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Stdlib Authors","url":"https://github.com/stdlib-js/stdlib/graphs/contributors"},"license":"Apache-2.0","homepage":"https://stdlib.io","keywords":["stdlib","stdml","statistics","stats","mathematics","math","machine learning","ml","incremental","regression","prediction","online","algorithm","gradient descent"],"repository":{"type":"git","url":"git://github.com/stdlib-js/ml-incr-sgd-regression.git"},"description":"Online regression via stochastic gradient descent (SGD).","contributors":[{"name":"The Stdlib Authors","url":"https://github.com/stdlib-js/stdlib/graphs/contributors"}],"maintainers":[{"name":"stdlib-bot","email":"kgryte+stdlibbot@gmail.com"},{"name":"kgryte","email":"kgryte@gmail.com"},{"name":"planeshifter","email":"pgb@andrew.cmu.edu"},{"name":"rreusser","email":"rsreusser@gmail.com"}],"readme":"<!--\n\n@license Apache-2.0\n\nCopyright (c) 2018 The Stdlib Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n-->\n\n\n<details>\n  <summary>\n    About stdlib...\n  </summary>\n  <p>We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.</p>\n  <p>The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.</p>\n  <p>When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.</p>\n  <p>To join us in bringing numerical computing to the web, get started by checking us out on <a href=\"https://github.com/stdlib-js/stdlib\">GitHub</a>, and please consider <a href=\"https://opencollective.com/stdlib\">financially supporting stdlib</a>. We greatly appreciate your continued support!</p>\n</details>\n\n# Online Regression\n\n[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->\n\n> Online regression via [Stochastic Gradient Descent][stochastic-gradient-descent].\n\n<section class=\"installation\">\n\n## Installation\n\n```bash\nnpm install @stdlib/ml-incr-sgd-regression\n```\n\n</section>\n\n<section class=\"usage\">\n\n## Usage\n\n```javascript\nvar incrSGDRegression = require( '@stdlib/ml-incr-sgd-regression' );\n```\n\n#### incrSGDRegression( \\[options] )\n\nCreates an online linear regression model fitted via [stochastic gradient descent][stochastic-gradient-descent]. The module performs [L2 regularization][l2-regularization] of the model coefficients, shrinking them towards zero by penalizing the squared [euclidean norm][euclidean-norm] of the coefficients.\n\n```javascript\nvar randu = require( '@stdlib/random-base-randu' );\nvar normal = require( '@stdlib/random-base-normal' );\nvar accumulator = incrSGDRegression();\n\nvar x1;\nvar x2;\nvar i;\nvar y;\n\n// Update model as data comes in...\nfor ( i = 0; i < 100000; i++ ) {\n    x1 = randu();\n    x2 = randu();\n    y = (3.0 * x1) + (-3.0 * x2) + 2.0 + normal( 0.0, 1.0 );\n    accumulator( [ x1, x2 ], y );\n}\n```\n\nThe function accepts the following `options`:\n\n-   **learningRate**: `string` denoting the learning rate to use. Can be `constant`, `pegasos` or `basic`. Default: `basic`.\n-   **loss**: `string` denoting the loss function to use. Can be `squaredError`, `epsilonInsensitive` or `huber`. Default: `squaredError`.\n-   **epsilon**: insensitivity parameter. Default: `0.1`.\n-   **lambda**: regularization parameter. Default: `1e-3`.\n-   **eta0**: constant learning rate. Default: `0.02`.\n-   **intercept**: `boolean` indicating whether to include an intercept. Default: `true`.\n\n<!-- run-disable -->\n\n```javascript\nvar accumulator = incrSGDRegression({\n    'loss': 'squaredError',\n    'lambda': 1e-4\n});\n```\n\nThe `learningRate` decides how fast or slow the weights will be updated towards the optimal weights. Let `i` denote the current iteration of the algorithm (i.e. the number of data points having arrived). The possible learning rates are:\n\n|      Option     |        Definition       |\n| :-------------: | :---------------------: |\n| basic (default) | 1000.0 / ( i + 1000.0 ) |\n|     constant    |           eta0          |\n|     pegasos     |  1.0 / ( lambda \\* i )  |\n\nThe used loss function is specified via the `loss` option. The available options are:\n\n-   **epsilonInsensitive**: Penalty is the absolute value of the error whenever the absolute error exceeds epsilon and zero otherwise.