Versions in this module Expand all Collapse all v0 v0.0.1 Apr 9, 2022 Changes in this version + const L1R_L2LOSS_SVC + const L1R_LR + const L2R_L1LOSS_SVC_DUAL + const L2R_L2LOSS_SVC + const L2R_L2LOSS_SVC_DUAL + const L2R_LR + const L2R_LR_DUAL + const MCSVM_CS + var NoTrainingDataError = errors.New("you need to Fit() before you can Predict()") + var NotEnoughDataError = errors.New("not enough rows to support this many variables.") + func Export(model *Model, filePath string) error + func Load(model *Model, filePath string) error + func Predict(model *Model, x []float64) float64 + type LinearRegression struct + func NewLinearRegression() *LinearRegression + func (lr *LinearRegression) Fit(inst base.FixedDataGrid) error + func (lr *LinearRegression) Predict(X base.FixedDataGrid) (base.FixedDataGrid, error) + type LinearSVC struct + Param *LinearSVCParams + func NewLinearSVC(loss, penalty string, dual bool, C float64, eps float64) (*LinearSVC, error) + func NewLinearSVCFromParams(params *LinearSVCParams) (*LinearSVC, error) + func (lr *LinearSVC) Fit(X base.FixedDataGrid) error + func (lr *LinearSVC) GetMetadata() base.ClassifierMetadataV1 + func (lr *LinearSVC) Load(filePath string) error + func (lr *LinearSVC) LoadWithPrefix(reader *base.ClassifierDeserializer, prefix string) error + func (lr *LinearSVC) Predict(X base.FixedDataGrid) (base.FixedDataGrid, error) + func (lr *LinearSVC) Save(filePath string) error + func (lr *LinearSVC) SaveWithPrefix(writer *base.ClassifierSerializer, prefix string) error + func (lr *LinearSVC) String() string + type LinearSVCParams struct + C float64 + ClassWeights []float64 + Dual bool + Eps float64 + SolverType int + WeightClassesAutomatically bool + func (p *LinearSVCParams) Copy() *LinearSVCParams + func (p *LinearSVCParams) SetKindFromStrings(loss, penalty string) error + type LogisticRegression struct + func NewLogisticRegression(penalty string, C float64, eps float64) (*LogisticRegression, error) + func (lr *LogisticRegression) Fit(X base.FixedDataGrid) error + func (lr *LogisticRegression) Predict(X base.FixedDataGrid) (base.FixedDataGrid, error) + func (lr *LogisticRegression) String() string + type Model struct + func Train(prob *Problem, param *Parameter) *Model + type Parameter struct + func NewParameter(solver_type int, C float64, eps float64) *Parameter + type Problem struct + func NewProblem(X [][]float64, y []float64, bias float64) *Problem
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