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PLearn 0.1
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#include <plearn/var/NaryVariable.h>

Go to the source code of this file.
Classes | |
| class | PLearn::NllSemisphericalGaussianVariable |
| This class implements the negative log-likelihood cost of a Markov chain that uses semispherical gaussian transition probabilities. More... | |
| class | PLearn::DiffTemplate< ObjectType, NllSemisphericalGaussianVariable > |
| class | PLearn::TypeTraits< NllSemisphericalGaussianVariable > |
Namespaces | |
| namespace | PLearn |
< for swap | |
Functions | |
| Object * | PLearn::toObjectPtr (const NllSemisphericalGaussianVariable &o) |
| PStream & | PLearn::operator>> (PStream &in, NllSemisphericalGaussianVariable &o) |
| PStream & | PLearn::operator>> (PStream &in, NllSemisphericalGaussianVariable *&o) |
| PStream & | PLearn::operator<< (PStream &out, const NllSemisphericalGaussianVariable &o) |
| PStream & | PLearn::operator>> (PStream &in, PP< NllSemisphericalGaussianVariable > &o) |
| template<class ObjectType > | |
| int | PLearn::diff (const string &refer, const string &other, const Option< ObjectType, NllSemisphericalGaussianVariable > *opt, PLearnDiff *diffs) |
| Var | PLearn::nll_semispherical_gaussian (Var tangent_plane_var, Var mu_var, Var sm_var, Var sn_var, Var neighbors_dist_var, Var p_target_var, Var p_neighbors_var, Var noise, Var mu_noisy, bool use_noise=false, real epsilon=1e-6, real min_p_x=0, int mu_n_neighbors=-1) |
1.7.4