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# Copyright (C) 2019 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import StOptTree
import StOptGrids
import StOptGeners
import StOptGlobal
# Simulate the optimal strategy , threaded version, for tree
# p_grid grid used for deterministic state (stocks for example)
# p_optimize optimizer defining the optimization between two time steps
# p_funcFinalValue function defining the final value
# p_pointStock initial point stock
# p_initialRegime regime at initial date
# p_fileToDump name of the file used to dump continuation values in optimization
def SimulateTreeControlHighLevel(p_grid, p_optimize, p_funcFinalValue, p_pointStock, p_initialRegime, p_fileToDump) :
simulator = p_optimize.getSimulator()
nbStep = simulator.getNbStep()
states = []
for i in range(simulator.getNbSimul()) :
states.append(StOptGlobal.StateTreeStocks(p_initialRegime, p_pointStock, 0))
ar = StOptGeners.BinaryFileArchive(p_fileToDump, "r")
# name for continuation object in archive
nameAr = "Continuation"
# cost function
costFunction = np.zeros((p_optimize.getSimuFuncSize(), simulator.getNbSimul()))
# iterate on time steps
for istep in range(nbStep) :
NewState = StOptGlobal.SimulateStepTreeControl(ar, nbStep - 1 - istep, nameAr, p_grid, p_optimize).oneStep(states, costFunction)
# different from C++
states = NewState[0]
costFunction = NewState[1]
# new stochastic state
simulator.stepForward()
for i in range(simulator.getNbSimul()) :
states[i].setStochasticRealization(simulator.getNodeAssociatedToSim(i))
# final : accept to exercise if not already done entirely
for i in range(simulator.getNbSimul()) :
costFunction[0,i] += p_funcFinalValue.set(states[i].getRegime(), states[i].getPtStock(),simulator.getValueAssociatedToNode(states[i].getStochasticRealization()))
# average gain/cost
return costFunction.mean()
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