feat: add ml/strided/dsgd-trainer

lib/node_modules/@stdlib/ml/strided/dsgd-trainer/benchmark/benchmark.js

@@ -0,0 +1,122 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2018 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 uniform = require( '@stdlib/random/array/uniform' );
+var format = require( '@stdlib/string/format' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Params = require( '@stdlib/ml/base/sgd/params/float64' );
+var pkg = require( './../package.json' ).name;
+var dsgdTrainer = require( './../lib/dsgd_trainer.js' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} N - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( N ) {
+	var ws = new Float64Array( N );
+	var x = uniform( N*N, -10.0, 10.0, options );
+	var y = uniform( N, -10.0, 10.0, options );
+	var w = uniform( N, -0.05, 0.05, options );
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var params;
+		var z;
+		var i;
+
+		params = new Params();
+
+		params.penalty = 'l2';
+		params.learningRate = 'constant';
+		params.lossFunction = 'hinge';
+		params.intercept = 0.0;
+		params.maxIter = 500;
+		params.penaltyParams = new Float64Array( [ 2.5, 0.0 ] );
+		params.learningRateParams = new Float64Array( [ 0.01, 0.0 ] );
+		params.lossFunctionParams = new Float64Array( [ 0.0 ] );
+		params.fitIntercept = true;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			z = dsgdTrainer( 'row-major', N, N, x, N, y, 1, w, 1, ws, 1, params );
+			if ( isnan( z[ i%z.length ] ) ) {
+				b.fail( 'should not return NaN' );
+			}
+		}
+		b.toc();
+		if ( isnan( z[ i%z.length ] ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var min;
+	var max;
+	var N;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+		f = createBenchmark( N );
+		bench( format( '%s:size=%d', pkg, N*N ), f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/ml/strided/dsgd-trainer/benchmark/benchmark.native.js

@@ -0,0 +1,127 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 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 resolve = require( 'path' ).resolve;
+var bench = require( '@stdlib/bench' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var format = require( '@stdlib/string/format' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Params = require( '@stdlib/ml/base/sgd/params/float64' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var dsgdTrainer = tryRequire( resolve( __dirname, './../lib/dsgd_trainer.native.js' ) );
+var opts = {
+	'skip': ( dsgdTrainer instanceof Error )
+};
+var options = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} N - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( N ) {
+	var ws = new Float64Array( N );
+	var x = uniform( N*N, -10.0, 10.0, options );
+	var y = uniform( N, -10.0, 10.0, options );
+	var w = uniform( N, -0.05, 0.05, options );
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var params;
+		var z;
+		var i;
+
+		params = new Params();
+
+		params.penalty = 'l2';
+		params.learningRate = 'constant';
+		params.lossFunction = 'hinge';
+		params.intercept = 0.0;
+		params.maxIter = 500;
+		params.penaltyParams = new Float64Array( [ 2.5, 0.0 ] );
+		params.learningRateParams = new Float64Array( [ 0.01, 0.0 ] );
+		params.lossFunctionParams = new Float64Array( [ 0.0 ] );
+		params.fitIntercept = true;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			z = dsgdTrainer( 'row-major', N, N, x, N, y, 1, w, 1, ws, 1, params );
+			if ( isnan( z[ i%z.length ] ) ) {
+				b.fail( 'should not return NaN' );
+			}
+		}
+		b.toc();
+		if ( isnan( z[ i%z.length ] ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var min;
+	var max;
+	var N;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+		f = createBenchmark( N );
+		bench( format( '%s::native:size=%d', pkg, N*N ), opts, f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/ml/strided/dsgd-trainer/benchmark/benchmark.ndarray.js

@@ -0,0 +1,122 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2018 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 uniform = require( '@stdlib/random/array/uniform' );
+var format = require( '@stdlib/string/format' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Params = require( '@stdlib/ml/base/sgd/params/float64' );
+var pkg = require( './../package.json' ).name;
+var dsgdTrainer = require( './../lib/ndarray.js' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} N - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( N ) {
+	var ws = new Float64Array( N );
+	var x = uniform( N*N, -10.0, 10.0, options );
+	var y = uniform( N, -10.0, 10.0, options );
+	var w = uniform( N, -0.05, 0.05, options );
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var params;
+		var z;
+		var i;
+
+		params = new Params();
+
+		params.penalty = 'l2';
+		params.learningRate = 'constant';
+		params.lossFunction = 'hinge';
+		params.intercept = 0.0;
+		params.maxIter = 500;
+		params.penaltyParams = new Float64Array( [ 2.5, 0.0 ] );
+		params.learningRateParams = new Float64Array( [ 0.01, 0.0 ] );
+		params.lossFunctionParams = new Float64Array( [ 0.0 ] );
+		params.fitIntercept = true;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			z = dsgdTrainer( N, N, x, N, 1, 0, y, 1, 0, w, 1, 0, ws, 1, 0, params ); // eslint-disable-line max-len
+			if ( isnan( z[ i%z.length ] ) ) {
+				b.fail( 'should not return NaN' );
+			}
+		}
+		b.toc();
+		if ( isnan( z[ i%z.length ] ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var min;
+	var max;
+	var N;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+		f = createBenchmark( N );
+		bench( format( '%s:ndarray:size=%d', pkg, N*N ), f );
+	}
+}
+
+main();