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save_indices.py
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save_indices.py
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# -*- coding: utf-8 -*-
import numpy as np
import scipy.io as sio
def Sampling(groundtruth): #divide dataset into train and test datasets
labeled = {}
test = {}
valid = {}
all = {}
m = max(groundtruth)
labeled_indices = []
test_indices = []
valid_indices = []
all_indices = []
for i in range(m+1):
indices = [j for j, x in enumerate(groundtruth.ravel().tolist()) if x == i]
if i != 0:
np.random.shuffle(indices)
all[i] = indices
test[i] = indices[200:]
valid[i] = indices[100:200]
labeled[i] = indices[:100]
labeled_indices += labeled[i]
valid_indices += valid[i]
test_indices += test[i]
all_indices += all[i]
np.random.shuffle(labeled_indices)
np.random.shuffle(valid_indices)
np.random.shuffle(test_indices)
np.random.shuffle(all_indices)
return labeled_indices, test_indices, valid_indices,all_indices
mat_gt = sio.loadmat("/data/pan/data/paviac/data/Pavia_gt.mat")
label = mat_gt['pavia_gt']
GT = label .reshape(np.prod(label.shape[:2]),)
labeled_indices, test_indices, valid_indices,all_indices= Sampling(GT)
np.save('/data/pan/data/paviac/data/labeled_index.npy', labeled_indices)
np.save('/data/pan/data/paviac/data/valid_index.npy', valid_indices)
np.save('/data/pan/data/paviac/data/test_index.npy', test_indices)
np.save('/data/pan/data/paviac/data/all_index.npy', all_indices)