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<div class="subTitle">org.apache.spark.mllib.regression</div>
<h2 title="Class StreamingLinearAlgorithm" class="title">Class StreamingLinearAlgorithm<M extends <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearModel.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearModel</a>,A extends <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearAlgorithm</a><M>></h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li>org.apache.spark.mllib.regression.StreamingLinearAlgorithm<M,A></li>
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<dl>
<dt>All Implemented Interfaces:</dt>
<dd><a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></dd>
</dl>
<dl>
<dt>Direct Known Subclasses:</dt>
<dd><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.html" title="class in org.apache.spark.mllib.regression">StreamingLinearRegressionWithSGD</a>, <a href="../../../../../org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.html" title="class in org.apache.spark.mllib.classification">StreamingLogisticRegressionWithSGD</a></dd>
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<br>
<pre>public abstract class <span class="strong">StreamingLinearAlgorithm<M extends <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearModel.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearModel</a>,A extends <a href="../../../../../org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.html" title="class in org.apache.spark.mllib.regression">GeneralizedLinearAlgorithm</a><M>></span>
extends java.lang.Object
implements <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></pre>
<div class="block">:: DeveloperApi ::
StreamingLinearAlgorithm implements methods for continuously
training a generalized linear model model on streaming data,
and using it for prediction on (possibly different) streaming data.
<p>
This class takes as type parameters a GeneralizedLinearModel,
and a GeneralizedLinearAlgorithm, making it easy to extend to construct
streaming versions of any analyses using GLMs.
Initial weights must be set before calling trainOn or predictOn.
Only weights will be updated, not an intercept. If the model needs
an intercept, it should be manually appended to the input data.
<p>
For example usage, see <code>StreamingLinearRegressionWithSGD</code>.
<p>
NOTE: In some use cases, the order in which trainOn and predictOn
are called in an application will affect the results. When called on
the same DStream, if trainOn is called before predictOn, when new data
arrive the model will update and the prediction will be based on the new
model. Whereas if predictOn is called first, the prediction will use the model
from the previous update.
<p>
NOTE: It is ok to call predictOn repeatedly on multiple streams; this
will generate predictions for each one all using the current model.
It is also ok to call trainOn on different streams; this will update
the model using each of the different sources, in sequence.
<p></div>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#StreamingLinearAlgorithm()">StreamingLinearAlgorithm</a></strong>()</code> </td>
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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 abstract <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">A</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#algorithm()">algorithm</a></strong>()</code>
<div class="block">The algorithm to use for updating.</div>
</td>
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<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">M</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#latestModel()">latestModel</a></strong>()</code>
<div class="block">Return the latest model.</div>
</td>
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<td class="colFirst"><code>protected abstract scala.Option<<a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">M</a>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#model()">model</a></strong>()</code>
<div class="block">The model to be updated and used for prediction.</div>
</td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOn(org.apache.spark.streaming.dstream.DStream)">predictOn</a></strong>(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</code>
<div class="block">Use the model to make predictions on batches of data from a DStream</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><java.lang.Double></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOn(org.apache.spark.streaming.api.java.JavaDStream)">predictOn</a></strong>(<a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</code>
<div class="block">Java-friendly version of <code>predictOn</code>.</div>
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<td class="colFirst"><code><K> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<K,java.lang.Object>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOnValues(org.apache.spark.streaming.dstream.DStream, scala.reflect.ClassTag)">predictOnValues</a></strong>(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<K,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
scala.reflect.ClassTag<K> evidence$1)</code>
<div class="block">Use the model to make predictions on the values of a DStream and carry over its keys.</div>
</td>
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<td class="colFirst"><code><K> <a href="../../../../../org/apache/spark/streaming/api/java/JavaPairDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairDStream</a><K,java.lang.Double></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#predictOnValues(org.apache.spark.streaming.api.java.JavaPairDStream)">predictOnValues</a></strong>(<a href="../../../../../org/apache/spark/streaming/api/java/JavaPairDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairDStream</a><K,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</code>
<div class="block">Java-friendly version of <code>predictOnValues</code>.</div>
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<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#trainOn(org.apache.spark.streaming.dstream.DStream)">trainOn</a></strong>(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</code>
<div class="block">Update the model by training on batches of data from a DStream.</div>
</td>
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<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html#trainOn(org.apache.spark.streaming.api.java.JavaDStream)">trainOn</a></strong>(<a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</code>
<div class="block">Java-friendly version of <code>trainOn</code>.</div>
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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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<pre>public StreamingLinearAlgorithm()</pre>
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<h4>model</h4>
<pre>protected abstract scala.Option<<a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">M</a>> model()</pre>
<div class="block">The model to be updated and used for prediction.</div>
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<pre>protected abstract <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">A</a> algorithm()</pre>
<div class="block">The algorithm to use for updating.</div>
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<pre>public <a href="../../../../../org/apache/spark/mllib/regression/StreamingLinearAlgorithm.html" title="type parameter in StreamingLinearAlgorithm">M</a> latestModel()</pre>
<div class="block">Return the latest model.
<p></div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public void trainOn(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</pre>
<div class="block">Update the model by training on batches of data from a DStream.
This operation registers a DStream for training the model,
and updates the model based on every subsequent
batch of data from the stream.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - DStream containing labeled data</dd></dl>
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<pre>public void trainOn(<a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><<a href="../../../../../org/apache/spark/mllib/regression/LabeledPoint.html" title="class in org.apache.spark.mllib.regression">LabeledPoint</a>> data)</pre>
<div class="block">Java-friendly version of <code>trainOn</code>.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - (undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><java.lang.Object> predictOn(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</pre>
<div class="block">Use the model to make predictions on batches of data from a DStream
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - DStream containing feature vectors</dd>
<dt><span class="strong">Returns:</span></dt><dd>DStream containing predictions
<p></dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><java.lang.Double> predictOn(<a href="../../../../../org/apache/spark/streaming/api/java/JavaDStream.html" title="class in org.apache.spark.streaming.api.java">JavaDStream</a><<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</pre>
<div class="block">Java-friendly version of <code>predictOn</code>.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <K> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<K,java.lang.Object>> predictOnValues(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<K,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
scala.reflect.ClassTag<K> evidence$1)</pre>
<div class="block">Use the model to make predictions on the values of a DStream and carry over its keys.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - DStream containing feature vectors</dd><dd><code>evidence$1</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>DStream containing the input keys and the predictions as values
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<pre>public <K> <a href="../../../../../org/apache/spark/streaming/api/java/JavaPairDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairDStream</a><K,java.lang.Double> predictOnValues(<a href="../../../../../org/apache/spark/streaming/api/java/JavaPairDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairDStream</a><K,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>> data)</pre>
<div class="block">Java-friendly version of <code>predictOnValues</code>.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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