Uses of Class
Facemorph.BigMat

Packages that use BigMat

Facemorph
Facemorph.aam
Facemorph.DataBase
Facemorph.oesf
Facemorph.psm
Facemorph.Stats
Facemorph.tensor

Uses of BigMat in Facemorph

Subclasses of BigMat in Facemorph

class

Gaussian A Gaussian distribution class

Methods in Facemorph that return BigMat

static BigMat	PCA.buildSplitModel(java.util.Vector<java.util.ArrayList<double[]>> sets) Calculates a matrix for transforming a vector from a generic model into a non-orthogonal sub-space model
BigMat	PCI.combine(PCI[] pci, Template targetShape, int targetWidth, int targetHeight, MaskInterface mask) Builds a PCI by combining a set of PCIs
BigMat	BigMat.copy() Create a copy of this matrix
BigMat	CSVFile.getAsBigMat()
BigMat	PCA.getComponents() Get the PCA components as a matrix
BigMat	PCA.getComponents(double[] average, boolean normalise) Gets the components as a matrix, pus 4 first rows hold linear approximations to rigid + scale parameters
BigMat	PCA.getComponents2(double[] average) Gets the components as a BigMat plus 4 initial cols for rigid params
BigMat	PCA.getComponents3D(double[] average, boolean normalise) Gets the components as a matrix, pus 7 first rows hold linear approximations to rigid + scale parameters for 3D
BigMat	Gaussian.getCovar(boolean invert) Uses the eigenvalues and eigenvectors to reconstruct the covariance matrix or it inverse
BigMat	PCI.getReduced(PCI pci, float[] filter, int m, MaskInterface mask) Shrinks by a factor of 2 using filter to downsample
static BigMat	BigMat.identity(int size)
BigMat	BigMat.invertSVD(double threshold) find the inverse (possibly in the least squares sense) using SVD
BigMat	BigMat.multiply(BigMat m) Multiply this matrix by another matrix
BigMat	BigMat.multiplyOtherTranspose(BigMat m) Multiply the this matrix by the transpose of another matrix
BigMat	BigMat.multiplySelfTranspose(boolean transpose, boolean skipFirst) Multiply this matrix by the transpose of itself
BigMat	BigMat.multiplyTranspose(BigMat m) Multiply this matrix by another matrix
BigMat	BigMat.reconstructQR(double[] c, double[] d) Construct the Q and R matrices explicitly, using the output from qrdcmp R.multiply(Q) will give the original input matrix
static BigMat	BigMat.removeNaNRecords(BigMat in) Removes any rows from the matrix that have NaNs of INF etc.
BigMat	BigMat.subMat(int[] rowIndex, int[] colIndex) Return a sub matrix containing only those columns points to by the indices.
BigMat	BigMat.subMat(int x, int y) Returns a matrix with row x and column y removed
BigMat	BigMat.subMat(int xstart, int xend, int ystart, int yend) Retrieve a sub-part of this matrix
BigMat	BigMat.subMatCols(int[] colIndex) Return a sub matrix containing only those columns pointed to by the indices.
BigMat	BigMat.subMatRows(int[] rowIndex) Return a sub matrix containing only those rows pointed to by the indices.
BigMat	BigMat.svbksb(double[] w, BigMat V, BigMat A, double threshold) SVD back substitution routine
BigMat	BigMat.svbksbt(double[] w, BigMat V, BigMat A, double threshold) SVD back substitution routine (transposed)
BigMat	BigMat.transposeCopy() Create a transposed copy of this matrix
BigMat	BigMat.upperTriangularInverseMultiply(BigMat M) Calculates A^(-1)M where A = this is upper triangular, by using repeated backsubstitutions.
BigMat	PCI.warp(Template targetShape, int targetWidth, int targetHeight, MaskInterface mask) Warps this PCI into targetShape

