LinearBatch (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.linear.LinearBatch

All Implemented Interfaces:

Serializable, Cloneable, Classifier, WarmClassifier, Parameterized, Regressor, WarmRegressor, SimpleWeightVectorModel
```
public class LinearBatch
extends Object
implements Classifier, Regressor, Parameterized, SimpleWeightVectorModel, WarmClassifier, WarmRegressor
```
LinearBatch learns either a classification or regression problem depending on the loss function ℓ(w,x) used. The solution attempts to minimize ∑_i ℓ(w,x_i) + λ₀/2 ||w||₂², and is trained using a batch optimization method.

LinearBatch can be warm started from any model implementing the SimpleWeightVectorModel interface.

Note: the current implementation does not currently use bias terms

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`class`	`LinearBatch.GradFunction` Function for using the single weight vector loss functions related to `LossC` and `LossR`
`class`	`LinearBatch.LossFunction` Function for using the single weight vector loss functions related to `LossC` and `LossR`.
`class`	`LinearBatch.LossMCFunction`

Constructor Summary

Constructors
Constructor and Description
`LinearBatch()` Creates a new Linear Batch learner for classification using a small regularization term
`LinearBatch(LinearBatch toCopy)` Copy constructor
`LinearBatch(LossFunc loss, double lambda0)` Creates a new Linear Batch learner
`LinearBatch(LossFunc loss, double lambda0, double tolerance)` Creates a new Linear Batch learner
`LinearBatch(LossFunc loss, double lambda0, double tolerance, Optimizer2 optimizer)` Creates a new Linear Batch learner

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`LinearBatch`	`clone()`
`double`	`getBias(int index)` Returns the bias term used with the weight vector for the given class index.
`double`	`getLambda0()` Returns the L₂ regularization term in use
`LossFunc`	`getLoss()` Returns the loss function in use
`Optimizer2`	`getOptimizer()` Returns the optimization method in use, or `null`.
`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(int index)` Returns the raw weight vector associated with the given class index.
`double`	`getTolerance()` Returns the value of the convergence tolerance parameter
`static Distribution`	`guessLambda0(DataSet d)` Guess the distribution to use for the regularization term `λ₀` .
`boolean`	`isUseBiasTerm()`
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`double`	`regress(DataPoint data)`
`void`	`setLambda0(double lambda0)` λ₀ controls the L₂ regularization penalty.
`void`	`setLoss(LossFunc loss)` Sets the loss function used for the model.
`void`	`setOptimizer(Optimizer2 optimizer)` Sets the method of batch optimization that will be used.
`void`	`setTolerance(double tolerance)` Sets the convergence tolerance to user for training.
`void`	`setUseBiasTerm(boolean useBiasTerm)`
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet D, ExecutorService threadPool)`
`void`	`train(RegressionDataSet dataSet, Regressor warmSolution)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`train(RegressionDataSet D, Regressor warmSolution, ExecutorService threadPool)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, Classifier warmSolution)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet D, Classifier warmSolution, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet D, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.
`boolean`	`warmFromSameDataOnly()` Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

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

- Constructor Detail
  - LinearBatch
```
public LinearBatch()
```
    Creates a new Linear Batch learner for classification using a small regularization term
  - LinearBatch
```
public LinearBatch(LossFunc loss,
                   double lambda0)
```
    Creates a new Linear Batch learner
    
    Parameters:
    
    loss - the loss function to use
    
    lambda0 - the L₂ regularization term
  - LinearBatch
```
public LinearBatch(LossFunc loss,
                   double lambda0,
                   double tolerance)
```
    Creates a new Linear Batch learner
    
    Parameters:
    
    loss - the loss function to use
    
    lambda0 - the L₂ regularization term
    
    tolerance - the threshold for convergence
  - LinearBatch
```
public LinearBatch(LossFunc loss,
                   double lambda0,
                   double tolerance,
                   Optimizer2 optimizer)
```
    Creates a new Linear Batch learner
    
