Uses of Class
de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix

Packages that use Matrix

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.data.type	Data type information, also used for type restrictions.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
de.lmu.ifi.dbs.elki.math.geometry	Algorithms from computational geometry.
de.lmu.ifi.dbs.elki.math.linearalgebra	Linear Algebra package provides classes and computational methods for operations on matrices.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.statistics	Statistical tests and methods.
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.

Uses of Matrix in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Matrix

Modifier and Type	Field and Description
(package private) Matrix	ORCLUS.ORCLUSCluster.basis The matrix defining the subspace of this cluster.
(package private) Matrix	LMCLUS.Separation.basis Basis of manifold

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Matrix

Modifier and Type	Method and Description
private Matrix	CASH.determineBasis(double[] alpha) Determines a basis defining a subspace described by the specified alpha values.
private Matrix	ORCLUS.findBasis(Relation<V> database, DistanceQuery<V> distFunc, ORCLUS.ORCLUSCluster cluster, int dim) Finds the basis of the subspace of dimensionality dim for the specified cluster.
private Matrix	LMCLUS.generateOrthonormalBasis(List<Vector> vectors) This Method generates an orthonormal basis from a set of Vectors.
private Matrix	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Matrix

Modifier and Type	Method and Description
private void	HiCO.adjust(Matrix v, Matrix e_czech, Vector vector, int corrDim) Inserts the specified vector into the given orthonormal matrix v at column corrDim.
private MaterializedRelation<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, DBIDs ids, Relation<ParameterizationFunction> relation) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private double	LMCLUS.deviation(Vector delta, Matrix beta) Deviation from a manifold described by beta.
private ParameterizationFunction	CASH.project(Matrix basis, ParameterizationFunction f) Projects the specified parameterization function into the subspace described by the given basis.

Uses of Matrix in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.em declared as Matrix

Modifier and Type	Field and Description
(package private) Matrix	MultivariateGaussianModel.covariance Covariance matrix, and inverse.
(package private) Matrix	MultivariateGaussianModel.invCovMatr Covariance matrix, and inverse.

Uses of Matrix in de.lmu.ifi.dbs.elki.data

Methods in de.lmu.ifi.dbs.elki.data with parameters of type Matrix

Modifier and Type	Method and Description
static double[]	VectorUtil.fastTimes(Matrix mat, NumberVector v) This is an ugly hack, but we don't want to have the Matrix class depend on NumberVector.

Uses of Matrix in de.lmu.ifi.dbs.elki.data.model

Fields in de.lmu.ifi.dbs.elki.data.model declared as Matrix

Modifier and Type	Field and Description
private Matrix	EMModel.covarianceMatrix Cluster covariance matrix
private Matrix	CorrelationAnalysisSolution.similarityMatrix The similarity matrix of the pca.
private Matrix	CorrelationAnalysisSolution.strongEigenvectors The strong eigenvectors of the hyperplane induced by the correlation.
private Matrix	CorrelationAnalysisSolution.weakEigenvectors The weak eigenvectors of the hyperplane induced by the correlation.

Methods in de.lmu.ifi.dbs.elki.data.model that return Matrix

Modifier and Type	Method and Description
Matrix	CorrelationAnalysisSolution.dataProjections(V p) Returns the data vectors after projection.
Matrix	EMModel.getCovarianceMatrix()
Matrix	CorrelationAnalysisSolution.getSimilarityMatrix() Returns the similarity matrix of the pca.
Matrix	CorrelationAnalysisSolution.getStrongEigenvectors() Returns the strong eigenvectors.
Matrix	CorrelationAnalysisSolution.getWeakEigenvectors() Returns the weak eigenvectors.

Methods in de.lmu.ifi.dbs.elki.data.model with parameters of type Matrix

Modifier and Type	Method and Description
void	EMModel.setCovarianceMatrix(Matrix covarianceMatrix)

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Matrix

Constructor and Description
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid) Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid, NumberFormat nf) Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.
EMModel(Vector mean, Matrix covarianceMatrix) Constructor.

