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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 math
import numpy as np
import unittest
import StOptGrids
import StOptReg
import StOptGlobal
import StOptGeners
import Utils
import Simulators as sim
import Optimizers as opt
import importlib
# unit test for global shape
############################
class OptimizerConstructionMpi(unittest.TestCase):
def test(self):
# test MPI
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
comm = MPI.COMM_WORLD
initialValues = np.zeros(1,dtype=float) + 1.
sigma = np.zeros(1) + 0.2
mu = np.zeros(1) + 0.05
corr = np.ones((1,1),dtype=float)
# number of step
nStep = 30
# exercice date
dates = np.linspace(0., 1., nStep + 1)
T= dates[len(dates) - 1]
nbSimul = 10 # simulation number (optimization and simulation)
# simulator
##########
bsSim = sim.BlackScholesSimulator(initialValues, sigma, mu, corr, T, len(dates) - 1, nbSimul, False)
strike = 1.
# Pay off
payOff= Utils.BasketCall(strike)
# optimizer
##########
N = 3 # number of exercize dates
swiOpt = opt.OptimizerSwingBlackScholes(payOff,N)
# link simulator to optimizer
swiOpt.setSimulator(bsSim)
# archive
########
nameArchive = "Archive" + str(comm.rank)
ar = StOptGeners.BinaryFileArchive(nameArchive,"w+")
# regressor
##########
nMesh = np.array([1])
regressor = StOptReg.LocalLinearRegression(nMesh)
# Grid
######
# low value for the meshes
lowValues =np.array([0.],dtype=float)
# size of the meshes
step = np.array([1.],dtype=float)
# number of steps
nbStep = np.array([N], dtype=np.int32)
gridArrival = StOptGrids.RegularSpaceGrid(lowValues,step,nbStep)
gridStart = StOptGrids.RegularSpaceGrid(lowValues,step,nbStep-1)
# pay off function for swing
############################
payOffBasket = Utils.BasketCall(strike);
payoff = Utils.PayOffSwing(payOffBasket,N)
# final step
############
asset =bsSim.getParticles()
bSplit = np.array([1],dtype=bool) #define which direction to split in parallel computing
fin = StOptGlobal.FinalStepDPDist(gridArrival,1,bSplit)
values = fin.set( payoff,asset)
# transition time step
#####################
# on step backward and get asset
asset = bsSim.stepBackwardAndGetParticles()
# update regressor
regressor.updateSimulations(0,asset)
transStep = StOptGlobal.TransitionStepRegressionDPDist(gridStart,gridArrival,swiOpt)
valuesNext=transStep.oneStep(values,regressor)
bOneFile=0 # multiple files
transStep.dumpContinuationValues(ar,"Continuation",1,valuesNext[0],valuesNext[1],regressor,bOneFile)
# simulate time step
####################
nbSimul= 10
vecOfStates =[] # state of each simulation
for i in np.arange(nbSimul):
# one regime, all with same stock level (dimension 2), same realization of simulation (dimension 3)
vecOfStates.append(StOptGlobal.StateWithStocks(1, np.array([0.]) , np.zeros(1)))
simStep = StOptGlobal.SimulateStepRegressionDist(ar,1,"Continuation",gridArrival,swiOpt,bOneFile)
phi = np.zeros((1,nbSimul))
NewState =VecOfStateNext = simStep.oneStep(vecOfStates, phi)
if __name__ == '__main__':
unittest.main()
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