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* @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 factory = require( '@stdlib/random/base/discrete-uniform' ).factory;
var incrmean = require( '@stdlib/stats/incr/mean' );
// MAIN //
/**
* Initializes centroids by randomly assigning each data point to cluster and computing centroids.
*
* ## References
*
* - Forgy, E. 1965. "Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification." _Biometrics_ 21 (3): 768–69.
*
* @private
* @param {ndarray} out - output centroids `kxd` matrix
* @param {ndarray} buffer - buffer containing data points
* @param {*} seed - PRNG seed
* @returns {ndarray} centroids
*/
function forgy( out, buffer, seed ) {
var randi;
var obuf;
var npts;
var buf;
var sb1;
var sb2;
var so1;
var so2;
var acc;
var oo;
var oa;
var ob;
var c;
var M;
var N;
var i;
var j;
M = out.shape[ 0 ];
N = out.shape[ 1 ];
obuf = out.data;
so1 = out.strides[ 0 ];
so2 = out.strides[ 1 ];
oo = out.offset;
buf = buffer.data;
npts = buffer.shape[ 0 ];
sb1 = buffer.strides[ 0 ];
sb2 = buffer.strides[ 1 ];
ob = buffer.offset;
// Initialize a PRNG for randomly assigning data points to clusters:
randi = factory( 0, M-1, {
'seed': seed
});
// Initialize a strided (MxN) array for storing accumulated centroids...
acc = [];
for ( i = 0; i < M*N; i++ ) {
acc.push( incrmean() );
}
// Randomly assign each data point to a cluster and update the respective cluster's centroid...
for ( i = 0; i < npts; i++ ) {
// Generate a random cluster index:
c = randi();
// Compute the accumulator index offset:
oa = N * c;
// Update the respective cluster centroid:
for ( j = 0; j < N; j++ ) {
acc[ oa+j ]( buf[ ob+(sb2*j) ] );
}
// Update the data point index offset:
ob += sb1;
}
// Update the output matrix...
oa = 0;
for ( i = 0; i < M; i++ ) {
for ( j = 0; j < N; j++ ) {
obuf[ oo+(so2*j) ] = acc[ oa ]();
oa += 1; // We can simply increment the array pointer as we know that the accumulator array is row-major single-segment contiguous
}
oo += so1;
}
return out;
}
// EXPORTS //
module.exports = forgy;
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