python 判断矩阵中每行非零个数的方法


						如下所示：





# -*- coding: utf-8 -*-

# @Time  : 2018/5/17 15:05

# @Author : Sizer

# @Site  : 

# @File  : test.py

# @Software: PyCharm

import time

import 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) * 1

for 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) * 1.0

factor = 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！

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