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| <h1>StreamingLinearRegressionWithSGD<span class="permalink"> |
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| <span class="name">StreamingLinearRegressionWithSGD</span><span class="result"> extends <a href="StreamingLinearAlgorithm.html" class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm">StreamingLinearAlgorithm</a>[<a href="LinearRegressionModel.html" class="extype" name="org.apache.spark.mllib.regression.LinearRegressionModel">LinearRegressionModel</a>, <a href="LinearRegressionWithSGD.html" class="extype" name="org.apache.spark.mllib.regression.LinearRegressionWithSGD">LinearRegressionWithSGD</a>] with <span class="extype" name="scala.Serializable">Serializable</span></span> |
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| |
| |
| <div id="comment" class="fullcommenttop"><div class="comment cmt"><p>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><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><p>Use a builder pattern to construct a streaming linear regression |
| analysis in an application, like:</p><p> val model = new StreamingLinearRegressionWithSGD() |
| .setStepSize(0.5) |
| .setNumIterations(10) |
| .setInitialWeights(Vectors.dense(...)) |
| .trainOn(DStream) |
| </p></div><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@Since</span><span class="args">(<span> |
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| <span class="symbol">"1.1.0"</span> |
| </span>)</span> |
| |
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| <p class="shortcomment cmt">Construct a StreamingLinearRegression object with default parameters: |
| {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.</p><div class="fullcomment"><div class="comment cmt"><p>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>) |
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| <span class="name">predictOn</span><span class="params">(<span name="data">data: <a href="../../streaming/api/java/JavaDStream.html" class="extype" name="org.apache.spark.streaming.api.java.JavaDStream">JavaDStream</a>[<a href="../linalg/Vector.html" class="extype" name="org.apache.spark.mllib.linalg.Vector">Vector</a>]</span>)</span><span class="result">: <a href="../../streaming/api/java/JavaDStream.html" class="extype" name="org.apache.spark.streaming.api.java.JavaDStream">JavaDStream</a>[<span class="extype" name="java.lang.Double">Double</span>]</span> |
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| <span class="name">predictOn</span><span class="params">(<span name="data">data: <a href="../../streaming/dstream/DStream.html" class="extype" name="org.apache.spark.streaming.dstream.DStream">DStream</a>[<a href="../linalg/Vector.html" class="extype" name="org.apache.spark.mllib.linalg.Vector">Vector</a>]</span>)</span><span class="result">: <a href="../../streaming/dstream/DStream.html" class="extype" name="org.apache.spark.streaming.dstream.DStream">DStream</a>[<span class="extype" name="scala.Double">Double</span>]</span> |
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| <p class="shortcomment cmt">Use the model to make predictions on batches of data from a DStream |
| </p><div class="fullcomment"><div class="comment cmt"><p>Use the model to make predictions on batches of data from a DStream |
| </p></div><dl class="paramcmts block"><dt class="param">data</dt><dd class="cmt"><p>DStream containing feature vectors</p></dd><dt>returns</dt><dd class="cmt"><p>DStream containing predictions</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="StreamingLinearAlgorithm.html" class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm">StreamingLinearAlgorithm</a></dd><dt>Annotations</dt><dd> |
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| <a id="predictOnValues[K](data:org.apache.spark.streaming.api.java.JavaPairDStream[K,org.apache.spark.mllib.linalg.Vector]):org.apache.spark.streaming.api.java.JavaPairDStream[K,Double]"></a><a id="predictOnValues[K](JavaPairDStream[K,Vector]):JavaPairDStream[K,Double]"></a> |
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| <span class="name">predictOnValues</span><span class="tparams">[<span name="K">K</span>]</span><span class="params">(<span name="data">data: <a href="../../streaming/api/java/JavaPairDStream.html" class="extype" name="org.apache.spark.streaming.api.java.JavaPairDStream">JavaPairDStream</a>[<span class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm.predictOnValues.K">K</span>, <a href="../linalg/Vector.html" class="extype" name="org.apache.spark.mllib.linalg.Vector">Vector</a>]</span>)</span><span class="result">: <a href="../../streaming/api/java/JavaPairDStream.html" class="extype" name="org.apache.spark.streaming.api.java.JavaPairDStream">JavaPairDStream</a>[<span class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm.predictOnValues.K">K</span>, <span class="extype" name="java.lang.Double">Double</span>]</span> |
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| <a id="predictOnValues[K](data:org.apache.spark.streaming.dstream.DStream[(K,org.apache.spark.mllib.linalg.Vector)])(implicitevidence$1:scala.reflect.ClassTag[K]):org.apache.spark.streaming.dstream.DStream[(K,Double)]"></a><a id="predictOnValues[K](DStream[(K,Vector)])(ClassTag[K]):DStream[(K,Double)]"></a> |
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| <span class="symbol"> |
| <span class="name">predictOnValues</span><span class="tparams">[<span name="K">K</span>]</span><span class="params">(<span name="data">data: <a href="../../streaming/dstream/DStream.html" class="extype" name="org.apache.spark.streaming.dstream.DStream">DStream</a>[(<span class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm.predictOnValues.K">K</span>, <a href="../linalg/Vector.html" class="extype" name="org.apache.spark.mllib.linalg.Vector">Vector</a>)]</span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm.predictOnValues.K">K</span>]</span>)</span><span class="result">: <a href="../../streaming/dstream/DStream.html" class="extype" name="org.apache.spark.streaming.dstream.DStream">DStream</a>[(<span class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm.predictOnValues.K">K</span>, <span class="extype" name="scala.Double">Double</span>)]</span> |
| </span> |
| |
| <p class="shortcomment cmt">Use the model to make predictions on the values of a DStream and carry over its keys.</p><div class="fullcomment"><div class="comment cmt"><p>Use the model to make predictions on the values of a DStream and carry over its keys.</p></div><dl class="paramcmts block"><dt class="tparam">K</dt><dd class="cmt"><p>key type</p></dd><dt class="param">data</dt><dd class="cmt"><p>DStream containing feature vectors</p></dd><dt>returns</dt><dd class="cmt"><p>DStream containing the input keys and the predictions as values</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="StreamingLinearAlgorithm.html" class="extype" name="org.apache.spark.mllib.regression.StreamingLinearAlgorithm">StreamingLinearAlgorithm</a></dd><dt>Annotations</dt><dd> |
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