Uses of Matrix in de.lmu.ifi.dbs.elki.data.type

Fields in de.lmu.ifi.dbs.elki.data.type with type parameters of type Matrix

Modifier and Type	Field and Description
static SimpleTypeInformation<Matrix>	TypeUtil.MATRIX Matrix type.

Uses of Matrix in de.lmu.ifi.dbs.elki.datasource.filter.transform

Methods in de.lmu.ifi.dbs.elki.datasource.filter.transform that return Matrix

Modifier and Type	Method and Description
protected Matrix	LinearDiscriminantAnalysisFilter.computeProjectionMatrix(List<V> vectorcolumn, List<? extends ClassLabel> classcolumn, int dim)
protected abstract Matrix	AbstractSupervisedProjectionVectorFilter.computeProjectionMatrix(List<V> vectorcolumn, List<? extends ClassLabel> classcolumn, int dim) computes the projection matrix

Uses of Matrix in de.lmu.ifi.dbs.elki.distance.distancefunction

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as Matrix

Modifier and Type	Field and Description
protected Matrix	MatrixWeightedDistanceFunction.weightMatrix The weight matrix.

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Matrix

Constructor and Description
MatrixWeightedDistanceFunction(Matrix weightMatrix) Constructor.

Uses of Matrix in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram that return Matrix

Modifier and Type	Method and Description
static Matrix	RGBHistogramQuadraticDistanceFunction.computeWeightMatrix(int bpp) Compute weight matrix for a RGB color histogram
static Matrix	HSBHistogramQuadraticDistanceFunction.computeWeightMatrix(int quanth, int quants, int quantb) Compute the weight matrix for HSB similarity.

Uses of Matrix in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel declared as Matrix

Modifier and Type	Field and Description
(package private) Matrix	KernelMatrix.kernel The kernel matrix

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel that return Matrix

Modifier and Type	Method and Description
static Matrix	KernelMatrix.centerKernelMatrix(KernelMatrix kernelMatrix) Centers the Kernel Matrix in Feature Space according to Smola et.
static Matrix	KernelMatrix.centerMatrix(Matrix matrix) Centers the matrix in feature space according to Smola et Schoelkopf, Learning with Kernels p. 431 Alters the input matrix.
Matrix	KernelMatrix.getKernel() Get the kernel matrix.
Matrix	KernelMatrix.getSubColumn(DBIDRef i1, DBIDs ids) Deprecated.
Matrix	KernelMatrix.getSubMatrix(DBIDs ids) Returns a sub kernel matrix for all objects in ids

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Matrix

Modifier and Type	Method and Description
static Matrix	KernelMatrix.centerMatrix(Matrix matrix) Centers the matrix in feature space according to Smola et Schoelkopf, Learning with Kernels p. 431 Alters the input matrix.

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Matrix

Constructor and Description
KernelMatrix(Matrix matrix) Makes a new kernel matrix from matrix (with data copying).

Uses of Matrix in de.lmu.ifi.dbs.elki.math

Methods in de.lmu.ifi.dbs.elki.math with parameters of type Matrix

Modifier and Type	Method and Description
static double	MathUtil.mahalanobisDistance(Matrix weightMatrix, Vector o1_minus_o2) Compute the Mahalanobis distance using the given weight matrix.
static double	MathUtil.mahalanobisDistance(Matrix weightMatrix, Vector o1, Vector o2) Compute the Mahalanobis distance using the given weight matrix.

Uses of Matrix in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity that return Matrix

Modifier and Type	Method and Description
Matrix	DimensionSimilarityMatrix.copyToFullMatrix() Transform linear triangle matrix into a full matrix.

Uses of Matrix in de.lmu.ifi.dbs.elki.math.geometry

Methods in de.lmu.ifi.dbs.elki.math.geometry with parameters of type Matrix

Modifier and Type	Method and Description
static int[]	PrimsMinimumSpanningTree.processDense(Matrix mat) Process a k x k distance matrix.

