def meanOfMeans():
initialExperiment = 25
finalExperiment = 35
suffix = "piConc"
experimentFolder = "170"
rootDir = os.path.join("/", "home", "rafaelbeirigo", "ql", "experiments", experimentFolder)
means = []
meanFileName = "W_avg_list_mean.out"
for experiment in range(initialExperiment, finalExperiment + 1):
meanFilePath = os.path.join(rootDir, str(experiment), "PRQL", meanFileName)
# open experiment the file that contains the "individual" mean
# into a list
mean = pl.loadtxt(meanFilePath)
# this must be done because the function meanError.meanMultiDim()
# needs each item in the mean list to be a list itself.
mean = [[item] for item in mean]
# append it to the list of means
means.append(mean)
# obtain the global mean considering the list of individual means
globalMean = meanError.meanMultiDim(means)
## print globalMean
# means2spreadsheet([globalMean])
pl.savetxt(os.path.join(rootDir, "W_avg_list_mean." + suffix + ".out"), globalMean, fmt="%1.6f")
return globalMean
def meanOfMeans():
initialExperiment = 25
finalExperiment = 35
suffix = 'piConc'
experimentFolder = '170'
rootDir = os.path.join('/', 'home', 'rafaelbeirigo', 'ql', 'experiments',
experimentFolder)
means = []
meanFileName = 'W_avg_list_mean.out'
for experiment in range(initialExperiment, finalExperiment + 1):
meanFilePath = os.path.join(rootDir, str(experiment), 'PRQL', meanFileName)
# open experiment the file that contains the "individual" mean
# into a list
mean = pl.loadtxt(meanFilePath)
# this must be done because the function meanError.meanMultiDim()
# needs each item in the mean list to be a list itself.
mean = [[item] for item in mean]
# append it to the list of means
means.append(mean)
# obtain the global mean considering the list of individual means
globalMean = meanError.meanMultiDim(means)
## print globalMean
#means2spreadsheet([globalMean])
pl.savetxt(os.path.join(rootDir, 'W_avg_list_mean.' + suffix + '.out'),
globalMean, fmt='%1.6f')
return globalMean
def saveOutputFiles(myQLearning, params, output):
filePath = params['filePath']
myQLearning.obtainPolicy()
myQLearning.printPolicy(filePath + 'policy.out')
myQLearning.printQ(filePath + 'Q.out')
f = open(filePath + 'parameters.out', 'w')
f.write('alpha = ' + params['alpha'] + '\n')
f.write('gamma = ' + params['gamma'] + '\n')
f.write('epsilon = ' + params['epsilon'] + '\n')
f.write('epsilonIncrement = ' + params['epsilonIncrement'] + '\n')
f.write('gammaPRQL = ' + params['gammaPRQL'] + '\n')
f.write('tau = ' + params['tau'] + '\n')
f.write('deltaTau = ' + params['deltaTau'] + '\n')
f.write('psi = ' + params['psi'] + '\n')
f.write('v = ' + params['v'] + '\n')
f.write('K = ' + params['K'] + '\n')
f.write('H = ' + params['H'] + '\n')
f.write('numberOfExecutions = ' + params['numberOfExecutions'] + '\n')
f.close()
#names = ['Ps']
for name in output.iterkeys():
#if name not in names: continue
print name
d = output[name]
if len(d) > 1:
dMean = meanError.meanMultiDim(d)
dCfdInt = meanError.confidenceIntervalMultiDim(d, dMean)
dMean = np.asarray(dMean)
dCfdInt = np.asarray(dCfdInt)
dMean.T
dCfdInt.T
np.savetxt(filePath + '/' + name + '_mean.out', dMean)
np.savetxt(filePath + '/' + name + '_cfdInt.out', dCfdInt)
else:
d = np.asarray(d)
d.T
np.savetxt(filePath + '/' + name + '.out', d)
