linear_models

Documentation

Overview

Package linear_models implements linear and logistic regression models.

Index

• Constants
• Variables
• func Predict(model *Model, x []float64) float64
• type LinearRegression
  • func NewLinearRegression() *LinearRegression
  • func (lr *LinearRegression) Fit(inst *base.Instances) error
  • func (lr *LinearRegression) Predict(X *base.Instances) (*base.Instances, error)
• type LogisticRegression
  • func NewLogisticRegression(penalty string, C float64, eps float64) *LogisticRegression
  • func (lr *LogisticRegression) Fit(X *base.Instances)
  • func (lr *LogisticRegression) Predict(X *base.Instances) *base.Instances
• type Model
  • func Train(prob *Problem, param *Parameter) *Model
• type Parameter
  • func NewParameter(solver_type int, C float64, eps float64) *Parameter
• type Problem
  • func NewProblem(X [][]float64, y []float64, bias float64) *Problem

Constants

const (
	L2R_LR              = C.L2R_LR
	L2R_L2LOSS_SVC_DUAL = C.L2R_L2LOSS_SVC_DUAL
	L2R_L2LOSS_SVC      = C.L2R_L2LOSS_SVC
	L2R_L1LOSS_SVC_DUAL = C.L2R_L1LOSS_SVC_DUAL
	MCSVM_CS            = C.MCSVM_CS
	L1R_L2LOSS_SVC      = C.L1R_L2LOSS_SVC
	L1R_LR              = C.L1R_LR
	L2R_LR_DUAL         = C.L2R_LR_DUAL
)

Variables

var (
	NotEnoughDataError  = errors.New("not enough rows to support this many variables.")
	NoTrainingDataError = errors.New("you need to Fit() before you can Predict()")
)

Functions

func Predict

func Predict(model *Model, x []float64) float64

Types

type LinearRegression

type LinearRegression struct {
	// contains filtered or unexported fields
}

func NewLinearRegression

func NewLinearRegression() *LinearRegression

func (*LinearRegression) Fit

func (lr *LinearRegression) Fit(inst *base.Instances) error

func (*LinearRegression) Predict

func (lr *LinearRegression) Predict(X *base.Instances) (*base.Instances, error)

type LogisticRegression

type LogisticRegression struct {
	// contains filtered or unexported fields
}

func NewLogisticRegression

func NewLogisticRegression(penalty string, C float64, eps float64) *LogisticRegression

func (*LogisticRegression) Fit

func (lr *LogisticRegression) Fit(X *base.Instances)

func (*LogisticRegression) Predict

func (lr *LogisticRegression) Predict(X *base.Instances) *base.Instances

type Model

type Model struct {
	// contains filtered or unexported fields
}

func Train

func Train(prob *Problem, param *Parameter) *Model

type Parameter

type Parameter struct {
	// contains filtered or unexported fields
}

func NewParameter

func NewParameter(solver_type int, C float64, eps float64) *Parameter

type Problem

type Problem struct {
	// contains filtered or unexported fields
}

func NewProblem

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