OpenCV
3.3.0
Open Source Computer Vision
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Stochastic Gradient Descent SVM classifier. More...
#include "ml.hpp"
Public Types | |
enum | MarginType { SOFT_MARGIN, HARD_MARGIN } |
enum | SvmsgdType { SGD, ASGD } |
Public Types inherited from cv::ml::StatModel | |
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 } |
Public Member Functions | |
virtual float | getInitialStepSize () const =0 |
Parameter initialStepSize of a SVMSGD optimization problem. More... | |
virtual float | getMarginRegularization () const =0 |
Parameter marginRegularization of a SVMSGD optimization problem. More... | |
virtual int | getMarginType () const =0 |
Margin type, one of SVMSGD::MarginType. More... | |
virtual float | getShift ()=0 |
virtual float | getStepDecreasingPower () const =0 |
Parameter stepDecreasingPower of a SVMSGD optimization problem. More... | |
virtual int | getSvmsgdType () const =0 |
Algorithm type, one of SVMSGD::SvmsgdType. More... | |
virtual TermCriteria | getTermCriteria () const =0 |
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). More... | |
virtual Mat | getWeights ()=0 |
virtual void | setInitialStepSize (float InitialStepSize)=0 |
Parameter initialStepSize of a SVMSGD optimization problem. More... | |
virtual void | setMarginRegularization (float marginRegularization)=0 |
Parameter marginRegularization of a SVMSGD optimization problem. More... | |
virtual void | setMarginType (int marginType)=0 |
Margin type, one of SVMSGD::MarginType. More... | |
virtual void | setOptimalParameters (int svmsgdType=SVMSGD::ASGD, int marginType=SVMSGD::SOFT_MARGIN)=0 |
Function sets optimal parameters values for chosen SVM SGD model. More... | |
virtual void | setStepDecreasingPower (float stepDecreasingPower)=0 |
Parameter stepDecreasingPower of a SVMSGD optimization problem. More... | |
virtual void | setSvmsgdType (int svmsgdType)=0 |
Algorithm type, one of SVMSGD::SvmsgdType. More... | |
virtual void | setTermCriteria (const cv::TermCriteria &val)=0 |
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). More... | |
Public Member Functions inherited from cv::ml::StatModel | |
virtual float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const |
Computes error on the training or test dataset. More... | |
virtual bool | empty () const |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More... | |
virtual int | getVarCount () const =0 |
Returns the number of variables in training samples. More... | |
virtual bool | isClassifier () const =0 |
Returns true if the model is classifier. More... | |
virtual bool | isTrained () const =0 |
Returns true if the model is trained. More... | |
virtual float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0 |
Predicts response(s) for the provided sample(s) More... | |
virtual bool | train (const Ptr< TrainData > &trainData, int flags=0) |
Trains the statistical model. More... | |
virtual bool | train (InputArray samples, int layout, InputArray responses) |
Trains the statistical model. More... | |
Public Member Functions inherited from cv::Algorithm | |
Algorithm () | |
virtual | ~Algorithm () |
virtual void | clear () |
Clears the algorithm state. More... | |
virtual String | getDefaultName () const |
virtual void | read (const FileNode &fn) |
Reads algorithm parameters from a file storage. More... | |
virtual void | save (const String &filename) const |
virtual void | write (FileStorage &fs) const |
Stores algorithm parameters in a file storage. More... | |
Static Public Member Functions | |
static Ptr< SVMSGD > | create () |
Creates empty model. Use StatModel::train to train the model. Since SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters. More... | |
static Ptr< SVMSGD > | load (const String &filepath, const String &nodeName=String()) |
Loads and creates a serialized SVMSGD from a file. More... | |
Static Public Member Functions inherited from cv::ml::StatModel | |
template<typename _Tp > | |
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0) |
Create and train model with default parameters. More... | |
Static Public Member Functions inherited from cv::Algorithm | |
template<typename _Tp > | |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
Loads algorithm from the file. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
Loads algorithm from a String. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | read (const FileNode &fn) |
Reads algorithm from the file node. More... | |
Additional Inherited Members | |
Protected Member Functions inherited from cv::Algorithm | |
void | writeFormat (FileStorage &fs) const |
Stochastic Gradient Descent SVM classifier.
SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, as presented in [15].
The classifier has following parameters:
The model type may have one of the following values: SGD and ASGD.
\[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\]
whereThe recommended model type is ASGD (following [15]).
The margin type may have one of the following values: SOFT_MARGIN or HARD_MARGIN.
The other parameters may be described as follows:
Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.
To use SVMSGD algorithm do as follows:
SVMSGD type. ASGD is often the preferable choice.
Enumerator | |
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SGD | Stochastic Gradient Descent. |
ASGD | Average Stochastic Gradient Descent. |
Creates empty model. Use StatModel::train to train the model. Since SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
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pure virtual |
Parameter initialStepSize of a SVMSGD optimization problem.
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pure virtual |
Parameter marginRegularization of a SVMSGD optimization problem.
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pure virtual |
Margin type, one of SVMSGD::MarginType.
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pure virtual |
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pure virtual |
Parameter stepDecreasingPower of a SVMSGD optimization problem.
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pure virtual |
Algorithm type, one of SVMSGD::SvmsgdType.
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pure virtual |
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).
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pure virtual |
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static |
Loads and creates a serialized SVMSGD from a file.
Use SVMSGD::save to serialize and store an SVMSGD to disk. Load the SVMSGD from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
filepath | path to serialized SVMSGD |
nodeName | name of node containing the classifier |
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pure virtual |
Parameter initialStepSize of a SVMSGD optimization problem.
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pure virtual |
Parameter marginRegularization of a SVMSGD optimization problem.
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pure virtual |
Margin type, one of SVMSGD::MarginType.
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pure virtual |
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pure virtual |
Parameter stepDecreasingPower of a SVMSGD optimization problem.
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pure virtual |
Algorithm type, one of SVMSGD::SvmsgdType.
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pure virtual |
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).