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<div class="subTitle">org.apache.spark.mllib.classification</div>
<h2 title="Class StreamingLogisticRegressionWithSGD" class="title">Class StreamingLogisticRegressionWithSGD</h2>
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<div class="contentContainer">
<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/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</a>></li>
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<li>org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD</li>
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<dd>java.io.Serializable, <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></dd>
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<pre>public class <span class="strong">StreamingLogisticRegressionWithSGD</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/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</a>>
implements scala.Serializable</pre>
<div class="block">:: Experimental ::
Train or predict a logistic 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>LogisticRegressionWithSGD</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 logistic regression
analysis in an application, like:
<p>
<pre><code>
val model = new StreamingLogisticRegressionWithSGD()
.setStepSize(0.5)
.setNumIterations(10)
.setInitialWeights(Vectors.dense(...))
.trainOn(DStream)
</code></pre></div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD">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/classification/StreamingLogisticRegressionWithSGD.html#StreamingLogisticRegressionWithSGD()">StreamingLogisticRegressionWithSGD</a></strong>()</code>
<div class="block">Construct a StreamingLogisticRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.</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>protected <a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.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/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html#model()">model</a></strong>()</code>
<div class="block">The model to be updated and used for prediction.</div>
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</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.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="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.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="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.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="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html#setRegParam(double)">setRegParam</a></strong>(double regParam)</code>
<div class="block">Set the regularization parameter.</div>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html#setStepSize(double)">setStepSize</a></strong>(double stepSize)</code>
<div class="block">Set the step size for gradient descent.</div>
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<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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<h3>Methods inherited from interface org.apache.spark.<a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></h3>
<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>StreamingLogisticRegressionWithSGD</h4>
<pre>public StreamingLogisticRegressionWithSGD()</pre>
<div class="block">Construct a StreamingLogisticRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.
Initial weights must be set before using trainOn or predictOn
(see <code>StreamingLinearAlgorithm</code>)</div>
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<h4>algorithm</h4>
<pre>protected <a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</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/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</a>></code></dd>
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<h4>model</h4>
<pre>protected scala.Option<<a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</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/classification/LogisticRegressionModel.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionModel</a>,<a href="../../../../../org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">LogisticRegressionWithSGD</a>></code></dd>
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<h4>setStepSize</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a> setStepSize(double stepSize)</pre>
<div class="block">Set the step size for gradient descent. Default: 0.1.</div>
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<pre>public <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a> setNumIterations(int numIterations)</pre>
<div class="block">Set the number of iterations of gradient descent to run per update. Default: 50.</div>
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<pre>public <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a> setMiniBatchFraction(double miniBatchFraction)</pre>
<div class="block">Set the fraction of each batch to use for updates. Default: 1.0.</div>
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<pre>public <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a> setRegParam(double regParam)</pre>
<div class="block">Set the regularization parameter. Default: 0.0.</div>
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<pre>public <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</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. Default: [0.0, 0.0].</div>
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