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<p class="subTitle">org.apache.spark.mllib.linalg.distributed</p>
<h2 title="Class RowMatrix" class="title">Class RowMatrix</h2>
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<pre>public class <strong>RowMatrix</strong>
extends Object
implements <a href="../../../../../../org/apache/spark/mllib/linalg/distributed/DistributedMatrix.html" title="interface in org.apache.spark.mllib.linalg.distributed">DistributedMatrix</a>, <a href="../../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></pre>
<div class="block">:: Experimental ::
Represents a row-oriented distributed Matrix with no meaningful row indices.
<p></div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../../serialized-form.html#org.apache.spark.mllib.linalg.distributed.RowMatrix">Serialized Form</a></dd></dl>
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<th class="colOne" scope="col">Constructor and Description</th>
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<td class="colOne"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#RowMatrix(org.apache.spark.rdd.RDD)">RowMatrix</a></strong>(<a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> rows)</code>
<div class="block">Alternative constructor leaving matrix dimensions to be determined automatically.</div>
</td>
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<tr class="rowColor">
<td class="colOne"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#RowMatrix(org.apache.spark.rdd.RDD, long, int)">RowMatrix</a></strong>(<a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> rows,
long nRows,
int nCols)</code> </td>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html" title="interface in org.apache.spark.mllib.stat">MultivariateStatisticalSummary</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()">computeColumnSummaryStatistics</a></strong>()</code>
<div class="block">Computes column-wise summary statistics.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeCovariance()">computeCovariance</a></strong>()</code>
<div class="block">Computes the covariance matrix, treating each row as an observation.</div>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeGramianMatrix()">computeGramianMatrix</a></strong>()</code>
<div class="block">Computes the Gramian matrix <code>A^T A</code>.</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computePrincipalComponents(int)">computePrincipalComponents</a></strong>(int k)</code>
<div class="block">Computes the top k principal components.</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/linalg/SingularValueDecomposition.html" title="class in org.apache.spark.mllib.linalg">SingularValueDecomposition</a><<a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html" title="class in org.apache.spark.mllib.linalg.distributed">RowMatrix</a>,<a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeSVD(int, boolean, double)">computeSVD</a></strong>(int k,
boolean computeU,
double rCond)</code>
<div class="block">Computes the singular value decomposition of this matrix.</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html" title="class in org.apache.spark.mllib.linalg.distributed">RowMatrix</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#multiply(org.apache.spark.mllib.linalg.Matrix)">multiply</a></strong>(<a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> B)</code>
<div class="block">Multiply this matrix by a local matrix on the right.</div>
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<td class="colFirst"><code>long</code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#numCols()">numCols</a></strong>()</code>
<div class="block">Gets or computes the number of columns.</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code>long</code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#numRows()">numRows</a></strong>()</code>
<div class="block">Gets or computes the number of rows.</div>
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<td class="colFirst"><code><a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html#rows()">rows</a></strong>()</code> </td>
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<code><a href="../../../../../../org/apache/spark/Logging.html#initialized()">initialized</a>, <a href="../../../../../../org/apache/spark/Logging.html#initializeIfNecessary()">initializeIfNecessary</a>, <a href="../../../../../../org/apache/spark/Logging.html#initializeLogging()">initializeLogging</a>, <a href="../../../../../../org/apache/spark/Logging.html#initLock()">initLock</a>, <a href="../../../../../../org/apache/spark/Logging.html#isTraceEnabled()">isTraceEnabled</a>, <a href="../../../../../../org/apache/spark/Logging.html#log_()">log_</a>, <a href="../../../../../../org/apache/spark/Logging.html#log()">log</a>, <a href="../../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0)">logDebug</a>, <a href="../../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0, java.lang.Throwable)">logDebug</a>, <a href="../../../../../../org/apache/spark/Logging.html#logError(scala.Function0)">logError</a>, <a href="../../../../../../org/apache/spark/Logging.html#logError(scala.Function0, java.lang.Throwable)">logError</a>, <a href="../../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0)">logInfo</a>, <a href="../../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0, java.lang.Throwable)">logInfo</a>, <a href="../../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0)">logTrace</a>, <a href="../../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0, java.lang.Throwable)">logTrace</a>, <a href="../../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0)">logWarning</a>, <a href="../../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0, java.lang.Throwable)">logWarning</a></code></li>
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<h4>RowMatrix</h4>
<pre>public RowMatrix(<a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> rows,
long nRows,
