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19-09-2019 - |
题
numpy.average()
有权选择,但是 numpy.std()
不。有人建议,为一个解决方法?
解决方案
如何关于以下简称 “人工计算”?
def weighted_avg_and_std(values, weights):
"""
Return the weighted average and standard deviation.
values, weights -- Numpy ndarrays with the same shape.
"""
average = numpy.average(values, weights=weights)
# Fast and numerically precise:
variance = numpy.average((values-average)**2, weights=weights)
return (average, math.sqrt(variance))
其他提示
有一堂课在 statsmodels
这使得计算加权统计数据变得容易: statsmodels.stats.weightstats.DescrStatsW
.
假设这个数据集和权重:
import numpy as np
from statsmodels.stats.weightstats import DescrStatsW
array = np.array([1,2,1,2,1,2,1,3])
weights = np.ones_like(array)
weights[3] = 100
您初始化该类(请注意,您必须传入校正因子,增量 自由程度 在此刻):
weighted_stats = DescrStatsW(array, weights=weights, ddof=0)
然后你可以计算:
.mean
这 加权平均数:>>> weighted_stats.mean 1.97196261682243
.std
这 加权标准差:>>> weighted_stats.std 0.21434289609681711
.var
这 加权方差:>>> weighted_stats.var 0.045942877107170932
-
>>> weighted_stats.std_mean 0.020818822467555047
如果您对标准误差和标准差之间的关系感兴趣:标准误是(对于
ddof == 0
) 计算方法为加权标准差除以权重总和减 1 的平方根 (对应的来源为statsmodels
GitHub 上的 0.9 版):standard_error = standard_deviation / sqrt(sum(weights) - 1)
那似乎不是这样的功能在顽固/这还没有,但是有一个 票 提出这一添加功能。包括在那里,你会找到 Statistics.py 它实现了加权标准偏差。
下面是一个选择:
np.sqrt(np.cov(values, aweights=weights))
有是通过提出一个很好的例子 gaborous :
import pandas as pd
import numpy as np
# X is the dataset, as a Pandas' DataFrame
mean = mean = np.ma.average(X, axis=0, weights=weights) # Computing the
weighted sample mean (fast, efficient and precise)
# Convert to a Pandas' Series (it's just aesthetic and more
# ergonomic; no difference in computed values)
mean = pd.Series(mean, index=list(X.keys()))
xm = X-mean # xm = X diff to mean
xm = xm.fillna(0) # fill NaN with 0 (because anyway a variance of 0 is
just void, but at least it keeps the other covariance's values computed
correctly))
sigma2 = 1./(w.sum()-1) * xm.mul(w, axis=0).T.dot(xm); # Compute the
unbiased weighted sample covariance
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