Retrieves the decision function

[alpha,svidx,rho] = model.getDecisionFunction(index)

Input

• index the index of the decision function (0-based). 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 N(N-1)/2 decision functions.

Output

• alpha 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 the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by cv.SVM.getSupportVectors. In the case of linear SVM each decision function consists of a single "compressed" support vector.
• rho rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.