Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

2 files changed, 6501 insertions, 0 deletions

diff --git a/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js b/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js
new file mode 100644
index 0000000000..bd6ccd3e3a
--- /dev/null
+++ b/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js
@@ -0,0 +1,171 @@
+/*
+ * Copyright (C) 2017 Apple Inc. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY APPLE INC. ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL APPLE INC. OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
+ */
+
+"use strict";
+
+let currentTime;
+if (this.performance && performance.now)
+    currentTime = function() { return performance.now() };
+else if (this.preciseTime)
+    currentTime = function() { return preciseTime() * 1000; };
+else
+    currentTime = function() { return +new Date(); };
+
+class MLBenchmark {
+    constructor() { }
+
+    runIteration()
+    {
+        let Matrix = MLMatrix;
+        let ACTIVATION_FUNCTIONS = FeedforwardNeuralNetworksActivationFunctions;
+
+        function run() {
+            
+            let it = (name, f) => {
+                f();
+            };
+
+            function assert(b) {
+                if (!b)
+                    throw new Error("Bad");
+            }
+
+            var functions = Object.keys(ACTIVATION_FUNCTIONS);
+
+            it('Training the neural network with XOR operator', function () {
+                var trainingSet = new Matrix([[0, 0], [0, 1], [1, 0], [1, 1]]);
+                var predictions = [false, true, true, false];
+
+                for (var i = 0; i < functions.length; ++i) {
+                    var options = {
+                        hiddenLayers: [4],
+                        iterations: 40,
+                        learningRate: 0.3,
+                        activation: functions[i]
+                    };
+                    var xorNN = new FeedforwardNeuralNetwork(options);
+
+                    xorNN.train(trainingSet, predictions);
+                    var results = xorNN.predict(trainingSet);
+                }
+            });
+
+            it('Training the neural network with AND operator', function () {
+                var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]];
+                var predictions = [[1, 0], [1, 0], [1, 0], [0, 1]];
+
+                for (var i = 0; i < functions.length; ++i) {
+                    var options = {
+                        hiddenLayers: [3],
+                        iterations: 75,
+                        learningRate: 0.3,
+                        activation: functions[i]
+                    };
+                    var andNN = new FeedforwardNeuralNetwork(options);
+                    andNN.train(trainingSet, predictions);
+
+                    var results = andNN.predict(trainingSet);
+                }
+            });
+
+            it('Export and import', function () {
+                var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]];
+                var predictions = [0, 1, 1, 1];
+
+                for (var i = 0; i < functions.length; ++i) {
+                    var options = {
+                        hiddenLayers: [4],
+                        iterations: 40,
+                        learningRate: 0.3,
+                        activation: functions[i]
+                    };
+                    var orNN = new FeedforwardNeuralNetwork(options);
+                    orNN.train(trainingSet, predictions);
+
+                    var model = JSON.parse(JSON.stringify(orNN));
+                    var networkNN = FeedforwardNeuralNetwork.load(model);
+
+                    var results = networkNN.predict(trainingSet);
+                }
+            });
+
+            it('Multiclass clasification', function () {
+                var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]];
+                var predictions = [2, 0, 1, 0];
+
+                for (var i = 0; i < functions.length; ++i) {
+                    var options = {
+                        hiddenLayers: [4],
+                        iterations: 40,
+                        learningRate: 0.5,
+                        activation: functions[i]
+                    };
+                    var nn = new FeedforwardNeuralNetwork(options);
+                    nn.train(trainingSet, predictions);
+
+                    var result = nn.predict(trainingSet);
+                }
+            });
+
+            it('Big case', function () {
+                var trainingSet = [[1, 1], [1, 2], [2, 1], [2, 2], [3, 1], [1, 3], [1, 4], [4, 1],
+                                    [6, 1], [6, 2], [6, 3], [6, 4], [6, 5], [5, 5], [4, 5], [3, 5]];
+                var predictions = [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0],
+                                    [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]];
+                for (var i = 0; i < functions.length; ++i) {
+                    var options = {
+                        hiddenLayers: [20],
+                        iterations: 60,
+                        learningRate: 0.01,
+                        activation: functions[i]
+                    };
+                    var nn = new FeedforwardNeuralNetwork(options);
+                    nn.train(trainingSet, predictions);
+
+                    var result = nn.predict([[5, 4]]);
+
+                    assert(result[0][0] < result[0][1]);
+                }
+            });
+        }
+
+        run();
+    }
+}
+
+function runBenchmark()
+{
+    const numIterations = 60;
+
+    let before = currentTime();
+
+    let benchmark = new Benchmark();
+
+    for (let iteration = 0; iteration < numIterations; ++iteration)
+        benchmark.runIteration();
+
+    let after = currentTime();
+    return after - before;
+}
diff --git a/third_party/webkit/PerformanceTests/ARES-6/ml/index.js b/third_party/webkit/PerformanceTests/ARES-6/ml/index.js
new file mode 100644
index 0000000000..03871ba081
--- /dev/null
+++ b/third_party/webkit/PerformanceTests/ARES-6/ml/index.js
@@ -0,0 +1,6330 @@
+/*
+ * The MIT License (MIT)
+ * 
+ * Copyright (c) 2015 ml.js
+ * 
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+*/
+'use strict';
+
+// ml-stat array.js
+const MLStatArray = {};
+{
+    function compareNumbers(a, b) {
+        return a - b;
+    }
+
+    /**
+     * Computes the sum of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.sum = function sum(values) {
+        var sum = 0;
+        for (var i = 0; i < values.length; i++) {
+            sum += values[i];
+        }
+        return sum;
+    };
+
+    /**
+     * Computes the maximum of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.max = function max(values) {
+        var max = values[0];
+        var l = values.length;
+        for (var i = 1; i < l; i++) {
+            if (values[i] > max) max = values[i];
+        }
+        return max;
+    };
+
+    /**
+     * Computes the minimum of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.min = function min(values) {
+        var min = values[0];
+        var l = values.length;
+        for (var i = 1; i < l; i++) {
+            if (values[i] < min) min = values[i];
+        }
+        return min;
+    };
+
+    /**
+     * Computes the min and max of the given values
+     * @param {Array} values
+     * @returns {{min: number, max: number}}
+     */
+    MLStatArray.minMax = function minMax(values) {
+        var min = values[0];
+        var max = values[0];
+        var l = values.length;
+        for (var i = 1; i < l; i++) {
+            if (values[i] < min) min = values[i];
+            if (values[i] > max) max = values[i];
+        }
+        return {
+            min: min,
+            max: max
+        };
+    };
+
+    /**
+     * Computes the arithmetic mean of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.arithmeticMean = function arithmeticMean(values) {
+        var sum = 0;
+        var l = values.length;
+        for (var i = 0; i < l; i++) {
+            sum += values[i];
+        }
+        return sum / l;
+    };
+
+    /**
+     * {@link arithmeticMean}
+     */
+    MLStatArray.mean = MLStatArray.arithmeticMean;
+
+    /**
+     * Computes the geometric mean of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.geometricMean = function geometricMean(values) {
+        var mul = 1;
+        var l = values.length;
+        for (var i = 0; i < l; i++) {
+            mul *= values[i];
+        }
+        return Math.pow(mul, 1 / l);
+    };
+
+    /**
+     * Computes the mean of the log of the given values
+     * If the return value is exponentiated, it gives the same result as the
+     * geometric mean.
