Uses of Interface
sugr.linalg.Matrix

Packages that use Matrix

sugr	This package (SUGR) is a library for Statistically Uncertain Geometric reasoning.
sugr.GUI	This package includes two simple applets generated with the SUGR library.
sugr.linalg	This package includes the Matrix and Vector interfaces and implementations for SUGR.

Uses of Matrix in sugr

Fields in sugr declared as Matrix

private Matrix	ProjectiveCamera.P Stores the Point-Projection and Line-Projection matrices (for speed-optimisation only)
private Matrix	ProjectiveCamera.Q Stores the Point-Projection and Line-Projection matrices (for speed-optimisation only)
private Matrix	ProjectiveCamera.Qdual Stores the Point-Projection and Line-Projection matrices (for speed-optimisation only)
protected Matrix	Relation.Sigma Represents the covariance matrix of the non-squared, non-normalized test value d.
protected Matrix	Element.cov Represents the Covariance Matrix

Methods in sugr that return Matrix

private static Matrix	ScaledMotion_3D.leaveOutProjectivePartsOfJacobian(Matrix A) Leave out those columns of the Jacobian of a ScaledMotion estimation which correspond to the 3 projective values (the last row of the transformation matrix)
Matrix	ScaledMotion_3D.getJacobianOfConstraint() Get the Jacobian of the constraint (H, G) if this entity is an unkown or an observation.
Matrix	Incident.getJacobian() Return Matrix Jacobian A of the first Entity and the relation type.
Matrix	Incident.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
Matrix	Parallel.getJacobian() Return Jacobian Matrix A of the first Entity and the relation type.
Matrix	Parallel.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
protected Matrix	Plane_3D.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
Matrix	Plane_3D.Pi() returns the pi-jacobian of this plane
static Matrix	Plane_3D.Pi(Vector v_in) returns the pi-jacobian of a 4x1 vector
Matrix	Plane_3D.PiDual() returns the pidual-jacobian of the dual of this plane
static Matrix	Plane_3D.PiDual(Vector v) Returns the pidual-jacobian of the dual of a 4x1 vector Uses now the PiDual function of Point to ensure consistency (T.Laebe, 15.11.05)
Matrix	Plane_3D.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
protected Matrix[]	Plane_3D.getReducedAB(RelationalProperty rprop) Get the reduced Matrix A and B from a relational property.
Matrix	Plane_3D.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
Matrix	ProjectiveCamera.getPointMatrix()
Matrix	ProjectiveCamera.getLineMatrix()
Matrix	ProjectiveCamera.getDualLineMatrix()
Matrix	ProjectiveCamera.getRotationMatrix() Returns the rotation matrix from the world to the camera coordiante system
Matrix	ProjectiveCamera.getCalibrationMatrix() Returns the calibration matrix containing the interior orientation parameters
Matrix	ProjectiveCamera.getInversePointMatrix(Vector A) Returns the Inverse Point Projection Matrix of the plane A.
Matrix	ProjectiveCamera.getJacobianAfterFirst(Entity e) The the Jacobian after the first entity of a trilinear relation.
Matrix	ProjectiveCamera.getJacobianAfterSecond(Entity e) The the Jacobian after the second entity of a trilinear relation.
protected Matrix	Point_3D.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
Matrix	Point_3D.Pi() returns the pi-jacobian of this point
static Matrix	Point_3D.Pi(Vector v_in) returns the pi-jacobian of this point
Matrix	Point_3D.PiDual() Returns C*PI matrix the dual PI-matrix
static Matrix	Point_3D.PiDual(Vector v_in) Returns C*PI matrix the dual PI-matrix This matrix differs from that of the dissertation because of other definition of C
Matrix	Point_3D.getJacobianHomogeneousPart() returns the part of the covariance matrix, which contains the homogeneous part
Matrix	Point_3D.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
protected Matrix[]	Point_3D.getReducedAB(RelationalProperty rprop) Get the reduced Matrix A and B from a relational property.
Matrix	Point_3D.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