Methods in Facemorph with parameters of type BigMat

void	BigMat.add(BigMat mat) Adds mat to this
static void	PCA.addToAverage(Template average1, PCA pca1, Template currentAverage, int n, int start, BigMat data) Incrementally updates the data matrix and currentAverage by adding the PCA data to them
int	PCA.build(BigMat data) Builds a PCA from the data in a matrix form
int	PCA.build(BigMat data, double[] weights) Builds a PCA from a set of weighted samples
boolean	Gaussian.build(java.util.Vector samples, BigMat dist) Build a Gaussian from the supplied samples
int	PCA.buildPCAandAverage(BigMat data, double[] average) Builds a PCA from the data in a matrix form
void	BigMat.copy(BigMat mat) Copy the matrix given into this
boolean	BigMat.equals(BigMat mat) Checks if the two matrices are identical
boolean	BigMat.equals(BigMat mat, double delta) Checks if the two matrices are the same to within a given tollerance
void	Template.fitTensor(int warpType, java.awt.Image subject, java.awt.Image averageImage, Template avrg, MaskInterface mask, Tensor shapeTensor, Tensor imageTensor, BigMat shapeLinearModel, BigMat shapeAverage, BigMat imageLinearModel, BigMat imageAverage, float scale) Attempt at fitting an Tensor AAM to an image using efficient reverse method, with linear projection model, includes colour information
double[]	Template.getTensorandRBparameters(BigMat shapeModel, BigMat shapeAverage, Template average, Tensor shapeTensor) Analyses this Template using a tensor model
boolean	BigMat.jacobi(double[] d, BigMat v, int[] nrot) Jacobi computes all eigenvalues and eigenvectors of a real symmetric matrix The input matrix elements above the diagonal are destroyed.
BigMat	BigMat.multiply(BigMat m) Multiply this matrix by another matrix
BigMat	BigMat.multiplyOtherTranspose(BigMat m) Multiply the this matrix by the transpose of another matrix
BigMat	BigMat.multiplyTranspose(BigMat m) Multiply this matrix by another matrix
boolean	BigMat.qrdcmp(BigMat Q, BigMat R) Decomposes this square matrix into an orthonormal matrix Q and an upper triangular matrix R such that this = R.multiply(Q)
boolean	BigMat.qrdcmp(BigMat a, double[] c, double[] d) From numerical recipies in C ch2.10 Constructs the QR decomposition of this matrix returning the result in a[1..n][1..n].
static BigMat	BigMat.removeNaNRecords(BigMat in) Removes any rows from the matrix that have NaNs of INF etc.
void	PCA.solve(BigMat data) Builds this PCA from the data given
void	BigMat.subtractInPlace(BigMat mat) Subtracts mat to this
BigMat	BigMat.svbksb(double[] w, BigMat V, BigMat A, double threshold) SVD back substitution routine
void	BigMat.svbksb(double[] w, BigMat V, double[] b, double[] x, double threshold) Back substitution routine for SVD.
BigMat	BigMat.svbksbt(double[] w, BigMat V, BigMat A, double threshold) SVD back substitution routine (transposed)
void	BigMat.svbksbt(double[] w, BigMat V, double[] b, double[] x, double threshold) Back substitution routine for SVD.
boolean	BigMat.svdcmp(BigMat v, double[] w) Performs Singular Value Decomposition A = UwVt U replace A on output (this goes from being A to being U) v and w are filled in returns true if successful, false otherwise.
boolean	BigMat.svdcmpt(BigMat v, double[] w) Performs Singular Value Decomposition A = UwVt U replace A on output (this goes from being A to being U) v and w are filled in returns true if successful, false otherwise.
BigMat	BigMat.upperTriangularInverseMultiply(BigMat M) Calculates A^(-1)M where A = this is upper triangular, by using repeated backsubstitutions.

Constructors in Facemorph with parameters of type BigMat

BigMat(BigMat P)

BigMat(BigMat P, int x, int y)

Gaussian(double[] mean, BigMat covar, boolean invert)
Construct a Gaussian using the data given

Kalman(BigMat A, BigMat P, BigMat Q, BigMat R, BigMat H, double[] x)
Creates a new instance of Kalman

Uses of BigMat in Facemorph.aam

Methods in Facemorph.aam that return BigMat

BigMat

TaylorSolver.getV() Gets the matrix of principal components (derived from SVD of hessian matrix)

Constructors in Facemorph.aam with parameters of type BigMat

CAAMSolver(double[] f, double[][] df, BigMat hessian, double[] fdotdf, double fdotf)
Creates a new instance of CAAMSolver

