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From:  Yaswant Pradhan <Yaswant.Prad...@gmail.com>
Newsgroups: comp.lang.idl-pvwave
Subject: Re: Principle Componets Analysis
Date: Fri, 24 Aug 2007 10:24:47 -0000
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On Aug 24, 11:21 am, Yaswant Pradhan <Yaswant.Prad...@gmail.com>
wrote:
> On Aug 24, 3:59 am, David Fanning <n...@dfanning.com> wrote:
>
>
>
> > kBob writes:
> > >  I find the book Image Analysis, Classification and Change Detection
> > > in Remote Sensing, with Algorothms for ENIV/IDL by Morton Canty handy.
> > > He provides ENVI/IDL code to do the PCAs.
>
> > Well, I'm ashamed to say, I had read part's of Mort's book
> > earlier in the week and found I needed, well, more remedial
> > help. Quite frankly, I didn't understand a word of it. :-(
>
> > The Lindsay Smith tutorial, on the other hand, was crystal
> > clear. So much so that I came back to my office and wrote up
> > the example in IDL, just to see if I could follow it.
>
> > It turns out, that the PCOMP function in IDL gives essentially
> > the same answer as the tutorial (this for Jeff's benefit), but
> > the values are scaled slightly differently. However they
> > plot on exactly the same line in the end. Here is the code
> > I used.
>
> > ; Method according to the Lindsay Smith tutorial:
> > ;http://tinyurl.com/3aaeb
>
> > x = [2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2.0, 1.0, 1.5, 1.1]
> > y = [2.4, 0.7, 2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9]
>
> > xmean = x - Mean(x)
> > ymean = y - Mean(y)
> > Window, XSIZE=600, YSIZE=800
> > !P.MULTI=[0,1,2]
> > Plot, xmean, ymean, PSYM=7
>
> > dataAdjust = Transpose([ [xmean], [ymean] ])
> > covArray = Correlate(dataAdjust, /COVARIANCE, /DOUBLE)
> > eigenvalues = EIGENQL(covArray, EIGENVECTORS=eigenvectors, /DOUBLE)
>
> > Print, 'EIGENVALUES: ', eigenvalues
> > Print, 'EIGENVECTORS: '
> > Print, eigenvectors
>
> > rowFeatureVector = eigenvectors[0,*] ; Take first principle component.
> > ;rowFeatureVector = eigenvectors
> > finalData = Transpose(rowFeatureVector) ## Transpose(dataAdjust)
> > Plot, finaldata+Mean(x), finaldata+mean(y), PSYM=7
> > !P.MULTI=0
>
> > ; Method using PCOMP in IDL library.
> > data = Transpose([[x],[y]])
> > r = PCOMP(data, /COVARIANCE, NVARIABLES=1, EIGENVALUES=ev, /STANDARDIZE)
> > Print, 'IDL EIGENVALUES: ', ev
>
> > ; Compare methods.
> > Window, 1
> > PLOT, r
> > OPLOT, finalData, LINESTYLE=2
>
> > Window, 2
> > PLOT, r + Mean(x), r + Mean(y), PSYM=2
> > OPLOT, finalData + Mean(x), finalData + Mean(y), PSYM=7
> > END
>
> > This is really nice stuff and has me EXTREMELY jazzed about
> > the potential of it. :-)
>
> > Cheers,
>
> > David
> > --
> > David Fanning, Ph.D.
> > Fanning Software Consulting, Inc.
> > Coyote's Guide to IDL Programming:http://www.dfanning.com/
> > Sepore ma de ni thui. ("Perhaps thou speakest truth.")
>
> Hi David,
> Yes, both methods are essentially same except that the data in
> Method#1 are NOT standardised. You will get exactly same result if you
> do
> xmean = (x - Mean(x) / Stddev(x)
> ymean = (y - Mean(y) / STddev(y)
>
> --yas

whoops... missed a parenthesis, should read xmean = (x - Mean(x)) /
Stddev(x) and likewise.