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# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import dp.TransitionStepRegressionDP as trans
import dp.FinalStepDP as final
def DynamicProgrammingByRegression(p_timeChangeGrid, p_grids, p_optimize, p_regressor, p_funcFinalValue, p_pointStock, p_initialRegime, p_fileToDump) :
# from the optimizer get back the simulation
simulator = p_optimize.getSimulator()
currentTime = simulator.getCurrentStep()
iTime = len(p_timeChangeGrid) - 1
while currentTime < p_timeChangeGrid[iTime] - 1e3 * 2.220446049250313e-16:
iTime -= 1
gridCurrent = p_grids[iTime]
# final values
valuesNext = final.FinalStepDP(gridCurrent, p_optimize.getNbRegime()).operator(p_funcFinalValue, simulator.getParticles())
gridPrevious = gridCurrent
# iterate on time steps
for iStep in range(simulator.getNbStep()) :
asset = simulator.stepBackwardAndGetParticles()
currentTime = simulator.getCurrentStep()
while currentTime < p_timeChangeGrid[iTime] - 1e3 * 2.220446049250313e-16:
iTime -= 1
gridCurrent = p_grids[iTime]
# conditional expectation operator
if iStep == (simulator.getNbStep() - 1):
p_regressor.updateSimulations(True, asset)
else:
p_regressor.updateSimulations(False, asset)
# transition object
transStep = trans.TransitionStepRegressionDP(gridCurrent, gridPrevious, p_optimize)
valuesAndControl = transStep.oneStep(valuesNext, p_regressor)
valuesNext = valuesAndControl[0]
gridPrevious = gridCurrent
# interpolate at the initial stock point and initial regime
return (gridPrevious.createInterpolator(p_pointStock).applyVec(valuesNext[p_initialRegime])).mean()
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