[Numpy-discussion] Cross-covariance function
Pierre Haessig
pierre.haessig at crans.org
Thu Jan 26 11:07:15 EST 2012
Le 26/01/2012 15:57, Bruce Southey a écrit :
> Can you please provide a
> couple of real examples with expected output that clearly show what
> you want?
>
Hi Bruce,
Thanks for your ticket feedback ! It's precisely because I see a big
potential impact of the proposed change that I send first a ML message,
second a ticket before jumping to a pull-request like a Sergio Leone's
cowboy (sorry, I watched "for a few dollars more" last weekend...)
Now, I realize that in the ticket writing I made the wrong trade-off
between conciseness and accuracy which led to some of the errors you
raised. Let me try to use your example to try to share what I have in mind.
> >> X = array([-2.1, -1. , 4.3])
> >> Y = array([ 3. , 1.1 , 0.12])
Indeed, with today's cov behavior we have a 2x2 array:
> >> cov(X,Y)
array([[ 11.71 , -4.286 ],
[ -4.286 , 2.14413333]])
Now, when I used the word 'concatenation', I wasn't precise enough
because I meant assembling X and Y in the sense of 2 vectors of
observations from 2 random variables X and Y.
This is achieved by concatenate(X,Y) *when properly playing with
dimensions* (which I didn't mentioned) :
> >> XY = np.concatenate((X[None, :], Y[None, :]))
array([[-2.1 , -1. , 4.3 ],
[ 3. , 1.1 , 0.12]])
In this case, I can indeed say that "cov(X,Y) is equivalent to cov(XY)".
> >> np.cov(XY)
array([[ 11.71 , -4.286 ],
[ -4.286 , 2.14413333]])
(And indeed, the actual cov Python code does use concatenate() )
Now let me come back to my assertion about this behavior *usefulness*.
You'll acknowledge that np.cov(XY) is made of four blocks (here just 4
simple scalars blocks).
* diagonal blocks are just cov(X) and cov(Y) (which in this case comes
to var(X) and var(Y) when setting ddof to 1)
* off diagonal blocks are symetric and are actually the covariance
estimate of X, Y observations (from
http://en.wikipedia.org/wiki/Covariance)
that is :
> >> ((X-X.mean()) * (Y-Y.mean())).sum()/ (3-1)
-4.2860000000000005
The new proposed behaviour for cov is that cov(X,Y) would return :
array(-4.2860000000000005) instead of the 2*2 matrix.
* This would be in line with the cov(X,Y) mathematical definition, as
well as with R behavior.
* This would save memory and computing resources. (and therefore help
save the planet ;-) )
However, I do understand that the impact for this change may be big.
This indeed requires careful reviewing.
Pierre
More information about the NumPy-Discussion
mailing list