[Numpy-discussion] Example of numpy cov() not correct?

[Nadav Horesh]

If you read the cov function documentation you'll see that if a second vector is given, it joins the 2 into one matrix and calculate the covariance of it. In your case, you are looking for the off-diagonal elements.

  Nadav.


-----הודעה מקורית-----
מאת: numpy-discussion-bounces at scipy.org בשם Keith Goodman
נשלח: ד 30-יולי-08 17:04
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] Example of numpy cov() not correct?
 
On Tue, Jul 29, 2008 at 9:10 PM, Anthony Kong
<Anthony.Kong at macquarie.com> wrote:
> I am trying out the example here
> (http://www.scipy.org/Numpy_Example_List_With_Doc#cov)
>
>
>>>> from numpy import *
> ...
>>>> T = array([1.3, 4.5, 2.8, 3.9])
>>>> P = array([2.7, 8.7, 4.7, 8.2])
>>>> cov(T,P)
>
> The answer is supposed to be 3.9541666666666657
>
> The result I got is instead a cov matrix
> array([[ 1.97583333,  3.95416667],
>       [ 3.95416667,  8.22916667]])
> So, I just wanna confirm this particular example may be no longer
> correct.
>
> I am using python 2.4.3 with numpy 1.1.0 on MS win

It works for me (1.1 on GNU/Linux):

>> import numpy as np
>> T = np.array([1.3, 4.5, 2.8, 3.9])
>> P = np.array([2.7, 8.7, 4.7, 8.2])
>> np.cov(T,P)

array([[ 1.97583333,  3.95416667],
       [ 3.95416667,  8.22916667]])'
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