Uses of BigMat in Facemorph.DataBase

Methods in Facemorph.DataBase that return BigMat

BigMat	Batch.constructMatrix(boolean[] selectedColumns, boolean[] selectedRows) Construct a BigMat matrix from numerical data in the Batch file
BigMat	Batch.constructMatrix(boolean addImage, boolean addTemplate, int[] selectedColumns, int[] selectedRows) Construct a BigMat matrix from numerical data in the Batch file
BigMat	Batch.constructMatrix(int[] selectedColumns, int[] selectedRows) Construct a BigMat matrix from numerical data in the Batch file
BigMat	Batch.constructMatrix(java.util.Vector<java.lang.String> headers) Construct a BigMat matrix from numerical data in the Batch file
BigMat	Batch.constructMatrix(java.util.Vector<java.lang.String> headers, boolean[] selectedRows) Construct a BigMat matrix from numerical data in the Batch file
BigMat	Batch.constructMatrix(java.util.Vector<java.lang.String> headers, int[] selectedRows) Construct a BigMat matrix from numerical data in the Batch file
BigMat	iniFile.getBigMat(java.lang.String key)
BigMat	iniFile.getBigMat(java.lang.String key, BigMat fallback)

Methods in Facemorph.DataBase with parameters of type BigMat

void	Batch.addMatrix(BigMat matrix, int[] selectedColumns, int[] selectedRows)
void	Batch.addMatrix(BigMat matrix, java.util.Vector<java.lang.String> headers, int[] selectedRows, boolean createColumns)
BigMat	iniFile.getBigMat(java.lang.String key, BigMat fallback)
void	iniFile.set(java.lang.String key, BigMat value)

Uses of BigMat in Facemorph.oesf

Methods in Facemorph.oesf that return BigMat

static BigMat

ORASM3D.toRotation(BigMat M) Projects M into a valid roation matrix

Methods in Facemorph.oesf with parameters of type BigMat

boolean	Template3D.fitTo2DOrtho(Template template, int width, int height, Vector3 centre, BigMat R, double[] T, double[] scale)
void	FilterSet.initialisePriors(Template3D t3d, BigMat R, double[] T, double scale, java.util.ArrayList<double[]> priorsView0, java.util.ArrayList<java.lang.Boolean> visible) Initialise the priors
static BigMat	ORASM3D.toRotation(BigMat M) Projects M into a valid roation matrix
void	Template3D.transform(BigMat R, double[] T, double scale)

Uses of BigMat in Facemorph.psm

Methods in Facemorph.psm that return BigMat

static BigMat	PSM.minimumSpanningTree(BigMat graph) Calculate the MST
static BigMat	PSMNode.minimumSpanningTree(BigMat graph) Get the minimum spanning tree of the graph

Methods in Facemorph.psm with parameters of type BigMat

static int	PSM.findBestRoot(BigMat tree, BigMat graph) Find the best root of the tree
static int	PSMNode.findBestRoot(BigMat tree, BigMat graph) Finds the "best" root of the tree
static BigMat	PSM.minimumSpanningTree(BigMat graph) Calculate the MST
static BigMat	PSMNode.minimumSpanningTree(BigMat graph) Get the minimum spanning tree of the graph

Constructors in Facemorph.psm with parameters of type BigMat

PSM(int start, BigMat mst, DiagonalGMM[] appGauss, DiagonalGaussian[] gauss, boolean[] visited, java.util.ArrayList<Filter> filters, Filter lowpass, int w, int h, int levs)
PSM constructor

PSMNode(int start, BigMat mst, Feature appGauss, DiagonalGaussian[][] gauss, boolean[] visited, int w, int h)
Contruct a PSMNode