    Parameters:
    
    loss - the loss function to use
    
    lambda0 - the L₂ regularization term
    
    tolerance - the threshold for convergence
    
    optimizer - the batch optimization method to use
  - LinearBatch
```
public LinearBatch(LinearBatch toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setUseBiasTerm
```
public void setUseBiasTerm(boolean useBiasTerm)
```
  - isUseBiasTerm
```
public boolean isUseBiasTerm()
```
  - setLambda0
```
public void setLambda0(double lambda0)
```
    λ₀ controls the L₂ regularization penalty.
    
    Parameters:
    
    lambda0 - the L₂ regularization penalty to use
  - getLambda0
```
public double getLambda0()
```
    Returns the L₂ regularization term in use
    
    Returns:
    
    the L₂ regularization term in use
  - setLoss
```
public void setLoss(LossFunc loss)
```
    Sets the loss function used for the model. The loss function controls whether or not regression, binary classification, or multi-class classification is supported.
    
    Parameters:
    
    loss - the loss function to use
  - getLoss
```
public LossFunc getLoss()
```
    Returns the loss function in use
    
    Returns:
    
    the loss function in use
  - setOptimizer
```
public void setOptimizer(Optimizer2 optimizer)
```
    Sets the method of batch optimization that will be used. null is valid for this value, in which case the implementation will attempt to select a reasonable optimizer automatically.
    
    NOTE: the current implementation requires the optimizer to work based off only the function value and its derivative.
    
    Parameters:
    
    optimizer - the method to use for function minimization
  - getOptimizer
```
public Optimizer2 getOptimizer()
```
    Returns the optimization method in use, or null.
    
    Returns:
    
    the optimization method in use, or null.
  - setTolerance
```
public void setTolerance(double tolerance)
```
    Sets the convergence tolerance to user for training. Smaller values reach a more accuracy solution but may take longer to complete.
    While zero is a valid tolerance value, it is not usually useful in practice. Values in [10^-4, 10^-2] are usually more practical.
    
    Parameters:
    
    tolerance - the convergence tolerance
  - getTolerance
```
public double getTolerance()
```
    Returns the value of the convergence tolerance parameter
    
    Returns:
    
    the convergence tolerance parameter
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   Classifier warmSolution)
```
    Description copied from interface: WarmClassifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface WarmClassifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - trainC
```
public void trainC(ClassificationDataSet D,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    D - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet D,
                   Classifier warmSolution,
                   ExecutorService threadPool)
```
    Description copied from interface: WarmClassifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface WarmClassifier
    
    Parameters:
    
    D - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - train
```
public void train(RegressionDataSet D,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  Regressor warmSolution)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression using the given data set.
    
    Specified by:
    
    train in interface WarmRegressor
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - train
```
public void train(RegressionDataSet D,
                  Regressor warmSolution,
                  ExecutorService threadPool)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    train in interface WarmRegressor
    
    Parameters:
    
    D - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - 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.
  - warmFromSameDataOnly
```
public boolean warmFromSameDataOnly()
```
    Description copied from interface: WarmClassifier
    
    Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
    When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmClassifier
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmRegressor
    
    Returns:
    
    true if the algorithm can only be warm started from the model trained on the exact same data set.
  - 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.
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - clone
```
public LinearBatch clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - guessLambda0
```
public static Distribution guessLambda0(DataSet d)
```
    Guess the distribution to use for the regularization term λ₀ .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the λ₀ parameter

Class LinearBatch

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

LinearBatch

LinearBatch

LinearBatch

LinearBatch

LinearBatch

Method Detail

setUseBiasTerm

isUseBiasTerm

setLambda0

getLambda0

setLoss

getLoss

setOptimizer

getOptimizer

setTolerance

getTolerance

classify

regress

trainC

trainC

trainC

trainC

train

train

train

train

getParameters

getParameter

warmFromSameDataOnly

getRawWeight

getBias

numWeightsVecs

supportsWeightedData

clone

guessLambda0