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.logMean = function logMean(values) {
+        var lnsum = 0;
+        var l = values.length;
+        for (var i = 0; i < l; i++) {
+            lnsum += Math.log(values[i]);
+        }
+        return lnsum / l;
+    };
+
+    /**
+     * Computes the weighted grand mean for a list of means and sample sizes
+     * @param {Array} means - Mean values for each set of samples
+     * @param {Array} samples - Number of original values for each set of samples
+     * @returns {number}
+     */
+    MLStatArray.grandMean = function grandMean(means, samples) {
+        var sum = 0;
+        var n = 0;
+        var l = means.length;
+        for (var i = 0; i < l; i++) {
+            sum += samples[i] * means[i];
+            n += samples[i];
+        }
+        return sum / n;
+    };
+
+    /**
+     * Computes the truncated mean of the given values using a given percentage
+     * @param {Array} values
+     * @param {number} percent - The percentage of values to keep (range: [0,1])
+     * @param {boolean} [alreadySorted=false]
+     * @returns {number}
+     */
+    MLStatArray.truncatedMean = function truncatedMean(values, percent, alreadySorted) {
+        if (alreadySorted === undefined) alreadySorted = false;
+        if (!alreadySorted) {
+            values = [].concat(values).sort(compareNumbers);
+        }
+        var l = values.length;
+        var k = Math.floor(l * percent);
+        var sum = 0;
+        for (var i = k; i < (l - k); i++) {
+            sum += values[i];
+        }
+        return sum / (l - 2 * k);
+    };
+
+    /**
+     * Computes the harmonic mean of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.harmonicMean = function harmonicMean(values) {
+        var sum = 0;
+        var l = values.length;
+        for (var i = 0; i < l; i++) {
+            if (values[i] === 0) {
+                throw new RangeError('value at index ' + i + 'is zero');
+            }
+            sum += 1 / values[i];
+        }
+        return l / sum;
+    };
+
+    /**
+     * Computes the contraharmonic mean of the given values
+     * @param {Array} values
+     * @returns {number}
+     */
+    MLStatArray.contraHarmonicMean = function contraHarmonicMean(values) {
+        var r1 = 0;
+        var r2 = 0;
+        var l = values.length;
+        for (var i = 0; i < l; i++) {
+            r1 += values[i] * values[i];
+            r2 += values[i];
+        }
+        if (r2 < 0) {
+            throw new RangeError('sum of values is negative');
+        }
+        return r1 / r2;
+    };
+
+    /**
+     * Computes the median of the given values
+     * @param {Array} values
+     * @param {boolean} [alreadySorted=false]
+     * @returns {number}
+     */
+    MLStatArray.median = function median(values, alreadySorted) {
+        if (alreadySorted === undefined) alreadySorted = false;
+        if (!alreadySorted) {
+            values = [].concat(values).sort(compareNumbers);
+        }
+        var l = values.length;
+        var half = Math.floor(l / 2);
+        if (l % 2 === 0) {
+            return (values[half - 1] + values[half]) * 0.5;
+        } else {
+            return values[half];
+        }
+    };
+
+    /**
+     * Computes the variance of the given values
+     * @param {Array} values
+     * @param {boolean} [unbiased=true] - if true, divide by (n-1); if false, divide by n.
+     * @returns {number}
+     */
+    MLStatArray.variance = function variance(values, unbiased) {
+        if (unbiased === undefined) unbiased = true;
+        var theMean = MLStatArray.mean(values);
+        var theVariance = 0;
+        var l = values.length;
+
+        for (var i = 0; i < l; i++) {
+            var x = values[i] - theMean;
+            theVariance += x * x;
+        }
+
+        if (unbiased) {
+            return theVariance / (l - 1);
+        } else {
+            return theVariance / l;
+        }
+    };
+
+    /**
+     * Computes the standard deviation of the given values
+     * @param {Array} values
+     * @param {boolean} [unbiased=true] - if true, divide by (n-1); if false, divide by n.
+     * @returns {number}
+     */
+    MLStatArray.standardDeviation = function standardDeviation(values, unbiased) {
+        return Math.sqrt(MLStatArray.variance(values, unbiased));
+    };
+
+    MLStatArray.standardError = function standardError(values) {
+        return MLStatArray.standardDeviation(values) / Math.sqrt(values.length);
+    };
+
+    /**
+     * IEEE Transactions on biomedical engineering, vol. 52, no. 1, january 2005, p. 76-
+     * Calculate the standard deviation via the Median of the absolute deviation
+     *  The formula for the standard deviation only holds for Gaussian random variables.
+     * @returns {{mean: number, stdev: number}}
+     */
+    MLStatArray.robustMeanAndStdev = function robustMeanAndStdev(y) {
+        var mean = 0, stdev = 0;
+        var length = y.length, i = 0;
+        for (i = 0; i < length; i++) {
+            mean += y[i];
+        }
+        mean /= length;
+        var averageDeviations = new Array(length);
+        for (i = 0; i < length; i++)
+            averageDeviations[i] = Math.abs(y[i] - mean);
+        averageDeviations.sort(compareNumbers);
+        if (length % 2 === 1) {
+            stdev = averageDeviations[(length - 1) / 2] / 0.6745;
+        } else {
+            stdev = 0.5 * (averageDeviations[length / 2] + averageDeviations[length / 2 - 1]) / 0.6745;
+        }
+
+        return {
+            mean: mean,
+            stdev: stdev
+        };
+    };
+
+    MLStatArray.quartiles = function quartiles(values, alreadySorted) {
+        if (typeof (alreadySorted) === 'undefined') alreadySorted = false;
+        if (!alreadySorted) {
+            values = [].concat(values).sort(compareNumbers);
+        }
+
+        var quart = values.length / 4;
+        var q1 = values[Math.ceil(quart) - 1];
+        var q2 = MLStatArray.median(values, true);
+        var q3 = values[Math.ceil(quart * 3) - 1];
+
+        return {q1: q1, q2: q2, q3: q3};
+    };
+
+    MLStatArray.pooledStandardDeviation = function pooledStandardDeviation(samples, unbiased) {
+        return Math.sqrt(MLStatArray.pooledVariance(samples, unbiased));
+    };
+
+    MLStatArray.pooledVariance = function pooledVariance(samples, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var sum = 0;
+        var length = 0, l = samples.length;
+        for (var i = 0; i < l; i++) {
+            var values = samples[i];
+            var vari = MLStatArray.variance(values);
+
+            sum += (values.length - 1) * vari;
+
+            if (unbiased)
+                length += values.length - 1;
+            else
+                length += values.length;
+        }
+        return sum / length;
+    };
+
+    MLStatArray.mode = function mode(values) {
+        var l = values.length,
+            itemCount = new Array(l),
+            i;
+        for (i = 0; i < l; i++) {
+            itemCount[i] = 0;
+        }
+        var itemArray = new Array(l);
+        var count = 0;
+
+        for (i = 0; i < l; i++) {
+            var index = itemArray.indexOf(values[i]);
+            if (index >= 0)
+                itemCount[index]++;
+            else {
+                itemArray[count] = values[i];
+                itemCount[count] = 1;
+                count++;
+            }
+        }
+
+        var maxValue = 0, maxIndex = 0;
+        for (i = 0; i < count; i++) {
+            if (itemCount[i] > maxValue) {
+                maxValue = itemCount[i];
+                maxIndex = i;
+            }
+        }
+
+        return itemArray[maxIndex];
+    };
+
+    MLStatArray.covariance = function covariance(vector1, vector2, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var mean1 = MLStatArray.mean(vector1);
+        var mean2 = MLStatArray.mean(vector2);
+
+        if (vector1.length !== vector2.length)
+            throw 'Vectors do not have the same dimensions';
+
+        var cov = 0, l = vector1.length;
+        for (var i = 0; i < l; i++) {
