View Source Evision.ML.SVM (Evision v0.1.7)
Link to this section Summary
cv
Clears the algorithm state
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.
Reads algorithm parameters from a file storage
Positional Arguments
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
simplified API for language bindings
simplified API for language bindings
cv.ml
Computes error on the training or test dataset
Computes error on the training or test dataset
Creates empty model. Use StatModel::train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.
Python prototype (for reference):
@see setC
@see setClassWeights
@see setCoef0
Retrieves the decision function
Retrieves the decision function
Generates a grid for %SVM parameters.
@see setDegree
@see setGamma
Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
@see setNu
@see setP
Retrieves all the support vectors
@see setTermCriteria
@see setType
Retrieves all the uncompressed support vectors of a linear %SVM
Returns the number of variables in training samples
Returns true if the model is classifier
Returns true if the model is trained
Loads and creates a serialized svm from a file
Predicts response(s) for the provided sample(s)
Predicts response(s) for the provided sample(s)
Positional Arguments
- val:
double
@copybrief getC @see getC
Positional Arguments
- val:
double
@copybrief getCoef0 @see getCoef0
Positional Arguments
- val:
double
@copybrief getDegree @see getDegree
Positional Arguments
- val:
double
@copybrief getGamma @see getGamma
Positional Arguments
- kernelType:
int
Initialize with one of predefined kernels. See SVM::KernelTypes.
Positional Arguments
- val:
double
@copybrief getNu @see getNu
Positional Arguments
- val:
double
@copybrief getP @see getP
Positional Arguments
- val:
TermCriteria
@copybrief getTermCriteria @see getTermCriteria
Positional Arguments
- val:
int
@copybrief getType @see getType
Trains the statistical model
Trains the statistical model
Trains the statistical model
Trains an %SVM with optimal parameters
Trains an %SVM with optimal parameters
Functions
Raising version of calcError/3
.
Raising version of calcError/4
.
Raising version of clear/1
.
Raising version of empty/1
.
Raising version of getC/1
.
Raising version of getClassWeights/1
.
Raising version of getCoef0/1
.
Raising version of getDecisionFunction/2
.
Raising version of getDecisionFunction/3
.
Raising version of getDefaultGridPtr/1
.
Raising version of getDefaultName/1
.
Raising version of getDegree/1
.
Raising version of getGamma/1
.
Raising version of getKernelType/1
.
Raising version of getNu/1
.
Raising version of getP/1
.
Raising version of getSupportVectors/1
.
Raising version of getTermCriteria/1
.
Raising version of getType/1
.
Raising version of getUncompressedSupportVectors/1
.
Raising version of getVarCount/1
.
Raising version of isClassifier/1
.
Raising version of isTrained/1
.
Raising version of load/1
.
Raising version of predict/2
.
Raising version of predict/3
.
Raising version of read/2
.
Raising version of save/2
.
Raising version of setC/2
.
Raising version of setClassWeights/2
.
Raising version of setCoef0/2
.
Raising version of setDegree/2
.
Raising version of setGamma/2
.
Raising version of setKernel/2
.
Raising version of setNu/2
.
Raising version of setP/2
.
Raising version of setTermCriteria/2
.
Raising version of setType/2
.
Raising version of train/2
.
Raising version of train/3
.
Raising version of train/4
.
Raising version of trainAuto/4
.
Raising version of trainAuto/5
.
Raising version of write/2
.
Raising version of write/3
.
Link to this section cv
Clears the algorithm state
Python prototype (for reference):
clear() -> None
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.
Python prototype (for reference):
getDefaultName() -> retval
Reads algorithm parameters from a file storage
Positional Arguments
- fn_:
FileNode
Python prototype (for reference):
read(fn_) -> None
Positional Arguments
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Python prototype (for reference):
save(filename) -> None
simplified API for language bindings
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None
simplified API for language bindings
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None
Link to this section cv.ml
Computes error on the training or test dataset
Positional Arguments
data:
Ptr<TrainData>
.the training data
test:
bool
.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.
Return
resp:
Evision.Mat
.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%).
Python prototype (for reference):
calcError(data, test[, resp]) -> retval, resp
Computes error on the training or test dataset
Positional Arguments
data:
Ptr<TrainData>
.the training data
test:
bool
.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.
