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# Copyright (C) 2018 The Regents of the University of California
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
# Authors - Aditya Maheshwari and Mike Ludkovski
# Department of Statistics and Applied Probability,
# University of California at Santa Barbara
from __future__ import division
import numpy as np
import math
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
import time
import parameters as bv
import simulateState_new as simX
import valueNext_new as fv
import StOptGrids
import StOptReg
import StOptGeners
if __name__ == "__main__":
outSampleSimul = 10000
param = bv.basicVariables()
condExpFilePath = 'condExp_rmcType_gd_regType_piecewiseLinear'
I0 = np.random.uniform(param.I_minMax[0],param.I_minMax[1],(outSampleSimul,1))
Xt = np.random.uniform(-8,8,(outSampleSimul,1))
r,c = Xt.shape
It = np.zeros((r,2)) # inventory evolution
It[:,0] = I0[:,0]
Dt = np.zeros((r,2), dtype=np.int) # clock
Dt[:,0] = 1 #initial regime
St = np.zeros((r,c)) # stores Xt - dt - Bt
Bt = np.zeros((r,c)) # battery output
dt = np.zeros((r,c)) # diesel output
archiveToRead = StOptGeners.BinaryFileArchive(condExpFilePath,"r")
contValues = archiveToRead.readGridAndRegressedValue(20,"toStore")
for sim in range(outSampleSimul):
_, dt[sim, 0], St[sim, 0], It[sim, 1] ,Bt[sim, 0] = fv.findOptimalControl(Xt[sim,0], It[sim,0], Dt[sim,0], contValues[0], param)
plt.scatter(Xt[:,0],It[:,0],c=dt[:,0])
plt.show()
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