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Packages that use BigMat | |
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Facemorph | |
Facemorph.aam | |
Facemorph.oesf | |
Facemorph.psm | |
Facemorph.tensor |
Uses of BigMat in Facemorph |
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Subclasses of BigMat in Facemorph | |
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class |
Gaussian
A Gaussian distribution class |
Methods in Facemorph that return BigMat | |
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BigMat |
PCI.combine(PCI[] pci,
Template targetShape,
int targetWidth,
int targetHeight,
Mask 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,
Mask mask)
Shrinks by a factor of 2 using filter to downsample |
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,
Mask mask)
Warps this PCI into targetShape |
Methods in Facemorph with parameters of type BigMat | |
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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,
Mask 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.subtract(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 | |
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BigMat(BigMat P)
|
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BigMat(BigMat P,
int x,
int y)
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Gaussian(double[] mean,
BigMat covar,
boolean invert)
Construct a Gaussian using the data given |
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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 |
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Methods in Facemorph.aam that return BigMat | |
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BigMat |
TaylorSolver.getV()
Gets the matrix of principal components (derived from SVD of hessian matrix) |
Constructors in Facemorph.aam with parameters of type BigMat | |
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CAAMSolver(double[] f,
double[][] df,
BigMat hessian,
double[] fdotdf,
double fdotf)
Creates a new instance of CAAMSolver |
Uses of BigMat in Facemorph.oesf |
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Methods in Facemorph.oesf that return BigMat | |
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static BigMat |
ORASM3D.toRotation(BigMat M)
Projects M into a valid roation matrix |
Methods in Facemorph.oesf with parameters of type BigMat | |
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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 |
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Methods in Facemorph.psm that return BigMat | |
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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 | |
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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 | |
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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.tensor |
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Methods in Facemorph.tensor that return BigMat | |
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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.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 | |
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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 |
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