+            var x = vector1[i] - mean1;
+            var y = vector2[i] - mean2;
+            cov += x * y;
+        }
+
+        if (unbiased)
+            return cov / (l - 1);
+        else
+            return cov / l;
+    };
+
+    MLStatArray.skewness = function skewness(values, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var theMean = MLStatArray.mean(values);
+
+        var s2 = 0, s3 = 0, l = values.length;
+        for (var i = 0; i < l; i++) {
+            var dev = values[i] - theMean;
+            s2 += dev * dev;
+            s3 += dev * dev * dev;
+        }
+        var m2 = s2 / l;
+        var m3 = s3 / l;
+
+        var g = m3 / (Math.pow(m2, 3 / 2.0));
+        if (unbiased) {
+            var a = Math.sqrt(l * (l - 1));
+            var b = l - 2;
+            return (a / b) * g;
+        } else {
+            return g;
+        }
+    };
+
+    MLStatArray.kurtosis = function kurtosis(values, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var theMean = MLStatArray.mean(values);
+        var n = values.length, s2 = 0, s4 = 0;
+
+        for (var i = 0; i < n; i++) {
+            var dev = values[i] - theMean;
+            s2 += dev * dev;
+            s4 += dev * dev * dev * dev;
+        }
+        var m2 = s2 / n;
+        var m4 = s4 / n;
+
+        if (unbiased) {
+            var v = s2 / (n - 1);
+            var a = (n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3));
+            var b = s4 / (v * v);
+            var c = ((n - 1) * (n - 1)) / ((n - 2) * (n - 3));
+
+            return a * b - 3 * c;
+        } else {
+            return m4 / (m2 * m2) - 3;
+        }
+    };
+
+    MLStatArray.entropy = function entropy(values, eps) {
+        if (typeof (eps) === 'undefined') eps = 0;
+        var sum = 0, l = values.length;
+        for (var i = 0; i < l; i++)
+            sum += values[i] * Math.log(values[i] + eps);
+        return -sum;
+    };
+
+    MLStatArray.weightedMean = function weightedMean(values, weights) {
+        var sum = 0, l = values.length;
+        for (var i = 0; i < l; i++)
+            sum += values[i] * weights[i];
+        return sum;
+    };
+
+    MLStatArray.weightedStandardDeviation = function weightedStandardDeviation(values, weights) {
+        return Math.sqrt(MLStatArray.weightedVariance(values, weights));
+    };
+
+    MLStatArray.weightedVariance = function weightedVariance(values, weights) {
+        var theMean = MLStatArray.weightedMean(values, weights);
+        var vari = 0, l = values.length;
+        var a = 0, b = 0;
+
+        for (var i = 0; i < l; i++) {
+            var z = values[i] - theMean;
+            var w = weights[i];
+
+            vari += w * (z * z);
+            b += w;
+            a += w * w;
+        }
+
+        return vari * (b / (b * b - a));
+    };
+
+    MLStatArray.center = function center(values, inPlace) {
+        if (typeof (inPlace) === 'undefined') inPlace = false;
+
+        var result = values;
+        if (!inPlace)
+            result = [].concat(values);
+
+        var theMean = MLStatArray.mean(result), l = result.length;
+        for (var i = 0; i < l; i++)
+            result[i] -= theMean;
+    };
+
+    MLStatArray.standardize = function standardize(values, standardDev, inPlace) {
+        if (typeof (standardDev) === 'undefined') standardDev = MLStatArray.standardDeviation(values);
+        if (typeof (inPlace) === 'undefined') inPlace = false;
+        var l = values.length;
+        var result = inPlace ? values : new Array(l);
+        for (var i = 0; i < l; i++)
+            result[i] = values[i] / standardDev;
+        return result;
+    };
+
+    MLStatArray.cumulativeSum = function cumulativeSum(array) {
+        var l = array.length;
+        var result = new Array(l);
+        result[0] = array[0];
+        for (var i = 1; i < l; i++)
+            result[i] = result[i - 1] + array[i];
+        return result;
+    };
+}
+
+
+// ml-stat matrix.js
+const MLStatMatrix = {};
+{
+    let arrayStat = MLStatArray;
+
+    function compareNumbers(a, b) {
+        return a - b;
+    }
+
+    MLStatMatrix.max = function max(matrix) {
+        var max = -Infinity;
+        for (var i = 0; i < matrix.length; i++) {
+            for (var j = 0; j < matrix[i].length; j++) {
+                if (matrix[i][j] > max) max = matrix[i][j];
+            }
+        }
+        return max;
+    };
+
+    MLStatMatrix.min = function min(matrix) {
+        var min = Infinity;
+        for (var i = 0; i < matrix.length; i++) {
+            for (var j = 0; j < matrix[i].length; j++) {
+                if (matrix[i][j] < min) min = matrix[i][j];
+            }
+        }
+        return min;
+    };
+
+    MLStatMatrix.minMax = function minMax(matrix) {
+        var min = Infinity;
+        var max = -Infinity;
+        for (var i = 0; i < matrix.length; i++) {
+            for (var j = 0; j < matrix[i].length; j++) {
+                if (matrix[i][j] < min) min = matrix[i][j];
+                if (matrix[i][j] > max) max = matrix[i][j];
+            }
+        }
+        return {
+            min:min,
+            max:max
+        };
+    };
+
+    MLStatMatrix.entropy = function entropy(matrix, eps) {
+        if (typeof (eps) === 'undefined') {
+            eps = 0;
+        }
+        var sum = 0,
+            l1 = matrix.length,
+            l2 = matrix[0].length;
+        for (var i = 0; i < l1; i++) {
+            for (var j = 0; j < l2; j++) {
+                sum += matrix[i][j] * Math.log(matrix[i][j] + eps);
+            }
+        }
+        return -sum;
+    };
+
+    MLStatMatrix.mean = function mean(matrix, dimension) {
+        if (typeof (dimension) === 'undefined') {
+            dimension = 0;
+        }
+        var rows = matrix.length,
+            cols = matrix[0].length,
+            theMean, N, i, j;
+
+        if (dimension === -1) {
+            theMean = [0];
+            N = rows * cols;
+            for (i = 0; i < rows; i++) {
+                for (j = 0; j < cols; j++) {
+                    theMean[0] += matrix[i][j];
+                }
+            }
+            theMean[0] /= N;
+        } else if (dimension === 0) {
+            theMean = new Array(cols);
+            N = rows;
+            for (j = 0; j < cols; j++) {
+                theMean[j] = 0;
+                for (i = 0; i < rows; i++) {
+                    theMean[j] += matrix[i][j];
+                }
+                theMean[j] /= N;
+            }
+        } else if (dimension === 1) {
+            theMean = new Array(rows);
+            N = cols;
+            for (j = 0; j < rows; j++) {
+                theMean[j] = 0;
+                for (i = 0; i < cols; i++) {
+                    theMean[j] += matrix[j][i];
+                }
+                theMean[j] /= N;
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+        return theMean;
+    };
+
+    MLStatMatrix.sum = function sum(matrix, dimension) {
+        if (typeof (dimension) === 'undefined') {
+            dimension = 0;
+        }
+        var rows = matrix.length,
+            cols = matrix[0].length,
+            theSum, i, j;
+
+        if (dimension === -1) {
+            theSum = [0];
+            for (i = 0; i < rows; i++) {
+                for (j = 0; j < cols; j++) {
+                    theSum[0] += matrix[i][j];
+                }
+            }
+        } else if (dimension === 0) {
+            theSum = new Array(cols);
+            for (j = 0; j < cols; j++) {
+                theSum[j] = 0;
+                for (i = 0; i < rows; i++) {
+                    theSum[j] += matrix[i][j];
+                }
+            }
+        } else if (dimension === 1) {
+            theSum = new Array(rows);
+            for (j = 0; j < rows; j++) {
+                theSum[j] = 0;
+                for (i = 0; i < cols; i++) {
+                    theSum[j] += matrix[j][i];
+                }
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+        return theSum;
+    };
+
+    MLStatMatrix.product = function product(matrix, dimension) {
