diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/README.md b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/README.md
new file mode 100644
index 000000000000..9c966f7e84cd
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/README.md
@@ -0,0 +1,213 @@
+
+
+# structFactory
+
+> Create a new [`struct`][@stdlib/dstructs/struct] constructor tailored to a specified floating-point data type.
+
+
+
+
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var structFactory = require( '@stdlib/ml/base/sgd/params/struct-factory' );
+```
+
+#### structFactory( dtype )
+
+Returns a new [`struct`][@stdlib/dstructs/struct] constructor tailored to a specified floating-point data type.
+
+```javascript
+var Struct = structFactory( 'float64' );
+// returns
+
+var s = new Struct();
+// returns
+```
+
+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.
+
+
+
+
+
+
+
+
+
+## Notes
+
+- A [`struct`][@stdlib/dstructs/struct] provides a fixed-width composite data structure for storing SGD trainer parameters and provides an ABI-stable data layout for JavaScript-C interoperation.
+- Each parameter array is a fixed-length array which is large enough to accommodate the option requiring the most parameters (`penaltyParams`: `2`, `learningRateParams`: `2`, `lossFunctionParams`: `1`). Accordingly, one must provide an array having the expected length, with any unused elements set to zero (e.g., `[ lambda, 0.0 ]`), as providing an array having an unexpected length, including an empty array, raises an exception. As `struct` instances are zero-filled upon initialization, one may omit an array when the corresponding option requires no parameters.
+- Consumers should only read as many elements as are applicable to the corresponding penalty, learning rate scheduler, or loss function, with any remaining elements being unused.
+
+
+
+
+
+
+
+
+
+## Examples
+
+
+
+```javascript
+var resolveLREnum = require( '@stdlib/ml/base/sgd/learning-rate-resolve-enum' );
+var resolveLossFunctionEnum = require( '@stdlib/ml/base/sgd/loss-function-resolve-enum' );
+var resolvePenaltyEnum = require( '@stdlib/ml/base/sgd/penalty-resolve-enum' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Float32Array = require( '@stdlib/array/float32' );
+var structFactory = require( '@stdlib/ml/base/sgd/params/struct-factory' );
+
+var Struct = structFactory( 'float64' );
+var params = new Struct({
+ 'penaltyParams': new Float64Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float64Array( [ 0.01, 0.0 ] ),
+ 'intercept': 0.0,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+});
+
+var str = params.toString({
+ 'format': 'linear'
+});
+console.log( str );
+
+Struct = structFactory( 'float32' );
+params = new Struct({
+ 'penaltyParams': new Float32Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float32Array( [ 0.01, 0.0 ] ),
+ 'intercept': 0.0,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+});
+
+str = params.toString({
+ 'format': 'linear'
+});
+console.log( str );
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[@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
+
+
+
+
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/benchmark/benchmark.js b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/benchmark/benchmark.js
new file mode 100644
index 000000000000..f06fde75adee
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/benchmark/benchmark.js
@@ -0,0 +1,54 @@
+/**
+* @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 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();
+});
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/repl.txt b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/repl.txt
new file mode 100644
index 000000000000..44d00f06a186
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/repl.txt
@@ -0,0 +1,24 @@
+
+{{alias}}( dtype )
+ Returns a new struct constructor tailored to a specified floating-point data
+ type.
+
+ Parameters
+ ----------
+ dtype: string
+ Floating-point data type for storing floating-point parameters.
+
+ Returns
+ -------
+ fcn: Function
+ Struct constructor.
+
+ Examples
+ --------
+ > var S = {{alias}}( 'float64' );
+ > var r = new S();
+ > r.toString()
+
+
+ See Also
+ --------
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/index.d.ts b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/index.d.ts
new file mode 100644
index 000000000000..099128ed58f4
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/index.d.ts
@@ -0,0 +1,240 @@
+/*
+* @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.
+*/
+
+// TypeScript Version: 4.1
+
+/**
+* Interface describing SGD trainer parameters.
+*/
+interface Params {
+ /**
+ * Parameters specific to the regularization function being used.
+ *
+ * ## Notes
+ *
+ * - Must be an array having length `2`, with any unused elements set to zero. The expected array contents depend on the 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.
+ */
+ penaltyParams?: T;
+
+ /**
+ * Parameters specific to the learning rate scheduler being used.
