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

function ellipse(width, height, options) {
  const matrix = Matrix.zeros(height, width, options);
  let yEven = 1 - height % 2;
  let xEven = 1 - width % 2;
  let a = Math.floor((width - 1) / 2); // horizontal ellipse axe
  let b = Math.floor((height - 1) / 2); // vertical ellipse axe
  let a2 = a * a;
  let b2 = b * b;
  if (options.filled) {
    for (let y = 0; y <= b; y++) {
      let shift = Math.floor(Math.sqrt(a2 - a2 * y * y / b2));
      for (let x = a - shift; x <= a; x++) {
        matrix.set(b - y, x, 1);
        matrix.set(b + y + yEven, x, 1);
        matrix.set(b - y, width - x - 1, 1);
        matrix.set(b + y + yEven, width - x - 1, 1);
      }
    }

forward(input) {
        this.input = input.slice();
        this.input.push(1); // bias
        var offs = 0; // offset used to get the current weights in the current perceptron
        this.output = Matrix.zeros(1, this.output.length).getRow(0);

        for (var i = 0; i < this.output.length; ++i) {
            for (var j = 0; j < this.input.length; ++j) {
                this.output[i] += this.weights[offs + j] * this.input[j];
            }
            if (this.isSigmoid)
                this.output[i] = sigmoid(this.output[i]);

            offs += this.input.length;
        }

        return this.output.slice();
    }

/**
 * Created by acastillo on 10/6/15.
 */
'use strict';

var Matrix = require("ml-matrix");
var ridgeRegression = require("../..").KernelRidgeRegression;

var nSamples = 10;
var nVars = 2;
var nSteps = 10, i, j;

var Xs = Matrix.random(nSamples,nVars);
Xs.sub(0.5);
var Ys = Matrix.zeros(nSamples,1);
for(i=0;i

constructor(inputSize, outputSize) {
        this.output = Matrix.zeros(1, outputSize).getRow(0);
        this.input = Matrix.zeros(1, inputSize + 1).getRow(0); //+1 for bias term
        this.deltaWeights = Matrix.zeros(1, (1 + inputSize) * outputSize).getRow(0);
        this.weights = randomInitializeWeights(this.deltaWeights.length, inputSize, outputSize);
        this.isSigmoid = true;
    }

train(error, learningRate, momentum) {
        var offs = 0;
        var nextError = Matrix.zeros(1, this.input.length).getRow(0);//new Array(this.input.length);

        for (var i = 0; i < this.output.length; ++i) {
            var delta = error[i];

            if (this.isSigmoid)
                delta *= sigmoidGradient(this.output[i]);

            for (var j = 0; j < this.input.length; ++j) {
                var index = offs + j;
                nextError[j] += this.weights[index] * delta;

                var deltaWeight = this.input[j] * delta * learningRate;
                this.weights[index] += this.deltaWeights[index] * momentum + deltaWeight;
                this.deltaWeights[index] = deltaWeight;
            }

function triangle(width, height, options) {
  if (!options.filled) {
    throw new Error('Non filled triangle is not implemented');
  }
  const matrix = Matrix.zeros(height, width, options);
  for (let y = 0; y < height; y++) {
    let shift = Math.floor((1 - y / height) * width / 2);
    for (let x = shift; x < (width - shift); x++) {
      matrix.set(y, x, 1);
    }
  }
  return matrix;
}

constructor(inputSize, outputSize) {
        this.output = Matrix.zeros(1, outputSize).getRow(0);
        this.input = Matrix.zeros(1, inputSize + 1).getRow(0); //+1 for bias term
        this.deltaWeights = Matrix.zeros(1, (1 + inputSize) * outputSize).getRow(0);
        this.weights = randomInitializeWeights(this.deltaWeights.length, inputSize, outputSize);
        this.isSigmoid = true;
    }

constructor(inputSize, outputSize) {
        this.output = Matrix.zeros(1, outputSize).getRow(0);
        this.input = Matrix.zeros(1, inputSize + 1).getRow(0); //+1 for bias term
        this.deltaWeights = Matrix.zeros(1, (1 + inputSize) * outputSize).getRow(0);
        this.weights = randomInitializeWeights(this.deltaWeights.length, inputSize, outputSize);
        this.isSigmoid = true;
    }

function rectangle(width, height, options) {
  const matrix = Matrix.zeros(height, width);
  if (options.filled) {
    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        matrix.set(y, x, 1);
      }
    }
  } else {
    for (let y of [0, height - 1]) {
      for (let x = 0; x < width; x++) {
        matrix.set(y, x, 1);
      }
    }
    for (let y = 0; y < height; y++) {
      for (let x of [0, width - 1]) {
        matrix.set(y, x, 1);
      }