在哪里可以找到scipy的疯狂（平均绝对偏差）？

It seems scipy once provided a function mad to calculate the mean absolute deviation for a set of numbers:

似乎scipy曾经提供了一个疯狂的函数来计算一组数字的平均绝对偏差:

http://projects.scipy.org/scipy/browser/trunk/scipy/stats/models/utils.py?rev=3473

However, I can not find it anywhere in current versions of scipy. Of course it is possible to just copy the old code from repository but I prefer to use scipy's version. Where can I find it, or has it been replaced or removed?

但是,我无法在当前版本的scipy中找到它。当然可以从存储库中复制旧代码,但我更喜欢使用scipy的版本。我在哪里可以找到它,或者它已被替换或删除?

9 个解决方案

#1

The current version of statsmodels has mad in statsmodels.robust:

当前版本的statsmodels在statsmodels.Bust中疯狂:

>>> import numpy as np
>>> from statsmodels import robust
>>> a = np.matrix( [
...     [ 80, 76, 77, 78, 79, 81, 76, 77, 79, 84, 75, 79, 76, 78 ],
...     [ 66, 69, 76, 72, 79, 77, 74, 77, 71, 79, 74, 66, 67, 73 ]
...  ], dtype=float )
>>> robust.mad(a, axis=1)
array([ 2.22390333,  5.18910776])

Note that by default this computes the robust estimate of the standard deviation assuming a normal distribution by scaling the result a scaling factor; from help:

请注意,默认情况下,这会通过将结果缩放比例因子来计算标准偏差的稳健估计值(假设正态分布);来自帮助:

Signature: robust.mad(a, 
                      c=0.67448975019608171, 
                      axis=0, 
                      center=<function median at 0x10ba6e5f0>)

The version in R makes a similar normalization. If you don't want this, obviously just set c=1.

R中的版本进行了类似的规范化。如果你不想要这个,显然只需设置c = 1。

(An earlier comment mentioned this being in statsmodels.robust.scale. The implementation is in statsmodels/robust/scale.py (see github) but the robust package does not export scale, rather it exports the public functions in scale.py explicitly.)

(之前的评论提到这是在statsmodels.robust.scale。实现在statsmodels / robust / scale.py(请参阅github),但是强大的包不会导出规模,而是显式地在scale.py中导出公共函数。 )

#2

[EDIT] Since this keeps on getting downvoted: I know that median absolute deviation is a more commonly-used statistic, but the questioner asked for mean absolute deviation, and here's how to do it:

[编辑]因为这继续被投票:我知道中位数绝对偏差是一个更常用的统计数据,但提问者要求平均绝对偏差,这里是如何做到的:

from numpy import mean, absolute

def mad(data, axis=None):
    return mean(absolute(data - mean(data, axis)), axis)

#3

For what its worth, I use this for MAD:

为了它的价值,我将它用于MAD:

def mad(arr):
    """ Median Absolute Deviation: a "Robust" version of standard deviation.
        Indices variabililty of the sample.
        https://en.wikipedia.org/wiki/Median_absolute_deviation 
    """
    arr = np.ma.array(arr).compressed() # should be faster to not use masked arrays.
    med = np.median(arr)
    return np.median(np.abs(arr - med))

#4

It looks like scipy.stats.models was removed in august 2008 due to insufficient baking. Development has migrated to statsmodels.

看起来scipy.stats.models因为烘烤不足而于2008年8月被删除。发展已经转移到statsmodels。

#5

If you enjoy working in Pandas (like I do), it has a useful function for the mean absolute deviation:

如果你喜欢在Pandas工作(就像我一样),它对平均绝对偏差有一个很有用的功能:

import pandas as pd
df = pd.DataFrame()
df['a'] = [1, 1, 2, 2, 4, 6, 9]
df['a'].mad()

Output: 2.3673469387755106

#6

It's not the scipy version, but here's an implementation of the MAD using masked arrays to ignore bad values: http://code.google.com/p/agpy/source/browse/trunk/agpy/mad.py

它不是scipy版本,但是这里是MAD的一个实现,使用掩码数组来忽略错误的值:http://code.google.com/p/agpy/source/browse/trunk/agpy/mad.py

Edit: A more recent version is available here.

编辑:此处提供了更新版本。

Edit 2: There's also a version in astropy here.

编辑2:这里还有一个astropy版本。

#7

I'm using:

from math import fabs

a = [1, 1, 2, 2, 4, 6, 9]

median = sorted(a)[len(a)//2]

for b in a:
    mad = fabs(b - median)
    print b,mad

#8

I'm just learning Python and Numpy, but here is the code I wrote to check my 7th grader's math homework which wanted the M(ean)AD of 2 sets of numbers:

我只是在学习Python和Numpy,但这里是我写的代码,用于检查我的7年级学生的数学作业,该作业需要2组数字的M(ean)AD:

Data in Numpy matrix rows:

Numpy矩阵行中的数据:

import numpy as np

>>> a = np.matrix( [ [ 80, 76, 77, 78, 79, 81, 76, 77, 79, 84, 75, 79, 76, 78 ], \\    
... [ 66, 69, 76, 72, 79, 77, 74, 77, 71, 79, 74, 66, 67, 73 ] ], dtype=float )    
>>> matMad = np.mean( np.abs( np.tile( np.mean( a, axis=1 ), ( 1, a.shape[1] ) ) - a ), axis=1 )    
>>> matMad    
matrix([[ 1.81632653],
        [ 3.73469388]])

Data in Numpy 1D arrays:

Numpy 1D阵列中的数据:

>>> a1 = np.array( [ 80, 76, 77, 78, 79, 81, 76, 77, 79, 84, 75, 79, 76, 78 ], dtype=float )    
>>> a2 = np.array( [ 66, 69, 76, 72, 79, 77, 74, 77, 71, 79, 74, 66, 67, 73 ], dtype=float )    
>>> madA1 = np.mean( np.abs( np.tile( np.mean( a1 ), ( 1, len( a1 ) ) ) - a1 ) )    
>>> madA2 = np.mean( np.abs( np.tile( np.mean( a2 ), ( 1, len( a2 ) ) ) - a2 ) )    
>>> madA1, madA2    
(1.816326530612244, 3.7346938775510199)

#9

Using numpy only:

仅使用numpy:

def meanDeviation(numpyArray):
    mean = np.mean(numpyArray)
    f = lambda x: abs(x - mean)
    vf = np.vectorize(f)
    return (np.add.reduce(vf(numpyArray))) / len(numpyArray)

#1