+        if (typeof (dimension) === 'undefined') {
+            dimension = 0;
+        }
+        var rows = matrix.length,
+            cols = matrix[0].length,
+            theProduct, i, j;
+
+        if (dimension === -1) {
+            theProduct = [1];
+            for (i = 0; i < rows; i++) {
+                for (j = 0; j < cols; j++) {
+                    theProduct[0] *= matrix[i][j];
+                }
+            }
+        } else if (dimension === 0) {
+            theProduct = new Array(cols);
+            for (j = 0; j < cols; j++) {
+                theProduct[j] = 1;
+                for (i = 0; i < rows; i++) {
+                    theProduct[j] *= matrix[i][j];
+                }
+            }
+        } else if (dimension === 1) {
+            theProduct = new Array(rows);
+            for (j = 0; j < rows; j++) {
+                theProduct[j] = 1;
+                for (i = 0; i < cols; i++) {
+                    theProduct[j] *= matrix[j][i];
+                }
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+        return theProduct;
+    };
+
+    MLStatMatrix.standardDeviation = function standardDeviation(matrix, means, unbiased) {
+        var vari = MLStatMatrix.variance(matrix, means, unbiased), l = vari.length;
+        for (var i = 0; i < l; i++) {
+            vari[i] = Math.sqrt(vari[i]);
+        }
+        return vari;
+    };
+
+    MLStatMatrix.variance = function variance(matrix, means, unbiased) {
+        if (typeof (unbiased) === 'undefined') {
+            unbiased = true;
+        }
+        means = means || MLStatMatrix.mean(matrix);
+        var rows = matrix.length;
+        if (rows === 0) return [];
+        var cols = matrix[0].length;
+        var vari = new Array(cols);
+
+        for (var j = 0; j < cols; j++) {
+            var sum1 = 0, sum2 = 0, x = 0;
+            for (var i = 0; i < rows; i++) {
+                x = matrix[i][j] - means[j];
+                sum1 += x;
+                sum2 += x * x;
+            }
+            if (unbiased) {
+                vari[j] = (sum2 - ((sum1 * sum1) / rows)) / (rows - 1);
+            } else {
+                vari[j] = (sum2 - ((sum1 * sum1) / rows)) / rows;
+            }
+        }
+        return vari;
+    };
+
+    MLStatMatrix.median = function median(matrix) {
+        var rows = matrix.length, cols = matrix[0].length;
+        var medians = new Array(cols);
+
+        for (var i = 0; i < cols; i++) {
+            var data = new Array(rows);
+            for (var j = 0; j < rows; j++) {
+                data[j] = matrix[j][i];
+            }
+            data.sort(compareNumbers);
+            var N = data.length;
+            if (N % 2 === 0) {
+                medians[i] = (data[N / 2] + data[(N / 2) - 1]) * 0.5;
+            } else {
+                medians[i] = data[Math.floor(N / 2)];
+            }
+        }
+        return medians;
+    };
+
+    MLStatMatrix.mode = function mode(matrix) {
+        var rows = matrix.length,
+            cols = matrix[0].length,
+            modes = new Array(cols),
+            i, j;
+        for (i = 0; i < cols; i++) {
+            var itemCount = new Array(rows);
+            for (var k = 0; k < rows; k++) {
+                itemCount[k] = 0;
+            }
+            var itemArray = new Array(rows);
+            var count = 0;
+
+            for (j = 0; j < rows; j++) {
+                var index = itemArray.indexOf(matrix[j][i]);
+                if (index >= 0) {
+                    itemCount[index]++;
+                } else {
+                    itemArray[count] = matrix[j][i];
+                    itemCount[count] = 1;
+                    count++;
+                }
+            }
+
+            var maxValue = 0, maxIndex = 0;
+            for (j = 0; j < count; j++) {
+                if (itemCount[j] > maxValue) {
+                    maxValue = itemCount[j];
+                    maxIndex = j;
+                }
+            }
+
+            modes[i] = itemArray[maxIndex];
+        }
+        return modes;
+    };
+
+    MLStatMatrix.skewness = function skewness(matrix, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var means = MLStatMatrix.mean(matrix);
+        var n = matrix.length, l = means.length;
+        var skew = new Array(l);
+
+        for (var j = 0; j < l; j++) {
+            var s2 = 0, s3 = 0;
+            for (var i = 0; i < n; i++) {
+                var dev = matrix[i][j] - means[j];
+                s2 += dev * dev;
+                s3 += dev * dev * dev;
+            }
+
+            var m2 = s2 / n;
+            var m3 = s3 / n;
+            var g = m3 / Math.pow(m2, 3 / 2);
+
+            if (unbiased) {
+                var a = Math.sqrt(n * (n - 1));
+                var b = n - 2;
+                skew[j] = (a / b) * g;
+            } else {
+                skew[j] = g;
+            }
+        }
+        return skew;
+    };
+
+    MLStatMatrix.kurtosis = function kurtosis(matrix, unbiased) {
+        if (typeof (unbiased) === 'undefined') unbiased = true;
+        var means = MLStatMatrix.mean(matrix);
+        var n = matrix.length, m = matrix[0].length;
+        var kurt = new Array(m);
+
+        for (var j = 0; j < m; j++) {
+            var s2 = 0, s4 = 0;
+            for (var i = 0; i < n; i++) {
+                var dev = matrix[i][j] - means[j];
+                s2 += dev * dev;
+                s4 += dev * dev * dev * dev;
+            }
+            var m2 = s2 / n;
+            var m4 = s4 / n;
+
+            if (unbiased) {
+                var v = s2 / (n - 1);
+                var a = (n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3));
+                var b = s4 / (v * v);
+                var c = ((n - 1) * (n - 1)) / ((n - 2) * (n - 3));
+                kurt[j] = a * b - 3 * c;
+            } else {
+                kurt[j] = m4 / (m2 * m2) - 3;
+            }
+        }
+        return kurt;
+    };
+
+    MLStatMatrix.standardError = function standardError(matrix) {
+        var samples = matrix.length;
+        var standardDeviations = MLStatMatrix.standardDeviation(matrix);
+        var l = standardDeviations.length;
+        var standardErrors = new Array(l);
+        var sqrtN = Math.sqrt(samples);
+
+        for (var i = 0; i < l; i++) {
+            standardErrors[i] = standardDeviations[i] / sqrtN;
+        }
+        return standardErrors;
+    };
+
+    MLStatMatrix.covariance = function covariance(matrix, dimension) {
+        return MLStatMatrix.scatter(matrix, undefined, dimension);
+    };
+
+    MLStatMatrix.scatter = function scatter(matrix, divisor, dimension) {
+        if (typeof (dimension) === 'undefined') {
+            dimension = 0;
+        }
+        if (typeof (divisor) === 'undefined') {
+            if (dimension === 0) {
+                divisor = matrix.length - 1;
+            } else if (dimension === 1) {
+                divisor = matrix[0].length - 1;
+            }
+        }
+        var means = MLStatMatrix.mean(matrix, dimension);
+        var rows = matrix.length;
+        if (rows === 0) {
+            return [[]];
+        }
+        var cols = matrix[0].length,
+            cov, i, j, s, k;
+
+        if (dimension === 0) {
+            cov = new Array(cols);
+            for (i = 0; i < cols; i++) {
+                cov[i] = new Array(cols);
+            }
+            for (i = 0; i < cols; i++) {
+                for (j = i; j < cols; j++) {
+                    s = 0;
+                    for (k = 0; k < rows; k++) {
+                        s += (matrix[k][j] - means[j]) * (matrix[k][i] - means[i]);
+                    }
+                    s /= divisor;
+                    cov[i][j] = s;
+                    cov[j][i] = s;
+                }
+            }
+        } else if (dimension === 1) {
+            cov = new Array(rows);
+            for (i = 0; i < rows; i++) {
+                cov[i] = new Array(rows);
+            }
+            for (i = 0; i < rows; i++) {
+                for (j = i; j < rows; j++) {
+                    s = 0;
