def getRQA(TimeSeries, numPointInterp=200, dim=5, tau=2, epsilon=0.09, lambd=2, distNorm=1, interpolationKind="", typeReturn='array', showCRP=0): if (len(TimeSeries) > dim * tau + 1): norm01TimeSeries = myCrpFunctions.ConvertSetNumber(TimeSeries) _, interpTimeSeries = myCrpFunctions.myInterpolation( norm01TimeSeries, numNew=numPointInterp, myKind=interpolationKind) r_Binary = makeRPmatrix(interpTimeSeries, dim=dim, tau=tau, epsilon=epsilon, distNorm=distNorm) return rqaCalculate(r_Binary, keyDot=1, lambd=lambd, typeReturn=typeReturn, showCRP=showCRP) return None
if (minOfTrain > min(dataTest)): minOfNorm = min(dataTest) else: minOfNorm = minOfTrain if maxOfTrain < max(dataTest): maxOfNorm = max(dataTest) else: maxOfNorm = maxOfTrain # veDoThiTatCaTrainShape(allTrainSet, "Trước khi smoot") for i in range(len(allTrainSet)): allTrainSet[i] = myCrpFunctions.ConvertSetNumber(allTrainSet[i], minOfSet=minOfNorm, maxOfSet=maxOfNorm) testSet = myCrpFunctions.ConvertSetNumber(dataTest, minOfSet=minOfNorm, maxOfSet=maxOfNorm) print("len(trainSet): ", len(allTrainSet)) print("len(testSet): ", len(testSet)) for degree in [2, 3, 5, 7, 9]: pathFolder1 = "{}degree_{}".format(pathFolder, degree) allSmoothingTrainSet = [ myCrpFunctions.smoothListTriangle(trainSet, degree=degree) for trainSet in allTrainSet ] smoothingTestSet = myCrpFunctions.smoothListTriangle(testSet,