PLearn 0.1
Classes | Namespaces | Functions
ProjectionErrorVariable.h File Reference
#include "BinaryVariable.h"
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Classes

class  PLearn::ProjectionErrorVariable
 The first input is a set of n_dim vectors (possibly seen as a single vector of their concatenation) f_i, each in R^n The second input is a set of T vectors (possibly seen as a single vector of their concatenation) t_j, each in R^n The output is the following: sum_j min_{w_j} || t_j - sum_i w_{ji} f_i ||^2 where row w_j of w is optmized analytically and separately for each j. More...
class  PLearn::DiffTemplate< ObjectType, ProjectionErrorVariable >
class  PLearn::TypeTraits< ProjectionErrorVariable >

Namespaces

namespace  PLearn
 

< for swap


Functions

Object * PLearn::toObjectPtr (const ProjectionErrorVariable &o)
PStream & PLearn::operator>> (PStream &in, ProjectionErrorVariable &o)
PStream & PLearn::operator>> (PStream &in, ProjectionErrorVariable *&o)
PStream & PLearn::operator<< (PStream &out, const ProjectionErrorVariable &o)
PStream & PLearn::operator>> (PStream &in, PP< ProjectionErrorVariable > &o)
template<class ObjectType >
int PLearn::diff (const string &refer, const string &other, const Option< ObjectType, ProjectionErrorVariable > *opt, PLearnDiff *diffs)
Var PLearn::projection_error (Var f, Var t, real norm_penalization=0, int n=-1, bool normalize_by_neighbor_distance=true, bool use_subspace_distance=false, real epsilon=0, real regularization=0, bool ordered_vectors=true)
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