PLearn 0.1
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#include "BinaryVariable.h"
Go to the source code of this file.
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) |