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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 
From: Benjamin Root <ben.root@ou...>  20120616 14:19:27

On Friday, June 15, 2012, Mark Lawrence wrote: > Hi all, > > I regularly use matplotlib for plotting data relating to my personal > finances. At the moment I'm converting Decimals to floats. Do I still > have to do this? If yes, are there any plans to support Decimals? I've > tried searching the latest PDF document, my apologies if I've missed > anything, in which case could I have a pointer please. > >  > Cheers. > > Mark Lawrence. > > > Our support for python decimals falls in the same category as any other nonfloat datatypes (ints, datetime, etc.): it might work, but probably not everywhere and most likely will end up being converted into floats anyway somewhere in the process. If you see places where Decimal doesn't work, file a bug request so that we can try to better generalize our codebase. Cheers! Ben Root 