def getY(filename, trainIDs, testIDs, predict):
path = os.getcwd()+'/'
IDDict = futil.LoadDictFromTxt(path+filename, 'vid')
compressed = 'compressed' in filename
#filepath = makePathMR(filename, '-mergerMinRanges')
filepath = makeFullPath(filename, '-mergerRanges.txt')
MR = np.loadtxt(filepath, dtype='int')
Ytrain = np.array([]) #will have numTrain*numFrames rows and 1 column
Ytest = np.array([])
if not numUsing == 0:
MR = MR[:numUsing]
for row in MR:
YVID = np.ascontiguousarray(getYInner(row,IDDict[row[0]], predict, compressed))
if row[0] in trainIDs:
Ytrain=append(Ytrain,YVID) #uses append because Y is small in memory
print("Finished getting Y data for Merger with VID:",row[0]," and it is a training example")
else:
Ytest=append(Ytest,YVID)
print("Finished getting Y data for Merger with VID:",row[0]," and it is a test example")
return np.ascontiguousarray(Ytrain), np.ascontiguousarray(Ytest)
def getX(filename, trainIDs, testIDs, mean_centered):
#filename="res/101_trajectories/aug_trajectories-0750am-0805am.txt"
path = os.getcwd()+'/'
compressed = 'compressed' in filename
frameDict = futil.LoadDictFromTxt(path+filename, 'frame')
print("Gotten frameDict",time.ctime())
dictOfGrids = futil.GetGridsFromFrameDict(frameDict, mean_centered, compressed)
print("Gotten dictOfGrids",time.ctime())
#filepath = makePathMR(filename, '-mergerMinRanges')
filepath = makeFullPath(filename, '-mergerRanges.txt')
MR = np.loadtxt(filepath, dtype='int')
'''MR=MergeRanges. MR[:,0]=merge ids, MR[:,1]=start frame, MR[:,2] = end'''
print ("Done loading in getX", time.ctime())
start = getStartVals(filename)
Xtrain = np.array([]) #will have numTrain*numFrames rows and size(grid)+1 columns
Xtest = np.array([])
it = 0
trainEmpty = True
testEmpty = True
if not numUsing == 0:
MR = MR[:numUsing]
for row in MR:
thisStart = start[it]
XVID = sparse.csr_matrix(np.ascontiguousarray(getXInner(row, dictOfGrids,thisStart,frameDict, compressed)))
if row[0] in trainIDs:
if trainEmpty == True:
Xtrain = XVID
trainEmpty = False
else:
Xtrain = sparse.vstack((Xtrain,XVID))#,axis=0)
print("Finished getting X data for Merger with VID:",row[0]," and it is a training example", time.ctime())
else:
if testEmpty == True:
Xtest = XVID
testEmpty = False
else:
Xtest = sparse.vstack((Xtest,XVID))#np.append(Xtest,XVID,axis=0)
print("Finished getting X data for Merger with VID:",row[0]," and it is a test example")
it += 1
print(Xtrain.shape)
return Xtrain, Xtest
def main(argv):
frameDict = futil.LoadDictFromTxt(
"res/Lankershim/aug_trajectories-0750am-0805am.txt", 'frame')
futil.AnimateFrames(frameDict)