ynew = f(xnew)
# print("xnew: ", len(xnew), xnew)
print("lenTimeSeries = ", len(ynew))
return xnew.tolist(), ynew.tolist()
pathData = "data/GR-Emerald-3X_GR-Emerald-3X.csv"
# dataShape1, _, _ = myCrpFunctions.readCSVFileByShape(pathData, 2, 1, 2)
dataByShape = [[],[],[],[],[]]
for i in range(5):
allTimeseries, _, _ = myCrpFunctions.readCSVFileByShape(pathData, i+1, 1, 2)
for timeseries in allTimeseries:
dataByShape[i] = allTimeseries
data = dataByShape[4][2][10:15]
print(len(data))
folderOut = "out24032019/RP/"
myCrpFunctions.createFolder(folderOut)
nPredict = []
# a = [1, 5, 3, 6, 5]
# b = [1, 2 ,3 ,4 ,5 ]
pathFolder = "1301_smoothListTriangle/"
# checkRecall(pathFolder)
fileName = [
"RBA-3P_RBA-3P.csv", "RBA-6P_RBA-6P.csv", "RBA-12PST4_RBA-12PST4.csv",
"RUBY-1X_RUBY-1X_1.csv", "RUBY-4X_RUBY-4X_1.csv", "TN-3X_TN-3X.csv"
]
path = [("data/" + name) for name in fileName]
shape = 2
indexColOfShape = 3
indexColFeature = 2
allTrainSet, minOfTrain, maxOfTrain = myCrpFunctions.readCSVFileByShape(
path[0], shape, indexColFeature, indexColOfShape)
for i in range(1, 5):
allTrainSetI, minOfTrainI, maxOfTrainI = myCrpFunctions.readCSVFileByShape(
path[i], shape, indexColFeature, indexColOfShape)
allTrainSet += allTrainSetI
if (minOfTrainI < minOfTrain):
minOfTrain = minOfTrainI
if (maxOfTrainI > maxOfTrain):
maxOfTrain = maxOfTrainI
# dataTest = myCrpFunctions.readCSVFile(path[5], indexColFeature)
dataTest, testShape = myCrpFunctions.readCSVFileForTest(
path[5], indexColFeature)
shapePredict = [0 for i in range(len(testShape))]
if (__name__ == "__main__"):
fileName = [
"RBA-3P_RBA-3P.csv", "RBA-6P_RBA-6P.csv", "RBA-12PST4_RBA-12PST4.csv",
"RUBY-1X_RUBY-1X_1.csv", "RUBY-4X_RUBY-4X_1.csv", "TN-3X_TN-3X.csv"
]
path = [("data/" + name) for name in fileName]
shape = 2
indexColOfShape = 3
indexColFeature = 2
dataTrain, minOfTrain, maxOfTrain = myCrpFunctions.readCSVFileByShape(
path[1], shape, indexColFeature, indexColOfShape)
dataTest = myCrpFunctions.readCSVFile(path[4], indexColFeature)
if (minOfTrain > min(dataTest)):
minOfNorm = min(dataTest)
else:
minOfNorm = minOfTrain
if maxOfTrain < max(dataTest):
maxOfNorm = max(dataTest)
else:
maxOfNorm = maxOfTrain
trainSet = myCrpFunctions.ConvertSetNumber(dataTrain,
minOfSet=minOfNorm,
maxOfSet=maxOfNorm)
