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<div class="subTitle">org.apache.spark.mllib.regression</div>
<h2 title="Class StreamingLinearRegressionWithSGD" class="title">Class StreamingLinearRegressionWithSGD</h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">org.apache.spark.mllib.regression.StreamingLinearAlgorithm</a><<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a>></li>
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<li>org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD</li>
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<pre>public class <span class="strong">StreamingLinearRegressionWithSGD</span>
extends <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">StreamingLinearAlgorithm</a><<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a>>
implements scala.Serializable</pre>
<div class="block">:: Experimental ::
Train or predict a linear regression model on streaming data. Training uses
Stochastic Gradient Descent to update the model based on each new batch of
incoming data from a DStream (see <code>LinearRegressionWithSGD</code> for model equation)
<p>
Each batch of data is assumed to be an RDD of LabeledPoints.
The number of data points per batch can vary, but the number
of features must be constant. An initial weight
vector must be provided.
<p>
Use a builder pattern to construct a streaming linear regression
analysis in an application, like:
<p>
val model = new StreamingLinearRegressionWithSGD()
.setStepSize(0.5)
.setNumIterations(10)
.setInitialWeights(Vectors.dense(...))
.trainOn(DStream)</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD">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/regression/StreamingLinearRegressionWithSGD.html#StreamingLinearRegressionWithSGD()">StreamingLinearRegressionWithSGD</a></strong>()</code>
<div class="block">Construct a StreamingLinearRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.</div>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#algorithm()">algorithm</a></strong>()</code>
<div class="block">The algorithm to use for updating.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>protected scala.Option<<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#model()">model</a></strong>()</code>
<div class="block">The model to be updated and used for prediction.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#setConvergenceTol(double)">setConvergenceTol</a></strong>(double tolerance)</code>
<div class="block">Set the convergence tolerance.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#setInitialWeights(org.apache.spark.mllib.linalg.Vector)">setInitialWeights</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</code>
<div class="block">Set the initial weights.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#setMiniBatchFraction(double)">setMiniBatchFraction</a></strong>(double miniBatchFraction)</code>
<div class="block">Set the fraction of each batch to use for updates.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#setNumIterations(int)">setNumIterations</a></strong>(int numIterations)</code>
<div class="block">Set the number of iterations of gradient descent to run per update.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html#setStepSize(double)">setStepSize</a></strong>(double stepSize)</code>
<div class="block">Set the step size for gradient descent.</div>
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<h3>Methods inherited from class org.apache.spark.mllib.regression.<a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">StreamingLinearAlgorithm</a></h3>
<code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#latestModel()">latestModel</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOn(org.apache.spark.streaming.dstream.DStream)">predictOn</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOn(org.apache.spark.streaming.api.java.JavaDStream)">predictOn</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOnValues(org.apache.spark.streaming.dstream.DStream, scala.reflect.ClassTag)">predictOnValues</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOnValues(org.apache.spark.streaming.api.java.JavaPairDStream)">predictOnValues</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#trainOn(org.apache.spark.streaming.dstream.DStream)">trainOn</a>, <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#trainOn(org.apache.spark.streaming.api.java.JavaDStream)">trainOn</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>StreamingLinearRegressionWithSGD</h4>
<pre>public StreamingLinearRegressionWithSGD()</pre>
<div class="block">Construct a StreamingLinearRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.
Initial weights must be set before using trainOn or predictOn
(see <code>StreamingLinearAlgorithm</code>)</div>
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<h4>algorithm</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a> algorithm()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#algorithm()">StreamingLinearAlgorithm</a></code></strong></div>
<div class="block">The algorithm to use for updating.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#algorithm()">algorithm</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">StreamingLinearAlgorithm</a><<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a>></code></dd>
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<h4>model</h4>
<pre>protected scala.Option<<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>> model()</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#model()">StreamingLinearAlgorithm</a></code></strong></div>
<div class="block">The model to be updated and used for prediction.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#model()">model</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">StreamingLinearAlgorithm</a><<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionModel.html" title="class in org.apache.spark.mllib.regression">LinearRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/regression/LinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">LinearRegressionWithSGD</a>></code></dd>
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<h4>setStepSize</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a> setStepSize(double stepSize)</pre>
<div class="block">Set the step size for gradient descent. Default: 0.1.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>stepSize</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a> setNumIterations(int numIterations)</pre>
<div class="block">Set the number of iterations of gradient descent to run per update. Default: 50.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>numIterations</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a> setMiniBatchFraction(double miniBatchFraction)</pre>
<div class="block">Set the fraction of each batch to use for updates. Default: 1.0.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>miniBatchFraction</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setInitialWeights</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a> setInitialWeights(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</pre>
<div class="block">Set the initial weights.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>initialWeights</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>setConvergenceTol</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a> setConvergenceTol(double tolerance)</pre>
<div class="block">Set the convergence tolerance. Default: 0.001.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>tolerance</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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