Python 3实现，肖维勒准则 Chauvenet’s Criterion。
使用肖维勒准则过滤正态分布数据中的异常值。
Chauvenet’s criterion is a means of assessing whether one piece of experimental data — an outlier — from a set of observations, is likely to be spurious.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import numpy as np from scipy.stats import norm def Chauvenet(v): l = len(v) std = np.std(v) avg = np.average(v) z = np.abs(norm.ppf(0.25 / l)) if l < 5: return [] Xmin = avg - (z * std) Xmax = avg + (z * std) nv = [i for i in v if Xmin <= i <= Xmax] bv = [i for i in v if Xmin > i or i > Xmax] #print('nv:', nv) #print('bv:', bv) if bv == []: return v return Chauvenet(nv) |
示例 Example
1 2 3 4 5 6 7 8 9 10 11 | if __name__ == '__main__': l = [43, 1, 1, 40, 40, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 5, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] print("sorce:", l) print("result:", Chauvenet(l)) ''' Output: sorce: [43, 1, 1, 40, 40, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 5, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] result: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ''' |