Struct opencv::core::DownhillSolver

pub struct DownhillSolver { /* private fields */ }

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.

Note: term criteria should meet following condition:

   termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0

Implementations

impl DownhillSolver

pub fn create( f: &Ptr<MinProblemSolver_Function>, init_step: &impl ToInputArray, termcrit: TermCriteria ) -> Result<Ptr<DownhillSolver>>

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.

C++ default parameters

• f: PtrMinProblemSolver::Function()
• init_step: Mat_(1,1,0.0)
• termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)

pub fn create_def() -> Result<Ptr<DownhillSolver>>

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.

Note

This alternative version of DownhillSolver::create function uses the following default values for its arguments:

• f: PtrMinProblemSolver::Function()
• init_step: Mat_(1,1,0.0)
• termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)

Trait Implementations

impl AlgorithmTrait for DownhillSolver

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for DownhillSolver

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut impl FileStorageTrait) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut impl FileStorageTrait, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

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.

impl Boxed for DownhillSolver

unsafe fn from_raw( ptr: <DownhillSolver as OpenCVFromExtern>::ExternReceive ) -> Self

Wrap the specified raw pointer

fn into_raw( self ) -> <DownhillSolver as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <DownhillSolver as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut( &mut self ) -> <DownhillSolver as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl Debug for DownhillSolver

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl DownhillSolverTrait for DownhillSolver

fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void

fn set_init_step(&mut self, step: &impl ToInputArray) -> Result<()>

Sets the initial step that will be used in downhill simplex algorithm.

impl DownhillSolverTraitConst for DownhillSolver

fn as_raw_DownhillSolver(&self) -> *const c_void

fn get_init_step(&self, step: &mut impl ToOutputArray) -> Result<()>

Returns the initial step that will be used in downhill simplex algorithm.

impl Drop for DownhillSolver

fn drop(&mut self)

Executes the destructor for this type.

impl From<DownhillSolver> for Algorithm

fn from(s: DownhillSolver) -> Self

Converts to this type from the input type.

impl From<DownhillSolver> for MinProblemSolver

fn from(s: DownhillSolver) -> Self

Converts to this type from the input type.

impl MinProblemSolverTrait for DownhillSolver

fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void

fn set_function(&mut self, f: &Ptr<MinProblemSolver_Function>) -> Result<()>

Setter for the optimized function.

fn set_term_criteria(&mut self, termcrit: TermCriteria) -> Result<()>

Set terminal criteria for solver.

fn minimize(&mut self, x: &mut impl ToInputOutputArray) -> Result<f64>

actually runs the algorithm and performs the minimization.

impl MinProblemSolverTraitConst for DownhillSolver

fn as_raw_MinProblemSolver(&self) -> *const c_void

fn get_function(&self) -> Result<Ptr<MinProblemSolver_Function>>

Getter for the optimized function.

fn get_term_criteria(&self) -> Result<TermCriteria>

Getter for the previously set terminal criteria for this algorithm.

impl TryFrom<Algorithm> for DownhillSolver

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: Algorithm) -> Result<Self>

Performs the conversion.

impl TryFrom<MinProblemSolver> for DownhillSolver

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: MinProblemSolver) -> Result<Self>

Performs the conversion.

impl Send for DownhillSolver