Class Vector

java.lang.Object

smile.tensor.DenseMatrix

smile.tensor.Vector

All Implemented Interfaces:

Serializable, Matrix, Tensor

public abstract class Vector
extends DenseMatrix

Mathematical vector interface. Vectors are a specialized case of matrices. Specifically, a vector is a matrix with either one row (a row-vector) or one column (a column-vector). Column-vectors are the more common representation and are often simply referred to as vectors.

See Also:

• Serialized Form

Method Summary

Modifier and Type	Method	Description
abstract void	add(int i, double x)	Sets A[i] += x.
double	apply(int i)	Returns A[i] for Scala users.
double	asum()	Sums the absolute values of the elements of the vector.
void	axpy(double alpha, Vector x)	Computes a constant alpha times a vector x plus this vector y.
static Vector	column(double[] array)	Creates a column vector.
static Vector	column(float[] array)	Creates a column vector.
Vector	column(int j)	Returns the j-th column.
Vector	copy()	Returns a deep copy of matrix.
abstract Vector	copy(int from, int to)	Copies the specified range of the vector into a new vector.
static void	copy(Vector src, int pos, Vector dest, int destPos, int length)	Copies the specified range of the vector into another vector.
DenseMatrix	diagflat()	Returns the matrix with the elements of this vector as the diagonal.
abstract void	div(int i, double x)	Sets A[i] /= x.
double	dot(Vector x)	Computes the dot product of two vectors.
abstract void	fill(int from, int to, double value)	Assigns the specified value to each element of the specified range of the specified vector.
abstract double	get(int i)	Returns A[i].
int	iamax()	Searches a vector for the first occurrence of the maximum absolute value.
double	max()	Returns the maximal elements of the vector.
double	mean()	Returns the mean of the elements of the vector.
double	min()	Returns the minimal elements of the vector.
abstract void	mul(int i, double x)	Sets A[i] *= x.
double	norm1()	Computes the L1 norm of a vector.
double	norm2()	Computes the Euclidean (L2) norm of a vector.
double	normInf()	Computes the L-Infinity norm of a vector.
Vector	ones(int length)	Returns a one column vector of the same scalar type as this vector.
static Vector	ones(ScalarType scalarType, int length)	Returns a one column vector.
static Vector	row(double[] array)	Creates a row vector.
static Vector	row(float[] array)	Creates a row vector.
Vector	scale(double alpha)	A *= alpha
abstract void	set(int i, double x)	Sets A[i] = x.
abstract int	size()	Returns the number of elements.
abstract Vector	slice(int from, int to)	Returns a slice of vector, which shares the data storage.
abstract int	softmax()	The softmax function without overflow.
abstract void	sub(int i, double x)	Sets A[i] -= x.
double	sum()	Sums the elements of the vector.
void	swap(Vector x)	Swaps two vectors.
abstract double[]	toArray(double[] a)	Returns an array containing all the elements in this vector.
abstract float[]	toArray(float[] a)	Returns an array containing all the elements in this vector.
void	update(int i, double x)	Sets A[i] = x for Scala users.
Vector	zeros(int length)	Returns a zero column vector of the same scalar type as this vector.
static Vector	zeros(ScalarType scalarType, int length)	Returns a zero column vector.

Methods inherited from class DenseMatrix

aat, add, add, ata, axpy, cholesky, colMeans, colNames, colSds, colSums, columns, columns, diag, diagflat, diagflat, eigen, eigen, equals, eye, eye, eye, eye, fill, ger, inverse, isSymmetric, ld, lu, memory, mm, mm, mt, mv, ncol, nrow, of, of, order, qr, rand, rand, rand, randn, row, rowMeans, rowNames, rows, rows, rowSums, standardize, standardize, sub, submatrix, svd, svd, tm, toeplitz, toeplitz, toeplitz, toeplitz, toString, transpose, uplo, withColNames, withDiag, withRowNames, withUplo, zeros, zeros

Methods inherited from class Object

clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface Matrix

add, apply, diagonal, dim, div, get, get, length, mul, mv, mv, mv, mv, mv, reshape, set, set, shape, size, sub, toArray, toArray, toString, toString, trace, tv, tv, tv, tv, tv, update, vector, vector, vector, xAx

Methods inherited from interface Tensor

scalarType

Method Details

size

public abstract int size()

Returns the number of elements.

Returns:

the number of elements.

get

public abstract double get(int i)

Returns A[i].

Parameters:

i - the row index.

Returns:

the cell value.

apply

public double apply(int i)

Returns A[i] for Scala users.

Parameters:

i - the index.

Returns:

the cell value.

set

public abstract void set(int i,
 double x)

Sets A[i] = x.

Parameters:

i - the index.

x - the cell value.

update

public void update(int i,
 double x)

Sets A[i] = x for Scala users.

Parameters:

i - the index.

x - the cell value.

add

public abstract void add(int i,
 double x)

Sets A[i] += x.

Parameters:

i - the index.

x - the operand.

sub

public abstract void sub(int i,
 double x)

Sets A[i] -= x.

Parameters:

i - the index.

x - the operand.

mul

public abstract void mul(int i,
 double x)

Sets A[i] *= x.

Parameters:

i - the index.

x - the operand.

div

public abstract void div(int i,
 double x)

Sets A[i] /= x.

Parameters:

i - the index.

x - the operand.

slice

public abstract Vector slice(int from,
 int to)

Returns a slice of vector, which shares the data storage.

Parameters:

from - the initial index of the range to be copied, inclusive.

to - the final index of the range to be copied, exclusive.

