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<div class="subTitle">org.apache.spark.mllib.regression</div>
<h2 title="Class LassoWithSGD" class="title">Class LassoWithSGD</h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
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<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&gt;</li>
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<pre>public class <span class="strong">LassoWithSGD</span>
extends <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearAlgorithm</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&gt;
implements scala.Serializable</pre>
<div class="block">Train a regression model with L1-regularization using Stochastic Gradient Descent.
 This solves the l1-regularized least squares regression formulation
          f(weights) = 1/2n ||A weights-y||^2^  + regParam ||weights||_1
 Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with
 its corresponding right hand side label y.
 See also the documentation for the precise formulation.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.regression.LassoWithSGD">Serialized Form</a></dd></dl>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#LassoWithSGD()">LassoWithSGD</a></strong>()</code>
<div class="block">Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
 regParam: 0.01, miniBatchFraction: 1.0}.</div>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<td class="colFirst"><code>protected <a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#createModel(org.apache.spark.mllib.linalg.Vector, double)">createModel</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>&nbsp;weights,
           double&nbsp;intercept)</code>
<div class="block">Create a model given the weights and intercept</div>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#optimizer()">optimizer</a></strong>()</code>
<div class="block">The optimizer to solve the problem.</div>
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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#train(org.apache.spark.rdd.RDD, int)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
     int&nbsp;numIterations)</code>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs.</div>
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<tr class="rowColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#train(org.apache.spark.rdd.RDD, int, double, double)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
     int&nbsp;numIterations,
     double&nbsp;stepSize,
     double&nbsp;regParam)</code>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs.</div>
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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#train(org.apache.spark.rdd.RDD, int, double, double, double)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
     int&nbsp;numIterations,
     double&nbsp;stepSize,
     double&nbsp;regParam,
     double&nbsp;miniBatchFraction)</code>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs.</div>
</td>
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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/LassoWithSGD.html#train(org.apache.spark.rdd.RDD, int, double, double, double, org.apache.spark.mllib.linalg.Vector)">train</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
     int&nbsp;numIterations,
     double&nbsp;stepSize,
     double&nbsp;regParam,
     double&nbsp;miniBatchFraction,
     <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>&nbsp;initialWeights)</code>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs.</div>
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<code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#addIntercept()">addIntercept</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#getNumFeatures()">getNumFeatures</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#isAddIntercept()">isAddIntercept</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#numFeatures()">numFeatures</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#numOfLinearPredictor()">numOfLinearPredictor</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#run(org.apache.spark.rdd.RDD)">run</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#run(org.apache.spark.rdd.RDD, org.apache.spark.mllib.linalg.Vector)">run</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#setIntercept(boolean)">setIntercept</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#setValidateData(boolean)">setValidateData</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#validateData()">validateData</a>, <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#validators()">validators</a></code></li>
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<code><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#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#logName()">logName</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>LassoWithSGD</h4>
<pre>public&nbsp;LassoWithSGD()</pre>
<div class="block">Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
 regParam: 0.01, miniBatchFraction: 1.0}.</div>
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<a name="train(org.apache.spark.rdd.RDD, int, double, double, double, org.apache.spark.mllib.linalg.Vector)">
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<h4>train</h4>
<pre>public static&nbsp;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&nbsp;train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
               int&nbsp;numIterations,
               double&nbsp;stepSize,
               double&nbsp;regParam,
               double&nbsp;miniBatchFraction,
               <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>&nbsp;initialWeights)</pre>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
 of iterations of gradient descent using the specified step size. Each iteration uses
 <code>miniBatchFraction</code> fraction of the data to calculate a stochastic gradient. The weights used
 in gradient descent are initialized using the initial weights provided.
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of (label, array of features) pairs. Each pair describes a row of the data
              matrix A as well as the corresponding right hand side label y</dd><dd><code>numIterations</code> - Number of iterations of gradient descent to run.</dd><dd><code>stepSize</code> - Step size scaling to be used for the iterations of gradient descent.</dd><dd><code>regParam</code> - Regularization parameter.</dd><dd><code>miniBatchFraction</code> - Fraction of data to be used per iteration.</dd><dd><code>initialWeights</code> - Initial set of weights to be used. Array should be equal in size to
        the number of features in the data.
 <p></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>train</h4>
<pre>public static&nbsp;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&nbsp;train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
               int&nbsp;numIterations,
               double&nbsp;stepSize,
               double&nbsp;regParam,
               double&nbsp;miniBatchFraction)</pre>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
 of iterations of gradient descent using the specified step size. Each iteration uses
 <code>miniBatchFraction</code> fraction of the data to calculate a stochastic gradient.
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of (label, array of features) pairs. Each pair describes a row of the data
              matrix A as well as the corresponding right hand side label y</dd><dd><code>numIterations</code> - Number of iterations of gradient descent to run.</dd><dd><code>stepSize</code> - Step size to be used for each iteration of gradient descent.</dd><dd><code>regParam</code> - Regularization parameter.</dd><dd><code>miniBatchFraction</code> - Fraction of data to be used per iteration.
 <p></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>train</h4>
<pre>public static&nbsp;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&nbsp;train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
               int&nbsp;numIterations,
               double&nbsp;stepSize,
               double&nbsp;regParam)</pre>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
 of iterations of gradient descent using the specified step size. We use the entire data set to
 update the true gradient in each iteration.
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of (label, array of features) pairs. Each pair describes a row of the data
              matrix A as well as the corresponding right hand side label y</dd><dd><code>stepSize</code> - Step size to be used for each iteration of Gradient Descent.</dd><dd><code>regParam</code> - Regularization parameter.</dd><dd><code>numIterations</code> - Number of iterations of gradient descent to run.</dd>
<dt><span class="strong">Returns:</span></dt><dd>a LassoModel which has the weights and offset from training.
 <p></dd></dl>
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<h4>train</h4>
<pre>public static&nbsp;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&nbsp;train(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>&gt;&nbsp;input,
               int&nbsp;numIterations)</pre>
<div class="block">Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
 of iterations of gradient descent using a step size of 1.0. We use the entire data set to
 compute the true gradient in each iteration.
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>input</code> - RDD of (label, array of features) pairs. Each pair describes a row of the data
              matrix A as well as the corresponding right hand side label y</dd><dd><code>numIterations</code> - Number of iterations of gradient descent to run.</dd>
<dt><span class="strong">Returns:</span></dt><dd>a LassoModel which has the weights and offset from training.
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a>&nbsp;optimizer()</pre>
<div class="block"><strong>Description copied from class:&nbsp;<code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#optimizer()">GeneralizedLinearAlgorithm</a></code></strong></div>
<div class="block">The optimizer to solve the problem.
 <p></div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#optimizer()">optimizer</a></code>&nbsp;in class&nbsp;<code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearAlgorithm</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&gt;</code></dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>protected&nbsp;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&nbsp;createModel(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>&nbsp;weights,
                     double&nbsp;intercept)</pre>
<div class="block"><strong>Description copied from class:&nbsp;<code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#createModel(org.apache.spark.mllib.linalg.Vector, double)">GeneralizedLinearAlgorithm</a></code></strong></div>
<div class="block">Create a model given the weights and intercept</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html#createModel(org.apache.spark.mllib.linalg.Vector, double)">createModel</a></code>&nbsp;in class&nbsp;<code><a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearAlgorithm</a>&lt;<a href="../../../../../org/apache/spark/mllib/regression/LassoModel.html" title="class in org.apache.spark.mllib.regression">LassoModel</a>&gt;</code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>weights</code> - (undocumented)</dd><dd><code>intercept</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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