protected Matrix	Point_2D.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
static Matrix	Point_2D.SkewSym(Vector v) Returns the skew symmetric matrix of a 3x1 vector.
Matrix	Point_2D.SkewSym() Returns the skew symmetric matrix of this point
Matrix	Point_2D.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
Matrix	Point_2D.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
Matrix	Relation.getSigma() returns the covariance matrix of d
Matrix	Motion_3D.getJacobianOfConstraint() Get the Jacobian of the constraint (H, G) if this entity is an unkown or an observation.
Matrix	Transformation.getTransformationMatrix() If you want to get the Transformation-Matrix (Affinity, Motion, Rotation, Translation) use this method.
protected Matrix	Transformation.errorProp(Transformation a, Transformation b) calculates the error propagation between two transformations Note that there is only needed one method for 2D and 3D case
protected Matrix[]	Transformation.getReducedAB(RelationalProperty r) Get the reduced Matrix A and B from a relational property.
Matrix	Transformation.getJacobianOfRelation(RelationalProperty rp) Get the (joint) Jacobian matrix after the observation of a trilinear Relation between two entities and a tranformation.
abstract Matrix	Transformation.getJacobianAfterFirst(Entity e) The the Jacobian after the first entity of a trilinear relation.
abstract Matrix	Transformation.getJacobianAfterSecond(Entity e) The the Jacobian after the second entity of a trilinear relation.
static Matrix	Transformation.getTransposeTransMatrix(int size) Get a transformation matrix A which transforms vec(Ht)=A*vec(H).
Matrix	TriRelationalProperty.getJacobian() Return Jacobian Matrix A of the first Entity and the relation type.
Matrix	TriRelationalProperty.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
Matrix	TriRelationalProperty.getJacobianOfObservationConstraints()
Matrix	TriRelationalProperty.getCovarianceOfObservations() The the covariance matrix of the observation of that relation.
Matrix	TriRelationalProperty.getReducedCovarianceOfObservations() The "reduced" covariance matrix of the observations which takes care of the homogeneous constraint.
private static Matrix	TriRelationalProperty.ChangeOrderVecTrans(Matrix J) Change the order of columns of a matrix in that way that the resulting matrix is J with J*vec(H) =0 whereas the input matrix is J*vec(Ht).
protected Matrix	Line_3D.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
static Matrix	Line_3D.GammaDual(Vector v) Returns the gamma-dual of a 6x1 vector.
Matrix	Line_3D.GammaDual() Returns the gamma-dual of this line.
static Matrix	Line_3D.Gamma(Vector v) Returns the gamma matrix of a 6x1 vector.
Matrix	Line_3D.Gamma() Returns the gamma matrix of this line
Matrix	Line_3D.getLineLineJacobian() Returns the 16x6 jacobi-matrix for the line-equality-test This formula is from the dissertation (3.70).
protected Matrix[]	Line_3D.getReducedAB(RelationalProperty rprop) Get the reduced Matrix A and B from a relational property.
Matrix	Line_3D.getDeltaMatrix(int i) Compute Matrix Delta_i of this line.
Matrix	Line_3D.getReducedDeltaMatrix(int i) Compute reduced Matrix Delta_i of this line.
Matrix	Line_3D.getJacobianOfConstraint() Get the Jacobian of the constraint (H, G) if this entity is an unkown or an observation
Matrix	Line_3D.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
Matrix	Line_3D.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
static Matrix	Line_3D.JGamma() Get the Matrix JGamma. vec(GammaDual(L)) = JGamma*C*L.
static Matrix	Line_3D.DualMatrixC() Get the dualization matrix C with LDual=C*L.
Matrix	Element.getCov() returns the Covariance Matrix.
protected Matrix[]	Element.getReducedAB(RelationalProperty r) Get the reduced Matrix A and B from a relational property.
static Matrix	Element.reduceMatrix(Matrix A, int dof) Reduce a Jacobian matrix.
static Matrix[]	Element.reduceMatrix(Matrix A, Matrix B, int dof) Reduce two Jacobian matrixes consistently.
