lr

package
v0.0.0-...-14a59ba Latest Latest
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 Go to latest Published: Oct 27, 2017 License: MIT Imports: 11 Imported by: 0

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Index

Constants

View Source 
const MAX_BACKTRACKING_ITER = 50

Variables

This section is empty.

Functions

func NewRelativeMeanImprCriterion 

func NewRelativeMeanImprCriterion(tolerance float64) *relativeMeanImprCriterion

Types

type DiffFunction 

type DiffFunction interface {
	Value(pos *core.Vector) float64
	Gradient(pos *core.Vector) *core.Vector
}

Description: function for minimizer such as LBFGS and OWLQN

type EPLogisticRegression 

type EPLogisticRegression struct {
	Model map[int64]*utils.Gaussian
	// contains filtered or unexported fields
}

func (*EPLogisticRegression) Clear 

func (algo *EPLogisticRegression) Clear()

func (*EPLogisticRegression) Command 

func (algo *EPLogisticRegression) Command() cli.Command

func (*EPLogisticRegression) Init 

func (algo *EPLogisticRegression) Init(ctx *cli.Context)

func (*EPLogisticRegression) LoadModel 

func (algo *EPLogisticRegression) LoadModel(path string)

func (*EPLogisticRegression) Predict 

func (algo *EPLogisticRegression) Predict(sample *core.Sample) float64

func (*EPLogisticRegression) SaveModel 

func (algo *EPLogisticRegression) SaveModel(path string)

func (*EPLogisticRegression) Train 

func (algo *EPLogisticRegression) Train(dataset *core.DataSet)

type EPLogisticRegressionParams 

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

type FTRLFeatureWeight 

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

func (*FTRLFeatureWeight) Wi 

func (w *FTRLFeatureWeight) Wi(p FTRLLogisticRegressionParams) float64

type FTRLLogisticRegression 

type FTRLLogisticRegression struct {
	Model  map[int64]FTRLFeatureWeight
	Params FTRLLogisticRegressionParams
}

func (*FTRLLogisticRegression) Clear 

func (algo *FTRLLogisticRegression) Clear()

func (*FTRLLogisticRegression) Command 

func (algo *FTRLLogisticRegression) Command() cli.Command

func (*FTRLLogisticRegression) Init 

func (algo *FTRLLogisticRegression) Init(ctx *cli.Context)

func (*FTRLLogisticRegression) LoadModel 

func (algo *FTRLLogisticRegression) LoadModel(path string)

func (*FTRLLogisticRegression) Predict 

func (algo *FTRLLogisticRegression) Predict(sample *core.Sample) float64

func (*FTRLLogisticRegression) SaveModel 

func (algo *FTRLLogisticRegression) SaveModel(path string)

func (*FTRLLogisticRegression) Train 

func (algo *FTRLLogisticRegression) Train(dataset *core.DataSet)

type FTRLLogisticRegressionParams 

type FTRLLogisticRegressionParams struct {
	Alpha     float64
	Beta      float64
	Lambda1   float64
	Lambda2   float64
	IsBalance bool    // 正反例样本是否均衡
	SSR       float64 // 欠采样比例Sub-Sample Ratio (0,1]。默认值为1,表示正反例样本数量均衡,不均衡时表示负例(label=0)样本。
	SBR       float64 // Samples Balance Ratio 样本集正反例样本比率 (0,1]。默认值为1,表示正反例样本数量均衡。
	Steps     int     // 最大迭代次数
}

type LBFGSMinimizer 

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

func NewLBFGSMinimizer 

func NewLBFGSMinimizer() *LBFGSMinimizer

func (*LBFGSMinimizer) Evaluate 

func (m *LBFGSMinimizer) Evaluate(pos *core.Vector) float64

func (*LBFGSMinimizer) Minimize 

func (m *LBFGSMinimizer) Minimize(costfun DiffFunction, init *core.Vector) *core.Vector

func (*LBFGSMinimizer) NextPoint 

func (m *LBFGSMinimizer) NextPoint(curPos *core.Vector, dir *core.Vector, alpha float64) *core.Vector

type LROWLQN 

type LROWLQN struct {
	Model  *core.Vector
	Params LROWLQNParams
	// contains filtered or unexported fields
}

func (*LROWLQN) Clear 

func (lr *LROWLQN) Clear()

func (*LROWLQN) Command 

func (lr *LROWLQN) Command() cli.Command

func (*LROWLQN) Equals 

func (lr *LROWLQN) Equals(x *core.Vector, y *core.Vector) bool

func (*LROWLQN) Gradient 

func (lr *LROWLQN) Gradient(pos *core.Vector) *core.Vector

func (*LROWLQN) Init 

func (lr *LROWLQN) Init(ctx *cli.Context)

func (*LROWLQN) LoadModel 

func (lr *LROWLQN) LoadModel(path string)

func (*LROWLQN) Predict 

func (lr *LROWLQN) Predict(sample *core.Sample) float64

func (*LROWLQN) SaveModel 

func (lr *LROWLQN) SaveModel(path string)

func (*LROWLQN) Train 

func (lr *LROWLQN) Train(dataset *core.DataSet)

