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# Copyright (C) 2016, 2018 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import StOptReg
import StOptGeners
import dp.TransitionStepRegressionDP as trans
import dp.FinalStepDP as final
def DynamicProgrammingByRegression(p_grid, p_optimize, p_regressor, p_funcFinalValue, p_pointStock, p_initialRegime, p_fileToDump, key1="Continuation" , key2 = "Control"):
# from the optimizer get back the simulation
simulator = p_optimize.getSimulator()
# final values
valuesNext = final.FinalStepDP(p_grid, p_optimize.getNbRegime()).operator(p_funcFinalValue, simulator.getParticles())
archiveToWrite = StOptGeners.BinaryFileArchive(p_fileToDump, "w")
nsteps = simulator.getNbStep()
# iterate on time steps
for iStep in range(nsteps):
asset = simulator.stepBackwardAndGetParticles()
# conditional expectation operator
if iStep == (simulator.getNbStep() - 1):
p_regressor.updateSimulations(True, asset)
else:
p_regressor.updateSimulations(False, asset)
# transition object
transStep = trans.TransitionStepRegressionDP(p_grid, p_grid, p_optimize)
valuesAndControl = transStep.oneStep(valuesNext, p_regressor)
# Dump the continuation values in the archive:
control = valuesAndControl[1]
archiveToWrite.dumpGridAndRegressedValue(key1, nsteps - 1 - iStep, valuesNext, p_regressor, p_grid)
archiveToWrite.dumpGridAndRegressedValue(key2, nsteps - 1 - iStep, control, p_regressor, p_grid)
valuesNext = valuesAndControl[0]
# interpolate at the initial stock point and initial regime
return (p_grid.createInterpolator(p_pointStock).applyVec(valuesNext[p_initialRegime])).mean()
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