static Matrix[]	Element.reduceFirstMatrix(Matrix A, Matrix B, int dof) Reduce two Jacobian matrixes consistently based on solely the first matrix A.
abstract Matrix	Element.getJacobianOfRelation(RelationalProperty r) Return Jacobian Matrix in combination with a relation.
Matrix	Element.getJacobianOfConstraint() Get the Jacobian of the constraint (H, G) if this entity is an unkown or an observation.
protected static Matrix	Element.calcCov(Matrix U_x, Matrix Sigma_x, Matrix V_y, Matrix Sigma_y) starts the error propagation of the uncertainty elements
protected Matrix	Line_2D.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
Matrix	Line_2D.SkewSym() returns the skew symmetric matrix of this line
static Matrix	Line_2D.SkewSym(Vector v) returns the skew symmetic matrix of a vector
Matrix	Line_2D.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
Matrix	Line_2D.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
abstract Matrix	Entity.getJacobianOfRelation(java.lang.Class relationType, java.lang.Class partnerType) Return Jacobian Matrix of that entity in combination with a relation.
Matrix	Entity.getJacobianOfRelation(RelationalProperty r) Return Jacobian Matrix of that entity in combination with a relation.
abstract Matrix	Entity.getCovarianceOfJacobianRowOfRelation(java.lang.Class relationType, java.lang.Class partnerType, int row) Return Covariance of a Row of the Jacobian Matrix of that entity in combination with a relation.
protected abstract Matrix	Entity.getConditionMatrix(double f) Get a matrix W(f) for conditioning.
Matrix	Orthogonal.getJacobian() Return Jacobian Matrix A of the first Entity and the relation type.
Matrix	Orthogonal.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
abstract Matrix	BiRelationalProperty.getJacobian() Return Jacobian Matrix A of the first Entity and the relation type.
abstract Matrix	BiRelationalProperty.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
Matrix	BiRelationalProperty.getJacobianOfObservationConstraints()
Matrix	BiRelationalProperty.getCovarianceOfObservations() The the covariance matrix of the observation of that relation.
Matrix	BiRelationalProperty.getReducedCovarianceOfObservations() The "reduced" covariance matrix of the observations which takes care of the homogeneous constraint.
Matrix	Equal.getJacobian() Return Matrix Jacobian A of the first Entity and the relation type.
Matrix	Equal.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A of the first Entity and the relation type.
Matrix	Homography_3D.getPointMatrix() Creates a transformation-matrix for points.
Matrix	Homography_3D.getLineMatrix() Creates out of this transformation a transformation-matrix for Line_3D See Stephans Diss, pg 52
Matrix	Homography_3D.getPlaneMatrix() Creates out of this transformation a transformation-matrix for Plane_3D H_plane = H^{-T}
Matrix	Homography_3D.getJacobianAfterFirst(Entity e) The the Jacobian after the first entity of a trilinear relation.
Matrix	Homography_3D.getJacobianAfterSecond(Entity e) The the Jacobian after the second entity of a trilinear relation.
Matrix	Homography_2D.getJacobianAfterFirst(Entity e) The the Jacobian after the first entity of a trilinear relation.
Matrix	Homography_2D.getJacobianAfterSecond(Entity e) The the Jacobian after the second entity of a trilinear relation.
Matrix	RelationalProperty.getJacobianOfObservationConstraints() Get the Jacobian of the constraint of the observation
Matrix	RelationalProperty.getCovarianceOfObservations() The the covariance matrix of the observations of that relation.
Matrix	RelationalProperty.getReducedCovarianceOfObservations() The "reduced" covariance matrix of the observations which takes care of the homogeneous constraint.
Matrix	RelationalProperty.getJacobian() Return Jacobian Matrix A after the target type.
Matrix	RelationalProperty.getCovarianceOfJacobianRow(int row) Returns the Covariance Matrix of the row-vector of the Jacobian Matrix A

