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bench: add JS benchmarks for `ml/strided/dkmeans-init-plus-plus
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‎lib/node_modules/@stdlib/ml/strided/dkmeans-init-plus-plus/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var Float64Array = require( '@stdlib/array/float64' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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var dkmeansInitPlusPlus = require( './../lib/dkmeans_init_plus_plus.js' );
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// VARIABLES //
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var LAYOUTS = [
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'row-major',
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'column-major'
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];
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {string} order - storage layout
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* @param {PositiveInteger} k - number of clusters
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* @param {PositiveInteger} M - number of data points
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* @param {PositiveInteger} N - number of features
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* @param {string} metric - distance metric
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* @param {PositiveInteger} trials - number of potential centroids per iteration (>= 1)
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* @returns {Function} benchmark function
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*/
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function createBenchmark( order, k, M, N, metric, trials ) {
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var out = new Float64Array( k*N );
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var x = uniform( M*N, -100.0, 100.0, options );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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function benchmark( b ) {
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var c;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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c = dkmeansInitPlusPlus( order, k, M, N, out, N, x, N, metric, trials, 44 ); // eslint-disable-line max-len
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if ( isnan( c[ i%(k*N) ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( c[ 0 ] ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var metrics;
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var trials;
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var min;
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var max;
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var M;
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var N;
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var k;
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var t;
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var m;
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var i;
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var j;
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var f;
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min = 1; // 10^min
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max = 4; // 10^max
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// Benchmark: vary order with defaults k=2, M=100, N=2, metric=sqeuclidean, trials=1...
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for ( i = 0; i <= LAYOUTS.length; i++ ) {
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f = createBenchmark( LAYOUTS[ i ], 2, 100, 2, 'sqeuclidean', 1 );
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bench( format( '%s::vary_M:order=%s,k=2,M=100,N=2,metric=sqeuclidean,trials=1', pkg, LAYOUTS[ i ] ), f );
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}
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// Benchmark: vary number of data points (M) with defaults k=2, N=2, metric=sqeuclidean, trials=1...
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for ( i = min; i <= max; i++ ) {
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M = pow( 10, i );
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f = createBenchmark( 'row-major', 2, M, 2, 'sqeuclidean', 1 );
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bench( format( '%s::vary_M:order=row-major,k=2,M=%d,N=2,metric=sqeuclidean,trials=1', pkg, M ), f );
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}
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// Benchmark: vary number of features (N) with defaults k=2, M=10, metric=sqeuclidean, trials=1...
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for ( i = min; i <= max; i++ ) {
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N = pow( 10, i );
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f = createBenchmark( 'row-major', 2, 10, N, 'sqeuclidean', 1 );
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bench( format( '%s::vary_N:order=row-major,k=2,M=1000,N=%d,metric=sqeuclidean,trials=1', pkg, N ), f );
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}
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// Benchmark: vary number of clusters (k) with defaults M=1000, N=2, metric=sqeuclidean, trials=1...
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for ( i = 1; i <= 4; i++ ) {
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k = pow( 2, i );
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f = createBenchmark( 'row-major', k, 1000, 2, 'sqeuclidean', 1 );
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bench( format( '%s::vary_k:order=row-major,k=%d,M=1000,N=2,metric=sqeuclidean,trials=1', pkg, k ), f );
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}
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// Benchmark: vary number of trials with defaults k=2, M=1000, N=2, metric=sqeuclidean...
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trials = [ 1, 10, 100, 1000 ];
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for ( j = 0; j < trials.length; j++ ) {
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t = trials[ j ];
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f = createBenchmark( 'row-major', 2, 1000, 2, 'sqeuclidean', t );
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bench( format( '%s::vary_trials:order=row-major,k=2,M=1000,N=2,metric=sqeuclidean,trials=%d', pkg, t ), f );
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}
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// Benchmark: vary metric with defaults k=2, M=1000, N=2, trials=1...