func (*LROWLQN) Value 

func (lr *LROWLQN) Value(pos *core.Vector) float64

type LROWLQNParams 

type LROWLQNParams struct {
	Regularization float64
}

type LinearRegression 

type LinearRegression struct {
	Model  map[int64]float64
	Params LogisticRegressionParams
}

func (*LinearRegression) Command 

func (algo *LinearRegression) Command() cli.Command

func (*LinearRegression) Init 

func (algo *LinearRegression) Init(ctx *cli.Context)

func (*LinearRegression) LoadModel 

func (algo *LinearRegression) LoadModel(path string)

func (*LinearRegression) Predict 

func (algo *LinearRegression) Predict(sample *core.Sample) float64

func (*LinearRegression) SaveModel 

func (algo *LinearRegression) SaveModel(path string)

func (*LinearRegression) Train 

func (algo *LinearRegression) Train(dataset *core.DataSet)

type LogisticRegression 

type LogisticRegression struct {
	Model  map[int64]float64
	Params LogisticRegressionParams
}

func (*LogisticRegression) Clear 

func (algo *LogisticRegression) Clear()

func (*LogisticRegression) Command 

func (algo *LogisticRegression) Command() cli.Command

func (*LogisticRegression) Init 

func (algo *LogisticRegression) Init(ctx *cli.Context)

func (*LogisticRegression) LoadModel 

func (algo *LogisticRegression) LoadModel(path string)

func (*LogisticRegression) Predict 

func (algo *LogisticRegression) Predict(sample *core.Sample) float64

func (*LogisticRegression) SaveModel 

func (algo *LogisticRegression) SaveModel(path string)

func (*LogisticRegression) Train 

func (algo *LogisticRegression) Train(dataset *core.DataSet)

type LogisticRegressionParams 

type LogisticRegressionParams struct {
	LearningRate   float64
	Regularization float64
	Steps          int
}

type LogisticRegressionStream 

type LogisticRegressionStream struct {
	Model  map[int64]float64
	Params LogisticRegressionParams
}

func (*LogisticRegressionStream) Command 

func (algo *LogisticRegressionStream) Command() cli.Command

func (*LogisticRegressionStream) Init 

func (algo *LogisticRegressionStream) Init(ctx *cli.Context)

func (*LogisticRegressionStream) LoadModel 

func (algo *LogisticRegressionStream) LoadModel(path string)

func (*LogisticRegressionStream) Predict 

func (algo *LogisticRegressionStream) Predict(sample *core.Sample) float64

func (*LogisticRegressionStream) SaveModel 

func (algo *LogisticRegressionStream) SaveModel(path string)

func (*LogisticRegressionStream) Train 

func (algo *LogisticRegressionStream) Train(dataset *core.StreamingDataSet)

type Minimizer 

type Minimizer interface {
	NextPoint(curPos *core.Vector, dir *core.Vector, alpha float64) *core.Vector
	Evaluate(curPos *core.Vector) float64
}

type OWLQNMinimizer 

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

*

  • It's based the paper "Scalable Training of L1-Regularized Log-Linear Models"
  • by Galen Andrew and Jianfeng Gao
  • user: weixuan
  • To change this template use File | Settings | File Templates.

func NewOWLQNMinimizer 

func NewOWLQNMinimizer(l1reg float64) *OWLQNMinimizer

func (*OWLQNMinimizer) Evaluate 

func (m *OWLQNMinimizer) Evaluate(pos *core.Vector) float64

func (*OWLQNMinimizer) Minimize 

func (m *OWLQNMinimizer) Minimize(costfun DiffFunction, init *core.Vector) *core.Vector

func (*OWLQNMinimizer) NextPoint 

func (m *OWLQNMinimizer) NextPoint(curPos *core.Vector, dir *core.Vector, alpha float64) *core.Vector

type QuasiNewtonHelper 

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

*

  • It's based the paper "Scalable Training of L1-Regularized Log-Linear Models"
  • by Galen Andrew and Jianfeng Gao
  • user: weixuan
  • To change this template use File | Settings | File Templates.

func NewQuasiNewtonHelper 

func NewQuasiNewtonHelper(numHist int, minimizer Minimizer, curPos *core.Vector, curGrad *core.Vector) *QuasiNewtonHelper

Description: the pos and gradient arguments should NOT be modified outside

func (*QuasiNewtonHelper) ApplyQuasiInverseHession 

func (h *QuasiNewtonHelper) ApplyQuasiInverseHession(dir *core.Vector)

Description: Update the dir from -grad to optimal direction 

Dir will be modified directly

func (*QuasiNewtonHelper) BackTrackingLineSearch 

func (h *QuasiNewtonHelper) BackTrackingLineSearch(cost float64, pos *core.Vector, grad *core.Vector, dir *core.Vector, isInit bool) (nextCost float64, nextPos *core.Vector)

func (*QuasiNewtonHelper) UpdateState 

func (h *QuasiNewtonHelper) UpdateState(nextPos *core.Vector, nextGrad *core.Vector) (isOptimal bool)

Description: the pos and gradient arguments should NOT be modified outside

Source Files

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