DenseMatrix

Documentation of 'smile.math.matrix.DenseMatrix' Java class DenseMatrix

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java.lang.Object
- smile.math.matrix.Matrix
- - smile.math.matrix.DenseMatrix

All Implemented Interfaces:

java.io.Serializable, MatrixMultiplication<DenseMatrix,DenseMatrix>

Direct Known Subclasses:

JMatrix
```
public abstract class DenseMatrix
extends Matrix
implements MatrixMultiplication<DenseMatrix,DenseMatrix>
```
An abstract interface of dense matrix.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

DenseMatrix()

Constructors
Constructor and Description
`DenseMatrix()`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`abstract DenseMatrix`	`aat()` Returns A * A'
`DenseMatrix`	`add(DenseMatrix b)` In place addition A = A + B
`DenseMatrix`	`add(DenseMatrix b, DenseMatrix c)` C = A + B
`DenseMatrix`	`add(double x)` In place element-wise addition A = A + x
`DenseMatrix`	`add(double x, DenseMatrix c)` Element-wise addition C = A + x
`abstract double`	`add(int i, int j, double x)` A[i][j] += x
`double[][]`	`array()` Return the two-dimensional array of matrix.
`abstract DenseMatrix`	`ata()` Returns A' * A
`abstract Cholesky`	`cholesky()` Returns the Cholesky decomposition.
`Cholesky`	`cholesky(boolean inPlace)` Returns the Cholesky decomposition.
`double[]`	`colMeans()` Returns the mean of each column for a matrix.
`double[]`	`colSums()` Returns the sum of each column for a matrix.
`abstract DenseMatrix`	`copy()` Returns a copy of this matrix.
`abstract double[]`	`data()` Returns the array of storing the matrix.
`DenseMatrix`	`div(DenseMatrix b)` In place element-wise division A = A / B A = A - B
`DenseMatrix`	`div(DenseMatrix b, DenseMatrix c)` C = A / B
`DenseMatrix`	`div(double x)` In place element-wise division A = A / x
`DenseMatrix`	`div(double x, DenseMatrix c)` Element-wise addition C = A / x
`abstract double`	`div(int i, int j, double x)` A[i][j] /= x
`abstract double[]`	`eig()` Returns the eigen values in an array of size 2N.
`double[]`	`eig(boolean inPlace)` Returns the eigen values in an array of size 2N.
`abstract EVD`	`eigen()` Returns the eigen value decomposition.
`EVD`	`eigen(boolean inPlace)` Returns the eigen value decomposition.
`DenseMatrix`	`inverse()` Returns the inverse matrix.
`DenseMatrix`	`inverse(boolean inPlace)` Returns the inverse matrix.
`abstract int`	`ld()` The LDA (and LDB, LDC, etc.) parameter in BLAS is effectively the stride of the matrix as it is laid out in linear memory.
`abstract LU`	`lu()` Returns the LU decomposition.
`LU`	`lu(boolean inPlace)` Returns the LU decomposition.
`DenseMatrix`	`mul(DenseMatrix b)` In place element-wise multiplication A = A * B
`DenseMatrix`	`mul(DenseMatrix b, DenseMatrix c)` C = A * B
`DenseMatrix`	`mul(double x)` In place element-wise multiplication A = A * x
`DenseMatrix`	`mul(double x, DenseMatrix c)` Element-wise addition C = A * x
`abstract double`	`mul(int i, int j, double x)` A[i][j] *= x
`double`	`norm()` L2 matrix norm.
`double`	`norm1()` L1 matrix norm.
`double`	`norm2()` L2 matrix norm.
`double`	`normFro()` Frobenius matrix norm.
`double`	`normInf()` Infinity matrix norm.
`abstract QR`	`qr()` Returns the QR decomposition.
`QR`	`qr(boolean inPlace)` Returns the QR decomposition.
`DenseMatrix`	`replaceNaN(double x)` Replaces NaN's with given value.
`double[]`	`rowMeans()` Returns the mean of each row for a matrix.
`double[]`	`rowSums()` Returns the sum of each row for a matrix.
`abstract double`	`set(int i, int j, double x)` Set the entry value at row i and column j.
`DenseMatrix`	`sub(DenseMatrix b)` In place subtraction A = A - B
`DenseMatrix`	`sub(DenseMatrix b, DenseMatrix c)` C = A - B
`DenseMatrix`	`sub(double x)` In place element-wise subtraction A = A - x
`DenseMatrix`	`sub(double x, DenseMatrix c)` Element-wise addition C = A - x
`abstract double`	`sub(int i, int j, double x)` A[i][j] -= x
`double`	`sum()` Returns the sum of all elements in the matrix.
`abstract SVD`	`svd()` Returns the singular value decomposition.
`SVD`	`svd(boolean inPlace)` Returns the singular value decomposition.
`abstract DenseMatrix`	`transpose()` Returns the matrix transpose.
`double`	`update(int i, int j, double x)` Set the entry value at row i and column j.
`double`	`xax(double[] x)` Returns x' * A * x.

