How to use the ml-matrix.Matrix.zeros function in ml-matrix

this.activationFunction = function (i, j) {
      this.set(i, j, actFunction(this.get(i, j)));
    };
    this.derivate = function (i, j) {
      this.set(i, j, derFunction(this.get(i, j)));
    };

    if (options.model) {
      // load model
      this.W = Matrix.checkMatrix(options.W);
      this.b = Matrix.checkMatrix(options.b);
    } else {
      // default constructor
      this.W = Matrix.rand(this.inputSize, this.outputSize);
      this.b = Matrix.zeros(1, this.outputSize);

      this.W.apply(function (i, j) {
        this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));
      });
    }
  }

import { Matrix } from 'ml-matrix';

import { KernelRidgeRegression } from '..';

var nSamples = 10;
var nVars = 2;

var Xs = Matrix.random(nSamples, nVars);
Xs.sub(0.5);
var Ys = Matrix.zeros(nSamples, 1);
for (var i = 0; i < nSamples; i++) {
  Ys.set(
    i,
    0,
    Xs.get(i, 0) * Xs.get(i, 0) +
      2 * Xs.get(i, 0) * Xs.get(i, 1) +
      Xs.get(i, 1) * Xs.get(i, 1)
  );
}

describe('Kernel ridge regression', function () {
  it('constant outputs', function () {
    var model = new KernelRidgeRegression([[0, 0], [1, 1]], [[0], [0]]);
    expect(model.predict([[1, 1], [2, 5], [4, 7]])).toStrictEqual([
      [0],
      [0],

function sumRow(matrix) {
    var sum = Matrix.zeros(matrix.rows, 1);
    for (var i = 0; i < matrix.rows; ++i) {
        for (var j = 0; j < matrix.columns; ++j) {
            sum[i][0] += matrix[i][j];
        }
    }
    return sum;
}

}
    }

    var svd = new SVD(A.transpose(), {
      computeLeftSingularVectors: true,
      computeRightSingularVectors: true,
      autoTranspose: false
    });

    var qqs = Matrix.rowVector(svd.diagonal);
    qqs = qqs.apply(function (i, j) {
      if (this.get(i, j) >= 1e-15) this.set(i, j, 1 / this.get(i, j));
      else this.set(i, j, 0);
    });

    var qqs1 = Matrix.zeros(examples, coefficients);
    for (i = 0; i < coefficients; ++i) {
      qqs1.set(i, i, qqs.get(0, i));
    }

    qqs = qqs1;

    var U = svd.rightSingularVectors;
    var V = svd.leftSingularVectors;

    this.coefficients = V.mmul(qqs.transpose())
      .mmul(U.transpose())
      .mmul(y);

    col = 0;

    for (i = 0; i <= coefficients; ++i) {

function sumCol(matrix) {
    var sum = Matrix.zeros(1, matrix.columns);
    for (var i = 0; i < matrix.rows; ++i) {
        for (var j = 0; j < matrix.columns; ++j) {
            sum[0][j] += matrix[i][j];
        }
    }
    return sum;
}