Hello,
I am new in sklearn, but so far I'm impressed. I think it is a great
library!
I think I found a little bug in the Multidimensional Scaling Function. It
expects "perfect" similarity matrices, which it's often not possible with
real data. Moreover, sklearn's functions are unable to get the expected
precision.
For example if I use sklearn to computea similarity matrix of euclidean
distances:
similarities = euclidean_distances(data)
print np.abs(similarities - similarities.T).max()
# this outputs 5.26835606386e-09, which I'd it's precise enough
Unfortunately, when I try to calculate MDS:
mds = manifold.MDS(n_components=2, dissimilarity="precomputed", n_jobs=1)
pos = mds.fit(similarities)
I get a precision error. I have already tried casting to float64 as
suggested
here<http://stackoverflow.com/questions/16990996/multidimensional-scaling-fitting-in-numpy-pandas-and-sklearn-valueerror>,
but it doesn't work either. There's no way to change the precision of
this, only the convergence precision.
Am I overlooking at something?
Thanks,
Jose
--
José P. González-Brenes, Ph.D.
www.josepablogonzalez.com
------------------------------------------------------------------------------
Rapidly troubleshoot problems before they affect your business. Most IT
organizations don't have a clear picture of how application performance
affects their revenue. With AppDynamics, you get 100% visibility into your
Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics Pro!
http://pubads.g.doubleclick.net/gampad/clk?id=84349831&iu=/4140/ostg.clktrk
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