+ *
+ * ## Notes
+ *
+ * - Must be an array having length `2`, with any unused elements set to zero. The expected array contents depend on the learning rate scheduler:
+ *
+ * - **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.
+ */
+ learningRateParams?: T;
+
+ /**
+ * Parameters specific to the loss function being used.
+ *
+ * ## Notes
+ *
+ * - Must be an array having length `1`. The expected array contents depend on the loss function:
+ *
+ * - **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.
+ */
+ lossFunctionParams?: T;
+
+ /**
+ * Initial intercept value.
+ *
+ * ## Notes
+ *
+ * - Only applicable when `fitIntercept` is `true`.
+ */
+ intercept?: number;
+
+ /**
+ * Maximum number of iterations to run.
+ */
+ maxIter?: number;
+
+ /**
+ * Regularization function.
+ */
+ penalty?: number;
+
+ /**
+ * Learning rate scheduler.
+ */
+ learningRate?: number;
+
+ /**
+ * Loss function.
+ */
+ lossFunction?: number;
+
+ /**
+ * Boolean indicating whether to include intercept.
+ *
+ * ## Notes
+ *
+ * - 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.
+ */
+ fitIntercept?: boolean;
+}
+
+/**
+* Interface describing a struct data structure.
+*/
+declare class Struct {
+ /**
+ * Struct constructor.
+ *
+ * @param arg - buffer or data object
+ * @param byteOffset - byte offset
+ * @param byteLength - maximum byte length
+ * @returns struct
+ */
+ constructor( arg?: ArrayBuffer | Params, byteOffset?: number, byteLength?: number );
+
+ /**
+ * Parameters specific to the regularization function being used.
+ */
+ penaltyParams: T;
+
+ /**
+ * Parameters specific to the learning rate scheduler being used.
+ */
+ learningRateParams: T;
+
+ /**
+ * Parameters specific to the loss function being used.
+ */
+ lossFunctionParams: T;
+
+ /**
+ * Initial intercept value.
+ */
+ intercept: number;
+
+ /**
+ * Maximum number of iterations to run.
+ */
+ maxIter: number;
+
+ /**
+ * Regularization function.
+ */
+ penalty: number;
+
+ /**
+ * Learning rate scheduler.
+ */
+ learningRate: number;
+
+ /**
+ * Loss function.
+ */
+ lossFunction: number;
+
+ /**
+ * Boolean indicating whether to include intercept.
+ */
+ fitIntercept: boolean;
+}
+
+/**
+* Interface defining a struct constructor which is both "newable" and "callable".
+*/
+interface StructConstructor {
+ /**
+ * Struct constructor.
+ *
+ * @param arg - buffer or data object
+ * @param byteOffset - byte offset
+ * @param byteLength - maximum byte length
+ * @returns struct
+ */
+ new( arg?: ArrayBuffer | Params, byteOffset?: number, byteLength?: number ): Struct;
+
+ /**
+ * Struct constructor.
+ *
+ * @param arg - buffer or data object
+ * @param byteOffset - byte offset
+ * @param byteLength - maximum byte length
+ * @returns struct
+ */
+ ( arg?: ArrayBuffer | Params, byteOffset?: number, byteLength?: number ): Struct;
+}
+
+/**
+* Returns a new struct constructor tailored to a specified floating-point data type.
+*
+* @param dtype - floating-point data type for storing floating-point params
+* @returns struct constructor
+*
+* @example
+* var Struct = structFactory( 'float64' );
+* // returns
+*
+* var s = new Struct();
+* // returns
+*/
+declare function structFactory( dtype: 'float64' ): StructConstructor;
+
+/**
+* Returns a new struct constructor tailored to a specified floating-point data type.
+*
+* @param dtype - floating-point data type for storing floating-point params
+* @returns struct constructor
+*
+* @example
+* var Struct = structFactory( 'float32' );
+* // returns
+*
+* var s = new Struct();
+* // returns
+*/
+declare function structFactory( dtype: 'float32' ): StructConstructor;
+
+
+// EXPORTS //
+
+export = structFactory;
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/test.ts b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/test.ts
new file mode 100644
index 000000000000..1857d7901cfb
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/docs/types/test.ts
@@ -0,0 +1,54 @@
+/*
+* @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.
+*/
+
+import structFactory = require( './index' );
+
+
+// TESTS //
+
+// The function returns a function...