+                    for (k = 0; k < cols; k++) {
+                        s += (matrix[j][k] - means[j]) * (matrix[i][k] - means[i]);
+                    }
+                    s /= divisor;
+                    cov[i][j] = s;
+                    cov[j][i] = s;
+                }
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+
+        return cov;
+    };
+
+    MLStatMatrix.correlation = function correlation(matrix) {
+        var means = MLStatMatrix.mean(matrix),
+            standardDeviations = MLStatMatrix.standardDeviation(matrix, true, means),
+            scores = MLStatMatrix.zScores(matrix, means, standardDeviations),
+            rows = matrix.length,
+            cols = matrix[0].length,
+            i, j;
+
+        var cor = new Array(cols);
+        for (i = 0; i < cols; i++) {
+            cor[i] = new Array(cols);
+        }
+        for (i = 0; i < cols; i++) {
+            for (j = i; j < cols; j++) {
+                var c = 0;
+                for (var k = 0, l = scores.length; k < l; k++) {
+                    c += scores[k][j] * scores[k][i];
+                }
+                c /= rows - 1;
+                cor[i][j] = c;
+                cor[j][i] = c;
+            }
+        }
+        return cor;
+    };
+
+    MLStatMatrix.zScores = function zScores(matrix, means, standardDeviations) {
+        means = means || MLStatMatrix.mean(matrix);
+        if (typeof (standardDeviations) === 'undefined') standardDeviations = MLStatMatrix.standardDeviation(matrix, true, means);
+        return MLStatMatrix.standardize(MLStatMatrix.center(matrix, means, false), standardDeviations, true);
+    };
+
+    MLStatMatrix.center = function center(matrix, means, inPlace) {
+        means = means || MLStatMatrix.mean(matrix);
+        var result = matrix,
+            l = matrix.length,
+            i, j, jj;
+
+        if (!inPlace) {
+            result = new Array(l);
+            for (i = 0; i < l; i++) {
+                result[i] = new Array(matrix[i].length);
+            }
+        }
+
+        for (i = 0; i < l; i++) {
+            var row = result[i];
+            for (j = 0, jj = row.length; j < jj; j++) {
+                row[j] = matrix[i][j] - means[j];
+            }
+        }
+        return result;
+    };
+
+    MLStatMatrix.standardize = function standardize(matrix, standardDeviations, inPlace) {
+        if (typeof (standardDeviations) === 'undefined') standardDeviations = MLStatMatrix.standardDeviation(matrix);
+        var result = matrix,
+            l = matrix.length,
+            i, j, jj;
+
+        if (!inPlace) {
+            result = new Array(l);
+            for (i = 0; i < l; i++) {
+                result[i] = new Array(matrix[i].length);
+            }
+        }
+
+        for (i = 0; i < l; i++) {
+            var resultRow = result[i];
+            var sourceRow = matrix[i];
+            for (j = 0, jj = resultRow.length; j < jj; j++) {
+                if (standardDeviations[j] !== 0 && !isNaN(standardDeviations[j])) {
+                    resultRow[j] = sourceRow[j] / standardDeviations[j];
+                }
+            }
+        }
+        return result;
+    };
+
+    MLStatMatrix.weightedVariance = function weightedVariance(matrix, weights) {
+        var means = MLStatMatrix.mean(matrix);
+        var rows = matrix.length;
+        if (rows === 0) return [];
+        var cols = matrix[0].length;
+        var vari = new Array(cols);
+
+        for (var j = 0; j < cols; j++) {
+            var sum = 0;
+            var a = 0, b = 0;
+
+            for (var i = 0; i < rows; i++) {
+                var z = matrix[i][j] - means[j];
+                var w = weights[i];
+
+                sum += w * (z * z);
+                b += w;
+                a += w * w;
+            }
+
+            vari[j] = sum * (b / (b * b - a));
+        }
+
+        return vari;
+    };
+
+    MLStatMatrix.weightedMean = function weightedMean(matrix, weights, dimension) {
+        if (typeof (dimension) === 'undefined') {
+            dimension = 0;
+        }
+        var rows = matrix.length;
+        if (rows === 0) return [];
+        var cols = matrix[0].length,
+            means, i, ii, j, w, row;
+
+        if (dimension === 0) {
+            means = new Array(cols);
+            for (i = 0; i < cols; i++) {
+                means[i] = 0;
+            }
+            for (i = 0; i < rows; i++) {
+                row = matrix[i];
+                w = weights[i];
+                for (j = 0; j < cols; j++) {
+                    means[j] += row[j] * w;
+                }
+            }
+        } else if (dimension === 1) {
+            means = new Array(rows);
+            for (i = 0; i < rows; i++) {
+                means[i] = 0;
+            }
+            for (j = 0; j < rows; j++) {
+                row = matrix[j];
+                w = weights[j];
+                for (i = 0; i < cols; i++) {
+                    means[j] += row[i] * w;
+                }
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+
+        var weightSum = arrayStat.sum(weights);
+        if (weightSum !== 0) {
+            for (i = 0, ii = means.length; i < ii; i++) {
+                means[i] /= weightSum;
+            }
+        }
+        return means;
+    };
+
+    MLStatMatrix.weightedCovariance = function weightedCovariance(matrix, weights, means, dimension) {
+        dimension = dimension || 0;
+        means = means || MLStatMatrix.weightedMean(matrix, weights, dimension);
+        var s1 = 0, s2 = 0;
+        for (var i = 0, ii = weights.length; i < ii; i++) {
+            s1 += weights[i];
+            s2 += weights[i] * weights[i];
+        }
+        var factor = s1 / (s1 * s1 - s2);
+        return MLStatMatrix.weightedScatter(matrix, weights, means, factor, dimension);
+    };
+
+    MLStatMatrix.weightedScatter = function weightedScatter(matrix, weights, means, factor, dimension) {
+        dimension = dimension || 0;
+        means = means || MLStatMatrix.weightedMean(matrix, weights, dimension);
+        if (typeof (factor) === 'undefined') {
+            factor = 1;
+        }
+        var rows = matrix.length;
+        if (rows === 0) {
+            return [[]];
+        }
+        var cols = matrix[0].length,
+            cov, i, j, k, s;
+
+        if (dimension === 0) {
+            cov = new Array(cols);
+            for (i = 0; i < cols; i++) {
+                cov[i] = new Array(cols);
+            }
+            for (i = 0; i < cols; i++) {
+                for (j = i; j < cols; j++) {
+                    s = 0;
+                    for (k = 0; k < rows; k++) {
+                        s += weights[k] * (matrix[k][j] - means[j]) * (matrix[k][i] - means[i]);
+                    }
+                    cov[i][j] = s * factor;
+                    cov[j][i] = s * factor;
+                }
+            }
+        } else if (dimension === 1) {
+            cov = new Array(rows);
+            for (i = 0; i < rows; i++) {
+                cov[i] = new Array(rows);
+            }
+            for (i = 0; i < rows; i++) {
+                for (j = i; j < rows; j++) {
+                    s = 0;
+                    for (k = 0; k < cols; k++) {
+                        s += weights[k] * (matrix[j][k] - means[j]) * (matrix[i][k] - means[i]);
+                    }
+                    cov[i][j] = s * factor;
+                    cov[j][i] = s * factor;
+                }
+            }
+        } else {
+            throw new Error('Invalid dimension');
+        }
+
+        return cov;
+    };
+}
+
+// ml-stat index.js
+const MLStat = {};
+{
+    MLStat.array = MLStatArray;
+    MLStat.matrix = MLStatMatrix;
+}
+
+
+// ml-array-utils ArrayUtils.js
+const MLArrayUtilsArrayUtils = {};
+{
+    const Stat = MLStat.array;
+    /**
+     * Function that returns an array of points given 1D array as follows:
+     *
+     * [x1, y1, .. , x2, y2, ..]
+     *
+     * And receive the number of dimensions of each point.
+     * @param array
+     * @param dimensions
+     * @returns {Array} - Array of points.
