jsat.classifiers.knn

Class NearestNeighbour

java.lang.Object

jsat.classifiers.knn.NearestNeighbour

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Parameterized, Regressor

public class NearestNeighbour
extends java.lang.Object
implements Classifier, Regressor, Parameterized

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

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, VectorCollection<VecPaired<Vec,java.lang.Double>> vcf) Constructs a new Nearest Neighbor Classifier
NearestNeighbour(int k, VectorCollection<VecPaired<Vec,java.lang.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)
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(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet dataSet, boolean parallel)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.regression.Regressor

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

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,
                        VectorCollection<VecPaired<Vec,java.lang.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,
                        VectorCollection<VecPaired<Vec,java.lang.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)

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.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

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:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

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 java.lang.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