PLearn 0.1
|
#include <LimitedGaussianSmoother.h>
Public Types | |
typedef Smoother | inherited |
Public Member Functions | |
LimitedGaussianSmoother () | |
LimitedGaussianSmoother (real window_size_wrt_sigma_, real sigma_bin_) | |
virtual void | build () |
Post-constructor. | |
virtual void | makeDeepCopyFromShallowCopy (CopiesMap &copies) |
Transforms a shallow copy into a deep copy. | |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual LimitedGaussianSmoother * | deepCopy (CopiesMap &copies) const |
virtual real | smooth (const Vec &source_function, Vec &smoothed_function, Vec bin_positions=Vec(), Vec dest_bin_positions=Vec()) const |
Static Public Member Functions | |
static string | _classname_ () |
Declares name and deepCopy methods. | |
static OptionList & | _getOptionList_ () |
static RemoteMethodMap & | _getRemoteMethodMap_ () |
static Object * | _new_instance_for_typemap_ () |
static bool | _isa_ (const Object *o) |
static void | _static_initialize_ () |
static const PPath & | declaringFile () |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares this class' options. | |
Protected Attributes | |
real | window_size_wrt_sigma |
real | sigma_bin |
Private Member Functions | |
void | build_ () |
This does the actual building. |
Definition at line 58 of file LimitedGaussianSmoother.h.
Reimplemented from PLearn::Smoother.
Definition at line 70 of file LimitedGaussianSmoother.h.
PLearn::LimitedGaussianSmoother::LimitedGaussianSmoother | ( | ) |
Definition at line 46 of file LimitedGaussianSmoother.cc.
:Smoother() /* ### Initialise all fields to their default value */ { // ... // ### You may or may not want to call build_() to finish building the object // build_(); }
PLearn::LimitedGaussianSmoother::LimitedGaussianSmoother | ( | real | window_size_wrt_sigma_, |
real | sigma_bin_ | ||
) |
Definition at line 56 of file LimitedGaussianSmoother.cc.
:Smoother(), window_size_wrt_sigma(window_size_wrt_sigma_), sigma_bin(sigma_bin_) {}
string PLearn::LimitedGaussianSmoother::_classname_ | ( | ) | [static] |
Declares name and deepCopy methods.
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
OptionList & PLearn::LimitedGaussianSmoother::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
RemoteMethodMap & PLearn::LimitedGaussianSmoother::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
Object * PLearn::LimitedGaussianSmoother::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::Object.
Definition at line 60 of file LimitedGaussianSmoother.cc.
StaticInitializer LimitedGaussianSmoother::_static_initializer_ & PLearn::LimitedGaussianSmoother::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
void PLearn::LimitedGaussianSmoother::build | ( | ) | [virtual] |
Post-constructor.
The normal implementation should call simply inherited::build(), then this class's build_(). This method should be callable again at later times, after modifying some option fields to change the "architecture" of the object.
Reimplemented from PLearn::Smoother.
Definition at line 91 of file LimitedGaussianSmoother.cc.
References PLearn::Smoother::build(), and build_().
{ inherited::build(); build_(); }
void PLearn::LimitedGaussianSmoother::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::Smoother.
Definition at line 79 of file LimitedGaussianSmoother.cc.
Referenced by build().
{ // ### This method should do the real building of the object, // ### according to set 'options', in *any* situation. // ### Typical situations include: // ### - Initial building of an object from a few user-specified options // ### - Building of a "reloaded" object: i.e. from the complete set of all serialised options. // ### - Updating or "re-building" of an object after a few "tuning" options have been modified. // ### You should assume that the parent class' build_() has already been called. }
string PLearn::LimitedGaussianSmoother::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 60 of file LimitedGaussianSmoother.cc.
void PLearn::LimitedGaussianSmoother::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares this class' options.
Reimplemented from PLearn::Smoother.
Definition at line 62 of file LimitedGaussianSmoother.cc.
References PLearn::Smoother::declareOptions().
{ // ### Declare all of this object's options here // ### For the "flags" of each option, you should typically specify // ### one of OptionBase::buildoption, OptionBase::learntoption or // ### OptionBase::tuningoption. Another possible flag to be combined with // ### is OptionBase::nosave // ### ex: // declareOption(ol, "myoption", &LimitedGaussianSmoother::myoption, OptionBase::buildoption, // "Help text describing this option"); // ... // Now call the parent class' declareOptions inherited::declareOptions(ol); }
static const PPath& PLearn::LimitedGaussianSmoother::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::Smoother.
Definition at line 113 of file LimitedGaussianSmoother.h.
LimitedGaussianSmoother * PLearn::LimitedGaussianSmoother::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::Smoother.
Definition at line 60 of file LimitedGaussianSmoother.cc.
OptionList & PLearn::LimitedGaussianSmoother::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 60 of file LimitedGaussianSmoother.cc.
OptionMap & PLearn::LimitedGaussianSmoother::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 60 of file LimitedGaussianSmoother.cc.
RemoteMethodMap & PLearn::LimitedGaussianSmoother::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::Object.
Definition at line 60 of file LimitedGaussianSmoother.cc.
void PLearn::LimitedGaussianSmoother::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::Smoother.
Definition at line 98 of file LimitedGaussianSmoother.cc.
References PLearn::Smoother::makeDeepCopyFromShallowCopy(), and PLERROR.