Uses of BigMat in Facemorph.Stats

Fields in Facemorph.Stats declared as BigMat

protected BigMat	OvercompleteSPLS.B
protected BigMat	PLSReducer.B
protected BigMat	OvercompleteSPLS.C
protected BigMat	PLSReducer.C
BigMat	ModelResults.components
protected BigMat	DataMiner.dependent
protected BigMat	OvercompleteSPLS.E
protected BigMat	PLSReducer.E
protected BigMat	OvercompleteSPLS.F
protected BigMat	PLSReducer.F
protected BigMat	DataMiner.independent
protected BigMat	OvercompleteSPLS.P
protected BigMat	PLSReducer.P
BigMat	Model.DataDescription.processedDataMatrix The data in processed form in a matrix (null if unprocessed).
protected BigMat	NumericRegressor.ProjectionMatrix Projection Matix onto the space spanned by the columns of X (yhat = P.y)
protected BigMat	OvercompleteSPLS.R
protected BigMat	PLSReducer.R
protected BigMat	OvercompleteSPLS.T
protected BigMat	PLSReducer.T
protected BigMat	OvercompleteSPLS.U
protected BigMat	PLSReducer.U
protected BigMat	OvercompleteSPLS.W
protected BigMat	PLSReducer.W
protected BigMat	NumericRegressor.X A set of predictor variables
protected BigMat	OvercompleteSPLS.X
protected BigMat	PLSReducer.X
protected BigMat	ImagePLS.Y Set of independent values to decompose against
protected BigMat	OvercompleteSPLS.Y
protected BigMat	PLSReducer.Y

Methods in Facemorph.Stats that return BigMat

BigMat	BinaryLogisticRegression.apply(BigMat in)
BigMat	DifferenceOfAverages.apply(BigMat in)
BigMat	ICA.apply(BigMat in)
BigMat	ImagePLS.apply(BigMat in)
BigMat	ImageRegressor.apply(BigMat in)
BigMat	NumericRegressor.apply(BigMat in)
BigMat	OvercompleteSPLS.apply(BigMat in)
BigMat	PCAReducer.apply(BigMat in)
BigMat	PLSReducer.apply(BigMat in)
BigMat	StatisticalModel.apply(BigMat in) Apply the statistical model on the input data supplied in Matrix form
BigMat	ICA.getBasisMatrix() Outputs the basis matrix that is the result of PCA analysis
BigMat	OvercompleteSPLS.getBasisMatrix() Outputs the basis matrix that is the result of PCA analysis
BigMat	PCAReducer.getBasisMatrix() Outputs the basis matrix that is the result of PCA analysis
BigMat	PLSReducer.getBasisMatrix() Outputs the basis matrix that is the result of PCA analysis
BigMat	Model.DataDescription.getProcessedDataMatrix()
BigMat	DataReducer.reconstructDependent(BigMat in) Reconstruct (approximately) a high dimensional input given the low dimensional output
BigMat	ICA.reconstructDependent(BigMat in)
BigMat	ImagePLS.reconstructDependent(BigMat in)
BigMat	OvercompleteSPLS.reconstructDependent(BigMat in)
BigMat	PCAReducer.reconstructDependent(BigMat in)
BigMat	PLSReducer.reconstructDependent(BigMat in)
BigMat	DataReducer.reconstructIndependent(BigMat in) Reconstruct (approximately) a high dimensional input given the low dimensional output
BigMat	ICA.reconstructIndependent(BigMat in)
BigMat	ImagePLS.reconstructIndependent(BigMat in)
BigMat	OvercompleteSPLS.reconstructIndependent(BigMat in)
BigMat	PCAReducer.reconstructIndependent(BigMat in)
BigMat	PLSReducer.reconstructIndependent(BigMat in)
BigMat	DataReducer.reduceDependent(BigMat in) Perform dimensionality reduction on the BigMat
BigMat	ICA.reduceDependent(BigMat in)
BigMat	ImagePLS.reduceDependent(BigMat in)
BigMat	OvercompleteSPLS.reduceDependent(BigMat in)
BigMat	PCAReducer.reduceDependent(BigMat in)
BigMat	PLSReducer.reduceDependent(BigMat in)
BigMat	DataReducer.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat
BigMat	ICA.reduceIndependent(BigMat in)
BigMat	ImagePLS.reduceIndependent(BigMat in)
BigMat	OvercompleteSPLS.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat (in) using a Least Squares approximation
BigMat	PCAReducer.reduceIndependent(BigMat in)
BigMat	PLSReducer.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat (in) using a Least Squares approximation
BigMat	WeightedPLS.reweight(BigMat mat, double[] weights)
BigMat	WeightedPLS.reweight(BigMat mat, double[] weights, double factor)

