svm-smo

Usage

The svm module offers fitting, prediction and scoring using provided methods.

svm::SVM

SVM.SVM(Kernel kernel, size_t max_it=100, double C=1, double epsilon=10E-5)

• kernel: A function<double(arma::rowvec,arma::rowvec)> object which describes the kernel of the constructed SVM object.
• max_it: The maximum number of sucessive iterations of unchanged alphas before until the fit is completed.
• C: The regularization coefficient of the system.
• epsilon: The error threshold for violation of the KKT conditions.

SVM.SVM(string kernel, size_t max_it=100, double C=1, double epsilon=10E-5)

• kernel: A string containing the name of the standard kernel chosen. Current kernel choices are "linear".

SVM.fit(arma::mat x, arma::vec y)

Fit the SVM model to a provided dataset using sequential minimal optimization

• X: The feature set to be fitted of shape (n_features, n_training_samples)
• y: The binary categorisation of the dataset of shape (n_training_samples)

SVM.predict(arma::mat x)

Returns a arma::vec of predictions for the provided dataset.

• X: A feature set of shape (n_features, n_samples)

SVM.score(arna::mat X, arma::vec y)

Returns the mean prediction rate of the fitted model for samples X against targets y

• X: A feature set of shape (n_features, n_samples) to be predicted.
• y: A binary classification vector of shape (n_samples) to be compared to predictions on X

Requirements

• Armadillo
• C++14