+{
+ structFactory( 'float64' ); // $ExpectType StructConstructor
+ structFactory( 'float32' ); // $ExpectType StructConstructor
+}
+
+// The compiler throws an error if not provided a supported data type...
+{
+ structFactory( 10 ); // $ExpectError
+ structFactory( true ); // $ExpectError
+ structFactory( false ); // $ExpectError
+ structFactory( null ); // $ExpectError
+ structFactory( undefined ); // $ExpectError
+ structFactory( [] ); // $ExpectError
+ structFactory( {} ); // $ExpectError
+ structFactory( ( x: number ): number => x ); // $ExpectError
+}
+
+// The function returns a function which returns a struct object...
+{
+ const Struct = structFactory( 'float64' );
+
+ // eslint-disable-next-line @typescript-eslint/no-unused-vars
+ const s1 = new Struct( new ArrayBuffer( 92 ) ); // $ExpectType Struct
+
+ // eslint-disable-next-line @typescript-eslint/no-unused-vars
+ const s2 = new Struct( new ArrayBuffer( 100 ), 8 ); // $ExpectType Struct
+
+ // eslint-disable-next-line @typescript-eslint/no-unused-vars
+ const s3 = new Struct( new ArrayBuffer( 100 ), 8, 92 ); // $ExpectType Struct
+}
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/examples/index.js b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/examples/index.js
new file mode 100644
index 000000000000..db4606f2bc44
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/examples/index.js
@@ -0,0 +1,60 @@
+/**
+* @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';
+
+var resolveLREnum = require( '@stdlib/ml/base/sgd/learning-rate-resolve-enum' );
+var resolveLossFunctionEnum = require( '@stdlib/ml/base/sgd/loss-function-resolve-enum' );
+var resolvePenaltyEnum = require( '@stdlib/ml/base/sgd/penalty-resolve-enum' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Float32Array = require( '@stdlib/array/float32' );
+var structFactory = require( './../lib' );
+
+var Struct = structFactory( 'float64' );
+var params = new Struct({
+ 'penaltyParams': new Float64Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float64Array( [ 0.01, 0.0 ] ),
+ 'intercept': 0.0,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+});
+
+var str = params.toString({
+ 'format': 'linear'
+});
+console.log( str );
+
+Struct = structFactory( 'float32' );
+params = new Struct({
+ 'penaltyParams': new Float32Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float32Array( [ 0.01, 0.0 ] ),
+ 'intercept': 0.0,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+});
+
+str = params.toString({
+ 'format': 'linear'
+});
+console.log( str );
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/index.js b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/index.js
new file mode 100644
index 000000000000..578ebbe3aaed
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/index.js
@@ -0,0 +1,43 @@
+/**
+* @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';
+
+/**
+* Create a new struct constructor tailored to a specified floating-point data type.
+*
+* @module @stdlib/ml/base/sgd/params/struct-factory
+*
+* @example
+* var structFactory = require( '@stdlib/ml/base/sgd/params/struct-factory' );
+*
+* var Struct = structFactory( 'float64' );
+* // returns
+*
+* var s = new Struct();
+* // returns
+*/
+
+// MODULES //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/main.js b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/main.js
new file mode 100644
index 000000000000..00504531671d
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/lib/main.js
@@ -0,0 +1,111 @@
+/**
+* @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 struct = require( '@stdlib/dstructs/struct' );
+
+
+// MAIN //
+
+/**
+* Returns a new struct constructor tailored to a specified floating-point data type.
+*
+* ## Notes
+*
+* - Each parameter list is a fixed-length array which is zero-filled upon initialization. Consumers should only read as many elements as are applicable to the corresponding penalty, learning rate scheduler, or loss function, with any remaining elements being unused.