+     */
+    function coordArrayToPoints(array, dimensions) {
+        if(array.length % dimensions !== 0) {
+            throw new RangeError('Dimensions number must be accordance with the size of the array.');
+        }
+
+        var length = array.length / dimensions;
+        var pointsArr = new Array(length);
+
+        var k = 0;
+        for(var i = 0; i < array.length; i += dimensions) {
+            var point = new Array(dimensions);
+            for(var j = 0; j < dimensions; ++j) {
+                point[j] = array[i + j];
+            }
+
+            pointsArr[k] = point;
+            k++;
+        }
+
+        return pointsArr;
+    }
+
+
+    /**
+     * Function that given an array as follows:
+     * [x1, y1, .. , x2, y2, ..]
+     *
+     * Returns an array as follows:
+     * [[x1, x2, ..], [y1, y2, ..], [ .. ]]
+     *
+     * And receives the number of dimensions of each coordinate.
+     * @param array
+     * @param dimensions
+     * @returns {Array} - Matrix of coordinates
+     */
+    function coordArrayToCoordMatrix(array, dimensions) {
+        if(array.length % dimensions !== 0) {
+            throw new RangeError('Dimensions number must be accordance with the size of the array.');
+        }
+
+        var coordinatesArray = new Array(dimensions);
+        var points = array.length / dimensions;
+        for (var i = 0; i < coordinatesArray.length; i++) {
+            coordinatesArray[i] = new Array(points);
+        }
+
+        for(i = 0; i < array.length; i += dimensions) {
+            for(var j = 0; j < dimensions; ++j) {
+                var currentPoint = Math.floor(i / dimensions);
+                coordinatesArray[j][currentPoint] = array[i + j];
+            }
+        }
+
+        return coordinatesArray;
+    }
+
+    /**
+     * Function that receives a coordinate matrix as follows:
+     * [[x1, x2, ..], [y1, y2, ..], [ .. ]]
+     *
+     * Returns an array of coordinates as follows:
+     * [x1, y1, .. , x2, y2, ..]
+     *
+     * @param coordMatrix
+     * @returns {Array}
+     */
+    function coordMatrixToCoordArray(coordMatrix) {
+        var coodinatesArray = new Array(coordMatrix.length * coordMatrix[0].length);
+        var k = 0;
+        for(var i = 0; i < coordMatrix[0].length; ++i) {
+            for(var j = 0; j < coordMatrix.length; ++j) {
+                coodinatesArray[k] = coordMatrix[j][i];
+                ++k;
+            }
+        }
+
+        return coodinatesArray;
+    }
+
+    /**
+     * Tranpose a matrix, this method is for coordMatrixToPoints and
+     * pointsToCoordMatrix, that because only transposing the matrix
+     * you can change your representation.
+     *
+     * @param matrix
+     * @returns {Array}
+     */
+    function transpose(matrix) {
+        var resultMatrix = new Array(matrix[0].length);
+        for(var i = 0; i < resultMatrix.length; ++i) {
+            resultMatrix[i] = new Array(matrix.length);
+        }
+
+        for (i = 0; i < matrix.length; ++i) {
+            for(var j = 0; j < matrix[0].length; ++j) {
+                resultMatrix[j][i] = matrix[i][j];
+            }
+        }
+
+        return resultMatrix;
+    }
+
+    /**
+     * Function that transform an array of points into a coordinates array
+     * as follows:
+     * [x1, y1, .. , x2, y2, ..]
+     *
+     * @param points
+     * @returns {Array}
+     */
+    function pointsToCoordArray(points) {
+        var coodinatesArray = new Array(points.length * points[0].length);
+        var k = 0;
+        for(var i = 0; i < points.length; ++i) {
+            for(var j = 0; j < points[0].length; ++j) {
+                coodinatesArray[k] = points[i][j];
+                ++k;
+            }
+        }
+
+        return coodinatesArray;
+    }
+
+    /**
+     * Apply the dot product between the smaller vector and a subsets of the
+     * largest one.
+     *
+     * @param firstVector
+     * @param secondVector
+     * @returns {Array} each dot product of size of the difference between the
+     *                  larger and the smallest one.
+     */
+    function applyDotProduct(firstVector, secondVector) {
+        var largestVector, smallestVector;
+        if(firstVector.length <= secondVector.length) {
+            smallestVector = firstVector;
+            largestVector = secondVector;
+        } else {
+            smallestVector = secondVector;
+            largestVector = firstVector;
+        }
+
+        var difference = largestVector.length - smallestVector.length + 1;
+        var dotProductApplied = new Array(difference);
+
+        for (var i = 0; i < difference; ++i) {
+            var sum = 0;
+            for (var j = 0; j < smallestVector.length; ++j) {
+                sum += smallestVector[j] * largestVector[i + j];
+            }
+            dotProductApplied[i] = sum;
+        }
+
+        return dotProductApplied;
+    }
+    /**
+     * To scale the input array between the specified min and max values. The operation is performed inplace
+     * if the options.inplace is specified. If only one of the min or max parameters is specified, then the scaling
+     * will multiply the input array by min/min(input) or max/max(input)
+     * @param input
+     * @param options
+     * @returns {*}
+     */
+    function scale(input, options){
+        var y;
+        if(options.inPlace){
+            y = input;
+        }
+        else{
+            y = new Array(input.length);
+        }
+        const max = options.max;
+        const min = options.min;
+        if(typeof max === "number"){
+            if(typeof min === "number"){
+                var minMax = Stat.minMax(input);
+                var factor = (max - min)/(minMax.max-minMax.min);
+                for(var i=0;i< y.length;i++){
+                    y[i]=(input[i]-minMax.min)*factor+min;
+                }
+            }
+            else{
+                var currentMin = Stat.max(input);
+                var factor = max/currentMin;
+                for(var i=0;i< y.length;i++){
+                    y[i] = input[i]*factor;
+                }
+            }
+        }
+        else{
+            if(typeof min === "number"){
+                var currentMin = Stat.min(input);
+                var factor = min/currentMin;
+                for(var i=0;i< y.length;i++){
+                    y[i] = input[i]*factor;
+                }
+            }
+        }
+        return y;
+    }
+
+    MLArrayUtilsArrayUtils.coordArrayToPoints = coordArrayToPoints;
+    MLArrayUtilsArrayUtils.coordArrayToCoordMatrix = coordArrayToCoordMatrix;
+    MLArrayUtilsArrayUtils.coordMatrixToCoordArray = coordMatrixToCoordArray;
+    MLArrayUtilsArrayUtils.coordMatrixToPoints = transpose;
+    MLArrayUtilsArrayUtils.pointsToCoordArray = pointsToCoordArray;
+    MLArrayUtilsArrayUtils.pointsToCoordMatrix = transpose;
+    MLArrayUtilsArrayUtils.applyDotProduct = applyDotProduct;
+    MLArrayUtilsArrayUtils.scale = scale;
+}
+
+
+// ml-array-utils getEquallySpaced.js
+const MLArrayUtilsGetEquallySpaced = {};
+{
+    /**
+     *
+     * Function that returns a Number array of equally spaced numberOfPoints
+     * containing a representation of intensities of the spectra arguments x
+     * and y.
+     *
+     * The options parameter contains an object in the following form:
+     * from: starting point
+     * to: last point
+     * numberOfPoints: number of points between from and to
+     * variant: "slot" or "smooth" - smooth is the default option
+     *
+     * The slot variant consist that each point in the new array is calculated
+     * averaging the existing points between the slot that belongs to the current
+     * value. The smooth variant is the same but takes the integral of the range
+     * of the slot and divide by the step size between two points in the new array.
+     *
+     * @param x - sorted increasing x values
+     * @param y
+     * @param options
+     * @returns {Array} new array with the equally spaced data.