Uses of Matrix in de.lmu.ifi.dbs.elki.math.linearalgebra

Fields in de.lmu.ifi.dbs.elki.math.linearalgebra declared as Matrix

Modifier and Type	Field and Description
private Matrix	AffineTransformation.inv the inverse transformation
private Matrix	AffineTransformation.trans The transformation matrix of dim+1 x dim+1 for homogeneous coordinates

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra that return Matrix

Modifier and Type	Method and Description
Matrix	Matrix.appendColumns(Matrix columns) Returns a matrix which consists of this matrix and the specified columns.
Matrix	Matrix.cheatToAvoidSingularity(double constant) Adds a given value to the diagonal entries if the entry is smaller than the constant.
Matrix	Matrix.clone() Clone the Matrix object.
Matrix	Matrix.completeBasis() Completes this d x c basis of a subspace of R^d to a d x d basis of R^d, i.e. appends c-d columns to this basis.
Matrix	Matrix.completeToOrthonormalBasis() Completes this d x c basis of a subspace of R^d to a d x d basis of R^d, i.e. appends c-d columns to this basis.
static Matrix	Matrix.constructWithCopy(double[][] A) Construct a matrix from a copy of a 2-D array.
Matrix	Matrix.copy() Make a deep copy of a matrix.
Matrix	CovarianceMatrix.destroyToNaiveMatrix() Obtain the covariance matrix according to the population statistics: n degrees of freedom.
Matrix	CovarianceMatrix.destroyToSampleMatrix() Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom.
static Matrix	Matrix.diagonal(double[] diagonal) Returns a quadratic Matrix consisting of zeros and of the given values on the diagonal.
static Matrix	Matrix.diagonal(Vector diagonal) Returns a quadratic Matrix consisting of zeros and of the given values on the diagonal.
Matrix	SortedEigenPairs.eigenVectors() Returns the sorted eigenvectors.
Matrix	SortedEigenPairs.eigenVectors(int n) Returns the first n sorted eigenvectors as a matrix.
Matrix	EigenvalueDecomposition.getD() Return the block diagonal eigenvalue matrix
Matrix	QRDecomposition.getH() Return the Householder vectors
Matrix	AffineTransformation.getInverse() Get a copy of the inverse matrix
Matrix	LUDecomposition.getL() Return lower triangular factor
Matrix	CholeskyDecomposition.getL() Return triangular factor.
Matrix	Matrix.getMatrix(int[] r, int[] c) Get a submatrix.
Matrix	Matrix.getMatrix(int[] r, int j0, int j1) Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int[] c) Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int j0, int j1) Get a submatrix.
Matrix	QRDecomposition.getQ() Generate and return the (economy-sized) orthogonal factor
Matrix	QRDecomposition.getR() Return the upper triangular factor
Matrix	SingularValueDecomposition.getS() Return the diagonal matrix of singular values
Matrix	AffineTransformation.getTransformation() Get a copy of the transformation matrix
Matrix	SingularValueDecomposition.getU() Return the left singular vectors
Matrix	LUDecomposition.getU() Return upper triangular factor
Matrix	SingularValueDecomposition.getV() Return the right singular vectors
Matrix	EigenvalueDecomposition.getV() Return the eigenvector matrix
static Matrix	Matrix.identity(int m, int n) Generate identity matrix
Matrix	Matrix.increment(int i, int j, double s) Increments a single element.
Matrix	Matrix.inverse() Matrix inverse or pseudoinverse
Matrix	CovarianceMatrix.makeNaiveMatrix() Obtain the covariance matrix according to the population statistics: n degrees of freedom.
Matrix	CovarianceMatrix.makeSampleMatrix() Obtain the covariance matrix according to the sample statistics: (n-1) degrees of freedom.
Matrix	Matrix.minus(Matrix B) C = A - B
Matrix	Matrix.minusEquals(Matrix B) A = A - B
Matrix	Matrix.minusTimes(Matrix B, double s) C = A - s * B
Matrix	Matrix.minusTimesEquals(Matrix B, double s) A = A - s * B
Matrix	Matrix.orthonormalize() Returns an orthonormalization of this matrix.
Matrix	Matrix.plus(double s) C = A + s
Matrix	Matrix.plus(Matrix B) C = A + B
Matrix	Matrix.plusDiagonal(double s) C = A + s
Matrix	Matrix.plusDiagonalEquals(double s) A = A + s
Matrix	Matrix.plusEquals(double s) A = A + s
Matrix	Matrix.plusEquals(Matrix B) A = A + B
Matrix	Matrix.plusTimes(Matrix B, double s) C = A + s * B
Matrix	Matrix.plusTimesEquals(Matrix B, double s) A = A + s * B
static Matrix	Matrix.random(int m, int n) Generate matrix with random elements
static Matrix	Matrix.read(BufferedReader input) Read a matrix from a stream.
Matrix	SortedEigenPairs.reverseEigenVectors(int n) Returns the last n sorted eigenvectors as a matrix.
Matrix	Matrix.robustInverse() Matrix inverse for square matrixes.
Matrix	Matrix.set(int i, int j, double s) Set a single element.
Matrix	ProjectionResult.similarityMatrix() Projection matrix
Matrix	QRDecomposition.solve(Matrix B) Least squares solution of A*X = B
Matrix	Matrix.solve(Matrix B) Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B) Solve A*X = B
Matrix	CholeskyDecomposition.solve(Matrix B) Solve A*X = B
Matrix	Matrix.times(double s) Multiply a matrix by a scalar, C = s*A
Matrix	Vector.times(Matrix B) Linear algebraic matrix multiplication, A * B.
Matrix	Matrix.times(Matrix B) Linear algebraic matrix multiplication, A * B
Matrix	Matrix.timesEquals(double s) Multiply a matrix by a scalar in place, A = s*A
Matrix	Vector.timesTranspose(Matrix B) Linear algebraic matrix multiplication, A * B^T.
Matrix	Matrix.timesTranspose(Matrix B) Linear algebraic matrix multiplication, A * B^T
Matrix	Vector.timesTranspose(Vector B) Linear algebraic matrix multiplication, A * B^T.
Matrix	Matrix.transpose() Matrix transpose.
Matrix	Vector.transposeTimes(Matrix B) Linear algebraic matrix multiplication, AT * B.
Matrix	Matrix.transposeTimes(Matrix B) Linear algebraic matrix multiplication, AT * B
Matrix	Matrix.transposeTimesTranspose(Matrix B) Linear algebraic matrix multiplication, A^T * B^T.
static Matrix	Matrix.unitMatrix(int dim) Returns the unit matrix of the specified dimension.
static Matrix	Matrix.zeroMatrix(int dim) Returns the zero matrix of the specified dimension.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type Matrix

