jsat.linear.distancemetrics

Class ManhattanDistance

java.lang.Object

jsat.linear.distancemetrics.ManhattanDistance

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, DenseSparseMetric, DistanceMetric

public class ManhattanDistance
extends java.lang.Object
implements DenseSparseMetric

Manhattan Distance is the L₁ norm.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ManhattanDistance()

Method Summary

Modifier and Type	Method and Description
ManhattanDistance	clone()
double	dist(double summaryConst, Vec main, Vec target) Efficiently computes the distance from one main vector that is used many times, to some sparse target vector.
double	dist(Vec a, Vec b) Computes the distance between 2 vectors.
double	getVectorConstant(Vec vec) Computes a summary constant value for the vector that is based on the distance metric in use.
boolean	isIndiscemible() Returns true if this distance metric obeys the rule that, for any x and y ∈ S d(x, y) = 0 if and only if x = y
boolean	isSubadditive() Returns true if this distance metric obeys the rule that, for any x, y, and z ∈ S d(x, z) ≤ d(x, y) + d(y, z)
boolean	isSymmetric() Returns true if this distance metric obeys the rule that, for any x, y, and z ∈ S d(x, y) = d(y, x)
double	metricBound() All metrics must return values greater than or equal to 0.
java.lang.String	toString() Returns a descriptive name of the Distance Metric in use

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.linear.distancemetrics.DistanceMetric

dist, dist, dist, getAccelerationCache, getAccelerationCache, getQueryInfo, isValidMetric, supportsAcceleration

Constructor Detail

ManhattanDistance

public ManhattanDistance()

Method Detail

dist

public double dist(Vec a,
                   Vec b)

Description copied from interface: DistanceMetric

Computes the distance between 2 vectors. The smaller the value, the closer, and there for, more similar, the vectors are. 0 indicates the vectors are the same.

Specified by:

dist in interface DistanceMetric

Parameters:

a - the first vector

b - the second vector

Returns:

the distance between them

isSymmetric

public boolean isSymmetric()

Description copied from interface: DistanceMetric

Returns true if this distance metric obeys the rule that, for any x, y, and z ∈ S
d(x, y) = d(y, x)

Specified by:

isSymmetric in interface DistanceMetric

Returns:

true if this distance metric is symmetric, false if it is not

isSubadditive

public boolean isSubadditive()

Description copied from interface: DistanceMetric

Returns true if this distance metric obeys the rule that, for any x, y, and z ∈ S
d(x, z) ≤ d(x, y) + d(y, z)

Specified by:

isSubadditive in interface DistanceMetric

Returns:

true if this distance metric supports the triangle inequality, false if it does not.

isIndiscemible

public boolean isIndiscemible()

Description copied from interface: DistanceMetric

Returns true if this distance metric obeys the rule that, for any x and y ∈ S
d(x, y) = 0 if and only if x = y

Specified by:

isIndiscemible in interface DistanceMetric

Returns:

true if this distance metric is indicemible, false otherwise.

metricBound

public double metricBound()

Description copied from interface: DistanceMetric

All metrics must return values greater than or equal to 0. The upper bound on the value returned is different for different metrics. This method returns the theoretical maximal value that could be returned by this distance metric. That means Double.POSITIVE_INFINITY is a valid return value.

Specified by:

metricBound in interface DistanceMetric

Returns:

the maximal distance for any two points in that could exist by this distance metric.

toString

public java.lang.String toString()

Description copied from interface: DistanceMetric

Returns a descriptive name of the Distance Metric in use

Specified by:

toString in interface DistanceMetric

Overrides:

toString in class java.lang.Object

Returns:

the name of this metric

clone

public ManhattanDistance clone()

Specified by:

clone in interface DistanceMetric

Overrides:

clone in class java.lang.Object

getVectorConstant

public double getVectorConstant(Vec vec)

Description copied from interface: DenseSparseMetric

Computes a summary constant value for the vector that is based on the distance metric in use. This value will be used to perform efficient dense to sparse computations.

Specified by:

getVectorConstant in interface DenseSparseMetric

Parameters:

vec - the vector that will be used in many distance computations

Returns:

the summary value for the vector

dist

public double dist(double summaryConst,
                   Vec main,
                   Vec target)

Description copied from interface: DenseSparseMetric

Efficiently computes the distance from one main vector that is used many times, to some sparse target vector. If the target vector dose not return true for Vec.isSparse(), the distance will be calculated using DistanceMetric.dist(jsat.linear.Vec, jsat.linear.Vec) instead.

Specified by:

dist in interface DenseSparseMetric

Parameters:

summaryConst - the summary constant for the main vector obtained with DenseSparseMetric.getVectorConstant(jsat.linear.Vec)

main - the main vector the summary constant is for

target - the target vector to compute the distance to

Returns:

the distance between the two vectors dist(main, target)