-
Notifications
You must be signed in to change notification settings - Fork 0
/
cmpassDataloader.py
46 lines (34 loc) · 1.74 KB
/
cmpassDataloader.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
from cmapssdata import CMAPSSDataset
import numpy as np
class dataLoader():
def __init__(self, bs = 10, sl = 50):
self.batchSize = bs
self.sequence_length = sl
cmpass = CMAPSSDataset(fd_number='1', batch_size=bs, sequence_length=sl)
trainData = cmpass.get_train_data()
self.trainDataFeature = cmpass.get_feature_slice(trainData)
self.trainDataLabel = cmpass.get_label_slice(trainData)
self.trainEngineID = cmpass.get_engine_id(trainData)
index = np.random.permutation(self.trainDataFeature.shape[0])
self.trainDataFeature = self.trainDataFeature[index,:,:]
self.trainDataLabel = self.trainDataLabel[index]
self.trainEngineID = self.trainEngineID[index]
testData = cmpass.get_test_data()
self.testDataFeature = cmpass.get_feature_slice(testData)
self.testDataLabel = cmpass.get_label_slice(testData)
self.testEngineID = cmpass.get_engine_id(testData)
self.batches = self.trainDataFeature.shape[0]//self.batchSize
self.batchpoint = 0
def nextBatch(self):
if self.batchpoint >= self.batches - 1:
self.batchpoint = 0
return self.trainDataFeature[(self.batches-1)* self.batchSize : self.batches*self.batchSize,:,:],\
self.trainDataLabel[(self.batches-1) * self.batchSize : self.batches*self.batchSize]
else:
self.batchpoint = self.batchpoint + 1
return self.trainDataFeature[(self.batchpoint-1) * self.batchSize:self.batchpoint*self.batchSize,:,:],\
self.trainDataLabel[(self.batchpoint-1) * self.batchSize:self.batchpoint*self.batchSize]
if __name__ == '__main__':
d = dataLoader(10,50)
z , v= d.nextBatch()
co = 1