LogisticRegressionDCD (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class LogisticRegressionDCD
extends Object
implements Classifier, Parameterized, SingleWeightVectorModel
```
This provides an implementation of regularized logistic regression using Dual Coordinate Descent. This algorithm works well on both dense and sparse large data sets.

The regularized problem is of the form:
C Σ log(1+exp(-y_iw^Tx_i)) + w^Tw/2

See:
Yu, H.-F., Huang, F.-L.,&Lin, C.-J. (2010). Dual Coordinate Descent Methods for Logistic Regression and Maximum Entropy Models. Machine Learning, 85(1-2), 41–75. doi:10.1007/s10994-010-5221-8

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`LogisticRegressionDCD()` Creates a new Logistic Regression learner that does no more than 100 training iterations with a default regularization tradeoff of C = 1
	`LogisticRegressionDCD(double C)` Creates a new Logistic Regression learner that does no more than 100 training iterations.
	`LogisticRegressionDCD(double C, int maxIterations)` Creates a new Logistic Regression learner
`protected`	`LogisticRegressionDCD(LogisticRegressionDCD toCopy)` Copy constructor

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.
`Classifier`	`clone()`
`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.
`double`	`getC()` Returns the regularization tradeoff parameter
`int`	`getMaxIterations()` Returns the maximum number of iterations the algorithm is allowed to run
`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.
`Vec`	`getWeightVec()` Returns the weight vector used to compute results via a dot product.
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` in Logistic Regression.
`boolean`	`isUseBias()` Returns `true` if a bias term is in use, `false` otherwise.
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setC(double C)` Sets the regularization trade-off term.
`void`	`setMaxIterations(int maxIterations)` Sets the maximum number of iterations the algorithm is allowed to run for.
`void`	`setUseBias(boolean useBias)` Sets whether or not an implicit bias term should be added to the model.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

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

- Constructor Detail
  - LogisticRegressionDCD
```
public LogisticRegressionDCD()
```
    Creates a new Logistic Regression learner that does no more than 100 training iterations with a default regularization tradeoff of C = 1
  - LogisticRegressionDCD
```
public LogisticRegressionDCD(double C)
```
    Creates a new Logistic Regression learner that does no more than 100 training iterations.
    
    Parameters:
    
    C - the regularization tradeoff term
  - LogisticRegressionDCD
```
public LogisticRegressionDCD(double C,
                             int maxIterations)
```
    Creates a new Logistic Regression learner
    
    Parameters:
    
    C - the regularization tradeoff term
    
    maxIterations - the maximum number of iterations through the data set
  - LogisticRegressionDCD
```
protected LogisticRegressionDCD(LogisticRegressionDCD toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setC
```
public void setC(double C)
```
    Sets the regularization trade-off term. larger values reduce the amount of regularization, and smaller values increase the regularization.
    
    Parameters:
    
    C - the positive regularization tradeoff value
  - getC
```
public double getC()
```
    Returns the regularization tradeoff parameter
    
    Returns:
    
    the regularization tradeoff parameter
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximum number of iterations the algorithm is allowed to run for.
    
    Parameters:
    
    maxIterations - the maximum number of iterations
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations the algorithm is allowed to run
    
    Returns:
    
    the maximum number of iterations the algorithm is allowed to run
  - setUseBias
```
public void setUseBias(boolean useBias)
```
    Sets whether or not an implicit bias term should be added to the model.
    
    Parameters:
    
    useBias - true to add a bias term, false to exclude the bias term.
  - isUseBias
```
public boolean isUseBias()
```
    Returns true if a bias term is in use, false otherwise.
    
    Returns:
    
    true if a bias term is in use, false otherwise.
  - 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.
  - 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.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   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:
    
    dataSet - the data set to train on
    
    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
  - 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
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - clone
```
public Classifier clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - getWeightVec
```
public Vec getWeightVec()
```
    Returns the weight vector used to compute results via a dot product.
    Do not modify this value, or you will alter the results returned.
    
    Returns:
    
    the learned weight vector for prediction
  - 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.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C in Logistic Regression.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter

Class LogisticRegressionDCD

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

LogisticRegressionDCD

LogisticRegressionDCD

LogisticRegressionDCD

LogisticRegressionDCD

Method Detail

setC

getC

setMaxIterations

getMaxIterations

setUseBias

isUseBias

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

classify

trainC

trainC

supportsWeightedData

clone

getWeightVec

getParameters

getParameter

guessC