Screenshot instructions:
Windows
Mac
Red Hat Linux
Ubuntu
Click URL instructions:
Rightclick on ad, choose "Copy Link", then paste here →
(This may not be possible with some types of ads)
From: Justin R <justinbrowe@gm...>  20120612 06:03:47

operating system Windows 7 matplotlib version : 1.1.0 obtained from sourceforge the class seems to generate the same Wt matrix for every input. The every element of the weight matrix is either +sqrt(1/2) or sqrt(1/2). dat1 = 4*np.random.randn(200,1) + 2 dat2 = dat1*.25 + 1*np.random.randn(200,1) pcaObj1 = PCA(np.hstack((dat1,dat2))) print pcaObj1.Wt dat3 = 2*np.random.randn(200,1) + 2 dat4 = dat3*2 + 3*np.random.randn(200,1) pcaObj2 = PCA(np.hstack((dat1,dat2))) print pcaObj2.Wt The output Y seems to be correct, and the projection function works. only the Wt matrix seems to be messed up. Am I using this class incorrectly, or could this be a bug? thanks, Justin 
From: Paul Hobson <pmhobson@gm...>  20120612 16:59:30

On Mon, Jun 11, 2012 at 11:03 PM, Justin R <justinbrowe@...> wrote: > operating system Windows 7 > matplotlib version : 1.1.0 > obtained from sourceforge > > the class seems to generate the same Wt matrix for every input. The > every element of the weight matrix is either +sqrt(1/2) or sqrt(1/2). > > dat1 = 4*np.random.randn(200,1) + 2 > dat2 = dat1*.25 + 1*np.random.randn(200,1) > pcaObj1 = PCA(np.hstack((dat1,dat2))) > print pcaObj1.Wt > > dat3 = 2*np.random.randn(200,1) + 2 > dat4 = dat3*2 + 3*np.random.randn(200,1) > pcaObj2 = PCA(np.hstack((dat1,dat2))) > print pcaObj2.Wt > > The output Y seems to be correct, and the projection function works. > only the Wt matrix seems to be messed up. Am I using this class > incorrectly, or could this be a bug? > thanks, > Justin Justin, could you post a selfcontained script that demonstrates the issue? Where does this PCA function come from? In [1]: from pylab import * In [2]: PCA  NameError Traceback (most recent call last) C:\Users\phobson\<ipythoninput2dcf6991f51c0> in <module>() > 1 PCA NameError: name 'PCA' is not defined paul 
From: Warren Weckesser <warren.weckesser@en...>  20120614 14:47:31
Attachments:
Message as HTML

On Tue, Jun 12, 2012 at 11:59 AM, Paul Hobson <pmhobson@...> wrote: > On Mon, Jun 11, 2012 at 11:03 PM, Justin R <justinbrowe@...> wrote: > > operating system Windows 7 > > matplotlib version : 1.1.0 > > obtained from sourceforge > > > > the class seems to generate the same Wt matrix for every input. The > > every element of the weight matrix is either +sqrt(1/2) or sqrt(1/2). > > > > dat1 = 4*np.random.randn(200,1) + 2 > > dat2 = dat1*.25 + 1*np.random.randn(200,1) > > pcaObj1 = PCA(np.hstack((dat1,dat2))) > > print pcaObj1.Wt > > > > dat3 = 2*np.random.randn(200,1) + 2 > > dat4 = dat3*2 + 3*np.random.randn(200,1) > > pcaObj2 = PCA(np.hstack((dat1,dat2))) > > print pcaObj2.Wt > > > > The output Y seems to be correct, and the projection function works. > > only the Wt matrix seems to be messed up. Am I using this class > > incorrectly, or could this be a bug? > > thanks, > > Justin > > Justin, could you post a selfcontained script that demonstrates the > issue? Where does this PCA function come from? > > In [1]: from pylab import * > > In [2]: PCA >  > NameError Traceback (most recent call last) > C:\Users\phobson\<ipythoninput2dcf6991f51c0> in <module>() > > 1 PCA > > NameError: name 'PCA' is not defined > > Paul, In case you never got an answer to this: PCA is in the mlab submodule, so if you do "from pylab import *", you would use mlab.PCA. (At least that's the case in matplotlib 1.1.0). Warren > paul > > >  > Live Security Virtual Conference > Exclusive live event will cover all the ways today's security and > threat landscape has changed and how IT managers can respond. Discussions > will include endpoint security, mobile security and the latest in malware > threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers > 
From: Goyo <goyodiaz@gm...>  20120613 19:01:39

2012/6/12 Paul Hobson <pmhobson@...>: > On Mon, Jun 11, 2012 at 11:03 PM, Justin R <justinbrowe@...> wrote: > Justin, could you post a selfcontained script that demonstrates the > issue? Where does this PCA function come from? It comes from matplotlib.mlab. Just add these imports before the OP's code: import numpy as np from matplotlib.mlab import PCA But I don't know much about PCA and can't comment on this. Goyo 
From: Aronne Merrelli <aronne.merrelli@gm...>  20120616 15:38:56

On Tue, Jun 12, 2012 at 1:03 AM, Justin R <justinbrowe@...> wrote: > operating system Windows 7 > matplotlib version : 1.1.0 > obtained from sourceforge > > the class seems to generate the same Wt matrix for every input. The > every element of the weight matrix is either +sqrt(1/2) or sqrt(1/2). > > dat1 = 4*np.random.randn(200,1) + 2 > dat2 = dat1*.25 + 1*np.random.randn(200,1) > pcaObj1 = PCA(np.hstack((dat1,dat2))) > print pcaObj1.Wt > > dat3 = 2*np.random.randn(200,1) + 2 > dat4 = dat3*2 + 3*np.random.randn(200,1) > pcaObj2 = PCA(np.hstack((dat1,dat2))) > print pcaObj2.Wt > > The output Y seems to be correct, and the projection function works. > only the Wt matrix seems to be messed up. Am I using this class > incorrectly, or could this be a bug? Hi, I wouldn't call myself a PCA expert  so don't weight my answer too heavily  but here is what I think is happening: Looking at the code, the input data array is centered and scaled to unit variance in each dimension. The attribute .a of the class is a copy of the array that is actually sent to the SVD; note the centering/scaling. I don't have a proof of this, but intuitively I expect that the PCA axes associated with a 2dimension centered/scaled array will always be at 45" angles (e.g., [1,1], [1,1], etc., which are normalized to [sqrt(1/2), sqrt(1/2)], etc). I think one way to describe this is that after centering/scaling there are no degrees of freedom left if you only started with 2 dimensions. So I don't think there is a bug, but it is maybe unclear what the PCA class is doing. If you increase to > 2 dimensions, you can see there is random fluctuation in Wt: In [102]: pcaObj = PCA(np.random.randn(200,2)) In [103]: pcaObj.Wt Out[103]: array([[0.70710678, 0.70710678], [0.70710678, 0.70710678]]) In [104]: pcaObj = PCA(np.random.randn(200,3)) In [105]: pcaObj.Wt Out[105]: array([[ 0.65456366, 0.24141116, 0.7164266 ], [ 0.39843462, 0.91551401, 0.05553329], [ 0.64249223, 0.32179924, 0.69544877]]) In [106]: pcaObj = PCA(np.random.randn(200,3)) In [107]: pcaObj.Wt Out[107]: array([[0.29885902, 0.67436982, 0.67521007], [0.95428685, 0.21449891, 0.20815098], [0.00446109, 0.70655189, 0.70764718]]) Hope that helps, Aronne 
Sign up for the SourceForge newsletter:
No, thanks