具体功能与前一篇博文一样,但是这个是针对每列进行处理的。
import copy
import itertools
import operator
import numpy as np
L = np.array([[0,0,0,0,0,0,0,0,0],[0,2,0,6,0,0,0,0,0],[1,3,5,5,2,0,0,1,5],[4,2,0,0,4,0,2,1,0],[1,0,0,0,2,1,3,0,0],[0,0,0,0,0,4,2,0,0],[0,0,0,0,0,1,0,0,0]])
L1 = copy.deepcopy(L)
L2 = copy.deepcopy(L)
[a, b] = np.shape(L)
zipped_dataset = np.zeros((a, b))
for i in range(b):
col_array = L[:,i]
col_nonzeros_index = np.where(col_array != 0)
L1[:,i][col_nonzeros_index] = 1
col_nonzeros_index_array = [[i for i, value in it] for key, it in
itertools.groupby(enumerate(L1[:,i]), key=operator.itemgetter(1)) if key != 0]
for j in range(len(col_nonzeros_index_array)):
number_L2 = col_nonzeros_index_array[j]
if np.size(number_L2) > 1:
mean_value = np.mean(np.array(L2[:,i][number_L2]))
min_value_index = (np.abs(L2[:,i][number_L2] - mean_value)).argmax()
relativity_index = number_L2[min_value_index]
zipped_dataset[:,i][relativity_index] = 1
else:
zipped_dataset[:,i][number_L2] = 1
print(L)
print(zipped_dataset)
输入数组为:
[[0 0 0 0 0 0 0 0 0]
[0 2 0 6 0 0 0 0 0]
[1 3 5 5 2 0 0 1 5]
[4 2 0 0 4 0 2 1 0]
[1 0 0 0 2 1 3 0 0]
[0 0 0 0 0 4 2 0 0]
[0 0 0 0 0 1 0 0 0]]
输出数组为:
[[0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0. 0.]
[0. 1. 1. 0. 0. 0. 0. 1. 1.]
[1. 0. 0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0.]]
能够看到,每列的非零数块被提取之后,再最大值处置1,并将其与非零数置0