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algorithms.ts
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algorithms.ts
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import { Network } from "./network.ts";
import { ParsedCSV, base_id, NetworkArgs } from "./enums.ts";
/**
* Tries to generate a network with the given number of nodes and edges.
* @param {Object} args
* @param {number} args.number_vertices
* @param {number} args.number_edges
* @param {boolean} [args.is_directed]
* @returns Network
*/
export function genRandomNetwork(args: {
number_vertices: number;
number_edges: number;
is_directed?: boolean;
edge_tries?: number;
}): Network {
let { number_vertices, number_edges, is_directed } = args;
is_directed ??= false;
const net = new Network({ is_directed });
for (let vertex = 0; vertex < number_vertices; vertex++)
net.addVertex({ id: vertex });
let edge_tries = args.edge_tries ?? 30;
while (net.edges.size < number_edges && edge_tries > 0) {
const from = Math.floor(Math.random() * number_vertices);
const to = Math.floor(Math.random() * number_vertices);
if (from !== to && net.addEdge({ from, to, do_force: false }))
edge_tries = args.edge_tries ?? 30;
edge_tries--;
}
return net;
}
export function genCompleteNetwork(
size: number,
args: NetworkArgs = {}
): Network {
const complete_net = new Network(
Object.assign(args, {
vertex_limit: size,
edge_limit: Math.floor((size * (size - 1)) / 2),
})
);
for (let vertex = 0; vertex < size; vertex++) {
complete_net.addVertex({ id: vertex });
complete_net.vertices.forEach((v) => {
if (v.id !== vertex) {
complete_net.addEdge({ from: v.id, to: vertex });
}
});
}
return complete_net;
}
/**
* Reads an [adjacency matrix](https://www.wikiwand.com/en/Adjacency_matrix) CSV and returns a network object.
* @param {string} file_name
* @returns Network
*/
export async function loadAdjacencyMatrix(
file_name: string,
is_directed = false
): Promise<Network> {
const csv_file = await Deno.readTextFile(file_name);
const parsed_csv = parseCSV([...csv_file]);
const vertex_limit = parsed_csv[0].length;
const edge_limit = (parsed_csv[0].length * (parsed_csv[0].length - 1)) / 2;
const csv_network = new Network({ is_directed, vertex_limit, edge_limit });
parsed_csv[0].forEach((vertex, index) => {
if (index === 0) return;
csv_network.addVertex({ id: vertex });
});
if (!is_directed) {
parsed_csv.forEach((line, line_number) => {
if (line_number === 0) return;
line.forEach((edge_weight, column_number) => {
if (column_number === 0) return;
const weight = +edge_weight;
if (weight) {
const from = parsed_csv[line_number][0];
const to = parsed_csv[0][column_number];
csv_network.addEdge({ from, to, weight });
}
});
});
}
return csv_network;
}
/**
* Parses a CSV.
* @param {string[]} csv_file
* @returns ParsedCSV
*/
function parseCSV(csv_file: string[]): ParsedCSV {
const parsed_csv: ParsedCSV = [[]];
let current_line = parsed_csv.length - 1;
let current_char = "";
csv_file.forEach((char) => {
if (char === "\n") {
parsed_csv.push([]);
current_line = parsed_csv.length - 1;
current_char = "";
return;
} else if (char === ",") {
parsed_csv[current_line].push(current_char);
current_char = "";
return;
}
current_char += char;
});
return parsed_csv;
}
/**
* Write to a CSV file.
*/
async function writeCSV(
rows: Array<Array<string | number>>,
file_name = "adjacencyMatrix.csv"
) {
let csv = "";
rows.forEach((row) => {
row.forEach((element, i) => {
csv += `${element + (i === row.length - 1 ? "\n" : ",")}`;
});
});
await Deno.writeTextFile(file_name, csv);
}
/**
* Exports given network into an adjacency matrix in the form of a CSV file.
* @param {Network} network
* @param {} file_name="adjacencyMatrix.csv"
*/
export async function writeAdjacencyMatrix(
network: Network,
file_name = "adjacencyMatrix.csv"
) {
const number_of_rows = network.vertices.size + 1;
const rows: Array<Array<base_id>> = [...Array(number_of_rows)].map(() =>
Array(number_of_rows).fill(0)
);
network.vertex_list.forEach((vertex, i) => {
rows[0][i + 1] = vertex.id;
rows[i + 1][0] = vertex.id;
network.vertex_list.forEach((vertex2, j) => {
if (network.hasEdge(vertex2.id, vertex.id)) {
rows[i + 1][j + 1] = 1;
rows[j + 1][i + 1] = 1;
}
});
});
await writeCSV(rows, file_name);
}