jsat.linear

Class DenseVector

java.lang.Object

jsat.linear.Vec

jsat.linear.DenseVector

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, java.lang.Iterable<IndexValue>

public class DenseVector
extends Vec

A vector implementation that is dense, meaning all values are allocated - even if their values will be implicitly zero.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DenseVector(double[] array) Creates a new Dense Vector that uses the given array as its values.
DenseVector(double[] array, int start, int end) Creates a new Dense Vector that uses the given array as its values.
DenseVector(int length) Creates a new Dense Vector of zeros
DenseVector(java.util.List<java.lang.Double> list) Creates a new vector of the length of the given list, and values copied over in order.
DenseVector(Vec toCopy) Creates a new Dense Vector that contains a copy of the values in the given vector

Method Summary

Modifier and Type	Method and Description
double[]	arrayCopy() Creates a new array that contains all the values of this vector in the appropriate indices
DenseVector	clone()
DenseVector	deepCopy()
double	dot(Vec v) Computes the dot product between two vectors, which is equivalent to Σ thisi*vi This method should be overloaded for a serious implementation.
boolean	equals(java.lang.Object obj)
boolean	equals(java.lang.Object obj, double range)
double	get(int index) Gets the value stored at a specific index in the vector
boolean	isSparse() Indicates whether or not this vector is optimized for sparce computation, meaning that most values in the vector are zero - and considered implicit.
double	kurtosis() Computes the kurtosis of this vector, which is the 4th moment.
int	length() Returns the length of this vector
double	max() Returns the maximum value stored in this vector
double	median() Returns the median value in this vector
double	min() Returns the minimum value stored in this vector
void	multiply(double c, Matrix A, Vec b) If this is vector a, this this computes b = b + c aT*A
void	mutableAdd(double c) Alters this vector such that this = this + c This method should be overloaded for a serious implementation.
void	mutableAdd(double c, Vec b) Alters this vector such that this = this + c * b This method should be overloaded for a serious implementation.
void	mutableDivide(double c) Mutates this /= c This method should be overloaded for a serious implementation.
void	mutableMultiply(double c) Mutates this *= c This method should be overloaded for a serious implementation.
void	mutablePairwiseDivide(Vec b) Mutates this by dividing each value by the value in b that has the same index This method should be overloaded for a serious implementation.
void	mutablePairwiseMultiply(Vec b) Mutates this by multiplying each value by the value in b that has the same index.
void	mutableSubtract(double c) Alters this vector such that this = this - c
void	normalize() Mutates this vector to be normalized by the L2 norm
double	pNorm(double p) Returns the p-norm of this vector.
double	pNormDist(double p, Vec y) Returns the p-norm distance between this and another vector y.
void	set(int index, double val) Sets the value stored at a specified index in the vector
double	skewness() Computes the skewness of this vector, which is the 3rd moment.
DenseVector	sortedCopy() Returns a copy of this array with the values moved around so that they are in sorted order
double	sum() Computes the sum of the values in this vector
static DenseVector	toDenseVec(double... array) Returns a new dense vector backed by the given array.
double	variance() Computes the variance of the values in this vector, which is Vec.standardDeviation()2

Methods inherited from class jsat.linear.Vec

add, add, applyFunction, applyIndexFunction, canBeMutated, copyTo, copyToCol, copyToRow, countNaNs, divide, getNonZeroIterator, getNonZeroIterator, hashCode, increment, iterator, mean, multiply, multiply, multiply, mutableAdd, mutableSubtract, mutableSubtract, nnz, normalized, pairwiseDivide, pairwiseMultiply, random, random, standardDeviation, subtract, subtract, toString, zeroOut, zeros

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Constructor Detail

DenseVector

public DenseVector(int length)

Creates a new Dense Vector of zeros

Parameters:

length - the length of the vector

DenseVector

public DenseVector(java.util.List<java.lang.Double> list)

Creates a new vector of the length of the given list, and values copied over in order.

Parameters:

list - the list of values to copy into a new vector

DenseVector

public DenseVector(double[] array)

Creates a new Dense Vector that uses the given array as its values. Its values will not be copied, and raw access and mutations tot he given array may occur.

Parameters:

array - the backing array to use for a new vector of the same length

DenseVector

public DenseVector(double[] array,
                   int start,
                   int end)

Creates a new Dense Vector that uses the given array as its values. Its values will not be copied, and raw access and mutations tot he given array may occur.

Parameters:

array - the backing array to use for a new vector

start - the first index in the array, inclusive, to mark the start of the vector.

end - the last index in the array, exclusive, to mark the end of the vector.

DenseVector

public DenseVector(Vec toCopy)

Creates a new Dense Vector that contains a copy of the values in the given vector

Parameters:

toCopy - the vector to copy

Method Detail

length

public int length()

Description copied from class: Vec

Returns the length of this vector

Specified by:

length in class Vec

Returns:

the length of this vector

get

public double get(int index)

Description copied from class: Vec

Gets the value stored at a specific index in the vector

Specified by:

get in class Vec

Parameters:

index - the index to access

Returns:

the double value in the vector

set

public void set(int index,
                double val)

Description copied from class: Vec

Sets the value stored at a specified index in the vector

Specified by:

set in class Vec

Parameters:

index - the index to access

val - the value to store in the index

min

public double min()

Description copied from class: Vec

Returns the minimum value stored in this vector

Overrides:

min in class Vec

Returns:

the minimum value in this vector

max

public double max()

Description copied from class: Vec

Returns the maximum value stored in this vector

Overrides:

max in class Vec

Returns:

the maximum value in this vector

sum

public double sum()

Description copied from class: Vec

Computes the sum of the values in this vector

Overrides:

sum in class Vec

Returns:

the sum of this vector's values

median

public double median()

Description copied from class: Vec

Returns the median value in this vector

Overrides:

median in class Vec

Returns:

the median

skewness

public double skewness()

Description copied from class: Vec

Computes the skewness of this vector, which is the 3rd moment.

