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https://issues.apache.org/jira/browse/MAHOUT-792?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13095251#comment-13095251
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Hudson commented on MAHOUT-792:
-------------------------------
Integrated in Mahout-Quality #1012 (See
[https://builds.apache.org/job/Mahout-Quality/1012/])
MAHOUT-790 - kill the cardinality array and size() for matrices. Use
rowSize() and columnSize() instead.
MAHOUT-792 - Fix RTM to avoid size() and cardinality array.
tdunning :
http://svn.apache.org/viewcvs.cgi/?root=Apache-SVN&view=rev&rev=1164016
Files :
*
/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/eval/InMemoryFactorizationEvaluator.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/bayes/InMemoryBayesDatastore.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/discriminative/LinearTrainer.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/naivebayes/NaiveBayesModel.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/naivebayes/training/TrainUtils.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/classifier/sequencelearning/hmm/HmmUtils.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/math/MatrixWritable.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/math/hadoop/decomposer/EigenVerificationJob.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/math/hadoop/stochasticsvd/UpperTriangular.java
*
/mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/hadoop/als/ParallelALSFactorizationJobTest.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/AbstractMatrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/DenseMatrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/DiagonalMatrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/Matrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/MatrixView.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/PermutedVectorView.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/PivotedMatrix.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/RandomAccessSparseVector.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/RandomTrinaryMatrix.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/SequentialAccessSparseVector.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/SparseColumnMatrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/SparseMatrix.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/SparseRowMatrix.java
*
/mahout/trunk/math/src/main/java/org/apache/mahout/math/decomposer/hebbian/HebbianSolver.java
*
/mahout/trunk/math/src/test/java/org/apache/mahout/math/AbstractTestVector.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/MatrixTest.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/TestMatrixView.java
*
/mahout/trunk/math/src/test/java/org/apache/mahout/math/TestSparseColumnMatrix.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/TestSparseMatrix.java
*
/mahout/trunk/math/src/test/java/org/apache/mahout/math/TestSparseRowMatrix.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/TestVectorView.java
*
/mahout/trunk/math/src/test/java/org/apache/mahout/math/als/AlternateLeastSquaresSolverTest.java
*
/mahout/trunk/math/src/test/java/org/apache/mahout/math/decomposer/SolverTest.java
> Add new stochastic decomposition code
> -------------------------------------
>
> Key: MAHOUT-792
> URL: https://issues.apache.org/jira/browse/MAHOUT-792
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
> Attachments: MAHOUT-792.patch, MAHOUT-792.patch, sd-2.pdf
>
>
> I have figured out some simplification for our SSVD algorithms. This
> eliminates the QR decomposition and makes life easier.
> I will produce a patch that contains the following:
> - a CholeskyDecomposition implementation that does pivoting (and thus
> rank-revealing) or not. This should actually be useful for solution of large
> out-of-core least squares problems.
> - an in-memory SSVD implementation that should work for matrices up to
> about 1/3 of available memory.
> - an out-of-core SSVD threaded implementation that should work for very
> large matrices. It should take time about equal to the cost of reading the
> input matrix 4 times and will require working disk roughly equal to the size
> of the input.
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