Return
resp:
Evision.Mat
.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%).
Python prototype (for reference):
calcError(data, test[, resp]) -> retval, resp
Creates empty model. Use StatModel::train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.
Python prototype (for reference):
create() -> retval
Python prototype (for reference):
empty() -> retval
@see setC
Python prototype (for reference):
getC() -> retval
@see setClassWeights
Python prototype (for reference):
getClassWeights() -> retval
@see setCoef0
Python prototype (for reference):
getCoef0() -> retval
Retrieves the decision function
Positional Arguments
i:
int
.the index of the decision function. If the problem solved is regression, 1-class or 2-class classification, then there will be just one decision function and the index should always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$ decision functions.
Return
alpha:
Evision.Mat
.the optional output vector for weights, corresponding to different support vectors. In the case of linear %SVM all the alpha's will be 1's.
svidx:
Evision.Mat
.the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear %SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.
Python prototype (for reference):
getDecisionFunction(i[, alpha[, svidx]]) -> retval, alpha, svidx
Retrieves the decision function
Positional Arguments
i:
int
.the index of the decision function. If the problem solved is regression, 1-class or 2-class classification, then there will be just one decision function and the index should always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$ decision functions.
Return
alpha:
Evision.Mat
.the optional output vector for weights, corresponding to different support vectors. In the case of linear %SVM all the alpha's will be 1's.
svidx:
Evision.Mat
.the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear %SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.
Python prototype (for reference):
getDecisionFunction(i[, alpha[, svidx]]) -> retval, alpha, svidx
Generates a grid for %SVM parameters.
Positional Arguments
param_id:
int
.%SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID.
The function generates a grid pointer for the specified parameter of the %SVM algorithm. The grid may be passed to the function SVM::trainAuto.
Python prototype (for reference):
getDefaultGridPtr(param_id) -> retval
@see setDegree
Python prototype (for reference):
getDegree() -> retval
@see setGamma
Python prototype (for reference):
getGamma() -> retval
Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
Python prototype (for reference):
getKernelType() -> retval
@see setNu
Python prototype (for reference):
getNu() -> retval
@see setP
Python prototype (for reference):
getP() -> retval
Retrieves all the support vectors
The method returns all the support vectors as a floating-point matrix, where support vectors are stored as matrix rows.
Python prototype (for reference):
getSupportVectors() -> retval
@see setTermCriteria
Python prototype (for reference):
getTermCriteria() -> retval
@see setType
Python prototype (for reference):
getType() -> retval
Retrieves all the uncompressed support vectors of a linear %SVM
The method returns all the uncompressed support vectors of a linear %SVM that the compressed support vector, used for prediction, was derived from. They are returned in a floating-point matrix, where the support vectors are stored as matrix rows.
Python prototype (for reference):
getUncompressedSupportVectors() -> retval
Returns the number of variables in training samples
Python prototype (for reference):
getVarCount() -> retval
Returns true if the model is classifier
Python prototype (for reference):
isClassifier() -> retval
Returns true if the model is trained
Python prototype (for reference):
isTrained() -> retval
Loads and creates a serialized svm from a file
Positional Arguments
filepath:
String
.path to serialized svm
Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.
Python prototype (for reference):
load(filepath) -> retval
Predicts response(s) for the provided sample(s)
Positional Arguments
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
int
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
Predicts response(s) for the provided sample(s)
Positional Arguments
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
int
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
Positional Arguments
- val:
double
@copybrief getC @see getC
Python prototype (for reference):
setC(val) -> None
Positional Arguments
- val:
Evision.Mat
@copybrief getClassWeights @see getClassWeights
Python prototype (for reference):
setClassWeights(val) -> None
Positional Arguments
- val:
double
@copybrief getCoef0 @see getCoef0
Python prototype (for reference):
setCoef0(val) -> None
Positional Arguments
- val:
double
@copybrief getDegree @see getDegree
Python prototype (for reference):
setDegree(val) -> None
Positional Arguments
- val:
double
@copybrief getGamma @see getGamma
Python prototype (for reference):
setGamma(val) -> None
Positional Arguments
- kernelType:
int
Initialize with one of predefined kernels. See SVM::KernelTypes.
