如何计算三角形相似矩阵的加权平均值

Question

我有一个像这样的三角相似矩阵。

[[3, 1, 2, 0],
 [1, 3, 0, 0],
 [1, 0, 0, 0],
 [0, 0, 0, 0]]

如何在丢弃零元素的同时计算每行的加权平均值？

Answer 1

您可以沿第二个轴添加，然后除以 sum 每行 non-zero 个值的数量。然后 where in np.divide 你可以划分其中满足条件，通过将其设置为指定 non-zero 值所在的掩码，你可以防止被零除错误：

a = np.array([[3, 1, 2, 0],
              [1, 3, 0, 0],
              [1, 0, 0, 0],
              [0, 0, 0, 0]])

m = (a!=0).sum(1)
np.divide(a.sum(1), m, where=m!=0)
# array([2., 2., 1., 0.])

Answer 2

遍历每一行，然后遍历每个元素。遍历元素时，不要包含零。如果您只找到零元素，只需将零（或您想要的默认值）添加到您的列表中。

weighted_averages = []
for row in matrix:
  total_weight = 0
  number_of_weights = 0
  for element in row:
    if element != 0:
      total_weight += element
      number_of_weights += 1
  if number_of_weights == 0:
    weighted_averages.append(0)
  else:
    weighted_averages.append(total_weight/number_of_weights)

weighted_averages 在您的案例中返回为： [2.0, 2.0, 1.0, 0]

Answer 3

您可以使用 numpy 计算加权平均值。

import numpy as np
a = np.array([
 [3, 1, 2, 0],
 [1, 3, 0, 0],
 [1, 0, 0, 0],
 [0, 0, 0, 0]
])
weights = np.array([1,2,3,4])
#create an mask where element is 0
ma = np.ma.masked_equal(a,0)
#take masked weighted average
ans = np.ma.average(ma, weights=weights,axis = 1)
#fill masked points as 0
ans.filled(0)

输出：

array([1.83333333, 2.33333333, 1.        , 0.        ])

就Python:

ar = [[3, 1, 2, 0],
 [1, 3, 0, 0],
 [1, 0, 0, 0],
 [0, 0, 0, 0]]
weight = [1,2,3,4]
ans=[]
for li in ar:
    wa = 0 #weighted average
    we = 0 #weights
    for index,ele in enumerate(li): 
        if ele !=0:
            wa+=weight[index]*ele
            we+=weight[index]
    if we!=0:
        ans.append(wa/we)
    else:
        ans.append(0)
ans

如何计算三角形相似矩阵的加权平均值

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