+*
+* @param {string} dtype - floating-point data type
+* @returns {Function} struct constructor
+*
+* @example
+* var Struct = factory( 'float64' );
+* // returns
+*
+* var s = new Struct();
+* // returns
+*/
+function factory( dtype ) {
+ var schema = [
+ {
+ 'name': 'penaltyParams',
+ 'description': 'parameters specific to the regularization function being used',
+ 'type': dtype,
+ 'length': 2,
+ 'castingMode': 'mostly-safe'
+ },
+ {
+ 'name': 'learningRateParams',
+ 'description': 'parameters specific to the learning rate scheduler being used',
+ 'type': dtype,
+ 'length': 2,
+ 'castingMode': 'mostly-safe'
+ },
+ {
+ 'name': 'lossFunctionParams',
+ 'description': 'parameters specific to the loss function being used',
+ 'type': dtype,
+ 'length': 1,
+ 'castingMode': 'mostly-safe'
+ },
+ {
+ 'name': 'intercept',
+ 'description': 'initial intercept value',
+ 'type': dtype,
+ 'castingMode': 'mostly-safe'
+ },
+ {
+ 'name': 'maxIter',
+ 'description': 'maximum number of iterations to run',
+ 'type': 'int32',
+ 'castingMode': 'mostly-safe'
+ },
+ {
+ 'name': 'penalty',
+ 'description': 'regularization function to be used',
+ 'type': 'int8',
+ 'castingMode': 'none'
+ },
+ {
+ 'name': 'learningRate',
+ 'description': 'learning rate scheduler to be used',
+ 'type': 'int8',
+ 'castingMode': 'none'
+ },
+ {
+ 'name': 'lossFunction',
+ 'description': 'loss function to be used',
+ 'type': 'int8',
+ 'castingMode': 'none'
+ },
+ {
+ 'name': 'fitIntercept',
+ 'description': 'boolean indicating whether to include intercept',
+ 'type': 'bool',
+ 'castingMode': 'none'
+ }
+ ];
+ return struct( schema );
+}
+
+
+// EXPORTS //
+
+module.exports = factory;
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/package.json b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/package.json
new file mode 100644
index 000000000000..089cf5b84b66
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/package.json
@@ -0,0 +1,68 @@
+{
+ "name": "@stdlib/ml/base/sgd/params/struct-factory",
+ "version": "0.0.0",
+ "description": "Create a new struct constructor tailored to a specified floating-point data type.",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "lib": "./lib",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "ml",
+ "machine",
+ "learning",
+ "sgd",
+ "stochastic gradient descent",
+ "trainer",
+ "utilities",
+ "utility",
+ "utils",
+ "util",
+ "struct",
+ "params",
+ "parameters"
+ ],
+ "__stdlib__": {}
+}
diff --git a/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/test/test.js b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/test/test.js
new file mode 100644
index 000000000000..95ab0064b238
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/base/sgd/params/struct-factory/test/test.js
@@ -0,0 +1,219 @@
+/**
+* @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 tape = require( 'tape' );
+var isSameFloat64Array = require( '@stdlib/assert/is-same-float64array' );
+var isSameFloat32Array = require( '@stdlib/assert/is-same-float32array' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Float32Array = require( '@stdlib/array/float32' );
+var resolveLREnum = require( '@stdlib/ml/base/sgd/learning-rate-resolve-enum' );
+var resolveLossFunctionEnum = require( '@stdlib/ml/base/sgd/loss-function-resolve-enum' );
+var resolvePenaltyEnum = require( '@stdlib/ml/base/sgd/penalty-resolve-enum' );
+var f32 = require( '@stdlib/number/float64/base/to-float32' );
+var structFactory = require( './../lib' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof structFactory, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a first argument which is not a supported data type', function test( t ) {
+ var values;
+ var i;
+
+ values = [
+ '5',
+ 5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ structFactory( value );
+ };
+ }
+});
+
+tape( 'the function returns a constructor for creating a fixed-width parameters object (dtype=float64)', function test( t ) {
+ var expected;
+ var actual;
+ var Struct;
+ var lambda;
+ var eta0;
+
+ Struct = structFactory( 'float64' );
+ t.strictEqual( typeof Struct, 'function', 'returns expected value' );
+
+ lambda = 2.5;
+ eta0 = 0.01;
+
+ actual = new Struct({
+ 'penaltyParams': new Float64Array( [ lambda, 0.0 ] ),
+ 'learningRateParams': new Float64Array( [ eta0, 0.0 ] ),
+ 'lossFunctionParams': new Float64Array( [ 0.0 ] ),
+ 'intercept': 0.5,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+ });
+
+ expected = {
+ 'penaltyParams': new Float64Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float64Array( [ 0.01, 0.0 ] ),