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metrics = [ 'sqeuclidean', 'cosine', 'cityblock', 'correlation' ];
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for ( j = 0; j < metrics.length; j++ ) {
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m = metrics[ j ];
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f = createBenchmark( 'row-major', 2, 1000, 2, m, 1 );
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bench( format( '%s::vary_metric:order=row-major,k=2,M=1000,N=2,metric=%s,trials=1', pkg, m ), f );
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}
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}
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main();

‎lib/node_modules/@stdlib/ml/strided/dkmeans-init-plus-plus/benchmark/benchmark.ndarray.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var Float64Array = require( '@stdlib/array/float64' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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var dkmeansInitPlusPlus = require( './../lib/ndarray.js' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {PositiveInteger} k - number of clusters
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* @param {PositiveInteger} M - number of data points
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* @param {PositiveInteger} N - number of features
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* @param {string} metric - distance metric
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* @param {PositiveInteger} trials - number of potential centroids per iteration (>= 1)
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* @returns {Function} benchmark function
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*/
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function createBenchmark( k, M, N, metric, trials ) {
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var out = new Float64Array( k*N );
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var x = uniform( M*N, -100.0, 100.0, options );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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function benchmark( b ) {
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var c;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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c = dkmeansInitPlusPlus( k, M, N, out, N, 1, 0, x, N, 1, 0, metric, trials, 44 ); // eslint-disable-line max-len
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if ( isnan( c[ i%(k*N) ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( c[ 0 ] ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var metrics;
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var trials;
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var min;
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var max;
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var M;
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var N;
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var k;
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var t;
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var m;
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var i;
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var j;
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var f;
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min = 1; // 10^min
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max = 4; // 10^max
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// Benchmark: vary number of data points (M) with defaults k=2, N=2, metric=sqeuclidean, trials=1...
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for ( i = min; i <= max; i++ ) {
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M = pow( 10, i );
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f = createBenchmark( 2, M, 2, 'sqeuclidean', 1 );
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bench( format( '%s::vary_M:k=2,M=%d,N=2,metric=sqeuclidean,trials=1', pkg, M ), f );
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}
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// Benchmark: vary number of features (N) with defaults k=2, M=10, metric=sqeuclidean, trials=1...
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for ( i = min; i <= max; i++ ) {
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N = pow( 10, i );
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f = createBenchmark( 2, 10, N, 'sqeuclidean', 1 );
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bench( format( '%s::vary_N:k=2,M=1000,N=%d,metric=sqeuclidean,trials=1', pkg, N ), f );
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}
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// Benchmark: vary number of clusters (k) with defaults M=1000, N=2, metric=sqeuclidean, trials=1...
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for ( i = 1; i <= 4; i++ ) {
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k = pow( 2, i );
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f = createBenchmark( k, 1000, 2, 'sqeuclidean', 1 );
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bench( format( '%s::vary_k:k=%d,M=1000,N=2,metric=sqeuclidean,trials=1', pkg, k ), f );
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}
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// Benchmark: vary number of trials with defaults k=2, M=1000, N=2, metric=sqeuclidean...
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trials = [ 1, 10, 100 ];
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for ( j = 0; j < trials.length; j++ ) {
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t = trials[ j ];
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f = createBenchmark( 2, 1000, 2, 'sqeuclidean', t );
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bench( format( '%s::vary_trials:k=2,M=1000,N=2,metric=sqeuclidean,trials=%d', pkg, t ), f );
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}
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// Benchmark: vary metric with defaults k=2, M=1000, N=2, trials=1...
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metrics = [ 'sqeuclidean', 'cosine', 'cityblock', 'correlation' ];
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for ( j = 0; j < metrics.length; j++ ) {
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m = metrics[ j ];
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f = createBenchmark( 2, 1000, 2, m, 1 );
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bench( format( '%s::vary_metric:k=2,M=1000,N=2,metric=%s,trials=1', pkg, m ), f );
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}
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}
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main();

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