plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.savefig(figureDir + labelName.replace(".", "_") + "-ROC.eps")
plotInd += 1
#The last column is the mean value
meanMeanAUCs = numpy.mean(meanAUCs, 1)
meanStdAUCs = numpy.mean(stdAUCs, 1)
meanAUCs = numpy.c_[meanAUCs, meanMeanAUCs]
stdAUCs = numpy.c_[stdAUCs, meanStdAUCs]
print("\n")
print(Latex.listToRow(labelNames))
print(Latex.addRowNames(rowNames, Latex.array2DsToRows(meanAUCs, stdAUCs, 2)))
#----------------- Results for RankBoost and RankSVM ----------------------------------
algorithmNames = ["RankBoost", "RankSVM"]
numMethods = len(dataTypes)*len(algorithmNames)
rowNames = numpy.zeros(numMethods, "a20")
meanAUCs = numpy.zeros((numMethods, len(labelNames)))
stdAUCs = numpy.zeros((numMethods, len(labelNames)))
for m in range(len(algorithmNames)):
algorithmName = algorithmNames[m]
for i in range(len(labelNames)):
labelName = labelNames[i]
meanTotalInfo = numpy.mean(totalInfo, 0)/numVertices
stdTotalInfo = numpy.std(totalInfo, 0)/numVertices
meanAverageHops = numpy.mean(averageHops)
stdAverageHops = numpy.std(averageHops)
meanTotalInfo = meanTotalInfo.ravel()
stdTotalInfo = stdTotalInfo.ravel()
meanTotalInfo = meanTotalInfo[1:maxIters]-meanTotalInfo[0:maxIters-1]
stdTotalInfo = stdTotalInfo[0:maxIters-1]+stdTotalInfo[1:maxIters]
if infoProb == 0.5:
pyplot.plot(meanTotalInfo.ravel(), label= "p="+str(p)+",k="+str(k))
print((str(p) + " & " + str(k) + " & " + str(infoProb) + " & %.3f" % meanAverageHops), end=' ')
print(("(%.3f) & " % stdAverageHops), end=' ')
print(("%.3f (%.3f) & " % (meanReceiversToSenders, stdReceiversToSenders)), end=' ')
print((Latex.array2DsToRows(meanTotalInfo.ravel()[0:maxIters], stdTotalInfo.ravel()[0:maxIters]) + "\\\\"))
except IOError:
print(("File not found : " + outputFileName))
pyplot.xlabel('Total information')
pyplot.ylabel('Iteration')
pyplot.legend(loc=10, ncol=3, shadow=True)
#pyplot.show()
print("")
print("")
graphType = "ErdosRenyi"
ps = [0.001, 0.002, 0.003, 0.004]
