gmm

package

v0.0.0-...-0953495 Latest Latest Go to latest Published: Apr 23, 2015 License: BSD-3-Clause Imports: 14 Imported by: 0

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Repository

github.com/akualab/gjoa

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Documentation ¶

Index ¶

func RandomVector(mean, sd []float64, r *rand.Rand) []float64
type Model
type Option

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

func RandomVector ¶

func RandomVector(mean, sd []float64, r *rand.Rand) []float64

Returns a random vector using the mean and sd vectors.

Types ¶

type Model ¶

type Model struct {
	Type         string            `json:"type"`
	ModelName    string            `json:"name"`
	ModelDim     int               `json:"dim"`
	NSamples     float64           `json:"nsamples"`
	Diag         bool              `json:"diag"`
	NComponents  int               `json:"num_components"`
	PosteriorSum []float64         `json:"posterior_sum,omitempty"`
	Weights      []float64         `json:"-"`
	LogWeights   []float64         `json:"weights,omitempty"`
	Likelihood   float64           `json:"likelihood"`
	Components   []*gaussian.Model `json:"components,omitempty"`
	Iteration    int               `json:"iteration"`
	// contains filtered or unexported fields
}

Model is a mixture of Gaussian distributions.

func NewModel ¶

func NewModel(dim, numComponents int, options ...Option) *Model

NewModel creates a new Gaussian mixture model.

func RandomModel ¶

func RandomModel(mean, sd []float64, numComponents int,
	name string, seed int64) *Model

RandomModel generates a random Gaussian mixture model using mean and variance vectors as seed. Use this function to initialize the GMM before training. The mean and sd vector can be estimated from the data set using a Gaussian model.

func Read ¶

func Read(r io.Reader) (*Model, error)

Read unmarshals json data from an io.Reader into a model struct.

func ReadFile ¶

func ReadFile(fn string) (*Model, error)

ReadFile unmarshals json data from a file into a model struct.

func (*Model) Clear ¶

func (gmm *Model) Clear()

Clear resets sufficient statistics.

func (*Model) Dim ¶

func (gmm *Model) Dim() int

Dim is the dimensionality of the observation vector.

func (*Model) Estimate ¶

func (gmm *Model) Estimate() error

Estimate computes model parameters using sufficient statistics.

func (*Model) LogProb ¶

func (gmm *Model) LogProb(obs model.Obs) float64

LogProb returns log probability for observation.

func (*Model) Name ¶

func (gmm *Model) Name() string

Name returns the name of the model.

func (*Model) Predict ¶

func (gmm *Model) Predict(x model.Observer) ([]model.Labeler, error)

Predict returns a hypothesis given the observation.

func (*Model) Sample ¶

func (gmm *Model) Sample(r *rand.Rand) model.Obs

Sample returns a GMM sample.

func (*Model) SampleChan ¶

func (gmm *Model) SampleChan(r *rand.Rand, size int) <-chan model.Obs

SampleChan returns a channel with samples generated by the GMM model.

func (*Model) Update ¶

func (gmm *Model) Update(x model.Observer, w func(model.Obs) float64) error

Update updates sufficient statistics using observations.

func (*Model) UpdateOne ¶

func (gmm *Model) UpdateOne(o model.Obs, w float64)

Estimate computes model parameters using sufficient statistics.

func (*Model) Write ¶

func (gmm *Model) Write(w io.Writer) error

Write writes the model to an io.Writer.

func (*Model) WriteFile ¶

func (gmm *Model) WriteFile(fn string) error

WriteFile writes the model to file.

type Option ¶

type Option func(*Model)

Option type is used to pass options to NewModel().

func Clone ¶

func Clone(src *Model) Option

Clone create a clone of src.

func Components ¶

func Components(cs []*gaussian.Model) Option

Components sets the mixture components for the model.

func LogWeights ¶

func LogWeights(logw []float64) Option

LogWeights sets the mixture weights for the model using log(w) as the argument.

func Name ¶

func Name(name string) Option

Name is an option to set the model name.

func Weights ¶

func Weights(w []float64) Option

Weights sets the mixture weights for the model.

Source Files ¶

View all Source files

gmm.go

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