Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

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

Packages that use Matrix
de.lmu.ifi.dbs.elki.algorithm.clustering Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface. 
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation Correlation clustering algorithms 
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.database ELKI database layer - loading, storing, indexing and accessing data 
de.lmu.ifi.dbs.elki.distance.distancefunction Distance functions for use within ELKI. 
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation 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.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, ... 
 

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

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering with type arguments of type Matrix
protected  void EM.assignProbabilitiesToInstances(Database<V> database, List<Double> normDistrFactor, List<V> means, List<Matrix> invCovMatr, List<Double> clusterWeights)
          Assigns the current probability values to the instances in the database.
 

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
(package private)  Matrix ORCLUS.ORCLUSCluster.basis
          The matrix defining the subspace of this cluster.
 

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Matrix
private  Matrix CASH.determineBasis(double[] alpha)
          Determines a basis defining a subspace described by the specified alpha values.
private  Matrix ORCLUS.findBasis(Database<V> database, ORCLUS.ORCLUSCluster cluster, int dim)
          Finds the basis of the subspace of dimensionality dim for the specified cluster.
private  Matrix CASH.runDerivator(Database<ParameterizationFunction> database, int dim, CASHInterval interval, Set<Integer> 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
private  Database<ParameterizationFunction> CASH.buildDB(int dim, Matrix basis, Set<Integer> ids, Database<ParameterizationFunction> database)
          Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
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.data
 

Methods in de.lmu.ifi.dbs.elki.data that return Matrix
 Matrix FeatureVector.getRowVector()
          Returns a Matrix representing in one row and getDimensionality() columns the values of this FeatureVector of V.
 Matrix FloatVector.getRowVector()
           
 Matrix SparseFloatVector.getRowVector()
           
 Matrix DoubleVector.getRowVector()
           
 Matrix BitVector.getRowVector()
          Returns a Matrix representing in one row and getDimensionality() columns the values of this BitVector as double values.
 

Constructors in de.lmu.ifi.dbs.elki.data with parameters of type Matrix
DoubleVector(Matrix columnMatrix)
          Expects a matrix of one column.
 

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

Fields in de.lmu.ifi.dbs.elki.data.model declared as Matrix
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
 Matrix CorrelationAnalysisSolution.dataProjections(V p)
          Returns the data vectors after projection.
 Matrix CorrelationAnalysisSolution.dataVectors(Matrix p)
          Returns the data vectors after projection.
 Matrix CorrelationAnalysisSolution.errorVectors(Matrix p)
          Returns the error vectors after projection.
 Matrix CorrelationAnalysisSolution.errorVectors(V p)
          Returns the error vectors after projection.
 Matrix EMModel.getCovarianceMatrix()
           
 Matrix CorrelationAnalysisSolution.getSimilarityMatrix()
          Returns the similarity matrix of the pca.
 Matrix CorrelationAnalysisSolution.getStrongEigenvectors()
          Returns a copy of the strong eigenvectors.
 Matrix CorrelationAnalysisSolution.getWeakEigenvectors()
          Returns a copy of the weak eigenvectors.
 

Methods in de.lmu.ifi.dbs.elki.data.model with parameters of type Matrix
 Matrix CorrelationAnalysisSolution.dataVectors(Matrix p)
          Returns the data vectors after projection.
private  double CorrelationAnalysisSolution.distance(Matrix p)
          Returns the distance of Matrix p from the hyperplane underlying this solution.
 Matrix CorrelationAnalysisSolution.errorVectors(Matrix p)
          Returns the error vectors after projection.
 void EMModel.setCovarianceMatrix(Matrix covarianceMatrix)
           
 

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Matrix
CorrelationAnalysisSolution(LinearEquationSystem solution, Database<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid)
          Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Database<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid, NumberFormat nf)
          Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.
EMModel(V mean, Matrix covarianceMatrix)
          Constructor.
 

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

Fields in de.lmu.ifi.dbs.elki.database with type parameters of type Matrix
static AssociationID<Matrix> AssociationID.CACHED_MATRIX
          The association id to associate an arbitrary matrix of an object.
static AssociationID<Matrix> AssociationID.LOCALLY_WEIGHTED_MATRIX
          The association id to associate the locally weighted matrix of an object for the locally weighted distance function.
static AssociationID<Matrix> AssociationID.STRONG_EIGENVECTOR_MATRIX
          The association id to associate the strong eigenvector weighted matrix of an object.
 

