// Wrapper for the `train` function in liblinear.
//
// `model* train(const struct problem *prob, const struct parameter *param);`
//
// The explanation of parameters are:
//
// solverType:
//
// for multi-class classification
// 0 -- L2-regularized logistic regression (primal)
// 1 -- L2-regularized L2-loss support vector classification (dual)
// 2 -- L2-regularized L2-loss support vector classification (primal)
// 3 -- L2-regularized L1-loss support vector classification (dual)
// 4 -- support vector classification by Crammer and Singer
// 5 -- L1-regularized L2-loss support vector classification
// 6 -- L1-regularized logistic regression
// 7 -- L2-regularized logistic regression (dual)
// for regression
// 11 -- L2-regularized L2-loss support vector regression (primal)
// 12 -- L2-regularized L2-loss support vector regression (dual)
// 13 -- L2-regularized L1-loss support vector regression (dual)
//
// eps is the stopping criterion.
//
// C_ is the cost of constraints violation.
//
// p is the sensitiveness of loss of support vector regression.
//
// classWeights is a map from int to float64, with the key be the class and the
// value be the weight. For example, {1: 10, -1: 0.5} means giving weight=10 for
// class=1 while weight=0.5 for class=-1
//
// If you do not want to change penalty for any of the classes, just set
// classWeights to nil.
func Train(X, y *mat64.Dense, bias float64, pm *Parameter) *Model {
var problem C.struct_problem
nRows, nCols := X.Dims()
cY := mapCDouble(y.Col(nil, 0))
cX := toFeatureNodes(X)
problem.x = &cX[0]
problem.y = &cY[0]
problem.n = C.int(nCols)
problem.l = C.int(nRows)
problem.bias = C.double(bias)
model := C.train(&problem, pm.GetPtr())
return &Model{
cModel: model,
}
}
func Train(prob *Problem, param *Parameter) *Model {
return &Model{C.train(&prob.c_prob, ¶m.c_param)}
}
func Train(prob *Problem, param *Parameter) *Model {
libLinearHookPrintFunc() // Sets up logging
return &Model{C.train(&prob.c_prob, ¶m.c_param)}
}
