public class SVMWithSGD extends GeneralizedLinearAlgorithm<SVMModel> implements scala.Serializable
SVMWithSGD.optimizer
.
Constructor and Description |
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SVMWithSGD()
Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100,
regParam: 0.01, miniBatchFraction: 1.0}.
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Modifier and Type | Method and Description |
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GradientDescent |
optimizer()
The optimizer to solve the problem.
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static SVMModel |
train(RDD<LabeledPoint> input,
int numIterations)
Train a SVM model given an RDD of (label, features) pairs.
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static SVMModel |
train(RDD<LabeledPoint> input,
int numIterations,
double stepSize,
double regParam)
Train a SVM model given an RDD of (label, features) pairs.
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static SVMModel |
train(RDD<LabeledPoint> input,
int numIterations,
double stepSize,
double regParam,
double miniBatchFraction)
Train a SVM model given an RDD of (label, features) pairs.
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static SVMModel |
train(RDD<LabeledPoint> input,
int numIterations,
double stepSize,
double regParam,
double miniBatchFraction,
Vector initialWeights)
Train a SVM model given an RDD of (label, features) pairs.
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getNumFeatures, isAddIntercept, run, run, setIntercept, setValidateData
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public SVMWithSGD()
public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction, Vector initialWeights)
miniBatchFraction
fraction of the data to calculate the gradient. The weights used in
gradient descent are initialized using the initial weights provided.
input
- RDD of (label, array of features) pairs.numIterations
- Number of iterations of gradient descent to run.stepSize
- Step size to be used for each iteration of gradient descent.regParam
- Regularization parameter.miniBatchFraction
- Fraction of data to be used per iteration.initialWeights
- Initial set of weights to be used. Array should be equal in size to
the number of features in the data.
public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction)
miniBatchFraction
fraction of the data to calculate the gradient.
input
- RDD of (label, array of features) pairs.numIterations
- Number of iterations of gradient descent to run.stepSize
- Step size to be used for each iteration of gradient descent.regParam
- Regularization parameter.miniBatchFraction
- Fraction of data to be used per iteration.public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam)
input
- RDD of (label, array of features) pairs.stepSize
- Step size to be used for each iteration of Gradient Descent.regParam
- Regularization parameter.numIterations
- Number of iterations of gradient descent to run.public static SVMModel train(RDD<LabeledPoint> input, int numIterations)
input
- RDD of (label, array of features) pairs.numIterations
- Number of iterations of gradient descent to run.public GradientDescent optimizer()
GeneralizedLinearAlgorithm
optimizer
in class GeneralizedLinearAlgorithm<SVMModel>