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# Copyright (C) 2021 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import StOptGrids
import StOptReg
import StOptGlobal
import StOptGeners
def DynamicProgrammingSwitchingByRegressionHighLevel(p_grid, p_optimize, p_regressor, p_pointState, p_initialRegime, p_fileToDump) :
# from the optimizer get back the simulation
simulator = p_optimize.getSimulator()
print("simulator", simulator)
# final values
valuesNext= []
for iReg in range(len(p_grid)):
valuesNext.append( np.zeros([simulator.getNbSimul(),p_grid[iReg].getNbPoints()]))
ar = StOptGeners.BinaryFileArchive(p_fileToDump, "w")
nameAr = "ContinuationSwitching"
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 = StOptGlobal.TransitionStepRegressionSwitch(p_grid, p_grid, p_optimize)
values = transStep.oneStep(valuesNext, p_regressor)
transStep.dumpContinuationValues(ar, nameAr, nsteps - 1 -iStep, valuesNext, p_regressor)
valuesNext = values
# interpolate at the initial stock point and initial regime
return valuesNext[p_initialRegime][:,p_grid[p_initialRegime].globCoordPerDimToLocal( p_pointState)].mean()
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