+     *
+     */
+    function getEquallySpacedData(x, y, options) {
+        if (x.length>1 && x[0]>x[1]) {
+            x=x.slice().reverse();
+            y=y.slice().reverse();
+        }
+
+        var xLength = x.length;
+        if(xLength !== y.length)
+            throw new RangeError("the x and y vector doesn't have the same size.");
+
+        if (options === undefined) options = {};
+
+        var from = options.from === undefined ? x[0] : options.from
+        if (isNaN(from) || !isFinite(from)) {
+            throw new RangeError("'From' value must be a number");
+        }
+        var to = options.to === undefined ? x[x.length - 1] : options.to;
+        if (isNaN(to) || !isFinite(to)) {
+            throw new RangeError("'To' value must be a number");
+        }
+
+        var reverse = from > to;
+        if(reverse) {
+            var temp = from;
+            from = to;
+            to = temp;
+        }
+
+        var numberOfPoints = options.numberOfPoints === undefined ? 100 : options.numberOfPoints;
+        if (isNaN(numberOfPoints) || !isFinite(numberOfPoints)) {
+            throw new RangeError("'Number of points' value must be a number");
+        }
+        if(numberOfPoints < 1)
+            throw new RangeError("the number of point must be higher than 1");
+
+        var algorithm = options.variant === "slot" ? "slot" : "smooth"; // default value: smooth
+
+        var output = algorithm === "slot" ? getEquallySpacedSlot(x, y, from, to, numberOfPoints) : getEquallySpacedSmooth(x, y, from, to, numberOfPoints);
+
+        return reverse ? output.reverse() : output;
+    }
+
+    /**
+     * function that retrieves the getEquallySpacedData with the variant "smooth"
+     *
+     * @param x
+     * @param y
+     * @param from - Initial point
+     * @param to - Final point
+     * @param numberOfPoints
+     * @returns {Array} - Array of y's equally spaced with the variant "smooth"
+     */
+    function getEquallySpacedSmooth(x, y, from, to, numberOfPoints) {
+        var xLength = x.length;
+
+        var step = (to - from) / (numberOfPoints - 1);
+        var halfStep = step / 2;
+
+        var start = from - halfStep;
+        var output = new Array(numberOfPoints);
+
+        var initialOriginalStep = x[1] - x[0];
+        var lastOriginalStep = x[x.length - 1] - x[x.length - 2];
+
+        // Init main variables
+        var min = start;
+        var max = start + step;
+
+        var previousX = Number.MIN_VALUE;
+        var previousY = 0;
+        var nextX = x[0] - initialOriginalStep;
+        var nextY = 0;
+
+        var currentValue = 0;
+        var slope = 0;
+        var intercept = 0;
+        var sumAtMin = 0;
+        var sumAtMax = 0;
+
+        var i = 0; // index of input
+        var j = 0; // index of output
+
+        function getSlope(x0, y0, x1, y1) {
+            return (y1 - y0) / (x1 - x0);
+        }
+
+        main: while(true) {
+            while (nextX - max >= 0) {
+                // no overlap with original point, just consume current value
+                var add = integral(0, max - previousX, slope, previousY);
+                sumAtMax = currentValue + add;
+
+                output[j] = (sumAtMax - sumAtMin) / step;
+                j++;
+
+                if (j === numberOfPoints)
+                    break main;
+
+                min = max;
+                max += step;
+                sumAtMin = sumAtMax;
+            }
+
+            if(previousX <= min && min <= nextX) {
+                add = integral(0, min - previousX, slope, previousY);
+                sumAtMin = currentValue + add;
+            }
+
+            currentValue += integral(previousX, nextX, slope, intercept);
+
+            previousX = nextX;
+            previousY = nextY;
+
+            if (i < xLength) {
+                nextX = x[i];
+                nextY = y[i];
+                i++;
+            } else if (i === xLength) {
+                nextX += lastOriginalStep;
+                nextY = 0;
+            }
+            // updating parameters
+            slope = getSlope(previousX, previousY, nextX, nextY);
+            intercept = -slope*previousX + previousY;
+        }
+
+        return output;
+    }
+
+    /**
+     * function that retrieves the getEquallySpacedData with the variant "slot"
+     *
+     * @param x
+     * @param y
+     * @param from - Initial point
+     * @param to - Final point
+     * @param numberOfPoints
+     * @returns {Array} - Array of y's equally spaced with the variant "slot"
+     */
+    function getEquallySpacedSlot(x, y, from, to, numberOfPoints) {
+        var xLength = x.length;
+
+        var step = (to - from) / (numberOfPoints - 1);
+        var halfStep = step / 2;
+        var lastStep = x[x.length - 1] - x[x.length - 2];
+
+        var start = from - halfStep;
+        var output = new Array(numberOfPoints);
+
+        // Init main variables
+        var min = start;
+        var max = start + step;
+
+        var previousX = -Number.MAX_VALUE;
+        var previousY = 0;
+        var nextX = x[0];
+        var nextY = y[0];
+        var frontOutsideSpectra = 0;
+        var backOutsideSpectra = true;
+
+        var currentValue = 0;
+
+        // for slot algorithm
+        var currentPoints = 0;
+
+        var i = 1; // index of input
+        var j = 0; // index of output
+
+        main: while(true) {
+            if (previousX>=nextX) throw (new Error('x must be an increasing serie'));
+            while (previousX - max > 0) {
+                // no overlap with original point, just consume current value
+                if(backOutsideSpectra) {
+                    currentPoints++;
+                    backOutsideSpectra = false;
+                }
+
+                output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;
+                j++;
+
+                if (j === numberOfPoints)
+                    break main;
+
+                min = max;
+                max += step;
+                currentValue = 0;
+                currentPoints = 0;
+            }
+
+            if(previousX > min) {
+                currentValue += previousY;
+                currentPoints++;
+            }
+
+            if(previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1)
+                currentPoints--;
+
+            previousX = nextX;
+            previousY = nextY;
+
+            if (i < xLength) {
+                nextX = x[i];
+                nextY = y[i];
+                i++;
+            } else {
+                nextX += lastStep;
+                nextY = 0;
+                frontOutsideSpectra++;
+            }
+        }
+
+        return output;
+    }
+    /**
+     * Function that calculates the integral of the line between two
+     * x-coordinates, given the slope and intercept of the line.
+     *
+     * @param x0
+     * @param x1
+     * @param slope
+     * @param intercept
+     * @returns {number} integral value.
+     */
+    function integral(x0, x1, slope, intercept) {
+        return (0.5 * slope * x1 * x1 + intercept * x1) - (0.5 * slope * x0 * x0 + intercept * x0);
+    }
+
+    MLArrayUtilsGetEquallySpaced.getEquallySpacedData = getEquallySpacedData;
+    MLArrayUtilsGetEquallySpaced.integral = integral;
+}
+
+
+// ml-array-utils snv.js
+const MLArrayUtilsSNV = {};
+{
+    MLArrayUtilsSNV.SNV = SNV;
+    let Stat = MLStat.array;
+
+    /**
+     * Function that applies the standard normal variate (SNV) to an array of values.
+     *
+     * @param data - Array of values.
+     * @returns {Array} - applied the SNV.