Modifier and Type	Method and Description
void	AffineTransformation.addMatrix(Matrix m) Add a matrix operation to the matrix.
Matrix	Matrix.appendColumns(Matrix columns) Returns a matrix which consists of this matrix and the specified columns.
protected void	Matrix.checkMatrixDimensions(Matrix B) Check if size(A) == size(B)
boolean	Matrix.linearlyIndependent(Matrix columnMatrix) Returns true if the specified column matrix a is linearly independent to the columns of this matrix.
static CovarianceMatrix	CovarianceMatrix.make(Matrix mat) Static Constructor.
static Centroid	Centroid.make(Matrix mat) Static Constructor from an existing matrix columns.
Matrix	Matrix.minus(Matrix B) C = A - B
Matrix	Matrix.minusEquals(Matrix B) A = A - B
Matrix	Matrix.minusTimes(Matrix B, double s) C = A - s * B
Matrix	Matrix.minusTimesEquals(Matrix B, double s) A = A - s * B
Matrix	Matrix.plus(Matrix B) C = A + B
Matrix	Matrix.plusEquals(Matrix B) A = A + B
Matrix	Matrix.plusTimes(Matrix B, double s) C = A + s * B
Matrix	Matrix.plusTimesEquals(Matrix B, double s) A = A + s * B
Vector	Vector.projection(Matrix v) Projects this row vector into the subspace formed by the specified matrix v.
void	Matrix.setMatrix(int[] r, int[] c, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int[] r, int j0, int j1, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int[] c, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int j0, int j1, Matrix X) Set a submatrix.
Matrix	QRDecomposition.solve(Matrix B) Least squares solution of A*X = B
Matrix	Matrix.solve(Matrix B) Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B) Solve A*X = B
Matrix	CholeskyDecomposition.solve(Matrix B) Solve A*X = B
Matrix	Vector.times(Matrix B) Linear algebraic matrix multiplication, A * B.
Matrix	Matrix.times(Matrix B) Linear algebraic matrix multiplication, A * B
Matrix	Vector.timesTranspose(Matrix B) Linear algebraic matrix multiplication, A * B^T.
Matrix	Matrix.timesTranspose(Matrix B) Linear algebraic matrix multiplication, A * B^T
Matrix	Vector.transposeTimes(Matrix B) Linear algebraic matrix multiplication, AT * B.
Matrix	Matrix.transposeTimes(Matrix B) Linear algebraic matrix multiplication, AT * B
double	Vector.transposeTimesTimes(Matrix B, Vector c) Linear algebraic matrix multiplication, aT * B * c.
Matrix	Matrix.transposeTimesTranspose(Matrix B) Linear algebraic matrix multiplication, A^T * B^T.