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

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as Matrix
private  Matrix WeightedDistanceFunction.weightMatrix
          The weight matrix.
 

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction that return types with arguments of type Matrix
 AssociationID<Matrix> KernelBasedLocallyWeightedDistanceFunction.getAssociationID()
          Returns the association ID for the association to be set by the preprocessor.
 

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Matrix
WeightedDistanceFunction(Matrix weightMatrix)
          Provides the Weighted distance for feature vectors.
 

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with parameters of type Matrix
private  void PCABasedCorrelationDistanceFunction.adjust(Matrix v, Matrix e_czech, Matrix vector, int corrDim)
          Inserts the specified vector into the given orthonormal matrix v at column corrDim.
 

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
(package private)  Matrix KernelMatrix.kernel
          The kernel matrix
 

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel that return Matrix
static Matrix KernelMatrix.centerKernelMatrix(KernelMatrix<? extends RealVector<?,? extends Number>> 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.
 Matrix KernelMatrix.getKernel()
          Get the kernel matrix.
 Matrix KernelMatrix.getSubColumn(int i, List<Integer> ids)
          Returns the ith kernel matrix column for all objects in ids
 Matrix KernelMatrix.getSubMatrix(Collection<Integer> 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
static Matrix KernelMatrix.centerMatrix(Matrix matrix)
          Centers the matrix in feature space according to Smola et.
 

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Matrix
KernelMatrix(Matrix matrix)
          Makes a new kernel matrix from matrix.
 

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

Methods in de.lmu.ifi.dbs.elki.math with parameters of type Matrix
static double MathUtil.mahalanobisDistance(Matrix weightMatrix, Vector o1_minus_o2)
           
 

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

Subclasses of Matrix in de.lmu.ifi.dbs.elki.math.linearalgebra
 class Vector
          Provides a vector object that encapsulates an m x 1 - matrix object.
 

