import * as fs from 'fs';
import { join } from 'path';
import * as km from 'node-kmeans';

const FILENAME_ONE = join('datasets', 'KNN-1.txt')
const FILENAME_TWO = join('datasets', 'KNN-2.txt')

let fileStringArr: string[] = fs.readFileSync(FILENAME_TWO, 'utf8').split('\n');

let array = fileStringArr.slice(1).map(value => {
    let itemsString = value.split(' ');
    let itemsParsed: number[] = [];

    // itemsParsed.push(parseInt(itemsString[0]));

    for(let i=1; i<itemsString.length; i++) {
        itemsParsed.push(parseFloat(itemsString[i]));
    }

    return itemsParsed;
}).filter(value => value != null && typeof value == 'object' && value.length !== 0);

// let length = array[0].length;
// for(let i=0; i<array.length; i++) {
//     if(array[i].length !== length) console.log(`${i}\n ${array[i]}`)
// }


let points: {x: number; y:number}[] = [];

for(let clusters=1; clusters<11; clusters++) {

    km.clusterize(array, {k: clusters}, (error, results: {centroid: number[], cluster: number[][]}[]) => {
        if (error) {
            console.log(`Error: ${error}`);
            return;
        }

        let numClusters = results.length;

        let distance = results.reduce((acc , cluster) => {
            return acc + calculateDistanceSquared(cluster.centroid, cluster.cluster)
        }, 0);

        points.push({x: numClusters, y: distance});
        console.log(`${numClusters}, ${distance}`);
    });

}

function calculateDistanceSquared(centroid: number[], clusters: number[][]): number {
    let sum = 0;

    for(let i=0; i<clusters.length; i++) {
        let curCluster = clusters[i];

        for(let dim=0; dim<curCluster.length; dim++) {
            sum += Math.pow(Math.abs(centroid[dim] - curCluster[dim]),2);
        }
    }

    return sum;
}