Documentation
¶
Overview ¶
Package linearmodel is a collection of linear regression fitting implementations to be used in the forecaster
Index ¶
Constants ¶
const ( DefaultLambda = 1.0 DefaultIterations = 1000 DefaultTolerance = 1e-4 )
Variables ¶
var ( ErrNoOptions = errors.New("no initialized model options") ErrTargetLenMismatch = errors.New("target length does not match target rows") ErrNoTrainingMatrix = errors.New("no training matrix") ErrNoTargetMatrix = errors.New("no target matrix") ErrNoDesignMatrix = errors.New("no design matrix for inference") ErrFeatureLenMismatch = errors.New("number of features does not match number of model coefficients") )
var ( ErrNegativeLambda = errors.New("negative lambda") ErrNegativeIterations = errors.New("negative iterations") ErrNegativeTolerance = errors.New("negative tolerance") ErrWarmStartBetaSize = errors.New("warm start beta does not have the same number of coefficients as training features") ErrNoLambdas = errors.New("no lambdas provided to fit with") )
Functions ¶
func SoftThreshold ¶
SoftThreshold returns 0.0 if the value is less than or equal to the gamma input
Types ¶
type LassoAutoOptions ¶
type LassoAutoOptions struct {
// Lambda represents the L1 multiplier, controlling the regularization. Must be a non-negative. 0.0 results in converging
// to Ordinary Least Squares (OLS).
Lambdas []float64
// Iterations is the maximum number of times the fit loops through training all coefficients.
Iterations int
// Tolerance is the smallest coefficient channge on each iteration to determine when to stop iterating.
Tolerance float64
// FitIntercept adds a constant 1.0 feature as the first column if set to true
FitIntercept bool
// Parallelization sets how many fits to run in parallel. More will increase memory and compute usage.
Parallelization int
}
LassoAutoOptions represents input options to run the Lasso Regression with optimal regularization parameter lambda
func NewDefaultLassoAutoOptions ¶
func NewDefaultLassoAutoOptions() *LassoAutoOptions
NewDefaultLassoAutoOptions returns a default set of Lasso Auto Regression options
func (*LassoAutoOptions) Validate ¶
func (l *LassoAutoOptions) Validate() (*LassoAutoOptions, error)
Validate runs basic validation on Lasso Auto options
type LassoAutoRegression ¶
type LassoAutoRegression struct {
// contains filtered or unexported fields
}
LassoAutoRegression computes the lasso regression using coordinate descent. lambda is derived by finding the optimal regularization parameter
func NewLassoAutoRegression ¶
func NewLassoAutoRegression(opt *LassoAutoOptions) (*LassoAutoRegression, error)
NewLassoAutoRegression initializes a Lasso model ready for fitting using automated lambad parameter selection
func (*LassoAutoRegression) Coef ¶
func (l *LassoAutoRegression) Coef() []float64
Coef returns a slice of the trained coefficients in the same order of the training feature Matrix by column.
func (*LassoAutoRegression) Fit ¶
func (l *LassoAutoRegression) Fit(x, y mat.Matrix) error
Fit the model according to the given training data
func (*LassoAutoRegression) Intercept ¶
func (l *LassoAutoRegression) Intercept() float64
Intercept returns the computed intercept if FitIntercept is set to true. Defaults to 0.0 if not set.
type LassoOptions ¶
type LassoOptions struct {
// WarmStartBeta is used to prime the coordinate descent to reduce the training time if a previous
// fit has been performed.
WarmStartBeta []float64
// Lambda represents the L1 multiplier, controlling the regularization. Must be a non-negative. 0.0 results in converging
// to Ordinary Least Squares (OLS).
Lambda float64
// Iterations is the maximum number of times the fit loops through training all coefficients.
Iterations int
// Tolerance is the smallest coefficient channge on each iteration to determine when to stop iterating.
Tolerance float64
// FitIntercept adds a constant 1.0 feature as the first column if set to true
FitIntercept bool
}
LassoOptions represents input options to run the Lasso Regression
func NewDefaultLassoOptions ¶
func NewDefaultLassoOptions() *LassoOptions
NewDefaultLassoOptions returns a default set of Lasso Regression options
func (*LassoOptions) Validate ¶
func (l *LassoOptions) Validate() (*LassoOptions, error)
Validate runs basic validation on Lasso options
type LassoRegression ¶
type LassoRegression struct {
// contains filtered or unexported fields
}
LassoRegression computes the lasso regression using coordinate descent. lambda = 0 converges to OLS
func NewLassoRegression ¶
func NewLassoRegression(opt *LassoOptions) (*LassoRegression, error)
NewLassoRegression initializes a Lasso model ready for fitting
func (*LassoRegression) Coef ¶
func (l *LassoRegression) Coef() []float64
Coef returns a slice of the trained coefficients in the same order of the training feature Matrix by column.
func (*LassoRegression) Fit ¶
func (l *LassoRegression) Fit(x, y mat.Matrix) error
Fit the model according to the given training data
func (*LassoRegression) Intercept ¶
func (l *LassoRegression) Intercept() float64
Intercept returns the computed intercept if FitIntercept is set to true. Defaults to 0.0 if not set.
type OLSOptions ¶
type OLSOptions struct {
// FitIntercept adds a constant 1.0 feature as the first column if set to true
FitIntercept bool
}
OLSOptions represents input options to run the OLS Regression
func NewDefaultOLSOptions ¶
func NewDefaultOLSOptions() *OLSOptions
NewDefaultOLSOptions returns a default set of OLS Regression options
func (*OLSOptions) Validate ¶
func (o *OLSOptions) Validate() (*OLSOptions, error)
Validate runs basic validation on OLS options
type OLSRegression ¶
type OLSRegression struct {
// contains filtered or unexported fields
}
OLSRegression computes ordinary least squares using QR factorization
func NewOLSRegression ¶
func NewOLSRegression(opt *OLSOptions) (*OLSRegression, error)
NewOLSRegression initializes an ordinary least squares model ready for fitting
func (*OLSRegression) Coef ¶
func (o *OLSRegression) Coef() []float64
Coef returns a slice of the trained coefficients in the same order of the training feature Matrix by column.
func (*OLSRegression) Fit ¶
func (o *OLSRegression) Fit(x, y mat.Matrix) error
Fit the model according to the given training data
func (*OLSRegression) Intercept ¶
func (o *OLSRegression) Intercept() float64
Intercept returns the computed intercept if FitIntercept is set to true. Defaults to 0.0 if not set.
Source Files