Methods in Facemorph.Stats with parameters of type BigMat

BigMat	BinaryLogisticRegression.apply(BigMat in)
BigMat	DifferenceOfAverages.apply(BigMat in)
BigMat	ICA.apply(BigMat in)
BigMat	ImagePLS.apply(BigMat in)
BigMat	ImageRegressor.apply(BigMat in)
BigMat	NumericRegressor.apply(BigMat in)
BigMat	OvercompleteSPLS.apply(BigMat in)
BigMat	PCAReducer.apply(BigMat in)
BigMat	PLSReducer.apply(BigMat in)
BigMat	StatisticalModel.apply(BigMat in) Apply the statistical model on the input data supplied in Matrix form
void	WeightedPLS.calculateNewWeights(BigMat Fw, boolean[] preRemoved, double[] newWeights, boolean[] newRemoved)
void	OvercompleteSPLS.iterate(BigMat Ew, BigMat Fw)
void	PLSReducer.iterate(BigMat Ew, BigMat Fw)
static double	CrossValidation.kFold(Regressor r, BigMat independent, double[] dependent, int k)
double[]	BinaryLogisticRegression.predict(BigMat values)
double[]	DifferenceOfAverages.predict(BigMat values) The projection of a point onto a line is given by the inner produce of the point to the source divided by the square magnitude of the line
double[]	ImagePLS.predict(BigMat values)
double[]	ImageRegressor.predict(BigMat values)
double[]	Model.predict(BigMat values) Using the Model previously generated, predict a set of values.
double[]	NumericRegressor.predict(BigMat values) Predict using the model the values of the dependent from a set of independents
double[]	OvercompleteSPLS.predict(BigMat values)
double[]	PCAReducer.predict(BigMat values)
double[]	PLSReducer.predict(BigMat values)
double[]	Regressor.predict(BigMat values) Predict using the model the values of the dependent from a set of independents
BigMat	DataReducer.reconstructDependent(BigMat in) Reconstruct (approximately) a high dimensional input given the low dimensional output
BigMat	ICA.reconstructDependent(BigMat in)
BigMat	ImagePLS.reconstructDependent(BigMat in)
BigMat	OvercompleteSPLS.reconstructDependent(BigMat in)
BigMat	PCAReducer.reconstructDependent(BigMat in)
BigMat	PLSReducer.reconstructDependent(BigMat in)
BigMat	DataReducer.reconstructIndependent(BigMat in) Reconstruct (approximately) a high dimensional input given the low dimensional output
BigMat	ICA.reconstructIndependent(BigMat in)
BigMat	ImagePLS.reconstructIndependent(BigMat in)
BigMat	OvercompleteSPLS.reconstructIndependent(BigMat in)
BigMat	PCAReducer.reconstructIndependent(BigMat in)
BigMat	PLSReducer.reconstructIndependent(BigMat in)
BigMat	DataReducer.reduceDependent(BigMat in) Perform dimensionality reduction on the BigMat
BigMat	ICA.reduceDependent(BigMat in)
BigMat	ImagePLS.reduceDependent(BigMat in)
BigMat	OvercompleteSPLS.reduceDependent(BigMat in)
BigMat	PCAReducer.reduceDependent(BigMat in)
BigMat	PLSReducer.reduceDependent(BigMat in)
BigMat	DataReducer.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat
BigMat	ICA.reduceIndependent(BigMat in)
BigMat	ImagePLS.reduceIndependent(BigMat in)
BigMat	OvercompleteSPLS.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat (in) using a Least Squares approximation
BigMat	PCAReducer.reduceIndependent(BigMat in)
BigMat	PLSReducer.reduceIndependent(BigMat in) Perform dimensionality reduction on the BigMat (in) using a Least Squares approximation
BigMat	WeightedPLS.reweight(BigMat mat, double[] weights)
BigMat	WeightedPLS.reweight(BigMat mat, double[] weights, double factor)
void	BinaryLogisticRegression.setDependentData(BigMat data)
void	DifferenceOfAverages.setDependentData(BigMat data)
void	ICA.setDependentData(BigMat data)
void	ImagePLS.setDependentData(BigMat data)
void	ImageRegressor.setDependentData(BigMat data)
void	NumericRegressor.setDependentData(BigMat data) The dependent part of the regression
void	OvercompleteSPLS.setDependentData(BigMat data)
void	PCAReducer.setDependentData(BigMat data)
void	PLSReducer.setDependentData(BigMat data)
void	StatisticalModel.setDependentData(BigMat data) The dependent part of the regression
void	BinaryLogisticRegression.setIndependentData(BigMat data)
void	DifferenceOfAverages.setIndependentData(BigMat data)
void	ICA.setIndependentData(BigMat data)
void	ImagePLS.setIndependentData(BigMat data)
void	ImageRegressor.setIndependentData(BigMat data)
void	NumericRegressor.setIndependentData(BigMat data) The independent part of the regression
void	OvercompleteSPLS.setIndependentData(BigMat data)
void	PCAReducer.setIndependentData(BigMat data)
void	PLSReducer.setIndependentData(BigMat data)
void	StatisticalModel.setIndependentData(BigMat data) The independent part of the regression
int	PCAReducer.setInputData(BigMat data)
int[]	BinaryLogisticRegression.validate(BigMat ind, double[] dep)