Overrides:

skewness in class Vec

Returns:

the skewness

kurtosis

public double kurtosis()

Description copied from class: Vec

Computes the kurtosis of this vector, which is the 4th moment.

Overrides:

kurtosis in class Vec

Returns:

the kurtosis

sortedCopy

public DenseVector sortedCopy()

Description copied from class: Vec

Returns a copy of this array with the values moved around so that they are in sorted order

Overrides:

sortedCopy in class Vec

Returns:

a new array in sorted order

variance

public double variance()

Description copied from class: Vec

Computes the variance of the values in this vector, which is Vec.standardDeviation()²

Overrides:

variance in class Vec

Returns:

the variance

dot

public double dot(Vec v)

Description copied from class: Vec

Computes the dot product between two vectors, which is equivalent to
Σ this_i*v_i

This method should be overloaded for a serious implementation.

Overrides:

dot in class Vec

Parameters:

v - the other vector

Returns:

the dot product of this vector and another

deepCopy

public DenseVector deepCopy()

multiply

public void multiply(double c,
                     Matrix A,
                     Vec b)

Description copied from class: Vec

If this is vector a, this this computes b = b + c a^T*A

Overrides:

multiply in class Vec

Parameters:

c - the constant factor to multiply by

A - the matrix to multiple by

b - the vector to mutate by adding the result to

mutableAdd

public void mutableAdd(double c)

Description copied from class: Vec

Alters this vector such that this = this + c

This method should be overloaded for a serious implementation.

Overrides:

mutableAdd in class Vec

Parameters:

c - a scalar constant to add to each value in this vector

mutableAdd

public void mutableAdd(double c,
                       Vec b)

Description copied from class: Vec

Alters this vector such that this = this + c * b

This method should be overloaded for a serious implementation.

Overrides:

mutableAdd in class Vec

Parameters:

c - a scalar constant

b - the vector to add to this

mutableSubtract

public void mutableSubtract(double c)

Description copied from class: Vec

Alters this vector such that this = this - c

Overrides:

mutableSubtract in class Vec

Parameters:

c - the scalar constant to subtract from all values in this vector

mutableMultiply

public void mutableMultiply(double c)

Description copied from class: Vec

Mutates this *= c

This method should be overloaded for a serious implementation.

Overrides:

mutableMultiply in class Vec

Parameters:

c - the constant to multiply by

mutableDivide

public void mutableDivide(double c)

Description copied from class: Vec

Mutates this /= c

This method should be overloaded for a serious implementation.

Overrides:

mutableDivide in class Vec

Parameters:

c - the constant to divide by

pNormDist

public double pNormDist(double p,
                        Vec y)

Description copied from class: Vec

Returns the p-norm distance between this and another vector y.

Overrides:

pNormDist in class Vec

Parameters:

p - the distance type. 2 is the common value

y - the other vector to compare against

Returns:

the p-norm distance

pNorm

public double pNorm(double p)

Description copied from class: Vec

Returns the p-norm of this vector.

Overrides:

pNorm in class Vec

Parameters:

p - the norm type. 2 is a common value

Returns:

the p-norm of this vector

clone

public DenseVector clone()

Specified by:

clone in class Vec

normalize

public void normalize()

Description copied from class: Vec

Mutates this vector to be normalized by the L₂ norm

Overrides:

normalize in class Vec

mutablePairwiseMultiply

public void mutablePairwiseMultiply(Vec b)

Description copied from class: Vec

Mutates this by multiplying each value by the value in b that has the same index.

This method should be overloaded for a serious implementation.

Overrides:

mutablePairwiseMultiply in class Vec

Parameters:

b - the vector to pairwise multiply by

mutablePairwiseDivide

public void mutablePairwiseDivide(Vec b)

Description copied from class: Vec

Mutates this by dividing each value by the value in b that has the same index

This method should be overloaded for a serious implementation.

Overrides:

mutablePairwiseDivide in class Vec

Parameters:

b - the vector to pairwise divide by

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class Vec

equals

public boolean equals(java.lang.Object obj,
                      double range)

Overrides:

equals in class Vec

toDenseVec

public static DenseVector toDenseVec(double... array)

Returns a new dense vector backed by the given array. This is a weak reference, the given array should no longer be altered - as it will effect the values of the dense vector.

Parameters:

array - the array to use as the backing of a dense vector

Returns:

a Dense Vector that is backed using the given array

arrayCopy

public double[] arrayCopy()

Description copied from class: Vec

Creates a new array that contains all the values of this vector in the appropriate indices

Overrides:

arrayCopy in class Vec

Returns:

a new array that is a copy of this vector

isSparse

public boolean isSparse()

Description copied from class: Vec

Indicates whether or not this vector is optimized for sparce computation, meaning that most values in the vector are zero - and considered implicit. Only non-zero values are stored.

Specified by:

isSparse in class Vec

Returns:

true if the vector is sparce, false otherwise.