Joe Verbout
opencv on mbed
DownhillSolver Class Reference
[Optimization Algorithms]
This class is used to perform the non-linear non-constrained minimization of a function,. More...
#include <optim.hpp>
Inherits cv::MinProblemSolver.
Public Member Functions | |
| virtual void | getInitStep (OutputArray step) const =0 |
| Returns the initial step that will be used in downhill simplex algorithm. | |
| virtual void | setInitStep (InputArray step)=0 |
| Sets the initial step that will be used in downhill simplex algorithm. | |
| virtual Ptr< Function > | getFunction () const =0 |
| Getter for the optimized function. | |
| virtual void | setFunction (const Ptr< Function > &f)=0 |
| Setter for the optimized function. | |
| virtual TermCriteria | getTermCriteria () const =0 |
| Getter for the previously set terminal criteria for this algorithm. | |
| virtual void | setTermCriteria (const TermCriteria &termcrit)=0 |
| Set terminal criteria for solver. | |
| virtual double | minimize (InputOutputArray x)=0 |
| actually runs the algorithm and performs the minimization. | |
| virtual CV_WRAP void | clear () |
| Clears the algorithm state. | |
| virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage. | |
| virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage. | |
| virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. | |
| virtual CV_WRAP void | save (const String &filename) const |
| Saves the algorithm to a file. | |
| virtual CV_WRAP String | getDefaultName () const |
| Returns the algorithm string identifier. | |
Static Public Member Functions | |
| static Ptr< DownhillSolver > | create (const Ptr< MinProblemSolver::Function > &f=Ptr< MinProblemSolver::Function >(), InputArray initStep=Mat_< double >(1, 1, 0.0), TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5000, 0.000001)) |
| This function returns the reference to the ready-to-use DownhillSolver object. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. | |
Detailed Description
This class is used to perform the non-linear non-constrained minimization of a function,.
defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as downhill simplex method**. The basic idea about the method can be obtained from <http://en.wikipedia.org/wiki/Nelder-Mead_method>.
It should be noted, that this method, although deterministic, is rather a heuristic and therefore may converge to a local minima, not necessary a global one. It is iterative optimization technique, which at each step uses an information about the values of a function evaluated only at `n+1` points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At each step new point is chosen to evaluate function at, obtained value is compared with previous ones and based on this information simplex changes it's shape , slowly moving to the local minimum. Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear Conjugate Gradient method (which is also implemented in optim).
Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon.
- Note:
- DownhillSolver is a derivative of the abstract interface cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to encapsulate the functionality, common to all non-linear optimization algorithms in the optim module.
-
term criteria should meet following condition:
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
Definition at line 155 of file optim.hpp.
Member Function Documentation
| virtual CV_WRAP void clear | ( | ) | [virtual, inherited] |
Clears the algorithm state.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
| static Ptr<DownhillSolver> create | ( | const Ptr< MinProblemSolver::Function > & | f = Ptr< MinProblemSolver::Function >(), |
| InputArray | initStep = Mat_< double >(1, 1, 0.0), |
||
| TermCriteria | termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5000, 0.000001) |
||
| ) | [static] |
This function returns the reference to the ready-to-use DownhillSolver object.
All the parameters are optional, so this procedure can be called even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() should be called upon the obtained object, if the respective parameters were not given to create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely equivalent (and will drop the same errors in the same way, should invalid input be detected).
- Parameters:
-
f Pointer to the function that will be minimized, similarly to the one you submit via MinProblemSolver::setFunction. initStep Initial step, that will be used to construct the initial simplex, similarly to the one you submit via MinProblemSolver::setInitStep. termcrit Terminal criteria to the algorithm, similarly to the one you submit via MinProblemSolver::setTermCriteria.
| virtual bool empty | ( | ) | const [virtual, inherited] |
| virtual CV_WRAP String getDefaultName | ( | ) | const [virtual, inherited] |
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
Getter for the optimized function.
The optimized function is represented by Function interface, which requires derivatives to implement the sole method calc(double*) to evaluate the function.
- Returns:
- Smart-pointer to an object that implements Function interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
| virtual void getInitStep | ( | OutputArray | step ) | const [pure virtual] |
Returns the initial step that will be used in downhill simplex algorithm.
- Parameters:
-
step Initial step that will be used in algorithm. Note, that although corresponding setter accepts column-vectors as well as row-vectors, this method will return a row-vector.
- See also:
- DownhillSolver::setInitStep
| virtual TermCriteria getTermCriteria | ( | ) | const [pure virtual, inherited] |
Getter for the previously set terminal criteria for this algorithm.
- Returns:
- Deep copy of the terminal criteria used at the moment.
| static Ptr<_Tp> load | ( | const String & | filename, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from the file.
- Parameters:
-
filename Name of the file to read. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).
| static Ptr<_Tp> loadFromString | ( | const String & | strModel, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from a String.
- Parameters:
-
strModel The string variable containing the model you want to load. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
| virtual double minimize | ( | InputOutputArray | x ) | [pure virtual, inherited] |
actually runs the algorithm and performs the minimization.
The sole input parameter determines the centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria, initial step, function to be minimized) are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used.
- Parameters:
-
x The initial point, that will become a centroid of an initial simplex. After the algorithm will terminate, it will be setted to the point where the algorithm stops, the point of possible minimum.
- Returns:
- The value of a function at the point found.
| virtual void read | ( | const FileNode & | fn ) | [virtual, inherited] |
Reads algorithm parameters from a file storage.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
Reads algorithm from the file node.
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::read<SVM>(fn);
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
| virtual CV_WRAP void save | ( | const String & | filename ) | const [virtual, inherited] |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Setter for the optimized function.
It should be called at least once before the call to* minimize(), as default value is not usable.
- Parameters:
-
f The new function to optimize.
| virtual void setInitStep | ( | InputArray | step ) | [pure virtual] |
Sets the initial step that will be used in downhill simplex algorithm.
Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional vectors that are used to determine the shape of initial simplex. Roughly said, initial point determines the position of a simplex (it will become simplex's centroid), while step determines the spread (size in each dimension) of a simplex. To be more precise, if 
are the initial step and initial point respectively, the vertices of a simplex will be: 
and 
for 
where 
denotes projections of the initial step of *n*-th coordinate (the result of projection is treated to be vector given by 
, where 
form canonical basis)
- Parameters:
-
step Initial step that will be used in algorithm. Roughly said, it determines the spread (size in each dimension) of an initial simplex.
| virtual void setTermCriteria | ( | const TermCriteria & | termcrit ) | [pure virtual, inherited] |
Set terminal criteria for solver.
This method *is not necessary* to be called before the first call to minimize(), as the default value is sensible.
Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first.
- Parameters:
-
termcrit Terminal criteria to be used, represented as cv::TermCriteria structure.
| virtual void write | ( | FileStorage & | fs ) | const [virtual, inherited] |
Stores algorithm parameters in a file storage.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
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