Constructors in Facemorph.Stats with parameters of type BigMat

NumericRegressor(BigMat predictors, double[] responses)

Uses of BigMat in Facemorph.tensor

Methods in Facemorph.tensor that return BigMat

BigMat[]	Tensor.build(boolean rotate, double[][] weights) build N-way model, replace this with the core tensor and return the rotation components.
BigMat[]	Tensor.build(boolean rotate, double[][] weights, int dataDim) build N-way model, replace this with the core tensor and return the rotation components.
BigMat[]	Tensor.buildWithAverage(double[][] weights, int dataDim, double maxvar) build N-way model, replace this with the core tensor and return the rotation components
BigMat[]	Tensor.buildWithAverage(double[][] weights, int dataDim, int subDim, double maxvar) build N-way model, replace this with the core tensor and return the rotation components
BigMat[]	Tensor.buildWithAverageInPlace(double[][] weights, int dataDim, double maxvar) build N-way model, replace this with the core tensor and return the rotation components
static BigMat	Tensor.createAverageAndSubtractMatrix(int width) Creates the matrix that can be used to calculate and subtract the average from rows of length width
BigMat	Tensor.getCovar(int i, boolean skipFirst) unfold method, unfolds along a dimension i
BigMat	Tensor.getFullLinearAnalysisMatrix(int dataDim, BigMat average, boolean skipFirst) Get the fll tensor as a linear analysis matrix
BigMat	Tensor.getLinearAnalysisMatrix(int dataDim, BigMat average) Gets a linear analysis matrix
BigMat	Tensor.planeAverages(int dim, int dataDim) Average across all but dim and dataDim
BigMat	Tensor.reconstructForALS(double[][] weights, int dataDim, int studyDim) Reconstruction for alternating least squares method
BigMat	Tensor.rowPCAMatrix(int dim, int dataDim, double[] w) Calculates the PCA rotation matrix along one row
BigMat	Tensor.unfold(int i) unfold method, unfolds along a dimension i

Methods in Facemorph.tensor with parameters of type BigMat

void	Tensor.fold(int i, BigMat M) Fold the matrix into this Tensor along the axis given
static double[]	Tensor.getALSWeights(BigMat coeffs, double[] testData) Get the paramter weights for test data using linear matrix coeffs
BigMat	Tensor.getFullLinearAnalysisMatrix(int dataDim, BigMat average, boolean skipFirst) Get the fll tensor as a linear analysis matrix
BigMat	Tensor.getLinearAnalysisMatrix(int dataDim, BigMat average) Gets a linear analysis matrix
static double[]	Tensor.linearAnalyse(BigMat linearModel, BigMat average, double[] testData) Analyse some test data with a linear model
Tensor	Tensor.multiply(int i, BigMat M) multiply method for N-mode product with M along row i
void	Tensor.multiplyInPlace(int i, BigMat M) multiply method for N-mode product with M along row i
Tensor	Tensor.reconstruct(BigMat[] U) Tensor reconstruction method
double[]	Tensor.reconstruct(double[][] weights, int i, BigMat U) reconstruct first multiplying by transpose U along i