分散为N个类别,每个类别的观察量相等

Question

我有1-5个范围内的numpy数组,通常不是分布式的.我想找到N-1将这些值分隔成N箱的截止值,其中每个箱具有相同数量的观察值.并非总是可以平等分配,但尽可能接近完美.它将用于~1000次观察.

我已经使用所请求的方法创建了一个示例discretize.垃圾箱和临界值应按顺序递增.

import numpy as np
import random

dat = np.hstack(([random.uniform(1,5) for i in range(10)], [random.uniform(4,5) for i in range(5)]))
print dat # [4.0310121   3.53599004  1.7687312   4.94552008  2.00898982  4.5596209, ...

discrete_dat, cutoffs = discretize(dat, bins=3)
print cutoffs # 2.2, 3.8
print discrete_dat # 3, 2, 1, 3, 1, 3, ...

Answer 1

好吧,我只是快速攻击了这个,所以这样np.array_split做是为了对于非等大小的bin它不会barf,这会先对数据进行排序,然后执行计算以拆分并返回截止值:

import random
import numpy as np

dat = np.arange(1,13)/2.0

def discretize(data, bins):
    split = np.array_split(np.sort(data), bins)
    cutoffs = [x[-1] for x in split]
    cutoffs = cutoffs[:-1]
    discrete = np.digitize(data, cutoffs, right=True)
    return discrete, cutoffs

discrete_dat, cutoff = discretize(dat, 3)
print "dat: {}".format(dat)
print "discrete_dat: {}".format(discrete_dat)
print "cutoff: {}".format(cutoff)

>> dat: [ 0.5  1.   1.5  2.   2.5  3.   3.5  4.   4.5  5.   5.5  6. ]
>> discrete_dat: [0 0 0 0 1 1 1 1 2 2 2 2]
>> cutoff: [2.0, 4.0]