def readNewLables(lablesPath,start_index=0,end_index=5000,TRAIN_DATA_PRECENT=0.8,VALIDATION_DATA_PRECENT=0.8):
y = getTopCatVector(lablesPath,start_index,end_index);
# with open('topCat.pkl.gz','wb') as f:
# pickle.dump(y, f, -1)
# f.close()
dataAmount = end_index-start_index
train_index = np.floor(dataAmount*TRAIN_DATA_PRECENT);
validation_index = np.floor(dataAmount*VALIDATION_DATA_PRECENT)
test_index = dataAmount
# Divided dataset into 3 parts.
train_set_y = y[:train_index]
val_set_y = y[train_index:validation_index]
test_set_y = y[validation_index:]
return (train_set_y,test_set_y)
def readNewLables(lablesPath,categorysFileName,start_index=0,end_index=5000,TRAIN_DATA_PRECENT=0.8,VALIDATION_DATA_PRECENT=0.8):
y = getTopCatVector(lablesPath+"\\*"+categorysFileName+".txt",start_index,end_index);
with open(categorysFileName+".pkl.gz",'wb') as f:
pickle.dump(y, f, -1)
f.close()
dataAmount = end_index-start_index
train_index = np.floor(dataAmount*TRAIN_DATA_PRECENT);
validation_index = np.floor(dataAmount*VALIDATION_DATA_PRECENT)
test_index = dataAmount
# Divided dataset into 3 parts.
train_set_y = y[:train_index]
val_set_y = y[train_index:validation_index]
test_set_y = y[validation_index:]
return (train_set_y,test_set_y)
