numpy - Numpy 中 Scipy 模式功能的替代方案？

Question

在 numpy 中是否有另一种方法来实现 scipy.stats.mode 函数以沿轴获取 ndarrays 中最频繁的值？(不导入其他模块)即

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]

Answer 1

如果您知道没有很多不同的值(相对于输入“itemArray”的大小)，这样的事情可能会很有效:

uniqueValues = np.unique(itemArray).tolist()
uniqueCounts = [len(np.nonzero(itemArray == uv)[0])
                for uv in uniqueValues]

modeIdx = uniqueCounts.index(max(uniqueCounts))
mode = itemArray[modeIdx]

# All counts as a map
valueToCountMap = dict(zip(uniqueValues, uniqueCounts))