SVMWrapper

class pyopenms.SVMWrapper

Bases: object

Cython implementation of _SVMWrapper

Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1SVMWrapper.html

__init__()

  • Cython signature: void SVMWrapper()

  • Cython signature: void SVMWrapper(SVMWrapper &)

Methods

__init__

  • Cython signature: void SVMWrapper()

calculateGaussTable

Cython signature: void calculateGaussTable(size_t border_length, double sigma, libcpp_vector[double] & gauss_table)

createRandomPartitions

Cython signature: void createRandomPartitions(SVMData & problem, size_t number, libcpp_vector[SVMData] & problems)

getDoubleParameter

Cython signature: double getDoubleParameter(SVM_parameter_type type_)

getIntParameter

Cython signature: int getIntParameter(SVM_parameter_type type_)

getPValue

Cython signature: double getPValue(double sigma1, double sigma2, libcpp_pair[double,double] point)

getSVRProbability

Cython signature: double getSVRProbability()

getSignificanceBorders

Cython signature: void getSignificanceBorders(SVMData & data, libcpp_pair[double,double] & sigmas, double confidence, size_t number_of_runs, size_t number_of_partitions, double step_size, size_t max_iterations)

loadModel

Cython signature: void loadModel(String modelFilename) The svm-model is loaded.

mergePartitions

Cython signature: void mergePartitions(libcpp_vector[SVMData] & problems, size_t except_, SVMData & merged_problem)

predict

Cython signature: void predict(SVMData & problem, libcpp_vector[double] & results) The prediction process is started and the results are stored in 'predicted_labels'

saveModel

Cython signature: void saveModel(String modelFilename) The model of the trained svm is saved into 'modelFilename'

setParameter

  • Cython signature: void setParameter(SVM_parameter_type type_, int value)

setTrainingSample

Cython signature: void setTrainingSample(SVMData & training_sample)

setWeights

Cython signature: void setWeights(libcpp_vector[int] & weight_labels, libcpp_vector[double] & weights)

train

Cython signature: int train(SVMData & problem) The svm is trained with the data stored in the 'svm_problem' structure
SVM_kernel_type

alias of pyopenms.pyopenms_8.__SVM_kernel_type

SVM_parameter_type

alias of pyopenms.pyopenms_8.__SVM_parameter_type

calculateGaussTable()

Cython signature: void calculateGaussTable(size_t border_length, double sigma, libcpp_vector[double] & gauss_table)

createRandomPartitions()

Cython signature: void createRandomPartitions(SVMData & problem, size_t number, libcpp_vector[SVMData] & problems)

getDoubleParameter()

Cython signature: double getDoubleParameter(SVM_parameter_type type_)

getIntParameter()

Cython signature: int getIntParameter(SVM_parameter_type type_)

getPValue()

Cython signature: double getPValue(double sigma1, double sigma2, libcpp_pair[double,double] point)

getSVRProbability()

Cython signature: double getSVRProbability()

getSignificanceBorders()

Cython signature: void getSignificanceBorders(SVMData & data, libcpp_pair[double,double] & sigmas, double confidence, size_t number_of_runs, size_t number_of_partitions, double step_size, size_t max_iterations)

loadModel()

Cython signature: void loadModel(String modelFilename) The svm-model is loaded. After this, the svm is ready for prediction

mergePartitions()

Cython signature: void mergePartitions(libcpp_vector[SVMData] & problems, size_t except_, SVMData & merged_problem)

predict()

Cython signature: void predict(SVMData & problem, libcpp_vector[double] & results) The prediction process is started and the results are stored in ‘predicted_labels’

saveModel()

Cython signature: void saveModel(String modelFilename) The model of the trained svm is saved into ‘modelFilename’

setParameter()

  • Cython signature: void setParameter(SVM_parameter_type type_, int value)

  • Cython signature: void setParameter(SVM_parameter_type type_, double value)

setTrainingSample()

Cython signature: void setTrainingSample(SVMData & training_sample)

setWeights()

Cython signature: void setWeights(libcpp_vector[int] & weight_labels, libcpp_vector[double] & weights)

train()

Cython signature: int train(SVMData & problem) The svm is trained with the data stored in the ‘svm_problem’ structure