gov.sandia.cognition.math.matrix.mtj

Class AbstractSparseMatrix

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.math.AbstractRing<Matrix>

gov.sandia.cognition.math.matrix.AbstractMatrix

gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix

gov.sandia.cognition.math.matrix.mtj.AbstractSparseMatrix

All Implemented Interfaces:

Matrix, Vectorizable, Ring<Matrix>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable, java.lang.Iterable<MatrixEntry>

Direct Known Subclasses:

SparseColumnMatrix, SparseRowMatrix

@CodeReview(reviewer="Jonathan McClain",
            date="2006-05-19",
            changesNeeded=false,
            comments="Looks fine.")
public abstract class AbstractSparseMatrix
extends AbstractMTJMatrix

Implements some generic operations that any sparse-matrix representation must do.

Since:

1.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	AbstractSparseMatrix(no.uib.cipr.matrix.Matrix internalMatrix) Creates a new instance of AbstractSparseMatrix using the given MTJ matrix

Method Summary

Modifier and Type	Method and Description
abstract void	compact() Compact the memory used by the matrix, getting rid of any zero elements
SparseVector	getColumn(int columnIndex) Gets the specified column from the zero-based index and returns a vector that corresponds to that column.
SparseVector	getRow(int rowIndex) Gets the specified row from the zero-based index and returns a vector that corresponds to that column
boolean	isSparse() Returns true if this matrix has a potentially sparse underlying structure.
DenseMatrix	pseudoInverse(double effectiveZero) Computes the effective pseudo-inverse of this, using a rather expensive procedure (SVD)
void	setElement(int rowIndex, int columnIndex, double value) Sets the Matrix element at the specified zero-based indices throws ArrayIndexOutOfBoundsException if either rowIndex or columnIndex are less than 0, or greater than the number of rows (columns) minus one (0 <= index <= num-1)
SparseVector	solve(AbstractMTJVector b) This sparse-vector solver performs iterative solving for "x" in the equation: this*x = b, and the AbstractSparseMatrix "this" can be unstructured (e.g., asymmetric, indefinite, etc.)
SparseVector	times(AbstractMTJVector vector) Returns the column vector from the equation return = this * vector
java.lang.String	toString() Creates a string in the "(Row,Column): value" for the nonzero elements
java.lang.String	toString(java.text.NumberFormat format) Converts the vector to a String, using the given formatter.

Methods inherited from class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix

clone, convertFromVector, convertToVector, dotTimesEquals, dotTimesEquals, equals, get, getElement, getInternalMatrix, getNumColumns, getNumRows, getSubMatrixInto, identity, increment, inverse, isSquare, isSymmetric, iterator, logDeterminant, minusEquals, minusEquals, normFrobenius, normFrobeniusSquared, plusEquals, plusEquals, rank, scaledMinusEquals, scaledPlusEquals, scaledPlusEquals, scaleEquals, set, setInternalMatrix, solve, solve, solve, times, times, times, timesInto, timesInto, transposeInto, zero

Methods inherited from class gov.sandia.cognition.math.matrix.AbstractMatrix

assertMultiplicationDimensions, assertSameDimensions, checkMultiplicationDimensions, checkSameDimensions, decrement, decrement, dotDivide, dotDivideEquals, equals, equals, getColumnInto, getRowInto, hashCode, increment, isSymmetric, isZero, pseudoInverse, rank, setColumn, setRow, setSubMatrix, sumOfColumns, sumOfRows, toArray, trace, valuesAsList

Methods inherited from class gov.sandia.cognition.math.AbstractRing

dotTimes, isZero, minus, negative, negativeEquals, plus, scale, scaledMinus, scaledMinusEquals, scaledPlus

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.math.matrix.Matrix

getEntryCount, getMatrixFactory, getSubMatrix, transpose

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Methods inherited from interface gov.sandia.cognition.math.Ring

dotTimes, isZero, minus, negative, negativeEquals, plus, scale, scaledMinus, scaledMinusEquals, scaledPlus

Constructor Detail

AbstractSparseMatrix

protected AbstractSparseMatrix(no.uib.cipr.matrix.Matrix internalMatrix)

Creates a new instance of AbstractSparseMatrix using the given MTJ matrix

Parameters:

internalMatrix - internal MTJ matrix to base this on

Method Detail

compact

public abstract void compact()

Compact the memory used by the matrix, getting rid of any zero elements

times

public SparseVector times(AbstractMTJVector vector)

Description copied from class: AbstractMTJMatrix

Returns the column vector from the equation return = this * vector

Specified by:

times in class AbstractMTJMatrix

Parameters:

vector - Vector by which to post-multiply this, must have the same number of rows as this

Returns:

Vector with the same dimensionality as the number of rows as this and vector

setElement

public void setElement(int rowIndex,
                       int columnIndex,
                       double value)

Description copied from interface: Matrix

Sets the Matrix element at the specified zero-based indices throws ArrayIndexOutOfBoundsException if either rowIndex or columnIndex are less than 0, or greater than the number of rows (columns) minus one (0 <= index <= num-1)

Specified by:

setElement in interface Matrix

Overrides:

setElement in class AbstractMTJMatrix

Parameters:

rowIndex - Zero-based index into the rows of the Matrix

columnIndex - Zero-based index into the columns of the Matrix

value - Value to set at the specified index

solve

public SparseVector solve(AbstractMTJVector b)

This sparse-vector solver performs iterative solving for "x" in the equation: this*x = b, and the AbstractSparseMatrix "this" can be unstructured (e.g., asymmetric, indefinite, etc.)

Overrides:

solve in class AbstractMTJMatrix

Parameters:

b - must satisfy this.getNumColumns() == b.getDimensionality()

Returns:

x Vector with dimensions (this.getNumRows())

pseudoInverse

public DenseMatrix pseudoInverse(double effectiveZero)

Description copied from interface: Matrix

Computes the effective pseudo-inverse of this, using a rather expensive procedure (SVD)

Parameters:

effectiveZero - effective zero to pass along to the SVD

Returns:

effective pseudo-inverse of this

toString

public java.lang.String toString()

Creates a string in the "(Row,Column): value" for the nonzero elements

Specified by:

toString in interface Matrix

Overrides:

toString in class java.lang.Object

Returns:

String representing the sparse matrix

toString

public java.lang.String toString(java.text.NumberFormat format)

Description copied from interface: Matrix

Converts the vector to a String, using the given formatter.

Parameters:

format - The number format to use.

Returns:

The String representation of the Matrix.

getColumn

public SparseVector getColumn(int columnIndex)

Description copied from interface: Matrix

Gets the specified column from the zero-based index and returns a vector that corresponds to that column.

Parameters:

columnIndex - zero-based index into the matrix

Returns:

Vector with elements equal to the number of rows in this

getRow

public SparseVector getRow(int rowIndex)

Description copied from interface: Matrix

Gets the specified row from the zero-based index and returns a vector that corresponds to that column

Parameters:

rowIndex - zero-based index into the matrix

Returns:

Vector with elements equal to the number of columns in this

isSparse

public boolean isSparse()

Description copied from interface: Matrix

Returns true if this matrix has a potentially sparse underlying structure. This can indicate that it is faster to only process the non-zero elements rather than to do dense operations on it. Of course, even with a sparse structure, there may be no zero elements or conversely even with a non-sparse (dense) structure there may be many zero elements.

Returns:

True if the matrix has a potentially sparse structure. Otherwise, false.