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,