ML

package module
v0.0.0-...-afb026f Latest Latest
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 Go to latest Published: Oct 20, 2013 License: Apache-2.0 Imports: 13 Imported by: 0

README

MLiG

Machine Learning in Go

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func EntropyAccumulatorFactory 

func EntropyAccumulatorFactory(categoryValueCount int) func() CVAccumulator

func NewTreeNode 

func NewTreeNode(statistics CVAccumulator) *treeNode

func ShuffleData 

func ShuffleData(data []*Data)

func StatAccumulatorFactory 

func StatAccumulatorFactory() func() CVAccumulator

func TrainBag 

func TrainBag(data []*Data, classifier Classifier)

Types

type CVAccumulator 

type CVAccumulator interface {
	ErrorAccumulator
	Remove(float64)
	Metric() float64
}

type Classifier 

type Classifier interface {
	Classify(func(int32) float64) CVAccumulator
	Train(trainset []*Data)

	// Accumulate statistics for this classifier
	Add(error float64)
	// Return an error estimate
	Estimate() float64
}

type Data 

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

func CSVData 

func CSVData(legend string, filename string, outputCategories, skip int) []*Data

func DigitData 

func DigitData(filename string) []*Data

func GlassData 

func GlassData(filename string) []*Data

func (*Data) AppendFeatures 

func (d *Data) AppendFeatures(af []float64)

func (*Data) Features 

func (d *Data) Features() []float64

type Ensemble 

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

func NewEnsemble 

func NewEnsemble() *Ensemble

func (*Ensemble) AddClassifier 

func (te *Ensemble) AddClassifier(newClassifier Classifier)

func (*Ensemble) Error 

func (te *Ensemble) Error(data []*Data) float64

type EntropyAccumulator 

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

func NewEntropyAccumulator 

func NewEntropyAccumulator(categoryValueCount int) *EntropyAccumulator

func (*EntropyAccumulator) Add 

func (ea *EntropyAccumulator) Add(category float64)

func (*EntropyAccumulator) Clear 

func (ea *EntropyAccumulator) Clear()

func (*EntropyAccumulator) Clone 

func (ea *EntropyAccumulator) Clone() ErrorAccumulator

func (*EntropyAccumulator) Count 

func (ea *EntropyAccumulator) Count() int

func (*EntropyAccumulator) Dump 

func (ea *EntropyAccumulator) Dump(w io.Writer, indent int)

func (*EntropyAccumulator) Estimate 

func (ea *EntropyAccumulator) Estimate() float64

func (*EntropyAccumulator) FrequencyEstimate 

func (ea *EntropyAccumulator) FrequencyEstimate(value float64) float64

func (*EntropyAccumulator) Metric 

func (ea *EntropyAccumulator) Metric() float64

func (*EntropyAccumulator) Remove 

func (ea *EntropyAccumulator) Remove(category float64)

func (EntropyAccumulator) String 

func (ea EntropyAccumulator) String() string

type ErrorAccumulator 

type ErrorAccumulator interface {
	Clear()
	Add(float64)
	Count() int
	Estimate() float64
	Clone() ErrorAccumulator
	Dump(io.Writer, int)
}

type Feature 

type Feature interface {
	// contains filtered or unexported methods
}

type FeatureType 

type FeatureType int
const (
	CATEGORICAL FeatureType = iota
	CONTINUOUS
)

type GrayWithFeatures 

type GrayWithFeatures struct {
	*image.Gray
	// contains filtered or unexported fields
}

func (*GrayWithFeatures) Centroid 

func (gwf *GrayWithFeatures) Centroid() (x, y float64)

func (*GrayWithFeatures) Edges 

func (gwf *GrayWithFeatures) Edges() (vertical, horizontal float64)

imageEdges returns the average number of vertical and horizontal edges in the image. For vertical edges the average is taken over all rows. For horizontal edges the average is taken over all columns.

