feat: add ml/base/sgd/params/*

lib/node_modules/@stdlib/ml/base/sgd/params/factory/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 The Stdlib Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+-->
+
+# paramsFactory
+
+> Create a new constructor for creating an SGD trainer parameters object.
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- Package usage documentation. -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var paramsFactory = require( '@stdlib/ml/base/sgd/params/factory' );
+```
+
+#### paramsFactory( dtype )
+
+Returns a new constructor for creating an SGD trainer parameters object.
+
+```javascript
+var Params = paramsFactory( 'float64' );
+// returns <Function>
+
+var r = new Params();
+// returns <Params>
+```
+
+The function supports the following parameters:
+
+-   **dtype**: floating-point data type for storing floating-point parameters. Must be either `'float64'` or `'float32'`.
+
+A returned [`struct`][@stdlib/dstructs/struct] constructor supports the following fields:
+
+-   **penalty**: [regularization function][@stdlib/ml/base/sgd/penalties].
+
+-   **penaltyParams**: parameters specific to the regularization function being used. Must be an array having length `2`, with any unused elements set to zero. The expected array contents depend on `penalty`:
+
+    -   **l1**: `[ lambda, 0.0 ]`
+    -   **l2**: `[ lambda, 0.0 ]`
+    -   **elasticnet**: `[ lambda, l1Ratio ]`
+    -   **none**: `[ 0.0, 0.0 ]` (unused)
+
+    where
+
+    -   **lambda**: regularization parameter which determines the amount of shrinkage inflicted on the model coefficients.
+    -   **l1Ratio**: mixing parameter on the interval `[0,1]` which determines the relative contribution of the L1 and L2 penalties.
+
+-   **learningRate**: [learning rate scheduler][@stdlib/ml/base/sgd/learning-rates].
+
+-   **learningRateParams**: parameters specific to the learning rate scheduler being used. Must be an array having length `2`, with any unused elements set to zero. The expected array contents depend on `learningRate`:
+
+    -   **basic**: `[ 0.0, 0.0 ]` (unused)
+    -   **constant**: `[ eta0, 0.0 ]`
+    -   **invscaling**: `[ eta0, powerT ]`
+    -   **pegasos**: `[ lambda, 0.0 ]`
+
+    where
+
+    -   **eta0**: initial learning rate.
+    -   **powerT**: exponent controlling how quickly the learning rate decreases.
+    -   **lambda**: regularization parameter.
+
+-   **lossFunction**: [loss function][@stdlib/ml/base/sgd/loss-functions].
+
+-   **lossFunctionParams**: parameters specific to the loss function being used. Must be an array having length `1`. The expected array contents depend on `lossFunction`:
+
+    -   **epsilon-insensitive**: `[ epsilon ]`
+    -   **squared-epsilon-insensitive**: `[ epsilon ]`
+    -   **huber**: `[ threshold ]`
+    -   all other loss functions: `[ 0.0 ]` (unused)
+
+    where
+
+    -   **epsilon**: insensitivity parameter (i.e., errors whose absolute value is less than `epsilon` incur no penalty).
+    -   **threshold**: error magnitude at which the loss transitions from squared-error loss to linear loss.
+
+-   **fitIntercept**: boolean indicating whether to include an intercept. If `true`, an element equal to one is implicitly added to each provided feature vector. If `false`, the model assumes that feature vectors are already centered.
+
+-   **intercept**: initial intercept value. Only applicable when `fitIntercept` is `true`.
+
+-   **maxIter**: maximum number of iterations to run.
+
+</section>
+
+<!-- /.usage -->
+
+<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+## Notes
+
+-   A parameters object is a [`struct`][@stdlib/dstructs/struct] providing a fixed-width composite data structure for storing SGD trainer parameters and providing an ABI-stable data layout for JavaScript-C interoperation.
+
+</section>
+
+<!-- /.notes -->
+
+<!-- Package usage examples. -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Float32Array = require( '@stdlib/array/float32' );
+var paramsFactory = require( '@stdlib/ml/base/sgd/params/factory' );
+
+var Params = paramsFactory( 'float64' );
+var params = new Params({
+    'penaltyParams': new Float64Array( [ 2.5, 0.0 ] ),
+    'learningRateParams': new Float64Array( [ 0.01, 0.0 ] ),
+    'lossFunctionParams': new Float64Array( [ 0.0 ] ),
+    'intercept': 0.0,
+    'maxIter': 500,
+    'penalty': 'l2',
+    'learningRate': 'constant',
+    'lossFunction': 'hinge',
+    'fitIntercept': true
+});
+
+var str = params.toString({
+    'format': 'linear'
+});
+console.log( str );
+
+Params = paramsFactory( 'float32' );
+params = new Params({
+    'penaltyParams': new Float32Array( [ 2.5, 0.0 ] ),
+    'learningRateParams': new Float32Array( [ 0.01, 0.0 ] ),
+    'lossFunctionParams': new Float32Array( [ 0.0 ] ),
+    'intercept': 0.0,
+    'maxIter': 500,
+    'penalty': 'l2',
+    'learningRate': 'constant',
+    'lossFunction': 'hinge',
+    'fitIntercept': true
+});
+
+str = params.toString({
+    'format': 'linear'
+});
+console.log( str );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- Section to include cited references. If references are included, add a horizontal rule *before* the section. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="references">
+
+</section>
+
+<!-- /.references -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+[@stdlib/dstructs/struct]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/dstructs/struct
+
+[@stdlib/ml/base/sgd/penalties]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ml/base/sgd/penalties
+
+[@stdlib/ml/base/sgd/learning-rates]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ml/base/sgd/learning-rates
+
+[@stdlib/ml/base/sgd/loss-functions]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ml/base/sgd/loss-functions
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/ml/base/sgd/params/factory/benchmark/benchmark.js