Methods inherited from class smile.math.matrix.Matrix
apply, atx, atxpy, atxpy, ax, axpy, axpy, diag, diag, eigen, eigen, eye, eye, get, isSymmetric, ncols, newInstance, newInstance, newInstance, nrows, ones, randn, randn, setSymmetric, svd, svd, toString, toString, trace, zeros

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface smile.math.matrix.MatrixMultiplication
abmm, abtmm, atbmm

- Constructor Detail
  - DenseMatrix
```
public DenseMatrix()
```
- Method Detail
  - data
```
public abstract double[] data()
```
    Returns the array of storing the matrix.
  - ld
```
public abstract int ld()
```
    The LDA (and LDB, LDC, etc.) parameter in BLAS is effectively the stride of the matrix as it is laid out in linear memory. It is perfectly valid to have an LDA value which is larger than the leading dimension of the matrix which is being operated on. Typical cases where it is either useful or necessary to use a larger LDA value are when you are operating on a sub matrix from a larger dense matrix, and when hardware or algorithms offer performance advantages when storage is padded to round multiples of some optimal size (cache lines or GPU memory transaction size, or load balance in multiprocessor implementations, for example).
    
    Returns:
    
    the leading dimension
  - set
```
public abstract double set(int i,
                           int j,
                           double x)
```
    Set the entry value at row i and column j.
  - update
```
public double update(int i,
                     int j,
                     double x)
```
    Set the entry value at row i and column j. For Scala users.
  - lu
```
public abstract LU lu()
```
    Returns the LU decomposition. This input matrix will be overwritten with the decomposition.
  - lu
```
public LU lu(boolean inPlace)
```
    Returns the LU decomposition.
    
    Parameters:
    
    inPlace - if true, this matrix will be used for matrix decomposition.
  - cholesky
```
public abstract Cholesky cholesky()
```
    Returns the Cholesky decomposition. This input matrix will be overwritten with the decomposition.
    
    Throws:
    
    java.lang.IllegalArgumentException - if the matrix is not positive definite.
  - cholesky
```
public Cholesky cholesky(boolean inPlace)
```
    Returns the Cholesky decomposition.
    
    Parameters:
    
    inPlace - if true, this matrix will be used for matrix decomposition.
    
    Throws:
    
    java.lang.IllegalArgumentException - if the matrix is not positive definite.
  - qr
```
public abstract QR qr()
```
    Returns the QR decomposition. This input matrix will be overwritten with the decomposition.
  - qr
```
public QR qr(boolean inPlace)
```
    Returns the QR decomposition.
    
    Parameters:
    
    inPlace - if true, this matrix will be used for matrix decomposition.
  - svd
```
public abstract SVD svd()
```
    Returns the singular value decomposition. Note that the input matrix will hold U on output.
  - svd
```
public SVD svd(boolean inPlace)
```
    Returns the singular value decomposition.
    
    Parameters:
    
    inPlace - if true, this matrix will hold U on output.
  - eigen
```
public abstract EVD eigen()
```
    Returns the eigen value decomposition. Note that the input matrix will be overwritten on output.
  - eigen
```
public EVD eigen(boolean inPlace)
```
    Returns the eigen value decomposition.
    
    Parameters:
    
    inPlace - if true, this matrix will be overwritten U on output.
  - eig
```
public abstract double[] eig()
```
    Returns the eigen values in an array of size 2N. The first half and second half of returned array contain the real and imaginary parts, respectively, of the computed eigenvalues.
  - eig
```
public double[] eig(boolean inPlace)
```
    Returns the eigen values in an array of size 2N. The first half and second half of returned array contain the real and imaginary parts, respectively, of the computed eigenvalues.
    
    Parameters:
    
    inPlace - if true, this matrix will be overwritten U on output.
  - transpose
```
public abstract DenseMatrix transpose()
```
    Returns the matrix transpose.
    
    Specified by:
    
    transpose in class Matrix
  - inverse
```
public DenseMatrix inverse()
```
    Returns the inverse matrix.
  - inverse
```
public DenseMatrix inverse(boolean inPlace)
```
    Returns the inverse matrix.
    
