README
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regression
regression is a simple, written from scratch Go library for basic variants of two the most popular models from the Generalized Linear Models (GLM) family.
regression/linear provides implementation of the linear regression model.
regression/logistic provides implementation of the logistic model.
Why?
Do you need another not fancy machine learning library? Actually, you don't. The main puropose of regression library is learning. Machine learning and AI algorithms remains mystified and magic. So here it is a simple, written from scratch implementation of one of the most popular algorithms solving regression and classification problem. It doesn't throw the responsibility for underyling math (like matrix calculus, iterative optimisation) to external packages. Everything is embedded in this repository.
Install
Examples
Documentation
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Overview ¶
Package regression defines interfaces, structures and errors shared by other packages that implement a concrete regression algorithm.
Index ¶
Constants ¶
This section is empty.
Variables ¶
var ( // ErrCannotConverge is returned if gradient descent cannot converge. // It usually means that the learning rate is too large. ErrCannotConverge = errors.New("cannot converge") // ErrUnsupportedGradientDescentVariant is returned if unsupported gradient descent variant was chosen. ErrUnsupportedGradientDescentVariant = errors.New("unsupported gradient descent variant") // ErrUnsupportedConverganceType is returned if unsupported convergance type was chosen. ErrUnsupportedConverganceType = errors.New("unsupported convergance type") // ErrInvalidTrainingSet is returned if features vectors included in the set are not consistent. ErrInvalidTrainingSet = errors.New("invalid training set") // ErrInvalidFeatureVector is returned if feature vector is invalid. ErrInvalidFeatureVector = errors.New("invalid feature vector") )
Functions ¶
This section is empty.
Types ¶
type Model ¶
type Model[T TargetType] interface { // Predict returns the predicated target value for the given input. Predict(x []float64) (T, error) // Coefficients returns the trained regression model's coefficients. Coefficients() []float64 // Accuracy returns calculated accuracy for trained model. Accuracy() float64 }
A Model is a trained regression model.
type Regression ¶
type Regression[T TargetType] interface { // Run runs regression against input training set. // It returns trained Model if succeded, otherwise returns an error. Run(s TrainingSet) (Model[T], error) }
A Regression is a regression runner. It provides an abstraction for model training.
type RegressionFunc ¶
type RegressionFunc[T TargetType] func(s TrainingSet) (Model[T], error)
RegressionFunc is an adapter to allow the use of plain functions as regressions.
func (RegressionFunc[T]) Run ¶
func (f RegressionFunc[T]) Run(s TrainingSet) (Model[T], error)
Run calls f(s).
type TargetType ¶
TargetType is a constraint that permits two types (float64 or integer) for target value. Floating point numbers are used for continous value of y, while integer corresponds to the discrete one.
type TrainingExample ¶
TrainingExample represents a single (features, target) example.
type TrainingSet ¶
type TrainingSet struct {
// contains filtered or unexported fields
}
TrainingSet represents set of traning examples.
func NewTrainingSet ¶
func NewTrainingSet(exs []TrainingExample) (*TrainingSet, error)
NewTrainingSet creates a new training set if examples are consistent. It doesn't check for correctness of values. If examples are not consistent, returns an error.
func (*TrainingSet) AddDummyFeatures ¶
func (s *TrainingSet) AddDummyFeatures()
AddDummyFeatures puts dummy feature (equals 1) for each training example being a part of the training set.
func (*TrainingSet) Examples ¶
func (s *TrainingSet) Examples() []TrainingExample
Examples returns all training examples from training set.
func (*TrainingSet) GetDesignMatrix ¶
func (s *TrainingSet) GetDesignMatrix() [][]float64
GetDesignMatrix returns the design matrix from the training set.
func (*TrainingSet) GetTargetVector ¶
func (s *TrainingSet) GetTargetVector() []float64
GetTargetVector returns the target vector from the training set.
Source Files
Directories
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| Path | Synopsis |
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internal
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gd
Package gd provides gradient descent implementation.
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Package gd provides gradient descent implementation. |
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Package linear provides linear regression model implementation.
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Package linear provides linear regression model implementation. |
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Package options defines training options for specific regression variants.
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Package options defines training options for specific regression variants. |