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var isFunction = require( '@stdlib/assert/is-function' );
+var isObject = require( '@stdlib/assert/is-object' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var factory = require( './../lib' );
+
+
+// MAIN //
+
+bench( pkg, function benchmark( b ) {
+	var values;
+	var v;
+	var i;
+
+	values = [
+		'float64',
+		'float32'
+	];
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = factory( values[ i%values.length ] );
+		if ( typeof v !== 'function' ) {
+			b.fail( 'should return a function' );
+		}
+	}
+	b.toc();
+	if ( !isFunction( v ) ) {
+		b.fail( 'should return a function' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});
+
+bench( format( '%s::constructor,new', pkg ), function benchmark( b ) {
+	var values;
+	var v;
+	var i;
+
+	values = [
+		factory( 'float64' ),
+		factory( 'float32' )
+	];
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = new ( values[ i%values.length ] )();
+		if ( typeof v !== 'object' ) {
+			b.fail( 'should return an object' );
+		}
+	}
+	b.toc();
+	if ( !isObject( v ) ) {
+		b.fail( 'should return an object' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});
+
+bench( format( '%s::constructor,no_new', pkg ), function benchmark( b ) {
+	var values;
+	var v;
+	var i;
+
+	values = [
+		factory( 'float64' ),
+		factory( 'float32' )
+	];
+
+	b.tic();
+	for ( i = 0; i < b.iterations; i++ ) {
+		v = values[ i%values.length ]();
+		if ( typeof v !== 'object' ) {
+			b.fail( 'should return an object' );
+		}
+	}
+	b.toc();
+	if ( !isObject( v ) ) {
+		b.fail( 'should return an object' );
+	}
+	b.pass( 'benchmark finished' );
+	b.end();
+});

lib/node_modules/@stdlib/ml/base/sgd/params/factory/docs/repl.txt

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+
+{{alias}}( dtype )
+    Returns a constructor for creating an SGD trainer parameters object.
+
+    Parameters
+    ----------
+    dtype: string
+        Floating-point data type for storing floating-point parameters.
+
+    Returns
+    -------
+    fcn: Function
+        Constructor.
+
+    Examples
+    --------
+    > var P = {{alias}}( 'float64' );
+    > var p = new P();
+    > p.toString()
+    <string>
+
+    See Also
+    --------
+