Python prototype (for reference):
setKernel(kernelType) -> None
Positional Arguments
- val:
double
@copybrief getNu @see getNu
Python prototype (for reference):
setNu(val) -> None
Positional Arguments
- val:
double
@copybrief getP @see getP
Python prototype (for reference):
setP(val) -> None
Positional Arguments
- val:
TermCriteria
@copybrief getTermCriteria @see getTermCriteria
Python prototype (for reference):
setTermCriteria(val) -> None
Positional Arguments
- val:
int
@copybrief getType @see getType
Python prototype (for reference):
setType(val) -> None
Trains the statistical model
Positional Arguments
trainData:
Ptr<TrainData>
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
int
.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).
Python prototype (for reference):
train(trainData[, flags]) -> retval
Trains the statistical model
Positional Arguments
trainData:
Ptr<TrainData>
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
int
.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).
Python prototype (for reference):
train(trainData[, flags]) -> retval
Trains the statistical model
Positional Arguments
samples:
Evision.Mat
.training samples
layout:
int
.See ml::SampleTypes.
responses:
Evision.Mat
.vector of responses associated with the training samples.
Python prototype (for reference):
train(samples, layout, responses) -> retval
Trains an %SVM with optimal parameters
Positional Arguments
samples:
Evision.Mat
.training samples
layout:
int
.See ml::SampleTypes.
responses:
Evision.Mat
.vector of responses associated with the training samples.
Keyword Arguments
kFold:
int
.Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
cgrid:
Ptr<ParamGrid>
.gammaGrid:
Ptr<ParamGrid>
.grid for gamma
pGrid:
Ptr<ParamGrid>
.nuGrid:
Ptr<ParamGrid>
.coeffGrid:
Ptr<ParamGrid>
.grid for coeff
degreeGrid:
Ptr<ParamGrid>
.grid for degree
balanced:
bool
.If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
Python prototype (for reference):
trainAuto(samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval
Trains an %SVM with optimal parameters
Positional Arguments
samples:
Evision.Mat
.training samples
layout:
int
.See ml::SampleTypes.
responses:
Evision.Mat
.vector of responses associated with the training samples.
Keyword Arguments
kFold:
int
.Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
cgrid:
Ptr<ParamGrid>
.gammaGrid:
Ptr<ParamGrid>
.grid for gamma
pGrid:
Ptr<ParamGrid>
.nuGrid:
Ptr<ParamGrid>
.coeffGrid:
Ptr<ParamGrid>
.grid for coeff
degreeGrid:
Ptr<ParamGrid>
.grid for degree
balanced:
bool
.If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
Python prototype (for reference):
trainAuto(samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval
Link to this section Functions
Raising version of calcError/3
.
Raising version of calcError/4
.
Raising version of clear/1
.
Raising version of create/0
.
Raising version of empty/1
.
Raising version of getC/1
.
Raising version of getClassWeights/1
.
Raising version of getCoef0/1
.
Raising version of getDecisionFunction/2
.
Raising version of getDecisionFunction/3
.
Raising version of getDefaultGridPtr/1
.
Raising version of getDefaultName/1
.
Raising version of getDegree/1
.
Raising version of getGamma/1
.
Raising version of getKernelType/1
.
Raising version of getNu/1
.
Raising version of getP/1
.
Raising version of getSupportVectors/1
.
Raising version of getTermCriteria/1
.
Raising version of getType/1
.
Raising version of getUncompressedSupportVectors/1
.
Raising version of getVarCount/1
.
Raising version of isClassifier/1
.
Raising version of isTrained/1
.
Raising version of load/1
.
Raising version of predict/2
.
Raising version of predict/3
.
Raising version of read/2
.
Raising version of save/2
.
Raising version of setC/2
.
Raising version of setClassWeights/2
.
Raising version of setCoef0/2
.
Raising version of setDegree/2
.
Raising version of setGamma/2
.
Raising version of setKernel/2
.
Raising version of setNu/2
.
Raising version of setP/2
.
Raising version of setTermCriteria/2
.
Raising version of setType/2
.
Raising version of train/2
.
Raising version of train/3
.
Raising version of train/4
.
Raising version of trainAuto/4
.
Raising version of trainAuto/5
.
Raising version of write/2
.
Raising version of write/3
.