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Penalties

SGD penalties.

Installation

npm install @stdlib/ml-base-sgd-penalties

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var penalties = require( '@stdlib/ml-base-sgd-penalties' );

penalties()

Returns a list of SGD penalties.

var out = penalties();
// e.g., returns [ 'elasticnet', 'l1', 'l2', 'none' ]

The output array contains the following penalties:

• elasticnet: regularization method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.
• l1: L1 regularization (also called LASSO) leads to sparse models by adding a penalty based on the absolute value of coefficients.
• l2: L2 regularization (also called ridge regression) encourages smaller, more evenly distributed weights by adding a penalty based on the square of the coefficients.
• none: no regularization.

Examples

var contains = require( '@stdlib/array-base-assert-contains' ).factory;
var penalties = require( '@stdlib/ml-base-sgd-penalties' );

var isPenalty = contains( penalties() );

var bool = isPenalty( 'l1' );
// returns true

bool = isPenalty( 'l2' );
// returns true

bool = isPenalty( 'beep' );
// returns false

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C APIs

Usage

#include "stdlib/ml/base/sgd/penalties.h"

STDLIB_ML_SGD_PENALTY

An enumeration of SGD penalties with the following fields:

• STDLIB_ML_SGD_ELASTICNET: regularization method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.
• STDLIB_ML_SGD_L1: L1 regularization (also called LASSO) leads to sparse models by adding a penalty based on the absolute value of coefficients.
• STDLIB_ML_SGD_L2: L2 regularization (also called ridge regression) encourages smaller, more evenly distributed weights by adding a penalty based on the square of the coefficients.
• STDLIB_ML_SGD_NONE: no regularization.

#include "stdlib/ml/base/sgd/penalties.h"

const enum STDLIB_ML_SGD_PENALTY v = STDLIB_ML_SGD_ELASTICNET;

Notes

• Enumeration constants should be considered opaque values, and one should not rely on specific integer values.

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Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.