def makeTestFeatureZoom(trainSet,
testSet,
dim=3,
tau=2,
epsilon=0.0055,
lambd=3,
percent=1,
distNorm=1,
pathFolder="result/",
formatSave=".png",
trainSetID=None):
myCrpFunctions.createFolder(pathFolder)
if (trainSetID == None):
import time
import datetime
trainSetID = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y%m%d%Hh%Mp%S')
numSample = 45
for i in range(0, len(trainSet), 50):
title = "IDtrain_" + str(trainSetID) + "_" + str(i) + " - dim_" + str(
dim) + " - epsil_" + str(epsilon) + " - lambd_" + str(lambd)
print("\n------------------------------------------------", title,
"------------------------------------------------\n")
pathSave = pathFolder + title + formatSave
trainSetZoom = trainSet[i:i + numSample]
if (len(trainSetZoom) > dim * tau):
f2 = predict_diagonal(trainSetZoom,
testSet,
dim=dim,
tau=tau,
epsilon=epsilon,
lambd=lambd,
percent=percent,
distNorm=distNorm,
titleOfGraph=title,
figureName=title,
pathSaveFigure=pathSave)
shape = 1
indexColOfShape = 3
indexColFeature = 2
pathFolder = "out23012018_csv/"
interpolationKinds = ['linear', 'nearest', 'slinear', 'quadratic', 'cubic']
# for i in range(6):
# allTrainSetOrigin, minOfTrain, maxOfTrain = myCrpFunctions.readCSVFileByShape(path[i], shape, indexColFeature, indexColOfShape)
for interpKind in interpolationKinds:
for myEpsilon in [0.0085, 0.005, 0.08, 0.05]:
for myLambd in [2, 3]:
pathFolder1 = '{}interpKind_{}-Epsilon_{}-Lamb_{}/'.format(pathFolder, interpKind, str(myEpsilon), str(myLambd))
myCrpFunctions.createFolder(pathFolder1)
print("--------------------------------------{}--------------------------------------".format(pathFolder1))
for i, inputFileName in enumerate(fileNames):
csvName = '{}rqa_shape_{}-file_{}'.format(pathFolder1, shape, inputFileName)
print("--------------------------------------{}--------------------------------------".format(csvName))
allTrainSetOrigin, minOfTrain, maxOfTrain = myCrpFunctions.readCSVFileByShape(path[i], shape, indexColFeature, indexColOfShape)
makeRQAcsvFiles(allTrainSetOrigin, csvName, epsilon = myEpsilon, lambd = myLambd, interpolationKind = interpKind)
'''
rqas = [getRQA(myset, typeReturn = 'array', showCRP=0) for myset in allTrainSetOrigin]
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 ] # for item in a: # print(item in b) # print(data) # plt.plot(data) # plt.plot(data, 'x') # numInsert = 3