Facemorph.pga
Class SpherePGA

java.lang.Object
  Facemorph.pga.SpherePGA

public class SpherePGA
extends java.lang.Object

Principal Geodesic Analysis for hyperspherical data (Experimental, only part completed)

Constructor Summary

SpherePGA(java.util.Vector<double[]> data, double tol)
Creates a new instance of SpherePGA

Method Summary

static void	addToAverage(double[] average, double[] b, int n) Add an (unfolded) sample to the average
void	addToAverage(double[] v, int n) Adds a sample to the average
double[]	analyse(double[] v) Analyse the vector given this model
void	calculateMean(java.util.Vector<double[]> data) Estimate the mean
void	estimateMean(java.util.Vector<double[]> data, double tol) Estimate the mean of the dataset on the hypersphere
double[]	fold(double[] v) Folds the sample back into the sphere
double[]	getMean() Return the current estimate of the mean
static double	length(double[] vec) Returns the length of the vector
static void	main(java.lang.String[] args) The main method is used for testing
static void	normalise(double[] vec) Subtract the mean of the vector's coords and normlise length to 1
double[]	reconstruct(double[] v) Reconstruct using this model
double[]	unfold(double[] v) Unfolds a sample into the tangent plane

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SpherePGA

public SpherePGA(java.util.Vector<double[]> data,
                 double tol)

Creates a new instance of SpherePGA

Parameters:

data - the data vectors to build the SpherePGA from

tol - tolerence factor for intrinsic mean convergence

Method Detail

analyse

public double[] analyse(double[] v)

Analyse the vector given this model

Parameters:

v - the vector to analyse

Returns:

returns the model parameters

reconstruct

public double[] reconstruct(double[] v)

Reconstruct using this model

Parameters:

v - the parameter vector

Returns:

returns the spatial vector

calculateMean

public void calculateMean(java.util.Vector<double[]> data)

Estimate the mean

Parameters:

data - The data to use to estimate the mean

estimateMean

public void estimateMean(java.util.Vector<double[]> data,
                         double tol)

Estimate the mean of the dataset on the hypersphere

Parameters:

data - the data samples

tol - the tolerance, a change in the mean below this halts the iteration

getMean

public double[] getMean()

Return the current estimate of the mean

Returns:

returns the current estimate of the mean

addToAverage

public static void addToAverage(double[] average,
                                double[] b,
                                int n)

Add an (unfolded) sample to the average

Parameters:

average - the (unfolded) average to add to

b - the vector to add

n - the number currently in the average

unfold

public double[] unfold(double[] v)

Unfolds a sample into the tangent plane

Parameters:

v - the sample to unfold

Returns:

returns the vector unfolded into the tangent plane relative to the current estimate of the mean

fold

public double[] fold(double[] v)

Folds the sample back into the sphere

Parameters:

v - the (flattened) vector to fold

Returns:

returns the folded location of the point

length

public static double length(double[] vec)

Returns the length of the vector

Parameters:

vec - the vector to normalise

Returns:

returns the length

addToAverage

public void addToAverage(double[] v,
                         int n)

Adds a sample to the average

Parameters:

v - the sample to add

n - the number of samples previously added to the mean

normalise

public static void normalise(double[] vec)

Subtract the mean of the vector's coords and normlise length to 1

Parameters:

vec - the vector to normalise

main

public static void main(java.lang.String[] args)

The main method is used for testing

Parameters:

args -