Fields in de.lmu.ifi.dbs.elki.math.linearalgebra declared as Matrix
private  Matrix EigenPair.eigenvector
          The eigenvector as a matrix.
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
 Matrix Matrix.appendColumns(Matrix columns)
          Returns a matrix which consists of this matrix and the specified columns.
 Matrix Matrix.arrayLeftDivide(Matrix B)
          Element-by-element left division, C = A.
 Matrix Matrix.arrayLeftDivideEquals(Matrix B)
          Element-by-element left division in place, A = A.
 Matrix Matrix.arrayRightDivide(Matrix B)
          Element-by-element right division, C = A.
 Matrix Matrix.arrayRightDivideEquals(Matrix B)
          Element-by-element right division in place, A = A.
 Matrix Matrix.arrayTimes(Matrix B)
          Element-by-element multiplication, C = A.
 Matrix Matrix.arrayTimesEquals(Matrix B)
          Element-by-element multiplication in place, A = A.
 Matrix Matrix.cheatToAvoidSingularity(double constant)
          Adds a given value to the diagonal entries if the entry is smaller than the constant.
 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.
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 Matrix.exactGaussJordanElimination()
          Returns a matrix derived by Gauss-Jordan-elimination using RationalNumbers for the transformations.
 Matrix Matrix.getColumn(int j)
          Returns the jth column of this matrix.
 Matrix EigenvalueDecomposition.getD()
          Return the block diagonal eigenvalue matrix
 Matrix EigenPair.getEigenvector()
          Returns the eigenvector.
 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 Matrix.getRow(int i)
          Returns the ith row of this matrix.
 Matrix SingularValueDecomposition.getS()
          Return the diagonal matrix of singular values
 Matrix AffineTransformation.getTransformation()
          Get a copy of the transformation matrix
 Matrix LUDecomposition.getU()
          Return upper triangular factor
 Matrix SingularValueDecomposition.getU()
          Return the left singular vectors
 Matrix EigenvalueDecomposition.getV()
          Return the eigenvector matrix
 Matrix SingularValueDecomposition.getV()
          Return the right singular vectors
static Matrix Matrix.identity(int m, int n)
          Generate identity matrix
 Matrix Matrix.inverse()
          Matrix inverse or pseudoinverse
 Matrix Matrix.minus(Matrix B)
          C = A - B
 Matrix Matrix.minusEquals(Matrix B)
          A = A - B
 Matrix Matrix.orthonormalize()
          Returns an orthonormalization of this matrix.
 Matrix Matrix.plus(Matrix B)
          C = A + B
 Matrix Matrix.plusEquals(Matrix B)
          A = A + B
 Matrix Matrix.projection(Matrix v)
          Projects this row vector into the subspace formed by the specified matrix v.
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 LUDecomposition.solve(Matrix B)
          Solve A*X = B
 Matrix QRDecomposition.solve(Matrix B)
          Least squares solution of A*X = B
 Matrix Matrix.solve(Matrix B)
          Solve A*X = B
 Matrix CholeskyDecomposition.solve(Matrix B)
          Solve A*X = B
 Matrix Matrix.solveTranspose(Matrix B)
          Solve X*A = B, which is also A'*X' = B'
 Matrix Matrix.times(double s)
          Multiply a matrix by a scalar, C = s*A
 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 Matrix.transpose()
          Matrix transpose.
 Matrix Matrix.uminus()
          Unary minus
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
 void AffineTransformation.addMatrix(Matrix m)
          Add a matrix operation to the matrix.
 double Matrix.angle(int colA, Matrix B, int colB)
          Returns the angle of the colA col of this and the colB col of B.
 Matrix Matrix.appendColumns(Matrix columns)
          Returns a matrix which consists of this matrix and the specified columns.
 Matrix Matrix.arrayLeftDivide(Matrix B)
          Element-by-element left division, C = A.
 Matrix Matrix.arrayLeftDivideEquals(Matrix B)
          Element-by-element left division in place, A = A.
 Matrix Matrix.arrayRightDivide(Matrix B)
          Element-by-element right division, C = A.
 Matrix Matrix.arrayRightDivideEquals(Matrix B)
          Element-by-element right division in place, A = A.
 Matrix Matrix.arrayTimes(Matrix B)
          Element-by-element multiplication, C = A.
 Matrix Matrix.arrayTimesEquals(Matrix B)
          Element-by-element multiplication in place, A = A.
private  void Matrix.checkMatrixDimensions(Matrix B)
          Check if size(A) == size(B) *
 double Matrix.distanceCov(Matrix B)
          distanceCov returns distance of two Matrices A and B, i.e. the root of the sum of the squared distances Aij-Bij.
 boolean Matrix.linearlyIndependent(Matrix columnMatrix)
          Returns true if the specified column matrix a is linearly independent to the columns of this matrix.
 Matrix Matrix.minus(Matrix B)
          C = A - B
 Matrix Matrix.minusEquals(Matrix B)
          A = A - B
 Matrix Matrix.plus(Matrix B)
          C = A + B
 Matrix Matrix.plusEquals(Matrix B)
          A = A + B
 Matrix Matrix.projection(Matrix v)
          Projects this row vector into the subspace formed by the specified matrix v.
 double Matrix.scalarProduct(int colA, Matrix B, int colB)
          Returns the scalar product of the colA cols of this and the colB col of B.
 void Matrix.setColumn(int j, Matrix column)
          Sets the jth column of this matrix to the specified column.
 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 LUDecomposition.solve(Matrix B)
          Solve A*X = B
 Matrix QRDecomposition.solve(Matrix B)
          Least squares solution of A*X = B
 Matrix Matrix.solve(Matrix B)
          Solve A*X = B
 Matrix CholeskyDecomposition.solve(Matrix B)
          Solve A*X = B
 Matrix Matrix.solveTranspose(Matrix B)
          Solve X*A = B, which is also A'*X' = B'
 Matrix Matrix.times(Matrix B)
          Linear algebraic matrix multiplication, A * B
 Vector AffineTransformation.unhomogeneRelativeVector(Matrix v)
          Project an homogeneous vector back into the original space.
 Vector AffineTransformation.unhomogeneVector(Matrix v)
          Project an homogeneous vector back into the original space.
 

