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
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