Methods in sugr with parameters of type Matrix

private static Matrix	ScaledMotion_3D.leaveOutProjectivePartsOfJacobian(Matrix A) Leave out those columns of the Jacobian of a ScaledMotion estimation which correspond to the 3 projective values (the last row of the transformation matrix)
protected Entity	Transformation.transform(Matrix h, Matrix covariance, Entity e, java.lang.Class newType) Do a transformation with given transformation matrix and covariance of tranformation matrix.
private static Matrix	TriRelationalProperty.ChangeOrderVecTrans(Matrix J) Change the order of columns of a matrix in that way that the resulting matrix is J with J*vec(H) =0 whereas the input matrix is J*vec(Ht).
void	Element.setCov(Matrix C) Sets the covariance matrix of this element.
void	Element.checkCovDim(Matrix cov, int dim) Checks the dimension of a given covariance matrix if it matches the dimension of the element
static Matrix	Element.reduceMatrix(Matrix A, int dof) Reduce a Jacobian matrix.
static Matrix[]	Element.reduceMatrix(Matrix A, Matrix B, int dof) Reduce two Jacobian matrixes consistently.
static Matrix[]	Element.reduceFirstMatrix(Matrix A, Matrix B, int dof) Reduce two Jacobian matrixes consistently based on solely the first matrix A.
protected static Matrix	Element.calcCov(Matrix U_x, Matrix Sigma_x, Matrix V_y, Matrix Sigma_y) starts the error propagation of the uncertainty elements
static Entity	Factory.getEntity(Vector v, Matrix cov, java.lang.String classname) Initialize an Entity with given entities and class name.
static Entity	Factory.getEntity(Vector v, Matrix cov, java.lang.Class classid) Initialize an Entity with given entities and class ID.

Constructors in sugr with parameters of type Matrix

ScaledMotion_3D(Translation_3D trans, Rotation_3D rot, double scale, Matrix cov)
creates a motion with covariance by a given Translation_3D and a Rotation_3D and a scale factor.

Motion_2D(Translation_2D trans, Rotation_2D rot, Matrix cov)
creates a motion with covariance by a given Translation_2D and a Rotation_2D No error propagation will be performed if cov is not null.

Plane_3D(Vector _v, Matrix _cov)
Constructor for a new Plane_3D

Plane_3D(double x, double y, double z, double w, Matrix cov)
constructs a new plane by setting all entries directly.

Plane_3D(double x, double y, double z, Matrix cov)
constructs a new Plane_3D by specifying the homogeneous part.

Rotation_3D(double omega, double phi, double kappa, Matrix cov)
Represents a rotation by three angles around the three axis'.

Rotation_3D(Vector spinAxis, Matrix cov)
representation of rotation through RODRIGUEZ with covariance (only covariance of rotation matrix) Please note: rotations with 180° are not allowed Found in: http://www.ipb.uni-bonn.de/publications/papers99/foerstner99_rotation.ps.gz

Rotation_3D(double q0, double q1, double q2, double q3, Matrix cov)
representation of rotation with quaternions and covariance (of rotation matrix!)

Rotation_3D(Vector axis, double angle, Matrix cov)
Rotate around any abitrary axix with angle, with covariance

Rotation_2D(double angle, Matrix cov)
Creates a rotation matrix which rotates around angle

Rotation_2D(double a, double b, Matrix cov)
set rotation matrix with covariance by hand. the matrix has then following form: |a -b 0| |b a 0| |0 0 1|

Translation_3D(double x, double y, double z, Matrix cov)
Creates a translation matrix with covariance and single parameters

Translation_3D(Vector transVector, Matrix cov)
Creates a translation matrix by a vector with covariance

Translation_3D(Point_3D x, Matrix cov)
Creates a translation by a Point_3D

ProjectiveCamera(Matrix pointMatrix)
Creates a ProjectiveCamera from a 3x4 point projection matrix

ProjectiveCamera(Matrix inOrien, Matrix extOrien)
Creates a ProjectiveCamera by setting inner and exterior orientation.

ProjectiveCamera(Matrix inOrien, Matrix extOrien, Matrix cov)
Creates a ProjectiveCamera by setting inner and exterior orientation with covariance.

ProjectiveCamera(Motion_3D extOrien, Matrix cov)
Creates a ProjectiveCamera by setting exterior orientation with covariance.

ProjectiveCamera(Matrix inOrien, Motion_3D extOrien)
Creates a ProjectiveCamera by setting inner and exterior orientation.

