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<div class="subTitle">org.apache.spark.ml.regression</div>
<h2 title="Class LeastSquaresCostFun" class="title">Class LeastSquaresCostFun</h2>
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<li>java.lang.Object</li>
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<li>org.apache.spark.ml.regression.LeastSquaresCostFun</li>
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<dt>All Implemented Interfaces:</dt>
<dd>breeze.optimize.DiffFunction<breeze.linalg.DenseVector<java.lang.Object>>, breeze.optimize.StochasticDiffFunction<breeze.linalg.DenseVector<java.lang.Object>>, scala.Function1<breeze.linalg.DenseVector<java.lang.Object>,java.lang.Object></dd>
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<pre>public class <span class="strong">LeastSquaresCostFun</span>
extends java.lang.Object
implements breeze.optimize.DiffFunction<breeze.linalg.DenseVector<java.lang.Object>></pre>
<div class="block">LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost.
It returns the loss and gradient with L2 regularization at a particular point (weights).
It's used in Breeze's convex optimization routines.</div>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/ml/regression/LeastSquaresCostFun.html#LeastSquaresCostFun(org.apache.spark.rdd.RDD, double, double, boolean, boolean, double[], double[], double)">LeastSquaresCostFun</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<java.lang.Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
double labelStd,
double labelMean,
boolean fitIntercept,
boolean standardization,
double[] featuresStd,
double[] featuresMean,
double effectiveL2regParam)</code> </td>
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<td class="colFirst"><code>scala.Tuple2<java.lang.Object,breeze.linalg.DenseVector<java.lang.Object>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/ml/regression/LeastSquaresCostFun.html#calculate(breeze.linalg.DenseVector)">calculate</a></strong>(breeze.linalg.DenseVector<java.lang.Object> weights)</code> </td>
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<code>cached, throughLens</code></li>
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<code>andThen, andThen$mcDD$sp, andThen$mcDF$sp, andThen$mcDI$sp, andThen$mcDJ$sp, andThen$mcFD$sp, andThen$mcFF$sp, andThen$mcFI$sp, andThen$mcFJ$sp, andThen$mcID$sp, andThen$mcIF$sp, andThen$mcII$sp, andThen$mcIJ$sp, andThen$mcJD$sp, andThen$mcJF$sp, andThen$mcJI$sp, andThen$mcJJ$sp, andThen$mcVD$sp, andThen$mcVF$sp, andThen$mcVI$sp, andThen$mcVJ$sp, andThen$mcZD$sp, andThen$mcZF$sp, andThen$mcZI$sp, andThen$mcZJ$sp, apply$mcDD$sp, apply$mcDF$sp, apply$mcDI$sp, apply$mcDJ$sp, apply$mcFD$sp, apply$mcFF$sp, apply$mcFI$sp, apply$mcFJ$sp, apply$mcID$sp, apply$mcIF$sp, apply$mcII$sp, apply$mcIJ$sp, apply$mcJD$sp, apply$mcJF$sp, apply$mcJI$sp, apply$mcJJ$sp, apply$mcVD$sp, apply$mcVF$sp, apply$mcVI$sp, apply$mcVJ$sp, apply$mcZD$sp, apply$mcZF$sp, apply$mcZI$sp, apply$mcZJ$sp, compose, compose$mcDD$sp, compose$mcDF$sp, compose$mcDI$sp, compose$mcDJ$sp, compose$mcFD$sp, compose$mcFF$sp, compose$mcFI$sp, compose$mcFJ$sp, compose$mcID$sp, compose$mcIF$sp, compose$mcII$sp, compose$mcIJ$sp, compose$mcJD$sp, compose$mcJF$sp, compose$mcJI$sp, compose$mcJJ$sp, compose$mcVD$sp, compose$mcVF$sp, compose$mcVI$sp, compose$mcVJ$sp, compose$mcZD$sp, compose$mcZF$sp, compose$mcZI$sp, compose$mcZJ$sp, toString</code></li>
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<h4>LeastSquaresCostFun</h4>
<pre>public LeastSquaresCostFun(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<java.lang.Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
double labelStd,
double labelMean,
boolean fitIntercept,
boolean standardization,
double[] featuresStd,
double[] featuresMean,
double effectiveL2regParam)</pre>
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<h4>calculate</h4>
<pre>public scala.Tuple2<java.lang.Object,breeze.linalg.DenseVector<java.lang.Object>> calculate(breeze.linalg.DenseVector<java.lang.Object> weights)</pre>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code>calculate</code> in interface <code>breeze.optimize.StochasticDiffFunction<breeze.linalg.DenseVector<java.lang.Object>></code></dd>
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