LWL (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.knn.LWL

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor
```
public class LWL
extends Object
implements Classifier, Regressor, Parameterized
```
Locally Weighted Learner (LW) is the combined generalized implementation of Locally Weighted Regression (LWR) and Locally Weighted Naive Bayes (LWNB). The concept is simple, prediction begins in a normal NN style. Instead of returning the prediction result as the average / majority of the found neighbors, a classifier is trained to represent the local area that is represented by the neighbors. The prediction result is then based on this locally trained model.
Because NN search is already slow, and increasing the search size increases the search time, it is recommended to use moderate sized values of k with simple models such as NaiveBayes and LinearRegression.
If the learning algorithm used does not support weighted data points, it will be as if the uniform kernel fucntion was used, regardless of whatever kernel function was set.

See:
- Atkeson, C., Moore, A.,&Schaal, S. (1997). Locally Weighted Learning. Artificial intelligence review, 11–73.
- Frank, E., Hall, M.,&Pfahringer, B. (2003). Locally Weighted Naive Bayes. Proceedings of the Conference on Uncertainty in Artificial Intelligence (pp. 249–256). Morgan Kaufmann.
Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`LWL(Classifier classifier, int k, DistanceMetric dm)` Creates a new LWL classifier
`LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf)` Creates a new LWL classifier
`LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf)` Creates a new LWL classifier
`LWL(Regressor regressor, int k, DistanceMetric dm)` Creates a new LWL Regressor
`LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf)` Creates a new LWL Regressor
`LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf)` Creates a new LWL Regressor

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.
`LWL`	`clone()`
`DistanceMetric`	`getDistanceMetric()` Returns the distance metric in use
`KernelFunction`	`getKernelFunction()` Returns the kernel function that will be used to set the weights.
`int`	`getNeighbors()` Returns the number of neighbors that will be used to create each local model
`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.
`static Distribution`	`guessNeighbors(DataSet d)` Guesses the distribution to use for the number of neighbors to consider
`double`	`regress(DataPoint data)`
`void`	`setDistanceMetric(DistanceMetric dm)` Sets the distance metric that will be used for the nearest neighbor search
`void`	`setKernelFunction(KernelFunction kf)` Sets the kernel function that will be used to set the weights of each data point in the local set
`void`	`setNeighbors(int k)` Sets the number of neighbors that will be used to create the local model
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`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
  - LWL
```
public LWL(Classifier classifier,
           int k,
           DistanceMetric dm)
```
    Creates a new LWL classifier
    
    Parameters:
    
    classifier - the local classifier to
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
  - LWL
```
public LWL(Classifier classifier,
           int k,
           DistanceMetric dm,
           KernelFunction kf)
```
    Creates a new LWL classifier
    
    Parameters:
    
    classifier - the local classifier to
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
    
    kf - the kernel function used to weight the local points
  - LWL
```
public LWL(Classifier classifier,
           int k,
           DistanceMetric dm,
           KernelFunction kf,
           VectorCollectionFactory<VecPaired<Vec,Double>> vcf)
```
    Creates a new LWL classifier
    
    Parameters:
    
    classifier - the local classifier to
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
    
    kf - the kernel function used to weight the local points
    
    vcf - the factory to create vector collections for storing the points
  - LWL
```
public LWL(Regressor regressor,
           int k,
           DistanceMetric dm)
```
    Creates a new LWL Regressor
    
    Parameters:
    
    regressor - the local regressor
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
  - LWL
```
public LWL(Regressor regressor,
           int k,
           DistanceMetric dm,
           KernelFunction kf)
```
    Creates a new LWL Regressor
    
    Parameters:
    
    regressor - the local regressor
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
    
    kf - the kernel function used to weight the local points
  - LWL
```
public LWL(Regressor regressor,
           int k,
           DistanceMetric dm,
           KernelFunction kf,
           VectorCollectionFactory<VecPaired<Vec,Double>> vcf)
```
    Creates a new LWL Regressor
    
    Parameters:
    
    regressor - the local regressor
    
    k - the number of neighbors to create a local classifier from
    
    dm - the metric to use when selecting the nearest points to a query
    
    kf - the kernel function used to weight the local points
    
    vcf - the factory to create vector collections for storing the points
- Method Detail
  - 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
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - 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
  - clone
```
public LWL clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - setNeighbors
```
public void setNeighbors(int k)
```
    Sets the number of neighbors that will be used to create the local model
    
    Parameters:
    
    k - the number of neighbors to obtain
  - getNeighbors
```
public int getNeighbors()
```
    Returns the number of neighbors that will be used to create each local model
    
    Returns:
    
    the number of neighbors that will be used
  - setDistanceMetric
```
public void setDistanceMetric(DistanceMetric dm)
```
    Sets the distance metric that will be used for the nearest neighbor search
    
    Parameters:
    
    dm - the distance metric to use for nearest neighbor search
  - getDistanceMetric
```
public DistanceMetric getDistanceMetric()
```
    Returns the distance metric in use
    
    Returns:
    
    the distance metric in use
  - setKernelFunction
```
public void setKernelFunction(KernelFunction kf)
```
    Sets the kernel function that will be used to set the weights of each data point in the local set
    
    Parameters:
    
    kf - the kernel function to use for weighting
  - getKernelFunction
```
public KernelFunction getKernelFunction()
```
    Returns the kernel function that will be used to set the weights.
    
    Returns:
    
    the kernel function that will be used to set the weights
  - guessNeighbors
```
public static Distribution guessNeighbors(DataSet d)
```
    Guesses the distribution to use for the number of neighbors to consider
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the Neighbors parameter
  - 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 LWL

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

LWL

LWL

LWL

LWL

LWL

LWL

Method Detail

classify

trainC

trainC

supportsWeightedData

regress

train

train

clone

setNeighbors

getNeighbors

setDistanceMetric

getDistanceMetric

setKernelFunction

getKernelFunction

guessNeighbors

getParameters

getParameter