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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 isColumnMajor = require( '@stdlib/ndarray/base/assert/is-column-major-string' );
var resolveMetricStr = require( '@stdlib/ml/base/kmeans/metric-resolve-str' );
var isRowMajor = require( '@stdlib/ndarray/base/assert/is-row-major-string' );
var isPositiveInteger = require( '@stdlib/assert/is-positive-integer' );
var stride2offset = require( '@stdlib/strided/base/stride2offset' );
var isLayout = require( '@stdlib/blas/base/assert/is-layout' );
var max = require( '@stdlib/math/base/special/fast/max' );
var format = require( '@stdlib/string/format' );
var prng = require( './prng.js' );
var base = require( './base.js' );
// MAIN //
/**
* Initializes centroids by performing the k-means++ initialization procedure on double-precision floating-point data points.
*
* @param {string} order - storage layout
* @param {PositiveInteger} M - number of data points
* @param {PositiveInteger} N - number of features
* @param {PositiveInteger} k - number of clusters
* @param {PositiveInteger} trials - number of potential centroids per iteration (>= 1)
* @param {string} metric - distance metric
* @param {Float64Array} X - input array
* @param {integer} LDX - stride of the first dimension of `x` (a.k.a., leading dimension of the matrix `x`)
* @param {Float64Array} out - output array
* @param {integer} LDO - stride of the first dimension of `out` (a.k.a., leading dimension of the matrix `out`)
* @param {Float64Array} W1 - first workspace array of size `2*M` for tracking squared distances and probabilities
* @param {integer} sw1 - stride length of `W1`
* @param {Float64Array} W2 - second workspace array for tracking centroid candidates
* @param {integer} sw2 - stride length of `W2`
* @param {Options} [options] - function options
* @param {PRNG} [options.prng] - pseudorandom number generator which generates uniformly distributed pseudorandom numbers
* @param {PRNGSeedMT19937} [options.seed] - pseudorandom number generator seed
* @param {PRNGStateMT19937} [options.state] - pseudorandom number generator state
* @param {boolean} [options.copy=true] - boolean indicating whether to copy a provided pseudorandom number generator state
* @throws {RangeError} second argument must be greater than or equal to fourth argument
* @throws {RangeError} fifth argument must be a positive integer
* @throws {TypeError} sixth argument must be a valid supported metric
* @throws {RangeError} eighth argument must be greater than or equal to max(1,N)
* @throws {RangeError} tenth argument must be greater than or equal to max(1,N)
* @returns {Float64Array} centroids
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var Int32Array = require( '@stdlib/array/int32' );
*
* var k = 3;
* var M = 5;
* var N = 2;
*
* var out = new Float64Array( k*N );
* var W1 = new Float64Array( 2*M );
* var W2 = new Int32Array( k );
*
* // Specify data points:
* var xbuf = new Float64Array([
* 0.0, 0.0,
* 1.0, 1.0,
* 1.0, -1.0,
* -1.0, -1.0,
* -1.0, 1.0
* ]);
*
* var opts = {
* 'seed': 1234
* }
*
* dkmeansInitPlusPlus( 'row-major', M, N, k, 3, 'sqeuclidean', xbuf, 2, out, 2, W1, 1, W2, 1, opts );
* // out => <Float64Array>[ 1.0, -1.0, 0.0, 0.0, -1.0, 1.0 ]
*/
function dkmeansInitPlusPlus( order, M, N, k, trials, metric, X, LDX, out, LDO, W1, sw1, W2, sw2, options ) { // eslint-disable-line max-len, max-params
var generators;
var so1;
var so2;
var sx1;
var sx2;
var so;
var sx;
if ( k > M ) {
throw new RangeError( format( 'invalid argument. Second argument `M` must be greater than or equal to fourth argument `k`. Value: `M=%d, k=%d`.', M, k ) );
}
if ( !isPositiveInteger( trials ) ) {
throw new RangeError( format( 'invalid argument. Fifth argument must be a positive integer. Value: `%d`.', trials ) );
}
if ( resolveMetricStr( metric ) === null ) {
throw new TypeError( format( 'invalid argument. Sixth argument must be a valid supported metric. Value: `%s`.', metric ) );
}
if ( k < 1 || M < 1 || N < 1 ) {
return out;
}
if ( !isLayout( order ) ) {
throw new TypeError( format( 'invalid argument. First argument must be a valid order. Value: `%s`.', order ) );
}
if ( isRowMajor( order ) ) {
so = N;
sx = N;
} else {
so = k;
sx = M;
}
if ( LDX < max( 1, sx ) ) {
throw new RangeError( format( 'invalid argument. Eighth argument must be greater than or equal to max(1,%d). Value: `%d`.', sx, LDX ) );
}
if ( LDO < max( 1, so ) ) {
throw new RangeError( format( 'invalid argument. Tenth argument must be greater than or equal to max(1,%d). Value: `%d`.', so, LDO ) );
}
if ( isColumnMajor( order ) ) {
so1 = 1;
so2 = LDO;
sx1 = 1;
sx2 = LDX;
} else { // order === 'row-major'
so1 = LDO;
so2 = 1;
sx1 = LDX;
sx2 = 1;
}
generators = prng( arguments.length > 14, options );
return base( M, N, k, trials, metric, X, sx1, sx2, 0, out, so1, so2, 0, W1, sw1, stride2offset( 2*M, sw1 ), W2, sw2, stride2offset( k, sw2 ), generators ); // eslint-disable-line max-len
}
// EXPORTS //
module.exports = dkmeansInitPlusPlus;
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