Constructors in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type Matrix

Constructor and Description
AffineTransformation(int dim, Matrix trans, Matrix inv) Trivial constructor with all fields, mostly for cloning
CholeskyDecomposition(Matrix Arg) Cholesky algorithm for symmetric and positive definite matrix.
EigenvalueDecomposition(Matrix Arg) Check for symmetry, then construct the eigenvalue decomposition
LUDecomposition(Matrix A) LU Decomposition
Matrix(Matrix mat) Constructor, cloning an existing matrix.
QRDecomposition(Matrix A) QR Decomposition, computed by Householder reflections.
SingularValueDecomposition(Matrix Arg) Construct the singular value decomposition

Uses of Matrix in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Fields in de.lmu.ifi.dbs.elki.math.linearalgebra.pca declared as Matrix

Modifier and Type	Field and Description
private Matrix	PCAFilteredResult.adapatedStrongEigenvectors The diagonal matrix of adapted strong eigenvalues: eigenvectors * e_czech.
private Matrix	PCAFilteredResult.e_czech The selection matrix of the strong eigenvectors.
private Matrix	PCAFilteredResult.e_hat The selection matrix of the weak eigenvectors.
private Matrix	PCAResult.eigenvectors The eigenvectors in decreasing order to their corresponding eigenvalues.
(package private) Matrix	PCAFilteredAutotuningRunner.Cand.m Candidate matrix
private Matrix	PCAFilteredResult.m_czech The dissimilarity matrix.
private Matrix	PCAFilteredResult.m_hat The similarity matrix.
private Matrix	PCAFilteredResult.strongEigenvectors The strong eigenvectors to their corresponding filtered eigenvalues.
private Matrix	PCAFilteredResult.weakEigenvectors The weak eigenvectors to their corresponding filtered eigenvalues.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca that return Matrix