    Parameters:
    
    inPlace - if true, this matrix will be used for matrix decomposition.
  - norm1
```
public double norm1()
```
    L1 matrix norm. Maximum column sum.
  - norm2
```
public double norm2()
```
    L2 matrix norm. Maximum singular value.
  - norm
```
public double norm()
```
    L2 matrix norm. Maximum singular value.
  - normInf
```
public double normInf()
```
    Infinity matrix norm. Maximum row sum.
  - normFro
```
public double normFro()
```
    Frobenius matrix norm. Sqrt of sum of squares of all elements.
  - xax
```
public double xax(double[] x)
```
    Returns x' * A * x. The left upper submatrix of A is used in the computation based on the size of x.
  - rowSums
```
public double[] rowSums()
```
    Returns the sum of each row for a matrix.
  - rowMeans
```
public double[] rowMeans()
```
    Returns the mean of each row for a matrix.
  - colSums
```
public double[] colSums()
```
    Returns the sum of each column for a matrix.
  - colMeans
```
public double[] colMeans()
```
    Returns the mean of each column for a matrix.
  - copy
```
public abstract DenseMatrix copy()
```
    Returns a copy of this matrix.
  - ata
```
public abstract DenseMatrix ata()
```
    Description copied from class: Matrix
    
    Returns A' * A
    
    Specified by:
    
    ata in class Matrix
  - aat
```
public abstract DenseMatrix aat()
```
    Description copied from class: Matrix
    
    Returns A * A'
    
    Specified by:
    
    aat in class Matrix
  - add
```
public abstract double add(int i,
                           int j,
                           double x)
```
    A[i][j] += x
  - sub
```
public abstract double sub(int i,
                           int j,
                           double x)
```
    A[i][j] -= x
  - mul
```
public abstract double mul(int i,
                           int j,
                           double x)
```
    A[i][j] *= x
  - div
```
public abstract double div(int i,
                           int j,
                           double x)
```
    A[i][j] /= x
  - add
```
public DenseMatrix add(DenseMatrix b,
                       DenseMatrix c)
```
    C = A + B
    
    Returns:
    
    the result matrix
  - add
```
public DenseMatrix add(DenseMatrix b)
```
    In place addition A = A + B
    
    Returns:
    
    this matrix
  - sub
```
public DenseMatrix sub(DenseMatrix b,
                       DenseMatrix c)
```
    C = A - B
    
    Returns:
    
    the result matrix
  - sub
```
public DenseMatrix sub(DenseMatrix b)
```
    In place subtraction A = A - B
    
    Returns:
    
    this matrix
  - mul
```
public DenseMatrix mul(DenseMatrix b,
                       DenseMatrix c)
```
    C = A * B
    
    Returns:
    
    the result matrix
  - mul
```
public DenseMatrix mul(DenseMatrix b)
```
    In place element-wise multiplication A = A * B
    
    Returns:
    
    this matrix
  - div
```
public DenseMatrix div(DenseMatrix b,
                       DenseMatrix c)
```
    C = A / B
    
    Returns:
    
    the result matrix
  - div
```
public DenseMatrix div(DenseMatrix b)
```
    In place element-wise division A = A / B A = A - B
    
    Returns:
    
    this matrix
  - add
```
public DenseMatrix add(double x,
                       DenseMatrix c)
```
    Element-wise addition C = A + x
  - add
```
public DenseMatrix add(double x)
```
    In place element-wise addition A = A + x
  - sub
```
public DenseMatrix sub(double x,
                       DenseMatrix c)
```
    Element-wise addition C = A - x
  - sub
```
public DenseMatrix sub(double x)
```
    In place element-wise subtraction A = A - x
  - mul
```
public DenseMatrix mul(double x,
                       DenseMatrix c)
```
    Element-wise addition C = A * x
  - mul
```
public DenseMatrix mul(double x)
```
    In place element-wise multiplication A = A * x
  - div
```
public DenseMatrix div(double x,
                       DenseMatrix c)
```
    Element-wise addition C = A / x
  - div
```
public DenseMatrix div(double x)
```
    In place element-wise division A = A / x
  - replaceNaN
```
public DenseMatrix replaceNaN(double x)
```
    Replaces NaN's with given value.
  - sum
```
public double sum()
```
    Returns the sum of all elements in the matrix.
    
    Returns:
    
    the sum of all elements.
  - array
```
public double[][] array()
```
    Return the two-dimensional array of matrix.
    
    Returns:
    
    the two-dimensional array of matrix.

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Summary:
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Field |
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Detail:
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Constr |
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Class DenseMatrix

Constructor Summary

Method Summary

Methods inherited from class smile.math.matrix.Matrix

Methods inherited from class java.lang.Object

Methods inherited from interface smile.math.matrix.MatrixMultiplication

Constructor Detail

DenseMatrix

Method Detail

data

ld

set

update

lu

lu

cholesky

cholesky

qr

qr

svd

svd

eigen

eigen

eig

eig

transpose

inverse

inverse

norm1

norm2

norm

normInf

normFro

xax

rowSums

rowMeans

colSums

colMeans

copy

ata

aat

add

sub

mul

div

add

add

sub

sub

mul

mul

div

div

add

add

sub

sub

mul

mul

div

div

replaceNaN

sum

array