{ inherited::makeDeepCopyFromShallowCopy(copies); // ### Call deepCopyField on all "pointer-like" fields // ### that you wish to be deepCopied rather than // ### shallow-copied. // ### ex: // deepCopyField(trainvec, copies); // ### Remove this line when you have fully implemented this method. PLERROR("LimitedGaussianSmoother::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); }
real PLearn::LimitedGaussianSmoother::smooth | ( | const Vec & | source_function, |
Vec & | smoothed_function, | ||
Vec | bin_positions = Vec() , |
||
Vec | dest_bin_positions = Vec() |
||
) | const [virtual] |
Implements PLearn::Smoother.
Definition at line 113 of file LimitedGaussianSmoother.cc.
References PLearn::TVec< T >::fill(), PLearn::gauss_cum(), i, j, PLearn::TVec< T >::length(), PLearn::TVec< T >::resize(), sigma_bin, and window_size_wrt_sigma.
{ //parzen regressor?? kernel smoothing?? // smoothed_function[k] = sum_{j=max(0,k-window_size)}^{min(l-1,k+window_size)} w_{k,j} source_function[j] // / sum_{j=max(0,k-window_size)}^{min(l-1,k+window_size)} w_{k,j} // with w_{k,j} = phi(bin_positions[j+1];mu_k,sigma_k)-phi(bin_positions[j];mu_k,sigma_k) // where mu_k = 0.5*(bin_positions[k+1]+bin_positions[k]), // sigma_k = bin_positions[k+window_size]-bin_positions[k] // where phi(x;mu,sigma) = cdf of normal(mu,sigma) at x, // window_size = window_size_wrt_sigma * sigma_bin // for dest_bin_positions != bin_positions: 2 methods: // 1- trouver sigma_bin en fonction du voisinage // d'une position dest. Somme ponderee avec // gaussienne centree sur le pt. dest. // 2- Un sigma_bin pour chaque bin_position (src). // Une gaussienne centree sur sur ch. pos src. // smoothed_function.resize(source_function.length()); smoothed_function.fill(0.0); real window_size= window_size_wrt_sigma * sigma_bin; for(int i= 0; i < smoothed_function.length()-1; ++i) { int min_j= i-static_cast<int>(window_size), max_j= i+static_cast<int>(window_size); if(min_j < 0) min_j= 0; if(max_j > smoothed_function.length()) max_j= smoothed_function.length(); real sum_weights= 0.0; real mu= 0.5*(bin_positions[i+1]+bin_positions[i]), sigma= bin_positions[max_j-1]-bin_positions[i]; for(int j= min_j; j < max_j-1; ++j) { sum_weights+= gauss_cum(bin_positions[j+1], mu, sigma) - gauss_cum(bin_positions[j], mu, sigma); } for(int j= min_j; j < max_j-1; ++j) smoothed_function[i]+= ( gauss_cum(bin_positions[j+1], mu, sigma) - gauss_cum(bin_positions[j], mu, sigma) ) * source_function[j] / sum_weights; } return 0.0; //dummy - FIXME - xsm /* if(bin_positions.length() != 0 && source_function.length() != bin_positions.length()-1) PLERROR("in LimitedGaussianSmoother::smooth There must be one more bin_positions than the " "number of source_function points."); //if no bin_positions given, assume positions are 0, 1, 2, ..., n if(bin_positions.length() == 0) { int n= source_function.length()+1; bin_positions.resize(n); for(int i= 0; i < n; ++i) bin_positions[i]= i; } //if no dest_bin_positions given, assume same as bin_positions if(dest_bin_positions.length() == 0) dest_bin_positions= bin_positions; smoothed_function.resize(dest_bin_positions.length()-1); smoothed_function.fill(0.0); real window_size, mu, sigma, sum_weights; int n= smoothed_function.length(); for(int i= 0; i < n; ++i) { sum_weights= 0.0; int nj= source_function.length(); for(int j= 0; j < nj; ++j) { mu= 0.5*(bin_positions[i+1]+bin_positions[i]); sigma= bin_positions[i+1]-bin_positions[i]; window_size= window_size_wrt_sigma * sigma; real p1= mu - 0.5*window_size, p2= mu + 0.5*window_size; Vec::iterator it = find_if(options.begin(), options.end(), bind2nd(mem_fun(&OptionBase::isOptionNamed), optionname)); } / * int min_j= i-static_cast<int>(window_size), max_j= i+static_cast<int>(window_size); if(min_j < 0) min_j= 0; if(max_j > smoothed_function.length()) max_j= smoothed_function.length(); * / real sum_weights= 0.0; for(int j= min_j; j < max_j-1; ++j) { sum_weights+= gauss_cum(bin_positions[j+1], mu, sigma) - gauss_cum(bin_positions[j], mu, sigma); } for(int j= min_j; j < max_j-1; ++j) smoothed_function[i]+= ( gauss_cum(bin_positions[j+1], mu, sigma) - gauss_cum(bin_positions[j], mu, sigma) ) * source_function[j] / sum_weights; } */ }
Reimplemented from PLearn::Smoother.
Definition at line 113 of file LimitedGaussianSmoother.h.
real PLearn::LimitedGaussianSmoother::sigma_bin [protected] |
Definition at line 65 of file LimitedGaussianSmoother.h.
Referenced by smooth().
Definition at line 65 of file LimitedGaussianSmoother.h.
Referenced by smooth().