NumPy-计算直方图交点

def histogram_intersection(h1, h2):
    sm = 0
    for i in range(13):
        sm += min(h1[i], h2[i])
    return sm

首先要注意的是:在你的数据箱中是范围,在你的算法中是数字。你必须为此重新定义垃圾箱。

此外, min(bins[i]*h1[i], bins[i]*h2[i]) 是 bins[i]*min(h1[i], h2[i]) 因此,可以通过以下方法获得结果:

hists=pandas.read_clipboard(index_col=0) # your data
bins=arange(-4,112,9)   #  try for bins but edges are different here
mins=hists.min('rows')
intersection=dot(mins,bins) 

#!/usr/bin/env python3

import numpy as np

A = np.array([1.274580708,2.466224824,5.045757621,7.413716262,8.958855646,10.41325305,11.14150951,10.91949012,11.29095648,10.95054297,10.10976255,8.128781795,1.886568472])
B = np.array([0,1.700493692,4.059243006,5.320899616,6.747120132,7.899067471,9.434997257,11.24520022,12.94569391,12.83598464,12.6165661,10.80636314,4.388370817])

def histogram_intersection(h1, h2):
    sm = 0
    for i in range(13):
        sm += min(h1[i], h2[i])
    return sm

print(histogram_intersection(A,B))
print(np.sum(np.minimum(A,B)))

输出

88.44792356099998
88.447923561

但如果你计时,Numpy只需要60%的时间:

%timeit histogram_intersection(A,B)
5.02 µs ± 65.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit np.sum(np.minimum(A,B))
3.22 µs ± 11.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)