegressCuts)
hyperCube = np.array([[lowValues[0],lowValues[0]+step[0]*nbStep[0]],
[lowValues[1],lowValues[1]+step[1]*nbStep[1]],
[lowValues[2],lowValues[2]+step[2]*nbStep[2]]])
regressCuts = contOb.getCutsAllSimulations(hyperCube)
if __name__ == '__main__':
unittest.main()
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