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<div class="subTitle">org.apache.spark.mllib.util</div>
<h2 title="Class LinearDataGenerator" class="title">Class LinearDataGenerator</h2>
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<pre>public class <span class="strong">LinearDataGenerator</span>
extends java.lang.Object</pre>
<div class="block">:: DeveloperApi ::
Generate sample data used for Linear Data. This class generates
uniformly random values for every feature and adds Gaussian noise with mean <code>eps</code> to the
response variable <code>Y</code>.</div>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#LinearDataGenerator()">LinearDataGenerator</a></strong>()</code> </td>
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<td class="colFirst"><code>static scala.collection.Seq<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#generateLinearInput(double, double[], double[], double[], int, int, double)">generateLinearInput</a></strong>(double intercept,
double[] weights,
double[] xMean,
double[] xVariance,
int nPoints,
int seed,
double eps)</code> </td>
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<tr class="rowColor">
<td class="colFirst"><code>static scala.collection.Seq<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#generateLinearInput(double, double[], int, int, double)">generateLinearInput</a></strong>(double intercept,
double[] weights,
int nPoints,
int seed,
double eps)</code>
<div class="block">For compatibility, the generated data without specifying the mean and variance
will have zero mean and variance of (1.0/3.0) since the original output range is
[-1, 1] with uniform distribution, and the variance of uniform distribution
is (b - a)^2^ / 12 which will be (1.0/3.0)</div>
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<td class="colFirst"><code>static java.util.List<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#generateLinearInputAsList(double, double[], int, int, double)">generateLinearInputAsList</a></strong>(double intercept,
double[] weights,
int nPoints,
int seed,
double eps)</code>
<div class="block">Return a Java List of synthetic data randomly generated according to a multi
collinear model.</div>
</td>
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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#generateLinearRDD(org.apache.spark.SparkContext, int, int, double, int, double)">generateLinearRDD</a></strong>(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
int nexamples,
int nfeatures,
double eps,
int nparts,
double intercept)</code>
<div class="block">Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso,
and uregularized variants.</div>
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<td class="colFirst"><code>static void</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/util/LinearDataGenerator.html#main(java.lang.String[])">main</a></strong>(java.lang.String[] args)</code> </td>
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<h4>LinearDataGenerator</h4>
<pre>public LinearDataGenerator()</pre>
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<h4>generateLinearInputAsList</h4>
<pre>public static java.util.List<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> generateLinearInputAsList(double intercept,
double[] weights,
int nPoints,
int seed,
double eps)</pre>
<div class="block">Return a Java List of synthetic data randomly generated according to a multi
collinear model.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>intercept</code> - Data intercept</dd><dd><code>weights</code> - Weights to be applied.</dd><dd><code>nPoints</code> - Number of points in sample.</dd><dd><code>seed</code> - Random seed</dd><dd><code>eps</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>Java List of input.</dd></dl>
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<h4>generateLinearInput</h4>
<pre>public static scala.collection.Seq<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> generateLinearInput(double intercept,
double[] weights,
int nPoints,
int seed,
double eps)</pre>
<div class="block">For compatibility, the generated data without specifying the mean and variance
will have zero mean and variance of (1.0/3.0) since the original output range is
[-1, 1] with uniform distribution, and the variance of uniform distribution
is (b - a)^2^ / 12 which will be (1.0/3.0)
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>intercept</code> - Data intercept</dd><dd><code>weights</code> - Weights to be applied.</dd><dd><code>nPoints</code> - Number of points in sample.</dd><dd><code>seed</code> - Random seed</dd><dd><code>eps</code> - Epsilon scaling factor.</dd>
<dt><span class="strong">Returns:</span></dt><dd>Seq of input.</dd></dl>
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<h4>generateLinearInput</h4>
<pre>public static scala.collection.Seq<<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> generateLinearInput(double intercept,
double[] weights,
double[] xMean,
double[] xVariance,
int nPoints,
int seed,
double eps)</pre>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>intercept</code> - Data intercept</dd><dd><code>weights</code> - Weights to be applied.</dd><dd><code>xMean</code> - the mean of the generated features. Lots of time, if the features are not properly
standardized, the algorithm with poor implementation will have difficulty
to converge.</dd><dd><code>xVariance</code> - the variance of the generated features.</dd><dd><code>nPoints</code> - Number of points in sample.</dd><dd><code>seed</code> - Random seed</dd><dd><code>eps</code> - Epsilon scaling factor.</dd>
<dt><span class="strong">Returns:</span></dt><dd>Seq of input.</dd></dl>
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<h4>generateLinearRDD</h4>
<pre>public static <a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> generateLinearRDD(<a href="../../../../../org/apache/spark/SparkContext.html" title="class in org.apache.spark">SparkContext</a> sc,
int nexamples,
int nfeatures,
double eps,
int nparts,
double intercept)</pre>
<div class="block">Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso,
and uregularized variants.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sc</code> - SparkContext to be used for generating the RDD.</dd><dd><code>nexamples</code> - Number of examples that will be contained in the RDD.</dd><dd><code>nfeatures</code> - Number of features to generate for each example.</dd><dd><code>eps</code> - Epsilon factor by which examples are scaled.</dd><dd><code>nparts</code> - Number of partitions in the RDD. Default value is 2.
<p></dd><dd><code>intercept</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>RDD of LabeledPoint containing sample data.</dd></dl>
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<h4>main</h4>
<pre>public static void main(java.lang.String[] args)</pre>
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