linear_models

May 29, 2022

Changes in this version

type Model

+ C_model unsafe.Pointer

May 29, 2022

Changes in this version

type Parameter

+ C_param C.struct_parameter

type Problem

+ C_prob C.struct_problem

May 27, 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

+ Attrs []base.Attribute

+ Cls base.Attribute

+ Disturbance float64

+ RegressionCoefficients []float64

+ 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, f float64, eps float64) *Parameter

+ type Problem struct

+ func NewProblem(X [][]float64, y []float64, bias float64) *Problem