the nr_featurenr_class array w gives feature weights . we use one against the rest for multi-class classification , so each feature index corresponds to nr_class weight values . weights are organized in the following way + ------------------ + ------------------ + ------------ + | nr_class weights | nr_class weights | ... | for 1st feature | for 2nd feature | + ------------------ + ------------------ + ------------ + if bias > = 0 , x becomes x ; bias . the number of features is increased by one , so w is a nr_feature +1 nr_class array . the value of bias is stored in the variable bias . concode_field_sep int nr_feature concode_elem_sep int nr_class concode_elem_sep double bias concode_elem_sep double[] w concode_elem_sep int[] label concode_elem_sep SolverType solverType concode_elem_sep long serialVersionUID concode_field_sep int[] getLabels concode_elem_sep double getBias concode_elem_sep Model load concode_elem_sep Model load concode_elem_sep int hashCode concode_elem_sep boolean equals concode_elem_sep boolean equals concode_elem_sep boolean isProbabilityModel concode_elem_sep void save concode_elem_sep void save concode_elem_sep int getNrFeature concode_elem_sep String toString concode_elem_sep int getNrClass
double [ ] function ( ) { return Linear . copyOf ( w , w . length ) ; }