Facemorph.Stats
Class sPLSSVD_DWH

java.lang.Object
  extended by Facemorph.Stats.PLSReducer
      extended by Facemorph.Stats.sPLSSVD_DWH
All Implemented Interfaces:
IniHandler, DataReducer, Regressor, StatisticalModel

public class sPLSSVD_DWH
extends PLSReducer

A version of Le Cao's algorithm modified to be sparse on the patches not on patch


Nested Class Summary
 
Nested classes/interfaces inherited from interface Facemorph.Stats.DataReducer
DataReducer.DataReducerInformation
 
Nested classes/interfaces inherited from interface Facemorph.Stats.Regressor
Regressor.RegressorException
 
Field Summary
 
Fields inherited from class Facemorph.Stats.PLSReducer
B, c, C, E, epsilon, F, maxComponents, maxIter, meanX, meanY, noComponents, p, P, q, R, stdX, stdY, t, T, targetDependentVariance, targetIndependentVariance, u, U, varX, varY, w, W, X, Y
 
Constructor Summary
sPLSSVD_DWH()
           
 
Method Summary
protected  double[] calcG(double[] vec, int nonZero)
           
protected  double computeLambda(double[] vec, int noNonZero)
           
 boolean train(boolean stats)
          Perform multi-linear regression using the Ordinary Least Squares method.
 
Methods inherited from class Facemorph.Stats.PLSReducer
apply, build, center, getBasisMatrix, getEpsilon, getInputDimensions, getModelInformation, getModelInformation, getOutputDimensions, getSumSquaredResiduals, getTargetDependentVariance, getTargetIndpendentVariance, initialise, iterate, iterate, main, outputStatistics, outputStatistics, predict, print, read, read, reconstructDependent, reconstructDependent, reconstructIndependent, reconstructIndependent, reduceDependent, reduceDependent, reduceIndependent, reduceIndependent, setDependentData, setDependentData, setEpsilon, setIndependentData, setOutputDimensions, setTargetDependentVariance, setTargetIndependentVariance, setTargetKaiserGuttman, write, write, write
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

sPLSSVD_DWH

public sPLSSVD_DWH()
Method Detail

train

public boolean train(boolean stats)
Description copied from interface: StatisticalModel
Perform multi-linear regression using the Ordinary Least Squares method. Solves y = XB + e using b = inv(X'X)X'y

Specified by:
train in interface StatisticalModel
Overrides:
train in class PLSReducer
Parameters:
stats - calculate values for statistical analysis
Returns:
true if the regression succeeds.

calcG

protected double[] calcG(double[] vec,
                         int nonZero)

computeLambda

protected double computeLambda(double[] vec,
                               int noNonZero)