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Object org.apache.spark.mllib.regression.StreamingLinearAlgorithm<LinearRegressionModel,LinearRegressionWithSGD> org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
public class StreamingLinearRegressionWithSGD
:: 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 LinearRegressionWithSGD
for model equation)
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.
Use a builder pattern to construct a streaming linear regression analysis in an application, like:
val model = new StreamingLinearRegressionWithSGD() .setStepSize(0.5) .setNumIterations(10) .setInitialWeights(Vectors.dense(...)) .trainOn(DStream)
Constructor Summary | |
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StreamingLinearRegressionWithSGD()
Construct a StreamingLinearRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}. |
Method Summary | |
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LinearRegressionWithSGD |
algorithm()
The algorithm to use for updating. |
StreamingLinearRegressionWithSGD |
setInitialWeights(Vector initialWeights)
Set the initial weights. |
StreamingLinearRegressionWithSGD |
setMiniBatchFraction(double miniBatchFraction)
Set the fraction of each batch to use for updates. |
StreamingLinearRegressionWithSGD |
setNumIterations(int numIterations)
Set the number of iterations of gradient descent to run per update. |
StreamingLinearRegressionWithSGD |
setStepSize(double stepSize)
Set the step size for gradient descent. |
Methods inherited from class org.apache.spark.mllib.regression.StreamingLinearAlgorithm |
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latestModel, predictOn, predictOn, predictOnValues, predictOnValues, trainOn, trainOn |
Methods inherited from class Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.Logging |
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initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
Constructor Detail |
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public StreamingLinearRegressionWithSGD()
StreamingLinearAlgorithm
)
Method Detail |
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public LinearRegressionWithSGD algorithm()
StreamingLinearAlgorithm
public StreamingLinearRegressionWithSGD setStepSize(double stepSize)
public StreamingLinearRegressionWithSGD setNumIterations(int numIterations)
public StreamingLinearRegressionWithSGD setMiniBatchFraction(double miniBatchFraction)
public StreamingLinearRegressionWithSGD setInitialWeights(Vector initialWeights)
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