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Packages that use Matrix | |
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de.lmu.ifi.dbs.elki.algorithm.clustering | Package collects clustering algorithms. |
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation | Package to collect correlation clustering algorithms suitable as a task for the KDDTask main routine. |
de.lmu.ifi.dbs.elki.algorithm.result | Package to collect result classes for the results of algorithms. |
de.lmu.ifi.dbs.elki.algorithm.result.clustering | Package to collect result classes for the results of clustering algorithms. |
de.lmu.ifi.dbs.elki.data | Package collects basic classes for different data types, database object types and label types. |
de.lmu.ifi.dbs.elki.database | Package collects variants of databases and related classes. |
de.lmu.ifi.dbs.elki.distance.distancefunction | Package collects distance functions. |
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel | Package collects kernel functions. |
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.statistics | Package to support statistical tests and methods. |
de.lmu.ifi.dbs.elki.utilities | Package collects various classes and methods of global utility. |
de.lmu.ifi.dbs.elki.varianceanalysis | Classes for analysis of variance by different methods. |
Uses of Matrix in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering with type arguments of type Matrix | |
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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 |
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Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Matrix | |
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(package private) Matrix |
ORCLUS.Cluster.basis
The matrix defining the subspace of this cluster. |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Matrix | |
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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.Cluster 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 inerval 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 | |
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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 parametrization function into the subspace described by the given basis. |
Uses of Matrix in de.lmu.ifi.dbs.elki.algorithm.result |
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Fields in de.lmu.ifi.dbs.elki.algorithm.result declared as Matrix | |
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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.algorithm.result that return Matrix | |
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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.algorithm.result with parameters of type Matrix | |
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private double |
CorrelationAnalysisSolution.distance(Matrix p)
Returns the distance of Matrix p from the hyperplane underlying this solution. |
Constructors in de.lmu.ifi.dbs.elki.algorithm.result with parameters of type Matrix | |
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CorrelationAnalysisSolution(LinearEquationSystem solution,
Database<V> db,
Matrix strongEigenvectors,
Matrix weakEigenvectors,
Matrix similarityMatrix,
Vector centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix. |
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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. |
Uses of Matrix in de.lmu.ifi.dbs.elki.algorithm.result.clustering |
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Fields in de.lmu.ifi.dbs.elki.algorithm.result.clustering declared as Matrix | |
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private Matrix |
EMModel.covarianceMatrix
|
Constructors in de.lmu.ifi.dbs.elki.algorithm.result.clustering with parameters of type Matrix | |
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EMModel(Database<V> db,
V mean,
Matrix covarianceMatrix)
todo comment |
Uses of Matrix in de.lmu.ifi.dbs.elki.data |
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Methods in de.lmu.ifi.dbs.elki.data that return Matrix | |
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Matrix |
DoubleVector.getRowVector()
|
Matrix |
FeatureVector.getRowVector()
Returns a Matrix representing in one row and getDimensionality() columns the values of this
FeatureVector. |
Matrix |
SparseDoubleVector.getRowVector()
|
Matrix |
BitVector.getRowVector()
|
Matrix |
FloatVector.getRowVector()
|
Constructors in de.lmu.ifi.dbs.elki.data with parameters of type Matrix | |
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DoubleVector(Matrix columnMatrix)
Expects a matrix of one column. |
Uses of Matrix in de.lmu.ifi.dbs.elki.database |
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Fields in de.lmu.ifi.dbs.elki.database with type parameters of type Matrix | |
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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 eigencvector weighted matrix of an object. |
Uses of Matrix in de.lmu.ifi.dbs.elki.distance.distancefunction |
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Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as Matrix | |
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private Matrix |
WeightedDistanceFunction.weightMatrix
The weight matrix. |
Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Matrix | |
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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 . |
Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Matrix | |
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WeightedDistanceFunction(Matrix weightMatrix)
Provides the Weighted distance for feature vectors. |
Uses of Matrix in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
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Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel declared as Matrix | |
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(package private) Matrix |
KernelMatrix.kernel
The kernel matrix |
Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel that return Matrix | |
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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()
|
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 | |
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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 | |
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KernelMatrix(Matrix matrix)
Makes a new kernel matrix from matrix. |
Uses of Matrix in de.lmu.ifi.dbs.elki.math.linearalgebra |
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Subclasses of Matrix in de.lmu.ifi.dbs.elki.math.linearalgebra | |
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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 | |
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private Matrix |
EigenPair.eigenvector
The eigenvector as a matrix. |
Methods in de.lmu.ifi.dbs.elki.math.linearalgebra that return Matrix | |
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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. |
private Matrix |
Matrix.gaussElimination()
Recursive gauss-elimination (non-reduced form). |
Matrix |
Matrix.gaussJordanElimination()
