PLearn 0.1
Public Member Functions | Public Attributes
PLearn::ReconstructionCandidate Class Reference

description of the main class: TransformationLearner More...

#include <TransformationLearner.h>

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List of all members.

Public Member Functions

 ReconstructionCandidate (int targetIdx_=-1, int neighborIdx_=-1, int transformIdx_=-1, real weight_=0)

Public Attributes

int targetIdx
int neighborIdx
int transformIdx
real weight

Detailed Description

description of the main class: TransformationLearner

GENERATION PROCESS

We suppose a new point v is obtained from a point x by : 1) choosing a transformation t among a set of transformations T (with probability p(t)) 2) applying the choosen transformation t on x 3) add some noise in all the directions (noise = normally distributed random variable)

UNDERLYING PROBABILITY SPACE

variables of the distribution : X : real vector V : real vector, neighbor of X T : transformation P(x,v,t) = P(v is obtained by applying transformation t on x) = P(x,v|t)P(t) = N( T(t)(x)|v,sigma)P(t)

LEARNING BEHAVIOR

The parameters of the distributions are learned using a variant of E.M.algorithm

Definition at line 119 of file TransformationLearner.h.


Constructor & Destructor Documentation

PLearn::ReconstructionCandidate::ReconstructionCandidate ( int  targetIdx_ = -1,
int  neighborIdx_ = -1,
int  transformIdx_ = -1,
real  weight_ = 0 
) [inline]

Definition at line 125 of file TransformationLearner.h.

References neighborIdx, targetIdx, transformIdx, and weight.

                                                                                                         {
        targetIdx =  targetIdx_;
        neighborIdx =  neighborIdx_;
        transformIdx =  transformIdx_ ;
        weight =  weight_;            
    }

Member Data Documentation


The documentation for this class was generated from the following file:
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