Stochastic Gradient Descent SVM classifier. More...
#include <opencv2/ml.hpp>
Public Types | |
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 } |
Predict options. More... | |
enum | MarginType { SOFT_MARGIN, HARD_MARGIN } |
Margin type. More... | |
enum | SvmsgdType { SGD, ASGD } |
SVMSGD type. More... | |
Public Member Functions | |
virtual float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const |
Computes error on the training or test dataset. More... | |
virtual void | clear () |
Clears the algorithm state. More... | |
virtual bool | empty () const CV_OVERRIDE |
Returns true if the Algorithm is empty (e.g. More... | |
virtual String | getDefaultName () const |
Returns the algorithm string identifier. More... | |
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. More... | |
virtual int | getVarCount () const =0 |
Returns the number of variables in training samples. More... | |
virtual Mat | getWeights ()=0 |
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 void | read (const FileNode &fn) |
Reads algorithm parameters from a file storage. More... | |
virtual void | save (const String &filename) const |
Saves the algorithm to a file. More... | |
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. 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... | |
virtual void | write (FileStorage &fs) const |
Stores algorithm parameters in a file storage. More... | |
void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More... | |
Static Public Member Functions | |
static Ptr< SVMSGD > | create () |
Creates empty model. More... | |
static Ptr< SVMSGD > | load (const String &filepath, const String &nodeName=String()) |
Loads and creates a serialized SVMSGD from a 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... | |
template<typename _Tp > | |
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0) |
Create and train model with default parameters. More... | |
Protected Member Functions | |
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 [9].
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 [9]).
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:
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inherited |
SVMSGD type.
ASGD is often the preferable choice.
Enumerator | |
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SGD | Stochastic Gradient Descent. |
ASGD | Average Stochastic Gradient Descent. |
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virtualinherited |
Computes error on the training or test dataset.
data | the training data |
test | if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing. |
resp | the optional output responses. |
The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
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inlinevirtualinherited |
Clears the algorithm state.
Reimplemented in cv::FlannBasedMatcher, and cv::DescriptorMatcher.
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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virtualinherited |
Returns true if the Algorithm is empty (e.g.
in the very beginning or after unsuccessful read
Reimplemented from cv::Algorithm.
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virtualinherited |
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.
Reimplemented in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, and cv::Feature2D.
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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 virtualinherited |
Returns the number of variables in training samples.
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pure virtual |
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pure virtualinherited |
Returns true if the model is classifier.
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pure virtualinherited |
Returns true if the model is trained.
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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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inlinestaticinherited |
Loads algorithm from a String.
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):
References CV_WRAP, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.
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pure virtualinherited |
Predicts response(s) for the provided sample(s)
samples | The input samples, floating-point matrix |
results | The optional output matrix of results. |
flags | The optional flags, model-dependent. See cv::ml::StatModel::Flags. |
Implemented in cv::ml::LogisticRegression, and cv::ml::EM.
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inlinevirtualinherited |
Reads algorithm parameters from a file storage.
Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.
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inlinestaticinherited |
Reads algorithm from the file node.
This is static template method of Algorithm. It's usage is following (in the case of SVM):
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)
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virtualinherited |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
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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.
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virtualinherited |
Trains the statistical model.
trainData | training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create. |
flags | optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP). |
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virtualinherited |
Trains the statistical model.
samples | training samples |
layout | See ml::SampleTypes. |
responses | vector of responses associated with the training samples. |
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inlinestaticinherited |
Create and train model with default parameters.
The class must implement static create()
method with no parameters or with all default parameter values
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inlinevirtualinherited |
Stores algorithm parameters in a file storage.
Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.
References CV_WRAP.
Referenced by cv::Feature2D::write(), and cv::DescriptorMatcher::write().
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inherited |
simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
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protectedinherited |