Deprecated. use LinearEquationSystem instead |
Matrix |
Matrix.getColumn(int j)
Returns the j th 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 |
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 i th row of this matrix. |
Matrix |
SingularValueDecomposition.getS()
Return the diagonal matrix of singular values |
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 |
LUDecomposition.solve(Matrix B)
Solve A*X = B |
Matrix |
QRDecomposition.solve(Matrix B)
Least squares solution of A*X = B |
Matrix |
CholeskyDecomposition.solve(Matrix B)
Solve A*X = B |
Matrix |
Matrix.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 | |
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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 j th 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 |
CholeskyDecomposition.solve(Matrix B)
Solve A*X = B |
Matrix |
Matrix.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 |
Constructors in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type Matrix | |
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CholeskyDecomposition(Matrix Arg)
Cholesky algorithm for symmetric and positive definite matrix. |
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EigenPair(Matrix eigenvector,
double eigenvalue)
Creates a new EigenPair object. |
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EigenvalueDecomposition(Matrix Arg)
Check for symmetry, then construct the eigenvalue decomposition |
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LUDecomposition(Matrix A)
LU Decomposition |
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QRDecomposition(Matrix A)
QR Decomposition, computed by Householder reflections. |
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SingularValueDecomposition(Matrix Arg)
Construct the singular value decomposition |
Uses of Matrix in de.lmu.ifi.dbs.elki.math.statistics |
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Fields in de.lmu.ifi.dbs.elki.math.statistics declared as Matrix | |
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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 | |
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private static Matrix |
PolynomialRegression.xMatrix(Vector x,
int p)
|
Methods in de.lmu.ifi.dbs.elki.math.statistics with parameters of type Matrix | |
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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 | |
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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 |
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Methods in de.lmu.ifi.dbs.elki.utilities that return Matrix | ||
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static
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Util.covarianceMatrix(Database<O> database)
Determines the covariance matrix of the objects stored in the given database. |
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static
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Util.covarianceMatrix(Database<V> database,
Collection<Integer> ids)
Determines the covariance matrix of the objects stored in the given database. |
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static Matrix |
Util.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 | |
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static Vector |
Util.centroid(Matrix data)
Returns the centroid as a Vector object of the specified data matrix. |
static Matrix |
Util.covarianceMatrix(Matrix data)
Determines the d x d covariance matrix of the given n x d data matrix. |
Uses of Matrix in de.lmu.ifi.dbs.elki.varianceanalysis |
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Fields in de.lmu.ifi.dbs.elki.varianceanalysis declared as Matrix | |
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private Matrix |
LocalPCA.adapatedStrongEigenvectors
The diagonal matrix of adapted strong eigenvalues: eigenvectors * e_czech. |
private Matrix |
GlobalPCA.covarianceMatrix
Holds the covariance matrix. |
private Matrix |
LocalPCA.e_czech
The selection matrix of the strong eigenvectors. |
private Matrix |
LocalPCA.e_hat
The selection matrix of the weak eigenvectors. |
private Matrix |
AbstractPCA.eigenvectors
The eigenvectors in decreasing order to their corresponding eigenvalues. |
private Matrix |
LocalPCA.m_czech
The dissimilarity matrix. |
private Matrix |
LocalPCA.m_hat
The similarity matrix. |
private Matrix |
AbstractPCA.strongEigenvectors
The strong eigenvectors to their corresponding filtered eigenvalues. |
private Matrix |
AbstractPCA.weakEigenvectors
The weak eigenvectors to their corresponding filtered eigenvalues. |
Methods in de.lmu.ifi.dbs.elki.varianceanalysis that return Matrix | |
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Matrix |
LocalPCA.adapatedStrongEigenvectors()
Returns a copy of the adapted strong eigenvectors. |
Matrix |
LocalPCA.dissimilarityMatrix()
Returns a copy of the dissimilarity matrix (M_czech) of this LocalPCA. |
Matrix |
GlobalPCA.getCovarianceMatrix()
Returns the covariance matrix. |
Matrix |
AbstractPCA.getEigenvectors()
Returns a copy of the matrix of eigenvectors of the object to which this PCA belongs to. |
Matrix |
PCA.getEigenvectors()
Returns a copy of the matrix of eigenvectors of the object to which this PCA belongs to. |
Matrix |
AbstractPCA.getStrongEigenvectors()
Returns a copy of the matrix of strong eigenvectors after passing the eigen pair filter. |
Matrix |
PCA.getStrongEigenvectors()
Returns a copy of the matrix of strong eigenvectors after passing the eigen pair filter. |
Matrix |
AbstractPCA.getWeakEigenvectors()
Returns a copy of the matrix of weak eigenvectors after passing the eigen pair filter. |
Matrix |
PCA.getWeakEigenvectors()
Returns a copy of the matrix of weak eigenvectors after passing the eigen pair filter. |
protected Matrix |
LocalKernelPCA.pcaMatrix(Database<V> database,
Collection<Integer> ids)
Returns the local kernel matrix of the specified ids. |
protected Matrix |
LinearLocalPCA.pcaMatrix(Database<V> database,
Collection<Integer> ids)
Returns the covariance matrix of the specified ids. |
protected abstract Matrix |
LocalPCA.pcaMatrix(Database<V> database,
Collection<Integer> ids)
Determines and returns the matrix that is used for performaing the pca. |
Matrix |
LocalPCA.selectionMatrixOfStrongEigenvectors()
Returns a copy of the selection matrix of the strong eigenvectors (E_czech) of this LocalPCA. |
Matrix |
LocalPCA.selectionMatrixOfWeakEigenvectors()
Returns a copy of the selection matrix of the weak eigenvectors (E_hat) of the object to which this PCA belongs to. |
Matrix |
LocalPCA.similarityMatrix()
Returns a copy of the similarity matrix (M_hat) of this LocalPCA. |
Methods in de.lmu.ifi.dbs.elki.varianceanalysis with parameters of type Matrix | |
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protected void |
AbstractPCA.determineEigenPairs(Matrix pcaMatrix)
Determines the (strong and weak) eigenpairs (i.e. the eigenvectors and their corresponding eigenvalues) sorted in descending order of their eigenvalues of the specified matrix. |
void |
GlobalPCA.run(Matrix matrix)
Computes the principal components for objects of the given matrix. |
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