如下所示：
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# -*- coding: utf-8 -*-# @Time : 2018/5/17 15:05# @Author : Sizer# @Site : # @File : test.py# @Software: PyCharmimport timeimport numpy as np# data = np.array([# [5.0, 3.0, 4.0, 4.0, 0.0],# [3.0, 1.0, 2.0, 3.0, 3.0],# [4.0, 3.0, 4.0, 3.0, 5.0],# [3.0, 3.0, 1.0, 5.0, 4.0],# [1.0, 5.0, 5.0, 2.0, 1.0]# ])data = np.random.random((1000, 1000))print(data.shape)start_time = time.time()# avg = [float(np.mean(data[i, :])) for i in range(data.shape[0])]# print(avg)start_time = time.time()avg = []for i in range(data.shape[0]): sum = 0 cnt = 0 for rx in data[i, :]: if rx > 0: sum += rx cnt += 1 if cnt > 0: avg.append(sum/cnt) else: avg.append(0)end_time = time.time()print("op 1:", end_time - start_time)start_time = time.time()avg = []isexist = (data > 0) * 1for i in range(data.shape[0]): sum = np.dot(data[i, :], isexist[i, :]) cnt = np.sum(isexist[i, :]) if cnt > 0: avg.append(sum / cnt) else: avg.append(0)end_time = time.time()print("op 2:", end_time - start_time)## print(avg)factor = np.mat(np.ones(data.shape[1])).T# print("facotr :")# print(factor)exist = np.mat((data > 0) * 1.0)# print("exist :")# print(exist)# print("res :")res = np.array(exist * factor)end_time = time.time()print("op 3:", end_time-start_time)start_time = time.time()exist = (data > 0) * 1.0factor = np.ones(data.shape[1])res = np.dot(exist, factor)end_time = time.time()print("op 4:", end_time - start_time) |
经过多次验证, 第四种实现方式的事件效率最高！
以上这篇python 判断矩阵中每行非零个数的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/m0_37890477/article/details/80352041
