func (v *ArrayVector) ToString() []byte { sb := util.StringBuilder{} for _, value := range v.data { sb.Float(value) sb.Write("|") } return sb.Bytes() }
func (algo *LinearRegression) SaveModel(path string) { sb := util.StringBuilder{} for f, g := range algo.Model { sb.Int64(f) sb.Write("\t") sb.Float(g) sb.Write("\n") } sb.WriteToFile(path) }
func (self *LinearSVM) SaveModel(path string) { sb := util.StringBuilder{} for f, g := range self.w.Data { sb.Int64(f) sb.Write("\t") sb.Float(g) sb.Write("\n") } sb.WriteToFile(path) }
func (lr *LROWLQN) SaveModel(path string) { sb := util.StringBuilder{} for key, val := range lr.Model.Data { sb.Int64(key) sb.Write("\t") sb.Float(val) sb.Write("\n") } sb.WriteToFile(path) }
func (v *Vector) ToString() []byte { sb := util.StringBuilder{} for key, value := range v.Data { sb.Int64(key) sb.Write(":") sb.Float(value) sb.Write("|") } return sb.Bytes() }
func (s *Sample) ToString(includePrediction bool) []byte { sb := util.StringBuilder{} sb.Int(s.Label) sb.Write(" ") if includePrediction { sb.Float(s.Prediction) sb.Write(" ") } for _, feature := range s.Features { sb.Int64(feature.Id) sb.Write(":") sb.Float(feature.Value) sb.Write(" ") } return sb.Bytes() }
func (algo *EPLogisticRegression) SaveModel(path string) { sb := util.StringBuilder{} for f, g := range algo.Model { sb.Int64(f) sb.Write("\t") sb.Float(g.Mean) sb.Write("\t") sb.Float(g.Vari) sb.Write("\n") } sb.WriteToFile(path) }
func (t *Tree) ToString() []byte { sb := util.StringBuilder{} sb.Int(len(t.nodes)) sb.Write("\n") for i, node := range t.nodes { sb.Int(i) sb.Write("\t") sb.Int(node.left) sb.Write("\t") sb.Int(node.right) sb.Write("\t") sb.Int(node.depth) sb.Write("\t") sb.WriteBytes(node.prediction.ToString()) sb.Write("\t") sb.Int(node.sample_count) sb.Write("\t") sb.Int64(node.feature_split.Id) sb.Write("\t") sb.Float(node.feature_split.Value) sb.Write("\n") } return sb.Bytes() }