ProjectiveCamera(Matrix inOrien, Motion_3D extOrien, Matrix cov)
Creates a ProjectiveCamera by setting inner and exterior orientation.

ProjectiveCamera(double c, double shear, double scale, double ppX, double ppY, Matrix cov)
Sets a ProjectiveCamera with covariance by setting inner orientation parameters directly.

ProjectiveCamera(Translation_3D trans, Rotation_3D rot, Matrix cov)
Creates a ProjectiveCamera by setting exterior orientation.

ProjectiveCamera(double[] cam, Matrix cov)
Sets a ProjectiveCamera by setting whole projection matrix.

ProjectiveCamera(double c, Matrix cov)
Sets a ProjectiveCamera with covariance by only defining camera constant.

ProjectiveCamera(double c, double ppX, double ppY, Matrix cov)
Sets a ProjectiveCamera by camera constant and principal point (camera with euclidean sensor)
Exterior orientation is set to identity.

ProjectiveCamera(double p11, double p12, double p13, double p14, double p21, double p22, double p23, double p24, double p31, double p32, double p33, double p34, Matrix cov)
Sets a ProjectiveCamera by setting whole projection matrix.

Translation_2D(double x, double y, Matrix cov)
Creates a translation matrix with covariance and single parameters

Translation_2D(Point_2D x, Matrix cov)
Creates a translation by a Point_2D

Point_3D(Vector _v, Matrix _cov)
Constructor for a new Point_3D

Point_3D(double x, double y, double z, double w, Matrix cov)
Constructs a new point_3D by setting all values directly.

Point_3D(double x, double y, double z, Matrix cov)
/** Constructs a new point with covariance. homogeneous part is set to 1.

Point_2D(Vector _v, Matrix _cov)
Constructor for a new Point_2D

Point_2D(double x, double y, Matrix cov)
Constructor for a euclidean point with covariance. homogeneous part is set to 1.

Point_2D(double u, double v, double w, Matrix cov)
Sets all elements of point directly.

Motion_3D(Translation_3D trans, Rotation_3D rot, Matrix cov)
creates a motion with covariance by a given Translation_3D and a Rotation_3D

Affinity_3D(double[] aff, Matrix cov)
sets Affinity_3D with covariance by hand. positions are: |aff[0] aff[3]

Affinity_3D(Rotation_3D rot, Translation_3D trans, Vector scale, Vector shear, Matrix cov)
Creates an affinity_3D by 12 paramaters with covariance: Transformation(3) Rotation(3), Scale (3), in x and y different values possible Shear (3), No error propagation will be done.

Affinity_2D(double a, double b, double c, double d, double e, double f, Matrix cov)
sets the matrix with covariance by hand. positions are: |a c e| |b d f| |0 0 1|

Affinity_2D(Rotation_2D rot, Translation_2D trans, Vector scale, double shear, boolean shearSymmetric, Matrix cov)
Creates an affinity_2D with covariance by 6 paramaters: Transformation(2) Rotation(1), Scale (2), in x and y different values possible Shear (1), No error propagation will be done.

Line_3D(Vector _v, Matrix _cov)
Constructor for a new Line_3D

Line_3D(double l1, double l2, double l3, double l4, double l5, double l6, Matrix cov)
Creates a new Line_3D with covariance by specifying all elements directly.

Line_3D(double l1, double l2, double l3, double l4, double l5, double l6, Matrix cov, boolean testPlueckerCondition)
Creates a new Line_3D with covariance by specifying all elements directly.

Line_2D(Vector _v, Matrix _cov)
Constructor for a new Line_2D

Line_2D(double u, double v, double w, Matrix cov)
Creates a new Line_2D with covariance matrix.

Line_2D(double angle, double distance, Matrix cov)
Angle-distance constructor for Line_2D with covariance.

Homography_3D(Affinity_3D aff, Vector projective, Matrix cov)
Creates a homography by an affinity and the projective part with covariance.

