StochasticRidgeRegression (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.regression.StochasticRidgeRegression

All Implemented Interfaces:

Serializable, Cloneable, Parameterized, Regressor, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class StochasticRidgeRegression
extends Object
implements Regressor, Parameterized, SingleWeightVectorModel
```
A Stochastic implementation of Ridge Regression. Ridge Regression is equivalent to MultipleLinearRegression with an added L₂ penalty for the weight vector.

This algorithm works best for problems with a large number of data points or very high dimensional problems.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`StochasticRidgeRegression(double lambda, int epochs, int batchSize, double learningRate)` Creates a new stochastic Ridge Regression learner that does not use a decay rate
`StochasticRidgeRegression(double lambda, int epochs, int batchSize, double learningRate, DecayRate learningDecay)` Creates a new stochastic Ridge Regression learner

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`StochasticRidgeRegression`	`clone()`
`int`	`getBatchSize()` Returns the batch size for updates
`double`	`getBias()` Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
`double`	`getBias(int index)` Returns the bias term used with the weight vector for the given class index.
`int`	`getEpochs()` Returns the number of training iterations
`double`	`getLambda()` Returns the regularization constant in use
`DecayRate`	`getLearningDecay()` Returns the learning decay rate used
`double`	`getLearningRate()` Returns the learning rate in use.
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`Vec`	`getRawWeight()` Returns the only weight vector used for the model
`Vec`	`getRawWeight(int index)` Returns the raw weight vector associated with the given class index.
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`double`	`regress(DataPoint data)`
`void`	`setBatchSize(int batchSize)` Sets the batch size to learn from.
`void`	`setEpochs(int epochs)` Sets the number of iterations through the whole training set that will be performed.
`void`	`setLambda(double lambda)` Sets the regularization parameter used.
`void`	`setLearningDecay(DecayRate learningDecay)` Sets the learning rate decay function to use.
`void`	`setLearningRate(double learningRate)` Sets the learning rate used, and should be in the range (0, 1).
`boolean`	`supportsWeightedData()`
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - StochasticRidgeRegression
```
public StochasticRidgeRegression(double lambda,
                                 int epochs,
                                 int batchSize,
                                 double learningRate)
```
    Creates a new stochastic Ridge Regression learner that does not use a decay rate
    
    Parameters:
    
    lambda - the regularization term
    
    epochs - the number of training epochs to perform
    
    batchSize - the batch size for updates
    
    learningRate - the learning rate
  - StochasticRidgeRegression
```
public StochasticRidgeRegression(double lambda,
                                 int epochs,
                                 int batchSize,
                                 double learningRate,
                                 DecayRate learningDecay)
```
    Creates a new stochastic Ridge Regression learner
    
    Parameters:
    
    lambda - the regularization term
    
    epochs - the number of training epochs to perform
    
    batchSize - the batch size for updates
    
    learningRate - the learning rate
    
    learningDecay - the learning rate decay
- Method Detail
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the regularization parameter used.
    
    Parameters:
    
    lambda - the positive regularization constant in (0, Inf)
  - getLambda
```
public double getLambda()
```
    Returns the regularization constant in use
    
    Returns:
    
    the regularization constant in use
  - setLearningRate
```
public void setLearningRate(double learningRate)
```
    Sets the learning rate used, and should be in the range (0, 1).
    
    Parameters:
    
    learningRate - the learning rate to use
  - getLearningRate
```
public double getLearningRate()
```
    Returns the learning rate in use.
    
    Returns:
    
    the learning rate to use.
  - setLearningDecay
```
public void setLearningDecay(DecayRate learningDecay)
```
    Sets the learning rate decay function to use. The decay is applied after each epoch through the data set. Using a decay rate can reduce the time to converge and quality of the solution for difficult problems.
    
    Parameters:
    
    learningDecay - the decay function to apply to the learning rate
  - getLearningDecay
```
public DecayRate getLearningDecay()
```
    Returns the learning decay rate used
    
    Returns:
    
    the learning decay rate used
  - setBatchSize
```
public void setBatchSize(int batchSize)
```
    Sets the batch size to learn from. If larger than the training set, the problem will reduce to classic gradient descent.
    
    Parameters:
    
    batchSize - the number of training points to use in each batch update
  - getBatchSize
```
public int getBatchSize()
```
    Returns the batch size for updates
    
    Returns:
    
    the batch size for updates
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of iterations through the whole training set that will be performed.
    
    Parameters:
    
    epochs - the number of training iterations
  - getEpochs
```
public int getEpochs()
```
    Returns the number of training iterations
    
    Returns:
    
    the number of training iterations
  - getRawWeight
```
public Vec getRawWeight()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the only weight vector used for the model
    
    Specified by:
    
    getRawWeight in interface SingleWeightVectorModel
    
    Returns:
    
    the only weight vector used for the model
  - getBias
```
public double getBias()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
    
    Specified by:
    
    getBias in interface SingleWeightVectorModel
    
    Returns:
    
    the bias term for the model
  - getRawWeight
```
public Vec getRawWeight(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
    Do not alter the returned weight vector, as it will change the model's values.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getRawWeight in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the weight vector used for the specified class
  - getBias
```
public double getBias(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getBias in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the bias term for the specified class
  - numWeightsVecs
```
public int numWeightsVecs()
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).
    
    Specified by:
    
    numWeightsVecs in interface SimpleWeightVectorModel
    
    Returns:
    
    the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Specified by:
    
    supportsWeightedData in interface Regressor
  - clone
```
public StochasticRidgeRegression clone()
```
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class StochasticRidgeRegression

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

StochasticRidgeRegression

StochasticRidgeRegression

Method Detail

setLambda

getLambda

setLearningRate

getLearningRate

setLearningDecay

getLearningDecay

setBatchSize

getBatchSize

setEpochs

getEpochs

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

regress

train

train

supportsWeightedData

clone

getParameters

getParameter