// create let m1 = new SparseMatrix(5, 6); let m2 = new SparseMatrix(5, 5).identity(); let m3 = new SparseMatrix().fromArray([[0, 1], [-1, 0.5]]); // change m1.set(0, 0, -3); m1.transpose(); m1.reshape(3, 3); // copy m2 = m1.copy(); // get console.log(m1.get(0, 0)); // -3 // examples console.log(new SparseMatrix(3, 3).identity().toArray()); // [[1, 0, 0], [0, 1, 0], [0, 0, 1]] let a = 0.33; // angle let r = new SparseMatrix().fromArray([ // rotation matrix [Math.cos(a), -Math.sin(a)], [Math.sin(a), Math.cos(a)], ]); r.reshape(3, 3).set(2, 2, 1); // expand matrix and set new value let res = r.mul(r.copy().transpose()); // multiply console.log(res.toArray()); // [[1, 0, 0], [0, 1, 0], [0, 0, 1]]

let n = 13; // number of pages let damping_factor = 0.8; // links: (from, to, number of links) let links = [[0, 11, 2], [12, 11, 3], // ... more links ... [3, 10, 1]]; let mat = new SparseMatrix(n, n); links.forEach(function (link) { mat.set(link[1], link[0], link[2]); }); // set links // normalize matrix for (let i = 0; i < n; i++) { let column_sum = mat.computeByColumn(i, (a, b) => a + b, 0); if (column_sum > 0) mat.mapByColumn(i, x => x / column_sum); } let page_rank = new Array(n).fill(1 / n); // initialize page_rank with 1/n values for (let iteration = 0; iteration < 16; iteration++) { let next_page_rank = mat.mul(page_rank); // multiply vector by matrix (M*v) for (let i = 0; i < n; i++) { page_rank[i] = next_page_rank[i] * damping_factor + (1 - damping_factor) / n; // recompute page rank } } console.log(page_rank); // [0.0153, 0.0289, 0.0853, 0.0811, 0.0660, 0.1721, 0.0235, 0.0153, 0.0211, 0.0563, 0.1525, 0.2213, 0.0606]