Press n or j to go to the next uncovered block, b, p or k for the previous block.
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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 dsquaredEuclidean = require( '@stdlib/stats/strided/distances/dsquared-euclidean' ).ndarray;
var dcorrelation = require( '@stdlib/stats/strided/distances/dcorrelation' ).ndarray;
var dcityblock = require( '@stdlib/stats/strided/distances/dcityblock' ).ndarray;
var dcosine = require( '@stdlib/stats/strided/distances/dcosine-distance' ).ndarray;
// MAIN //
/**
* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
*
* @private
* @param {NonNegativeInteger} M - number of data points
* @param {NonNegativeInteger} N - number of features
* @param {NonNegativeInteger} k - number of centroids
* @param {string} metric - distance metric
* @param {Float64Array} X - input data matrix
* @param {integer} sx1 - stride of the first dimension of `X`
* @param {integer} sx2 - stride of the second dimension of `X`
* @param {NonNegativeInteger} ox - index offset for `X`
* @param {Float64Array} C - centroid matrix
* @param {integer} sc1 - stride of the first dimension of `C`
* @param {integer} sc2 - stride of the second dimension of `C`
* @param {NonNegativeInteger} oc - index offset for `C`
* @param {Int32Array} out - output array for closest centroid indices
* @param {integer} so - stride length for `out`
* @param {NonNegativeInteger} oo - index offset for `out`
* @param {Int32Array} counts - output array for per-centroid assignment counts
* @param {integer} sco - stride length for `counts`
* @param {NonNegativeInteger} oco - index offset for `counts`
* @returns {Int32Array} output array
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var Int32Array = require( '@stdlib/array/int32' );
*
* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
*
* var out = new Int32Array( 3 );
* var counts = new Int32Array( 2 );
*
* closestCentroids( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
* // out => <Int32Array>[ 0, 1, 0 ]
*/
function closestCentroids( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ) { // eslint-disable-line max-len, max-params
var bestDist;
var best;
var xidx;
var oidx;
var dist;
var d;
var i;
var j;
if ( metric === 'sqeuclidean' ) {
dist = dsquaredEuclidean;
} else if ( metric === 'correlation' ) {
dist = dcorrelation;
} else if ( metric === 'cityblock' ) {
dist = dcityblock;
} else {
dist = dcosine;
}
xidx = ox;
for ( i = 0; i < M; i++ ) {
oidx = oc;
best = 0;
bestDist = dist( N, X, sx2, xidx, C, sc2, oidx );
oidx += sc1; // move to the next centroid
for ( j = 1; j < k; j++ ) {
d = dist( N, X, sx2, xidx, C, sc2, oidx );
if ( d < bestDist ) {
bestDist = d;
best = j;
}
oidx += sc1;
}
out[ oo + ( i*so ) ] = best;
counts[ oco + ( sco*best ) ] += 1;
xidx += sx1;
}
return out;
}
module.exports = closestCentroids;
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