Constructors in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type Matrix
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.
EigenPair(Matrix eigenvector, double eigenvalue)
          Creates a new EigenPair object.
EigenvalueDecomposition(Matrix Arg)
          Check for symmetry, then construct the eigenvalue decomposition
LUDecomposition(Matrix A)
          LU Decomposition
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
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.
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
 Matrix PCAFilteredResult.adapatedStrongEigenvectors()
          Returns a copy of the adapted strong eigenvectors.
 Matrix PCAFilteredResult.dissimilarityMatrix()
          Returns a copy of the dissimilarity matrix (M_czech) of this LocalPCA.
 Matrix PCAResult.getEigenvectors()
          Returns a copy of the matrix of eigenvectors of the object to which this PCA belongs to.
 Matrix PCAFilteredResult.getStrongEigenvectors()
          Returns a copy of the matrix of strong eigenvectors after passing the eigen pair filter.
 Matrix PCAFilteredResult.getWeakEigenvectors()
          Returns a copy of the matrix of weak eigenvectors after passing the eigen pair filter.
 Matrix StandardCovarianceMatrixBuilder.processDatabase(Database<V> database)
          Compute Covariance Matrix for a complete database
 Matrix CovarianceMatrixBuilder.processDatabase(Database<V> database)
          Compute Covariance Matrix for a complete database
 Matrix WeightedCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Weighted Covariance Matrix for a set of IDs.
 Matrix KernelCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Returns the local kernel matrix of the specified ids.
 Matrix StandardCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Compute Covariance Matrix for a collection of database IDs
abstract  Matrix CovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Compute Covariance Matrix for a collection of database IDs
 Matrix CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 Matrix WeightedCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 Matrix CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 Matrix PCAFilteredResult.selectionMatrixOfStrongEigenvectors()
          Returns a copy of the selection matrix of the strong eigenvectors (E_czech) of this LocalPCA.
 Matrix PCAFilteredResult.selectionMatrixOfWeakEigenvectors()
          Returns a copy of the selection matrix of the weak eigenvectors (E_hat) of the object to which this PCA belongs to.
 Matrix PCAFilteredResult.similarityMatrix()
          Returns a copy of the similarity matrix (M_hat) of this LocalPCA.
 

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Matrix
 PCAFilteredResult PCAFilteredRunner.processCovarMatrix(Matrix covarMatrix)
          Process an existing Covariance Matrix
 PCAResult PCARunner.processCovarMatrix(Matrix covarMatrix)
          Process an existing covariance Matrix
 

Constructors in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Matrix
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
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
private static Matrix PolynomialRegression.xMatrix(Vector x, int p)
           
 

Methods in de.lmu.ifi.dbs.elki.math.statistics with parameters of type Matrix
 double MultipleLinearRegression.estimateY(Matrix x)
          Performes an estimatation of y on the specified matrix.
 

Constructors in de.lmu.ifi.dbs.elki.math.statistics with parameters of type Matrix
MultipleLinearRegression(Vector y, Matrix x)
          Provides a new multiple linear regression model with the specified parameters.
 

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

Methods in de.lmu.ifi.dbs.elki.utilities that return Matrix
static
<O extends RealVector<O,?>>
Matrix
DatabaseUtil.covarianceMatrix(Database<O> database)
          Determines the covariance matrix of the objects stored in the given database.
static
<O extends RealVector<O,?>>
Matrix
DatabaseUtil.covarianceMatrix(Database<O> database, O centroid)
           Determines the covariance matrix of the objects stored in the given database w.r.t. the given centroid.
static
<V extends RealVector<V,?>>
Matrix
DatabaseUtil.covarianceMatrix(Database<V> database, Collection<Integer> ids)
          Determines the covariance matrix of the objects stored in the given database.
static Matrix DatabaseUtil.covarianceMatrix(Matrix data)
          Determines the d x d covariance matrix of the given n x d data matrix.
 

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type Matrix
static Vector DatabaseUtil.centroid(Matrix data)
          Returns the centroid as a Vector object of the specified data matrix.
static Matrix DatabaseUtil.covarianceMatrix(Matrix data)
          Determines the d x d covariance matrix of the given n x d data matrix.
 


Release 0.2.1 (2009-07-13_1605)