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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 //
/**
* Compute inertia between double-precision floating-point centroids and data points.
*
* @private
* @param {PositiveInteger} M - number of samples.
* @param {PositiveInteger} N - number of features.
* @param {Function} metric - distance function.
* @param {Float64Array} X - input strided matrix.
* @param {integer} sx1 - stride length of first dimension of X.
* @param {integer} sx2 - stride length of second dimension of X.
* @param {integer} ox - starting index of X.
* @param {Float64Array} C - strided array centroid locations.
* @param {integer} sc1 - stride length of first dimension of c.
* @param {integer} sc2 - stride length of second dimension of c.
* @param {integer} oc - initial index of centroids.
* @param {Int32Array} labels - labels array containing cluster index of each data point.
* @param {integer} sl - stride length of labels.
* @param {integer} ol - initial index of labels.
* @returns {number} inertia.
*
* @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 labels = new Int32Array( [ 0, 1, 0 ] );
*
* var inertia = dcomputeInertia( 3, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, labels, 1, 0 );
* // returns 0.5
*/
function dcomputeInertia( M, N, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, labels, sl, ol ) { // eslint-disable-line max-len, max-params
var inertia;
var dist;
var xidx;
var cidx;
var lidx;
var d;
var c;
var i;
if ( metric === 'sqeuclidean' ) {
dist = dsquaredEuclidean;
} else if ( metric === 'correlation' ) {
dist = dcorrelation;
} else if ( metric === 'cityblock' ) {
dist = dcityblock;
} else {
dist = dcosine;
}
inertia = 0.0;
xidx = ox;
lidx = ol;
for ( i = 0; i < M; i++ ) {
c = labels[ lidx ];
cidx = oc + ( c*sc1 );
d = dist( N, X, sx2, xidx, C, sc2, cidx );
inertia += d;
xidx += sx1;
lidx += sl;
}
return inertia;
}
module.exports = dcomputeInertia;
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