int nCols)</pre>
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<pre>public RowMatrix(<a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> rows)</pre>
<div class="block">Alternative constructor leaving matrix dimensions to be determined automatically.</div>
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<h4>rows</h4>
<pre>public <a href="../../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> rows()</pre>
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<h4>numCols</h4>
<pre>public long numCols()</pre>
<div class="block">Gets or computes the number of columns.</div>
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<dd><code><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/DistributedMatrix.html#numCols()">numCols</a></code> in interface <code><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/DistributedMatrix.html" title="interface in org.apache.spark.mllib.linalg.distributed">DistributedMatrix</a></code></dd>
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<pre>public long numRows()</pre>
<div class="block">Gets or computes the number of rows.</div>
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<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/DistributedMatrix.html#numRows()">numRows</a></code> in interface <code><a href="../../../../../../org/apache/spark/mllib/linalg/distributed/DistributedMatrix.html" title="interface in org.apache.spark.mllib.linalg.distributed">DistributedMatrix</a></code></dd>
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<pre>public <a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> computeGramianMatrix()</pre>
<div class="block">Computes the Gramian matrix <code>A^T A</code>.</div>
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<h4>computeSVD</h4>
<pre>public <a href="../../../../../../org/apache/spark/mllib/linalg/SingularValueDecomposition.html" title="class in org.apache.spark.mllib.linalg">SingularValueDecomposition</a><<a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html" title="class in org.apache.spark.mllib.linalg.distributed">RowMatrix</a>,<a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a>> computeSVD(int k,
boolean computeU,
double rCond)</pre>
<div class="block">Computes the singular value decomposition of this matrix.
Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'.
<p>
There is no restriction on m, but we require <code>n^2</code> doubles to fit in memory.
Further, n should be less than m.
<p>
The decomposition is computed by first computing A'A = V S^2 V',
computing svd locally on that (since n x n is small), from which we recover S and V.
Then we compute U via easy matrix multiplication as U = A * (V * S^-1).
Note that this approach requires <code>O(n^3)</code> time on the master node.
<p>
At most k largest non-zero singular values and associated vectors are returned.
If there are k such values, then the dimensions of the return will be:
<p>
U is a RowMatrix of size m x k that satisfies U'U = eye(k),
s is a Vector of size k, holding the singular values in descending order,
and V is a Matrix of size n x k that satisfies V'V = eye(k).
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - number of singular values to keep. We might return less than k if there are
numerically zero singular values. See rCond.</dd><dd><code>computeU</code> - whether to compute U</dd><dd><code>rCond</code> - the reciprocal condition number. All singular values smaller than rCond * sigma(0)
are treated as zero, where sigma(0) is the largest singular value.</dd>
<dt><span class="strong">Returns:</span></dt><dd>SingularValueDecomposition(U, s, V)</dd></dl>
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<pre>public <a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> computeCovariance()</pre>
<div class="block">Computes the covariance matrix, treating each row as an observation.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>a local dense matrix of size n x n</dd></dl>
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<pre>public <a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> computePrincipalComponents(int k)</pre>
<div class="block">Computes the top k principal components.
Rows correspond to observations and columns correspond to variables.
The principal components are stored a local matrix of size n-by-k.
Each column corresponds for one principal component,
and the columns are in descending order of component variance.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - number of top principal components.</dd>
<dt><span class="strong">Returns:</span></dt><dd>a matrix of size n-by-k, whose columns are principal components</dd></dl>
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<h4>computeColumnSummaryStatistics</h4>
<pre>public <a href="../../../../../../org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html" title="interface in org.apache.spark.mllib.stat">MultivariateStatisticalSummary</a> computeColumnSummaryStatistics()</pre>
<div class="block">Computes column-wise summary statistics.</div>
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<h4>multiply</h4>
<pre>public <a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html" title="class in org.apache.spark.mllib.linalg.distributed">RowMatrix</a> multiply(<a href="../../../../../../org/apache/spark/mllib/linalg/Matrix.html" title="interface in org.apache.spark.mllib.linalg">Matrix</a> B)</pre>
<div class="block">Multiply this matrix by a local matrix on the right.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>B</code> - a local matrix whose number of rows must match the number of columns of this matrix</dd>
<dt><span class="strong">Returns:</span></dt><dd>a <a href="../../../../../../org/apache/spark/mllib/linalg/distributed/RowMatrix.html" title="class in org.apache.spark.mllib.linalg.distributed"><code>RowMatrix</code></a> representing the product,
which preserves partitioning</dd></dl>
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