Homography_3D(double[] hom, Matrix cov)
sets Homography_3D by hand with covariance. positions are: |hom[00] hom[01] hom[02] hom[03]| |hom[04] hom[05] hom[06] hom[07]| |hom[08] hom[09] hom[10] hom[11]| |hom[12] hom[13] hom[14] hom[15]|

Homography_3D(Matrix hom, Matrix cov)
Constructs a homography out of a given homography matrix and a covariance matrix for the given homography.

Homography_3D(Translation_3D trans, Rotation_3D rot, Vector scale, Vector shear, Vector projective, Matrix cov)
Creates a Homography_3D by 15 parameters PLUS covariance Matrix: Translation (3) Rotation (3) scale (3) shear (3) projective part of homography (3) No error propagation will be performed

Homography_3D(Motion_3D mot, Vector scale, Vector shear, Vector projective, Matrix cov)
Creates a Homography_3D by 15 parameters PLUS covariance Matrix: Motion (6) scale (3) shear (3) projective part of homography (3) No error propagation will be performed

Homography_2D(Affinity_2D aff, Vector projective, Matrix cov)
Creates a homography with covariane by an affinity and the projective part.

Homography_2D(double[] hom, Matrix cov)
sets Homography_2D with covariance by hand. positions are:
|hom[0] hom[1] hom[2] | |hom[3] hom[4] hom[5] | |hom[6] hom[7] hom[8] |

Homography_2D(Matrix hom, Matrix cov)
Constructs a homography out of a given homography matrix and a covariance matrix for the given homography.

Uses of Matrix in sugr.GUI

Methods in sugr.GUI that return Matrix

private Matrix	ConstructionApplet.getEntityCovMatrix(javax.swing.JTable table)
private Matrix	ConstructionApplet.checkConfidence(Matrix confidence)
private Matrix	RelationApplet.getEntityCovMatrix(javax.swing.JTable table)
private Matrix	RelationApplet.checkConfidence(Matrix confidence)

Methods in sugr.GUI with parameters of type Matrix

private Matrix	ConstructionApplet.checkConfidence(Matrix confidence)
private Matrix	RelationApplet.checkConfidence(Matrix confidence)

