查找数组Numpy中非零元素的模式

Question

What is most efficient way to find the mode per row in a multi-dimensional array of the non-zero elements?

For example:

[
 [0.  0.4 0.6 0.  0.6 0.  0.6 0.  0.  0.6 0.  0.6 0.6 0.6 0.  0.  0.  0.6
     0.  0.  0.  0.  0.  0.  0.  0.  0.5 0.6 0.  0.  0.6 0.6 0.6 0.  0.  0.6
     0.6 0.6 0.  0.5 0.6 0.6 0.  0.  0.6 0.  0.6 0.  0.  0.6],
 [0.  0.1 0.2 0.1 0.  0.1 0.1 0.1 0.  0.1 0.  0.  0.  0.1 0.1 0.  0.1 0.1
 0.  0.1 0.1 0.1 0.  0.1 0.1 0.1 0.  0.1 0.2 0.  0.1 0.1 0.  0.1 0.1 0.1
 0.  0.2 0.1 0.  0.1 0.  0.1 0.1 0.  0.1 0.  0.1 0.  0.1]
]

The mode of the above is [0, 0.1] , but ideally we want to return [0.6, 0.1] .

Answer 1

You would use the same method as this question (mentioned in the comments by @yatu), but instead make a call to the numpy.nonzero() method.

To get just the non-zero elements, we can just call the nonzero method, which will return the indices of the non-zero elements. We can do this using this command, if a is a numpy array:

a[nonzero(a)]

Example finding the mode (building off code from the other answer):

import numpy as np
from scipy import stats

a = np.array([
    [1, 0, 4, 2, 2, 7],
    [5, 2, 0, 1, 4, 1],
    [3, 3, 2, 0, 1, 1]]
)

def nonzero_mode(arr):
    return stats.mode(arr[np.nonzero(arr)]).mode

m = map(nonzero_mode, a)
print(m)

If you wanted to get the mode of each row, just use a loop through the array:

for row in a:
   print(nonzero_mode(row))