Except last I checked numpy.linalg doesn&#39;t have an efficient method for retrieving only a few PCA components.<br>So yeh, you can do PCA but it will be *really* slow on most of the types of problems that PCA is usually used for.
<br>You need something like an ARPACK wrapper, which I think they have in the scipy.sandbox.<br><br>--bb<br><br><div><span class="gmail_quote">On 6/25/07, <b class="gmail_sendername">Matthieu Brucher</b> &lt;<a href="mailto:matthieu.brucher@gmail.com">
matthieu.brucher@gmail.com</a>&gt; wrote:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Hi,<br><br>You have everything you need for PCA in 
numpy.linalg.<br><br>Matthieu<br><br><div><span class="gmail_quote">2007/6/21, Alex Torquato S. Carneiro &lt;<a href="mailto:archsheep@yahoo.com.br" target="_blank" onclick="return top.js.OpenExtLink(window,event,this)">archsheep@yahoo.com.br
</a>&gt;:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><div><div style="font-family: times new roman,new york,times,serif; font-size: 12pt;">

<div>I&#39;m doing some projects in Python (system GNU / Linux - Ubuntu 7.0) about image processing. I&#39;m needing a implementation of PCA, prefer to library for apt-get.<br><br>Thanks.<br><span>Alex.<br></span>
<span><br></span></div></div><span><br>



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