Uses of Matrix in sugr.linalg

Classes in sugr.linalg that implement Matrix

class

MatrixImplColt

Methods in sugr.linalg that return Matrix

Matrix	MatrixImplColt.plus(Matrix B)
Matrix	MatrixImplColt.minus(Matrix B)
Matrix	MatrixImplColt.mult(Matrix B)
Matrix	MatrixImplColt.trans()
Matrix	MatrixImplColt.invert()
Matrix	MatrixImplColt.mult(double f)
Matrix[]	MatrixImplColt.svd()
Matrix	MatrixImplColt.slice(int[] listOfRows, int[] listOfCols)
Matrix	MatrixImplColt.slice(int i0, int i1, int j0, int j1)
Matrix	MatrixImplColt.copy()
Matrix	MatrixImplColt.getMatrix(int i0, int i1, int j0, int j1)
Matrix	MatrixImplColt.pseudoInverse(int rank)
Matrix	MatrixImplColt.kronecker(Matrix B)
Matrix	MatrixImplColt.delColumn(int col)
Matrix	MatrixImplColt.delRow(int row)
Matrix[]	MatrixImplColt.qr()
Matrix	MatrixImplColt.pseudoInverse(Matrix H)
Matrix	VectorImplColt.getMatrix()
Matrix	VectorImplColt.SkewSym()
Matrix	Matrix.plus(Matrix B) C=A+B
Matrix	Matrix.minus(Matrix B) C=A-B
Matrix	Matrix.mult(Matrix B) C=A*B
Matrix	Matrix.trans() Matrix transpose
Matrix	Matrix.invert() Matrix inverse or pseudoinverse
Matrix	Matrix.mult(double f) B=A*f , where f is a scalar
Matrix[]	Matrix.svd() Computes the SingularValueDecomposition of given Matrix with dimension m x n (m>=n), so the result is given by: Matrix = U*S*V^t
Matrix	Matrix.slice(int[] listOfRows, int[] listOfCols) Get submatrix with given list of rows and given list of columns.
Matrix	Matrix.slice(int i0, int i1, int j0, int j1) Gets a submatrix of A
Matrix	Matrix.copy() Create a new matrix B, independent from A
Matrix	Matrix.getMatrix(int i0, int i1, int j0, int j1) Returns a Submatrix with dimension (i1-i0) x (j1-j0) Upper left element begin with indices (0,0)
Matrix	Matrix.pseudoInverse(int rank) returns the pseudo-inverse A^+ .
Matrix	Matrix.pseudoInverse(Matrix H) returns the pseudo-inverse A^+ with given Nullspace
Matrix	Matrix.kronecker(Matrix B) Calculates the kronecker-product of two matrices.
Matrix	Matrix.delColumn(int col) delete a column from A.
Matrix	Matrix.delRow(int row) delete a row from A.
Matrix[]	Matrix.qr() Computes the QR Decomposition of given Matrix
Matrix	Vector.getMatrix() Creates a matrix from s This is a "kind" of typecast.
Matrix	Vector.SkewSym() Creates a skew-symmetrix matrix from s. s must have dimension 3 to do that.
static Matrix	LinearAlgebraFactoryColt.getMatrix(int no_rows, int no_cols) Return a Matrix no_rows x no_cols filled with zeros.
static Matrix	LinearAlgebraFactoryColt.getMatrix(double[][] values) create a new matrix by a given double precision floating point array.
static Matrix	LinearAlgebraFactoryColt.getMatrix(Matrix A) create new Matrix given Matrix.
static Matrix	LinearAlgebraFactoryColt.getDiagMatrix(int size, double d) Create new diagonal matrix.
static Matrix	LinearAlgebraFactoryColt.getMatrixId(int dim) creates an identity Matrix
static Matrix	LinearAlgebraFactoryColt.getMatrix(Vector v) creates a Matrix from a given Vector.
static Matrix	LinearAlgebraFactory.getMatrix(int no_rows, int no_cols) Return a Matrix no_rows x no_cols filled with zeros.
static Matrix	LinearAlgebraFactory.getMatrix(double[][] values) create a new matrix by a given double precision floating point array.
static Matrix	LinearAlgebraFactory.getMatrix(Matrix A) create new Matrix given Matrix.
static Matrix	LinearAlgebraFactory.getDiagMatrix(int size, double d) Create new diagonal matrix.
static Matrix	LinearAlgebraFactory.getMatrix(Vector v, int times) creates a Matrix from a given Vector.
static Matrix	LinearAlgebraFactory.getMatrixId(int dim) creates an identity Matrix
static Matrix	LinearAlgebraFactory.getMatrix(Vector v) creates a Matrix from a given Vector.

Methods in sugr.linalg with parameters of type Matrix

Matrix	MatrixImplColt.plus(Matrix B)
Matrix	MatrixImplColt.minus(Matrix B)
Matrix	MatrixImplColt.mult(Matrix B)
void	MatrixImplColt.slice(int[] listOfRows, int[] listOfCols, Matrix B)
void	MatrixImplColt.slice(int i0, int i1, int j0, int j1, Matrix B)
Matrix	MatrixImplColt.kronecker(Matrix B)
Matrix	MatrixImplColt.pseudoInverse(Matrix H)
Matrix	Matrix.plus(Matrix B) C=A+B
Matrix	Matrix.minus(Matrix B) C=A-B
Matrix	Matrix.mult(Matrix B) C=A*B
void	Matrix.slice(int[] listOfRows, int[] listOfCols, Matrix B) As other slice, only: read values from Matrix B and put them in the object at the posisitions of the intersections of given rows and columns.
void	Matrix.slice(int i0, int i1, int j0, int j1, Matrix B) Sets a submatrix of A with B
Matrix	Matrix.pseudoInverse(Matrix H) returns the pseudo-inverse A^+ with given Nullspace
Matrix	Matrix.kronecker(Matrix B) Calculates the kronecker-product of two matrices.
static Matrix	LinearAlgebraFactoryColt.getMatrix(Matrix A) create new Matrix given Matrix.
static Matrix	LinearAlgebraFactory.getMatrix(Matrix A) create new Matrix given Matrix.

Constructors in sugr.linalg with parameters of type Matrix

MatrixImplColt(Matrix m)