ml. Machine Learning

The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data.

Most of the classification and regression algorithms are implemented as C++ classes. As the algorithms have different sets of features (like an ability to handle missing measurements or categorical input variables), there is a little common ground between the classes. This common ground is defined by the class CvStatModel that all the other ML classes are derived from.

• Statistical Models
  • StatModel
  • StatModel::train
  • StatModel::isTrained
  • StatModel::isClassifier
  • StatModel::getVarCount
  • StatModel::predict
  • StatModel::calcError
  • StatModel::save
  • StatModel::load
• Normal Bayes Classifier
  • NormalBayesClassifier
  • NormalBayesClassifier::create
  • NormalBayesClassifier::predictProb
• K-Nearest Neighbors
  • KNearest
  • KNearest::create
  • KNearest::findNearest
  • KNearest::getDefaultK
  • KNearest::setDefaultK
• Support Vector Machines
  • ParamGrid
  • ParamGrid::ParamGrid
  • SVM::Params
  • SVM::Params::Params
  • SVM
  • SVM::create
  • SVM::trainAuto
  • SVM::getDefaulltGrid
  • SVM::getParams
  • SVM::getSupportVectors
  • SVM::getDecisionFunction
  • Prediction with SVM
• Decision Trees
  • Predicting with Decision Trees
  • Training Decision Trees
  • Variable Importance
  • DTrees::Split
  • DTrees::Node
  • DTrees::Params
  • DTrees::Params::Params
  • DTrees
  • DTrees::create
  • DTrees::getDParams
  • DTrees::setDParams
  • DTrees::getRoots
  • DTrees::getNodes
  • DTrees::getSplits
  • DTrees::getSubsets
• Boosting
  • Boost::Params
  • Boost::Params::Params
  • Boost
  • Boost::create
  • Boost::getBParams
  • Boost::setBParams
  • Prediction with Boost
• Random Trees
  • RTrees::Params
  • RTrees::Params::Params
  • RTrees
  • RTrees::create
  • RTrees::getVarImportance
• Expectation Maximization
  • EM
  • EM::Params
  • EM::Params::Params
  • EM::create
  • EM::train
  • EM::predict2
  • EM::getMeans
  • EM::getWeights
  • EM::getCovs
• Neural Networks
  • ANN_MLP::Params
  • ANN_MLP::Params::Params
  • ANN_MLP
  • ANN_MLP::create
  • ANN_MLP::setParams
  • ANN_MLP::getParams
• Logistic Regression
  • LogisticRegression::Params
  • LogisticRegression::Params::Params
  • LogisticRegression
  • LogisticRegression::create
  • LogisticRegression::train
  • LogisticRegression::predict
  • LogisticRegression::get_learnt_thetas
  • LogisticRegression::read
  • LogisticRegression::write
• Training Data
  • TrainData
  • TrainData::loadFromCSV
  • TrainData::create
  • TrainData::getTrainSamples
  • TrainData::getTrainResponses
  • TrainData::getClassLabels
  • TrainData::getTrainNormCatResponses
  • TrainData::setTrainTestSplitRatio
  • Other methods