Returns:

a slice of this vector.

column

public Vector column(int j)

Description copied from class: DenseMatrix

Returns the j-th column. Negative index -j means the j-th row from the end.

Specified by:

column in class DenseMatrix

Parameters:

j - the column index.

Returns:

the column.

copy

public Vector copy()

Description copied from interface: Matrix

Returns a deep copy of matrix.

Specified by:

copy in interface Matrix

Specified by:

copy in class DenseMatrix

Returns:

a deep copy of matrix.

copy

public abstract Vector copy(int from,
 int to)

Copies the specified range of the vector into a new vector. The final index of the range may be greater than vector length, in which case 0 is placed in all extra elements.

Parameters:

from - the initial index of the range to be copied, inclusive.

to - the final index of the range to be copied, exclusive. This index may lie outside the array.

Returns:

a copy of this vector.

copy

public static void copy(Vector src,
 int pos,
 Vector dest,
 int destPos,
 int length)

Copies the specified range of the vector into another vector.

Parameters:

src - the destination vector.

pos - starting position in this vector.

dest - the destination vector.

destPos - starting position in the destination vector.

length - the number of vector elements to be copied.

toArray

public abstract double[] toArray(double[] a)

Returns an array containing all the elements in this vector.

Parameters:

a - the array into which the elements of the vector are to be stored if it is big enough; otherwise, a new array is allocated.

Returns:

an array containing the elements of the vector.

toArray

public abstract float[] toArray(float[] a)

Returns an array containing all the elements in this vector.

Parameters:

a - the array into which the elements of the vector are to be stored, if it is big enough; otherwise, a new array is allocated.

Returns:

an array containing the elements of the vector.

fill

public abstract void fill(int from,
 int to,
 double value)

Assigns the specified value to each element of the specified range of the specified vector.

Parameters:

from - the index of the first element (inclusive) to be filled with the specified value.

to - the index of the last element (exclusive) to be filled with the specified value.

value - the value to be stored in specified range of the vector.

diagflat

public DenseMatrix diagflat()

Returns the matrix with the elements of this vector as the diagonal.

Returns:

the diagonal matrix.

column

public static Vector column(double[] array)

Creates a column vector.

Parameters:

array - the primitive array backing the vector.

Returns:

a column vector.

column

public static Vector column(float[] array)

Creates a column vector.

Parameters:

array - the primitive array backing the vector.

Returns:

a column vector.

row

public static Vector row(double[] array)

Creates a row vector.

Parameters:

array - the primitive array backing the vector.

Returns:

a row vector.

row

public static Vector row(float[] array)

Creates a row vector.

Parameters:

array - the primitive array backing the vector.

Returns:

a row vector.

zeros

public static Vector zeros(ScalarType scalarType,
 int length)

Returns a zero column vector.

Parameters:

scalarType - the scalar type.

length - the length of vector.

Returns:

a zero column vector.

zeros

public Vector zeros(int length)

Returns a zero column vector of the same scalar type as this vector.

Parameters:

length - the length of vector.

Returns:

a zero column vector.

ones

public static Vector ones(ScalarType scalarType,
 int length)

Returns a one column vector.

Parameters:

scalarType - the scalar type.

length - the length of vector.

Returns:

a one column vector.

ones

public Vector ones(int length)

Returns a one column vector of the same scalar type as this vector.

Parameters:

length - the length of vector.

Returns:

a one column vector.

min

public double min()

Returns the minimal elements of the vector.

Returns:

the minimal elements of the vector.

max

public double max()

Returns the maximal elements of the vector.

Returns:

the maximal elements of the vector.

sum

public double sum()

Sums the elements of the vector.

Returns:

Sum of the elements of the vector.

mean

public double mean()

Returns the mean of the elements of the vector.

Returns:

the mean of the elements of the vector.

softmax

public abstract int softmax()

The softmax function without overflow. The function normalizes the vector into a probability distribution proportional to the exponentials of the input numbers. That is, prior to applying softmax, some vector components could be negative, or greater than one; and might not sum to 1; but after applying softmax, each component will be in the interval (0,1), and the components will add up to 1, so that they can be interpreted as probabilities. Furthermore, the larger input components will correspond to larger probabilities.

Returns:

the index of largest posteriori probability.

asum

public double asum()

Sums the absolute values of the elements of the vector.

Returns:

Sum of the absolute values of the elements of the vector.

axpy

public void axpy(double alpha,
 Vector x)

Computes a constant alpha times a vector x plus this vector y. The result overwrites the initial values of this vector y.

Parameters:

alpha - If alpha = 0 this routine returns without any computation.

x - Input vector.

dot

public double dot(Vector x)

Computes the dot product of two vectors.

Parameters:

x - Input vector contains the second vector operand.

Returns:

dot product.

norm2

public double norm2()

Computes the Euclidean (L2) norm of a vector.

Returns:

Euclidean norm.

norm1

public double norm1()

Computes the L1 norm of a vector.

Returns:

L1 norm.

normInf

public double normInf()

Computes the L-Infinity norm of a vector.

Returns:

L-Infinity norm.

scale

public Vector scale(double alpha)

Description copied from interface: Matrix

A *= alpha

Specified by:

scale in interface Matrix

Overrides:

scale in class DenseMatrix

Parameters:

alpha - the scaling factor.

Returns:

this matrix.

swap

public void swap(Vector x)

Swaps two vectors.

Parameters:

x - Vector to be swapped.

iamax

public int iamax()

Searches a vector for the first occurrence of the maximum absolute value.

Returns:

The first index of the maximum absolute value of vector x.