GitHub user rezazadeh opened a pull request:
https://github.com/apache/spark/pull/88
Sparkpca
# Principal Component Analysis
Computes the top k principal component coefficients for the m-by-n data
matrix X. Rows of X correspond to observations and columns correspond to
variables. The coefficient matrix is n-by-k. Each column of the coefficients
return matrix contains coefficients for one principal component, and the
columns are in descending order of component variance. This function centers
the data and uses the singular value decomposition (SVD) algorithm.
## Testing
Tests included:
* All principal components
* Only top k principal components
The results are tested against MATLAB's pca:
http://www.mathworks.com/help/stats/pca.html
## Documentation
Added to mllib-guide.md
## Example Usage
Added to examples directory under SparkPCA.scala
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/rezazadeh/spark sparkpca
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/88.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #88
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commit 78738a9de0d99df3b2cb8966172ef2e09277a156
Author: Reza Zadeh <[email protected]>
Date: 2014-03-06T03:24:44Z
initial files
commit 1dfd2cf27a420dfb265ca8de0368286bc23c0b83
Author: Reza Zadeh <[email protected]>
Date: 2014-03-06T03:26:53Z
all files from old pr
commit 1841d78710c88e8eed3a3bdb3c2b7fff2ee678f0
Author: Reza Zadeh <[email protected]>
Date: 2014-03-06T03:30:52Z
bad chnage undo
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