\n-   **huber**: Squared-error loss for observations with error smaller than epsilon in magnitude, linear loss otherwise. Should be used in order to decrease the influence of outliers on the model fit.\n-   **squaredError**: Squared error loss, i.e. the squared difference of the observed and fitted values.\n\nThe `lambda` parameter determines the amount of shrinkage inflicted on the model coefficients:\n\n```javascript\nvar createRandom = require( '@stdlib/random-base-randu' ).factory;\n\nvar accumulator;\nvar coefs;\nvar opts;\nvar rand;\nvar x1;\nvar x2;\nvar i;\nvar y;\n\nopts = {\n    'seed': 23\n};\nrand = createRandom( opts );\n\naccumulator = incrSGDRegression({\n    'lambda': 1e-5\n});\n\nfor ( i = 0; i < 100; i++ ) {\n    x1 = rand();\n    x2 = rand();\n    y = (3.0 * x1) + (-3.0 * x2) + 2.0;\n    accumulator( [ x1, x2 ], y );\n}\n\ncoefs = accumulator.coefs;\n// returns [ ~3.007, ~-3.002, ~2 ]\n\nrand = createRandom( opts );\naccumulator = incrSGDRegression({\n    'lambda': 1e-2\n});\n\nfor ( i = 0; i < 100; i++ ) {\n    x1 = rand();\n    x2 = rand();\n    y = (3.0 * x1) + (-3.0 * x2) + 2.0;\n    accumulator( [ x1, x2 ], y );\n}\n\ncoefs = accumulator.coefs;\n// returns [ ~2.893, ~-2.409, ~1.871 ]\n```\n\nHigher values of `lambda` reduce the variance of the model coefficient estimates at the expense of introducing bias.\n\nBy default, the model contains an `intercept` term. To omit the `intercept`, set the corresponding option to `false`:\n\n```javascript\nvar accumulator = incrSGDRegression({\n    'intercept': false\n});\naccumulator( [ 1.4, 0.5 ], 2.0 );\n\nvar dim = accumulator.coefs.length;\n// returns 2\n\naccumulator = incrSGDRegression();\naccumulator( [ 1.4, 0.5 ], 2.0 );\n\ndim = accumulator.coefs.length;\n// returns 3\n```\n\nIf `intercept` is `true`, an element equal to one is implicitly added to each `x` vector. Hence, this module performs regularization of the intercept term.\n\n#### accumulator( x, y )\n\nUpdates the model coefficients in light of incoming data. `y` must be a numeric response value, `x` a `numeric array` of predictors. The number of predictors is decided upon first invocation of this method. All subsequent calls must supply `x` vectors of the same dimensionality.\n\n<!-- run-disable -->\n\n<!-- eslint-disable stdlib/doctest -->\n\n```javascript\naccumulator( [ 1.0, 0.0 ], 5.0 );\n```\n\n#### accumulator.predict( x )\n\nPredicts the response for a new feature vector `x`, where `x` must be a `numeric array` of predictors. Given feature vector `x = [x_0, x_1, ...]` and model coefficients `c = [c_0, c_1, ...]`, the prediction is equal to `x_0*c_0 + x_1*c_1 + ... + c_intercept`.\n\n<!-- run-disable -->\n\n<!-- eslint-disable stdlib/doctest -->\n\n```javascript\nvar yhat = accumulator.predict( [ 0.5, 2.0 ] );\n// returns <number>\n```\n\n#### accumulator.coefs\n\nGetter for the model coefficients / feature weights stored in an `array`. The coefficients are ordered as `[c_0, c_1,..., c_intercept]`, where `c_0` corresponds to the first feature in `x` and so on.\n\n<!-- run-disable -->\n\n<!-- eslint-disable stdlib/doctest -->\n\n```javascript\nvar coefs = accumulator.coefs;\n// returns <Array>\n```\n\n</section>\n\n<!-- /.usage -->\n\n<section class=\"notes\">\n\n## Notes\n\n-   Stochastic gradient descent is sensitive to the scaling of the features. One is best advised to either scale each attribute to `[0,1]` or `[-1,1]` or to transform them into z-scores with zero mean and unit variance. One should keep in mind that the same scaling has to be applied to test vectors in order to obtain accurate predictions.\n-   Since this module performs regularization of the intercept term, scaling the response variable to an appropriate scale is also highly recommended.\n\n</section>\n\n<!-- /.notes -->\n\n<section class=\"examples\">\n\n## Examples\n\n<!