func (*GrayWithFeatures) Mass 

func (gwf *GrayWithFeatures) Mass() float64

func (*GrayWithFeatures) Moments 

func (gwf *GrayWithFeatures) Moments() (rxx, ryy, rxy float64)

func (*GrayWithFeatures) RandomFeature 

func (gwf *GrayWithFeatures) RandomFeature(s int32) float64

type HierarchicalFeatures 

type HierarchicalFeatures struct {
	*image.Gray
	// contains filtered or unexported fields
}

func NewHierarchicalFeatures 

func NewHierarchicalFeatures(gs *image.Gray) *HierarchicalFeatures

func (*HierarchicalFeatures) Centroid 

func (hf *HierarchicalFeatures) Centroid(r image.Rectangle) (xBar, yBar float64)

func (*HierarchicalFeatures) Dump 

func (hf *HierarchicalFeatures) Dump(w io.Writer)

func (*HierarchicalFeatures) Edges 

func (hf *HierarchicalFeatures) Edges(r image.Rectangle) (horizEdges, vertEdges float64)

func (*HierarchicalFeatures) MassSums 

func (hf *HierarchicalFeatures) MassSums(r image.Rectangle) (mass, xMass, yMass int32, x2Mass, y2Mass, xyMass int64)

func (*HierarchicalFeatures) RandomFeature 

func (hf *HierarchicalFeatures) RandomFeature(s int32) float64

type RandomFeatureSelector 

type RandomFeatureSelector interface {
	RandomFeature(s int32) float64
}

type SplitInfo 

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

func (*SplitInfo) String 

func (si *SplitInfo) String() string

type StatAccumulator 

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

A new StatAccumulator may be declared without initialization. The Go default initialization is correct.

func (*StatAccumulator) Add 

func (sa *StatAccumulator) Add(x float64)

func (*StatAccumulator) Clear 

func (sa *StatAccumulator) Clear()

func (*StatAccumulator) Clone 

func (sa *StatAccumulator) Clone() ErrorAccumulator

func (*StatAccumulator) Count 

func (sa *StatAccumulator) Count() int

func (*StatAccumulator) Dump 

func (sa *StatAccumulator) Dump(w io.Writer, indent int)

func (*StatAccumulator) Estimate 

func (sa *StatAccumulator) Estimate() float64

func (*StatAccumulator) Metric 

func (sa *StatAccumulator) Metric() float64

func (*StatAccumulator) Remove 

func (sa *StatAccumulator) Remove(x float64)

type Tree 

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

func NewTree 

func NewTree(accumulatorFactory func() CVAccumulator) *Tree

func (*Tree) Add 

func (tree *Tree) Add(error float64)

func (*Tree) Classify 

func (tree *Tree) Classify(featureSelector func(int32) float64) CVAccumulator

func (*Tree) Depth 

func (tree *Tree) Depth() int

func (*Tree) Dump 

func (tree *Tree) Dump(w io.Writer)

func (*Tree) Estimate 

func (tree *Tree) Estimate() float64

func (*Tree) Leaves 

func (tree *Tree) Leaves() int

func (*Tree) SetFeaturesToTry 

func (tree *Tree) SetFeaturesToTry(n int)

func (*Tree) SetMaxDepth 

func (tree *Tree) SetMaxDepth(depth int)

func (*Tree) SetMinLeafSize 

func (tree *Tree) SetMinLeafSize(size int)

func (*Tree) Size 

func (tree *Tree) Size() int

func (*Tree) Train 

func (tree *Tree) Train(trainingSet []*Data)

type WeightedCVAccumulator 

type WeightedCVAccumulator interface {
	WeightedErrorAccumulator
	Remove(cateogry, weight float64)
	Metric() float64
}

type WeightedErrorAccumulator 

type WeightedErrorAccumulator interface {
	Clear()
	Add(category, weight float64)
	Count() int
	WeightedCount() float64
	Estimate() float64
	Clone() WeightedErrorAccumulator
	Dump(io.Writer, int)
}

Source Files

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