+ 'lossFunctionParams': new Float64Array( [ 0.0 ] ),
+ 'intercept': 0.5,
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+ };
+
+ t.strictEqual( actual instanceof Struct, true, 'returns expected value' );
+ t.strictEqual( actual.penalty, expected.penalty, 'returns expected value' );
+ t.strictEqual( actual.learningRate, expected.learningRate, 'returns expected value' );
+ t.strictEqual( actual.lossFunction, expected.lossFunction, 'returns expected value' );
+ t.strictEqual( actual.fitIntercept, expected.fitIntercept, 'returns expected value' );
+ t.strictEqual( actual.intercept, expected.intercept, 'returns expected value' );
+ t.strictEqual( actual.maxIter, expected.maxIter, 'returns expected value' );
+ t.strictEqual( isSameFloat64Array( actual.penaltyParams, expected.penaltyParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat64Array( actual.learningRateParams, expected.learningRateParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat64Array( actual.lossFunctionParams, expected.lossFunctionParams ), true, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function returns a constructor for creating a fixed-width parameters object (dtype=float32)', function test( t ) {
+ var expected;
+ var actual;
+ var Struct;
+ var lambda;
+ var eta0;
+
+ Struct = structFactory( 'float32' );
+ t.strictEqual( typeof Struct, 'function', 'returns expected value' );
+
+ lambda = 2.5;
+ eta0 = 0.01;
+
+ actual = new Struct({
+ 'penaltyParams': new Float32Array( [ lambda, 0.0 ] ),
+ 'learningRateParams': new Float32Array( [ eta0, 0.0 ] ),
+ 'lossFunctionParams': new Float32Array( [ 0.0 ] ),
+ 'intercept': f32( 0.5 ),
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+ });
+
+ expected = {
+ 'penaltyParams': new Float32Array( [ 2.5, 0.0 ] ),
+ 'learningRateParams': new Float32Array( [ 0.01, 0.0 ] ),
+ 'lossFunctionParams': new Float32Array( [ 0.0 ] ),
+ 'intercept': f32( 0.5 ),
+ 'maxIter': 500,
+ 'penalty': resolvePenaltyEnum( 'l2' ),
+ 'learningRate': resolveLREnum( 'constant' ),
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' ),
+ 'fitIntercept': true
+ };
+
+ t.strictEqual( actual instanceof Struct, true, 'returns expected value' );
+ t.strictEqual( actual.penalty, expected.penalty, 'returns expected value' );
+ t.strictEqual( actual.learningRate, expected.learningRate, 'returns expected value' );
+ t.strictEqual( actual.lossFunction, expected.lossFunction, 'returns expected value' );
+ t.strictEqual( actual.fitIntercept, expected.fitIntercept, 'returns expected value' );
+ t.strictEqual( actual.intercept, expected.intercept, 'returns expected value' );
+ t.strictEqual( actual.maxIter, expected.maxIter, 'returns expected value' );
+ t.strictEqual( isSameFloat32Array( actual.penaltyParams, expected.penaltyParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat32Array( actual.learningRateParams, expected.learningRateParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat32Array( actual.lossFunctionParams, expected.lossFunctionParams ), true, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function returns a constructor which zero-fills parameter lists which are not provided', function test( t ) {
+ var expected;
+ var actual;
+ var Struct;
+
+ Struct = structFactory( 'float64' );
+
+ actual = new Struct({
+ 'lossFunction': resolveLossFunctionEnum( 'hinge' )
+ });
+
+ expected = {
+ 'penaltyParams': new Float64Array( [ 0.0, 0.0 ] ),
+ 'learningRateParams': new Float64Array( [ 0.0, 0.0 ] ),
+ 'lossFunctionParams': new Float64Array( [ 0.0 ] )
+ };
+
+ t.strictEqual( isSameFloat64Array( actual.penaltyParams, expected.penaltyParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat64Array( actual.learningRateParams, expected.learningRateParams ), true, 'returns expected value' );
+ t.strictEqual( isSameFloat64Array( actual.lossFunctionParams, expected.lossFunctionParams ), true, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function returns a constructor which throws an error if provided a parameter list having an unexpected length', function test( t ) {
+ var Struct;
+ var values;
+ var i;
+
+ Struct = structFactory( 'float64' );
+
+ values = [
+ new Float64Array( [] ),
+ new Float64Array( [ 2.5 ] ),
+ new Float64Array( [ 2.5, 0.0, 0.0 ] ),
+ new Float64Array( [ 2.5, 0.0, 0.0, 0.0 ] )
+ ];
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided an array having length ' + values[ i ].length );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ return new Struct({
+ 'penaltyParams': value
+ });
+ };
+ }
+});