1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
|
# 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 StOptGeners
import StOptGlobal
import imp
# Simulate the optimal strategy with switching
# p_grids grid used for int deterministic state for each regime
# 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 SimulateSwitchingRegression(p_grids, p_optimize, p_pointState, p_initialRegime, p_fileToDump) :
simulator = p_optimize.getSimulator()
nbStep = simulator.getNbStep()
states = []
particle0 = simulator.getParticles()[:,0]
for i in range(simulator.getNbSimul()) :
states.append(StOptGlobal.StateWithIntState(p_initialRegime, p_pointState, particle0))
ar = StOptGeners.BinaryFileArchive(p_fileToDump, "r")
# name for continuation object in archive
nameAr = "ContinuationSwitching"
# cost function
costFunction = np.zeros((p_optimize.getSimuFuncSize(), simulator.getNbSimul()))
# iterate on time steps
for istep in range(nbStep) :
NewState = StOptGlobal.SimulateStepSwitch(ar, istep, nameAr, p_grids, p_optimize).oneStep(states, costFunction)
# different from C++
states = NewState[0]
costFunction = NewState[1]
# new stochastic state
particules = simulator.stepForwardAndGetParticles()
# update stochastic state
for i in range(simulator.getNbSimul()) :
states[i].setStochasticRealization(particules[:,i])
# average gain/cost
return costFunction.mean()
|