Modifier and Type	Method and Description
Matrix	PCAFilteredResult.adapatedStrongEigenvectors() Returns the adapted strong eigenvectors.
Matrix	PCAFilteredResult.dissimilarityMatrix() Returns the dissimilarity matrix (M_czech) of this LocalPCA.
Matrix	PCAResult.getEigenvectors() Returns the matrix of eigenvectors of the object to which this PCA belongs to.
Matrix	PCAFilteredResult.getStrongEigenvectors() Returns the matrix of strong eigenvectors after passing the eigen pair filter.
Matrix	PCAFilteredResult.getWeakEigenvectors() Returns the matrix of weak eigenvectors after passing the eigen pair filter.
Matrix	StandardCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database) Compute Covariance Matrix for a complete database.
Matrix	CovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database) Compute Covariance Matrix for a complete database.
Matrix	AbstractCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database)
Matrix	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation) Weighted Covariance Matrix for a set of IDs.
Matrix	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database) Compute Covariance Matrix for a collection of database IDs.
Matrix	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)
Matrix	CovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database) Compute Covariance Matrix for a collection of database IDs.
abstract Matrix	AbstractCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)
Matrix	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database) Compute Covariance Matrix for a QueryResult Collection.
Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database)
Matrix	WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) Compute Covariance Matrix for a QueryResult Collection.
Matrix	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) Compute Covariance Matrix for a QueryResult Collection.
Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)
Matrix	PCAFilteredResult.selectionMatrixOfStrongEigenvectors() Returns the selection matrix of the strong eigenvectors (E_czech) of this LocalPCA.
Matrix	PCAFilteredResult.selectionMatrixOfWeakEigenvectors() Returns the selection matrix of the weak eigenvectors (E_hat) of the object to which this PCA belongs to.
Matrix	PCAFilteredResult.similarityMatrix() Returns the similarity matrix (M_hat) of this LocalPCA.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Matrix

Modifier and Type	Method and Description
PCAResult	PCARunner.processCovarMatrix(Matrix covarMatrix) Process an existing covariance Matrix.
PCAFilteredResult	PCAFilteredRunner.processCovarMatrix(Matrix covarMatrix) Process an existing Covariance Matrix.

Constructors in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Matrix

Constructor and Description
PCAFilteredAutotuningRunner.Cand(Matrix m, double explain, int dim) Constructor.
PCAResult(double[] eigenvalues, Matrix eigenvectors, SortedEigenPairs eigenPairs) Build a PCA result object.

Uses of Matrix in de.lmu.ifi.dbs.elki.math.statistics

Fields in de.lmu.ifi.dbs.elki.math.statistics declared as Matrix

Modifier and Type	Field and Description
private Matrix	MultipleLinearRegression.x The (n x p+1)-matrix holding the x-values, where the i-th row has the form (1 x1i ... x1p).
private Matrix	MultipleLinearRegression.xx_inverse Holds the matrix (x'x)^-1.

Methods in de.lmu.ifi.dbs.elki.math.statistics that return Matrix

Modifier and Type	Method and Description
private static Matrix	PolynomialRegression.xMatrix(Vector x, int p)

Methods in de.lmu.ifi.dbs.elki.math.statistics with parameters of type Matrix

Modifier and Type	Method and Description
double	MultipleLinearRegression.estimateY(Matrix x) Perform an estimation of y on the specified matrix.

Constructors in de.lmu.ifi.dbs.elki.math.statistics with parameters of type Matrix

Constructor and Description
MultipleLinearRegression(Vector y, Matrix x) Constructor.

Uses of Matrix in de.lmu.ifi.dbs.elki.utilities

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type Matrix

Modifier and Type	Method and Description
static String	FormatUtil.format(Matrix m) returns String-representation of Matrix.
static String	FormatUtil.format(Matrix m, int w, int d) Returns a string representation of this matrix.
static String	FormatUtil.format(Matrix m, NumberFormat nf) returns String-representation of Matrix.
static String	FormatUtil.format(Matrix m, String pre) Returns a string representation of this matrix.
static String	FormatUtil.format(Matrix m, String pre, NumberFormat nf) Returns a string representation of this matrix.

Uses of Matrix in de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike

Methods in de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike with parameters of type Matrix

Modifier and Type	Method and Description
Double	FlatMatrixAdapter.get(Matrix array, int off) Deprecated.
byte	FlatMatrixAdapter.getByte(Matrix array, int off)
double	FlatMatrixAdapter.getDouble(Matrix array, int off)
float	FlatMatrixAdapter.getFloat(Matrix array, int off)
int	FlatMatrixAdapter.getInteger(Matrix array, int off)
long	FlatMatrixAdapter.getLong(Matrix array, int off)
short	FlatMatrixAdapter.getShort(Matrix array, int off)
int	FlatMatrixAdapter.size(Matrix array)