-- eslint no-undef: \"error\" -->\n\n```javascript\nvar randu = require( '@stdlib/random-base-randu' );\nvar normal = require( '@stdlib/random-base-normal' );\nvar incrSGDRegression = require( '@stdlib/ml-incr-sgd-regression' );\n\nvar accumulator;\nvar rnorm;\nvar x1;\nvar x2;\nvar y;\nvar i;\n\nrnorm = normal.factory( 0.0, 1.0 );\n\n// Create model:\naccumulator = incrSGDRegression({\n    'lambda': 1e-7,\n    'loss': 'squaredError',\n    'intercept': true\n});\n\n// Update model as data comes in...\nfor ( i = 0; i < 10000; i++ ) {\n    x1 = randu();\n    x2 = randu();\n    y = (3.0 * x1) + (-3.0 * x2) + 2.0 + rnorm();\n    accumulator( [ x1, x2 ], y );\n}\n\n// Extract model coefficients:\nconsole.log( accumulator.coefs );\n\n// Predict new observations:\nconsole.log( 'y_hat = %d; x1 = %d; x2 = %d', accumulator.predict( [0.9, 0.1] ), 0.9, 0.1 );\nconsole.log( 'y_hat = %d; x1 = %d; x2 = %d', accumulator.predict( [0.1, 0.9] ), 0.1, 0.9 );\nconsole.log( 'y_hat = %d; x1 = %d; x2 = %d', accumulator.predict( [0.9, 0.9] ), 0.9, 0.9 );\n```\n\n</section>\n\n<!-- /.examples -->\n\n<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->\n\n<section class=\"related\">\n\n* * *\n\n## See Also\n\n-   <span class=\"package-name\">[`@stdlib/ml-incr/binary-classification`][@stdlib/ml/incr/binary-classification]</span><span class=\"delimiter\">: </span><span class=\"description\">incrementally perform binary classification using stochastic gradient descent (SGD).</span>\n\n</section>\n\n<!-- /.related -->\n\n<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->\n\n\n<section class=\"main-repo\" >\n\n* * *\n\n## Notice\n\nThis package is part of [stdlib][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.\n\nFor more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].\n\n#### Community\n\n[![Chat][chat-image]][chat-url]\n\n---\n\n## License\n\nSee [LICENSE][stdlib-license].\n\n\n## Copyright\n\nCopyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].\n\n</section>\n\n<!-- /.stdlib -->\n\n<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->\n\n<section class=\"links\">\n\n[npm-image]: http://img.shields.io/npm/v/@stdlib/ml-incr-sgd-regression.svg\n[npm-url]: https://npmjs.org/package/@stdlib/ml-incr-sgd-regression\n\n[test-image]: https://github.com/stdlib-js/ml-incr-sgd-regression/actions/workflows/test.yml/badge.svg?branch=v0.2.3\n[test-url]: https://github.com/stdlib-js/ml-incr-sgd-regression/actions/workflows/test.yml?query=branch:v0.2.3\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/ml-incr-sgd-regression/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/ml-incr-sgd-regression?branch=main\n\n<!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/ml-incr-sgd-regression.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/ml-incr-sgd-regression/main\n\n-->\n\n[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg\n[chat-url]: https://stdlib.zulipchat.com\n\n[stdlib]: https://github.com/stdlib-js/stdlib\n\n[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors\n\n[umd]: https://github.com/umdjs/umd\n[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules\n\n[deno-url]: https://github.com/stdlib-js/ml-incr-sgd-regression/tree/deno\n[deno-readme]: https://github.com/stdlib-js/ml-incr-sgd-regression/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/ml-incr-sgd-regression/tree/umd\n[umd-readme]: https://github.com/stdlib-js/ml-incr-sgd-regression/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/ml-incr-sgd-regression/tree/esm\n[esm-readme]: https://github.com/stdlib-js/ml-incr-sgd-regression/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/ml-incr-sgd-regression/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/ml-incr-sgd-regression/main/LICENSE\n\n[euclidean-norm]: https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm\n\n[l2-regularization]: https://en.wikipedia.org/wiki/Tikhonov_regularization\n\n[stochastic-gradient-descent]: https://en.wikipedia.org/wiki/Stochastic_gradient_descent\n\n<!-- <related-links> -->\n\n[@stdlib/ml/incr/binary-classification]: https://www.npmjs.com/package/@stdlib/ml-incr-binary-classification\n\n<!-- </related-links> -->\n\n</section>\n\n<!-- /.links -->\n","readmeFilename":"README.md"}