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temp.py

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+from util import *
+"""将positive data中各种size的cell均衡化,避免某一类cell过多"""
+NB_EACH_BIN = 1000
+
+filename = './data/rect_data.csv'
+data = np.loadtxt(filename, dtype=np.int32, comments='#', delimiter=' ')
+np.random.shuffle(data)
+X = data[:,4:]
+y = data[:,0:4]
+area = np.array([(yy[1]-yy[0])*(yy[3]-yy[2]) for yy in list(y)])
+sepes = np.histogram(area, bins=20)[1]
+counts = np.histogram(area, bins=20)[0]
+
+data2 = list()
+for idx in range(20):
+    data2.append([])
+for idx in range(data.shape[0]):
+    for ii in range(20):
+        if(sepes[ii] <= area[idx] < sepes[ii+1]):
+            data2[ii].append(data[idx])
+
+data3 = []
+for idx_bin in range(20):
+    if(len(data2[idx_bin]) != 0):
+        data3.append(np.vstack(tuple(data2[idx_bin])))
+
+for idx_bin in range(len(data3)):
+    if(data3[idx_bin].shape[0] > NB_EACH_BIN):
+        np.random.shuffle(data3[idx_bin])
+        data3[idx_bin] = data3[idx_bin][0:NB_EACH_BIN, :]
+data4 = np.vstack(tuple(data3))
+np.savetxt('./data/rect_data2.csv', data4, fmt='%d', header = 'rect_examples, first 4 cols are target')