jsat.classifiers.knn

Class NearestNeighbour

java.lang.Object

jsat.classifiers.knn.NearestNeighbour

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor

public class NearestNeighbour
extends Object
implements Classifier, Regressor, Parameterized

An implementation of the Nearest Neighbor algorithm, but with a British spelling! How fancy.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NearestNeighbour(int k) Constructs a new Nearest Neighbor Classifier
NearestNeighbour(int k, boolean weighted) Constructs a new Nearest Neighbor Classifier
NearestNeighbour(int k, boolean weighted, DistanceMetric distanceMetric) Constructs a new Nearest Neighbor Classifier
NearestNeighbour(int k, boolean weighted, DistanceMetric distanceMetric, VectorCollectionFactory<VecPaired<Vec,Double>> vcf) Constructs a new Nearest Neighbor Classifier
NearestNeighbour(int k, VectorCollectionFactory<VecPaired<Vec,Double>> vcf) Constructs a new Nearest Neighbor Classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
NearestNeighbour	clone()
DistanceMetric	getDistanceMetric()
int	getNeighbors() Returns the number of neighbors currently consulted to make decisions
int	getNeighbors(int k)
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 distanceMetric)
void	setNeighbors(int k) Sets the number of neighbors to consult when making decisions
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

NearestNeighbour

public NearestNeighbour(int k)

Constructs a new Nearest Neighbor Classifier

Parameters:

k - the number of neighbors to use

NearestNeighbour

public NearestNeighbour(int k,
                        VectorCollectionFactory<VecPaired<Vec,Double>> vcf)

Constructs a new Nearest Neighbor Classifier

Parameters:

k - the number of neighbors to use

vcf - the vector collection factory to use for storing and querying

NearestNeighbour

public NearestNeighbour(int k,
                        boolean weighted)

Constructs a new Nearest Neighbor Classifier

Parameters:

k - the number of neighbors to use

weighted - whether or not to weight the influence of neighbors by their distance

NearestNeighbour

public NearestNeighbour(int k,
                        boolean weighted,
                        DistanceMetric distanceMetric)

Constructs a new Nearest Neighbor Classifier

Parameters:

k - the number of neighbors to use

weighted - whether or not to weight the influence of neighbors by their distance

distanceMetric - the method of computing distance between two vectors.

NearestNeighbour

public NearestNeighbour(int k,
                        boolean weighted,
                        DistanceMetric distanceMetric,
                        VectorCollectionFactory<VecPaired<Vec,Double>> vcf)

Constructs a new Nearest Neighbor Classifier

Parameters:

k - the number of neighbors to use

weighted - whether or not to weight the influence of neighbors by their distance

distanceMetric - the method of computing distance between two vectors.

vcf - the vector collection factory to use for storing and querying

Method Detail

getNeighbors

public int getNeighbors()

Returns the number of neighbors currently consulted to make decisions

Returns:

the number of neighbors

setNeighbors

public void setNeighbors(int k)

Sets the number of neighbors to consult when making decisions

Parameters:

k - the number of neighbors to use

getNeighbors

public int getNeighbors(int k)

getDistanceMetric

public DistanceMetric getDistanceMetric()

setDistanceMetric

public void setDistanceMetric(DistanceMetric distanceMetric)

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.

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)

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

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.

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet)

Specified by:

train in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)

Specified by:

train in interface Regressor

clone

public NearestNeighbour clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class Object

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

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