可替代Numpy中的Scipy模式功能?

问题描述：

在numpy中还有另一种方法来实现scipy.stats.mode函数以沿轴获取ndarray中最频繁的值吗(无需导入其他模块)，即

Is there another way in numpy to realize scipy.stats.mode function to get the most frequent values in ndarrays along axis?(without importing other modules) i.e.

import numpy as np
from scipy.stats import mode

a = np.array([[[ 0,  1,  2,  3,  4],
                  [ 5,  6,  7,  8,  9],
                  [10, 11, 12, 13, 14],
                  [15, 16, 17, 18, 19]],

                 [[ 0,  1,  2,  3,  4],
                  [ 5,  6,  7,  8,  9],
                  [10, 11, 12, 13, 14],
                  [15, 16, 17, 18, 19]],

                 [[40, 40, 42, 43, 44],
                  [45, 46, 47, 48, 49],
                  [50, 51, 52, 53, 54],
                  [55, 56, 57, 58, 59]]])

mode= mode(data, axis=0)
mode = mode[0]
print mode
>>>[ 0,  1,  2,  3,  4],
   [ 5,  6,  7,  8,  9],
   [10, 11, 12, 13, 14],
   [15, 16, 17, 18, 19]

答

scipy.stats.mode函数是使用以下代码定义的，该代码仅依赖于numpy:

The scipy.stats.mode function is defined with this code, which only relies on numpy:

def mode(a, axis=0):
    scores = np.unique(np.ravel(a))       # get ALL unique values
    testshape = list(a.shape)
    testshape[axis] = 1
    oldmostfreq = np.zeros(testshape)
    oldcounts = np.zeros(testshape)

    for score in scores:
        template = (a == score)
        counts = np.expand_dims(np.sum(template, axis),axis)
        mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
        oldcounts = np.maximum(counts, oldcounts)
        oldmostfreq = mostfrequent

    return mostfrequent, oldcounts

来源: https://github.com/scipy/scipy/blob /master/scipy/stats/stats.py#L609

可替代Numpy中的Scipy模式功能?

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