+     */
+    function SNV(data) {
+        var mean = Stat.mean(data);
+        var std = Stat.standardDeviation(data);
+        var result = data.slice();
+        for (var i = 0; i < data.length; i++) {
+            result[i] = (result[i] - mean) / std;
+        }
+        return result;
+    }
+}
+
+// ml-array-utils index.js
+const MLArrayUtils = {};
+{
+    MLArrayUtils.getEquallySpacedData = MLArrayUtilsGetEquallySpaced.getEquallySpacedData;
+    MLArrayUtils.SNV = MLArrayUtilsSNV.SNV;
+}
+
+
+
+// do this early so things can use it. This is from ml-matrix src/matrix.js
+const MLMatrixMatrix = {};
+
+// ml-matrix src/util.js
+const MLMatrixUtil = {};
+{
+    let exports = MLMatrixUtil;
+    let Matrix = MLMatrixMatrix;
+
+    /**
+     * @private
+     * Check that a row index is not out of bounds
+     * @param {Matrix} matrix
+     * @param {number} index
+     * @param {boolean} [outer]
+     */
+    exports.checkRowIndex = function checkRowIndex(matrix, index, outer) {
+        var max = outer ? matrix.rows : matrix.rows - 1;
+        if (index < 0 || index > max) {
+            throw new RangeError('Row index out of range');
+        }
+    };
+
+    /**
+     * @private
+     * Check that a column index is not out of bounds
+     * @param {Matrix} matrix
+     * @param {number} index
+     * @param {boolean} [outer]
+     */
+    exports.checkColumnIndex = function checkColumnIndex(matrix, index, outer) {
+        var max = outer ? matrix.columns : matrix.columns - 1;
+        if (index < 0 || index > max) {
+            throw new RangeError('Column index out of range');
+        }
+    };
+
+    /**
+     * @private
+     * Check that the provided vector is an array with the right length
+     * @param {Matrix} matrix
+     * @param {Array|Matrix} vector
+     * @return {Array}
+     * @throws {RangeError}
+     */
+    exports.checkRowVector = function checkRowVector(matrix, vector) {
+        if (vector.to1DArray) {
+            vector = vector.to1DArray();
+        }
+        if (vector.length !== matrix.columns) {
+            throw new RangeError('vector size must be the same as the number of columns');
+        }
+        return vector;
+    };
+
+    /**
+     * @private
+     * Check that the provided vector is an array with the right length
+     * @param {Matrix} matrix
+     * @param {Array|Matrix} vector
+     * @return {Array}
+     * @throws {RangeError}
+     */
+    exports.checkColumnVector = function checkColumnVector(matrix, vector) {
+        if (vector.to1DArray) {
+            vector = vector.to1DArray();
+        }
+        if (vector.length !== matrix.rows) {
+            throw new RangeError('vector size must be the same as the number of rows');
+        }
+        return vector;
+    };
+
+    exports.checkIndices = function checkIndices(matrix, rowIndices, columnIndices) {
+        var rowOut = rowIndices.some(r => {
+            return r < 0 || r >= matrix.rows;
+
+        });
+
+        var columnOut = columnIndices.some(c => {
+            return c < 0 || c >= matrix.columns;
+        });
+
+        if (rowOut || columnOut) {
+            throw new RangeError('Indices are out of range');
+        }
+
+        if (typeof rowIndices !== 'object' || typeof columnIndices !== 'object') {
+            throw new TypeError('Unexpected type for row/column indices');
+        }
+        if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);
+        if (!Array.isArray(columnIndices)) rowIndices = Array.from(columnIndices);
+
+        return {
+            row: rowIndices,
+            column: columnIndices
+        };
+    };
+
+    exports.checkRange = function checkRange(matrix, startRow, endRow, startColumn, endColumn) {
+        if (arguments.length !== 5) throw new TypeError('Invalid argument type');
+        var notAllNumbers = Array.from(arguments).slice(1).some(function (arg) {
+            return typeof arg !== 'number';
+        });
+        if (notAllNumbers) throw new TypeError('Invalid argument type');
+        if (startRow > endRow || startColumn > endColumn || startRow < 0 || startRow >= matrix.rows || endRow < 0 || endRow >= matrix.rows || startColumn < 0 || startColumn >= matrix.columns || endColumn < 0 || endColumn >= matrix.columns) {
+            throw new RangeError('Submatrix indices are out of range');
+        }
+    };
+
+    exports.getRange = function getRange(from, to) {
+        var arr = new Array(to - from + 1);
+        for (var i = 0; i < arr.length; i++) {
+            arr[i] = from + i;
+        }
+        return arr;
+    };
+
+    exports.sumByRow = function sumByRow(matrix) {
+        var sum = Matrix.Matrix.zeros(matrix.rows, 1);
+        for (var i = 0; i < matrix.rows; ++i) {
+            for (var j = 0; j < matrix.columns; ++j) {
+                sum.set(i, 0, sum.get(i, 0) + matrix.get(i, j));
+            }
+        }
+        return sum;
+    };
+
+    exports.sumByColumn = function sumByColumn(matrix) {
+        var sum = Matrix.Matrix.zeros(1, matrix.columns);
+        for (var i = 0; i < matrix.rows; ++i) {
+            for (var j = 0; j < matrix.columns; ++j) {
+                sum.set(0, j, sum.get(0, j) + matrix.get(i, j));
+            }
+        }
+        return sum;
+    };
+
+    exports.sumAll = function sumAll(matrix) {
+        var v = 0;
+        for (var i = 0; i < matrix.rows; i++) {
+            for (var j = 0; j < matrix.columns; j++) {
+                v += matrix.get(i, j);
+            }
+        }
+        return v;
+    };
+}
+
+// ml-matrix symbolsspecies.js
+if (!Symbol.species) {
+    Symbol.species = Symbol.for('@@species');
+}
+
+
+// ml-matrix src/dc/util.js
+const MLMatrixDCUtil = {};
+{
+    let exports = MLMatrixDCUtil;
+    exports.hypotenuse = function hypotenuse(a, b) {
+        var r;
+        if (Math.abs(a) > Math.abs(b)) {
+            r = b / a;
+            return Math.abs(a) * Math.sqrt(1 + r * r);
+        }
+        if (b !== 0) {
+            r = a / b;
+            return Math.abs(b) * Math.sqrt(1 + r * r);
+        }
+        return 0;
+    };
+
+    // For use in the decomposition algorithms. With big matrices, access time is
+    // too long on elements from array subclass
+    // todo check when it is fixed in v8
+    // http://jsperf.com/access-and-write-array-subclass
+    exports.getEmpty2DArray = function (rows, columns) {
+        var array = new Array(rows);
+        for (var i = 0; i < rows; i++) {
+            array[i] = new Array(columns);
+        }
+        return array;
+    };
+
+    exports.getFilled2DArray = function (rows, columns, value) {
+        var array = new Array(rows);
+        for (var i = 0; i < rows; i++) {
+            array[i] = new Array(columns);
+            for (var j = 0; j < columns; j++) {
+                array[i][j] = value;
+            }
+        }
+        return array;
+    };
+}
+
+// ml-matrix src/dc/lu.js
+let MLMatrixDCLU = {};
+{
+    let Matrix = MLMatrixMatrix;
+
+    // https://github.com/lutzroeder/Mapack/blob/master/Source/LuDecomposition.cs
+    function LuDecomposition(matrix) {
+        if (!(this instanceof LuDecomposition)) {
+            return new LuDecomposition(matrix);
+        }
+
+        matrix = Matrix.Matrix.checkMatrix(matrix);
+
+        var lu = matrix.clone(),
+            rows = lu.rows,
+            columns = lu.columns,
+            pivotVector = new Array(rows),
+            pivotSign = 1,
+            i, j, k, p, s, t, v,
+            LUrowi, LUcolj, kmax;
+
+        for (i = 0; i < rows; i++) {
+            pivotVector[i] = i;
+        }
+
+        LUcolj = new Array(rows);
+
+        for (j = 0; j < columns; j++) {
+
+            for (i = 0; i < rows; i++) {
+                LUcolj[i] = lu[i][j];
+            }
+
+            for (i = 0; i < rows; i++) {
+                LUrowi = lu[i];
+                kmax = Math.min(i, j);
+                s = 0;
+                for (k = 0; k < kmax; k++) {
+                    s += LUrowi[k] * LUcolj[k];
+                }
+                LUrowi[j] = LUcolj[i] -= s;
+            }
+
+            p = j;
+            for (i = j + 1; i < rows; i++) {
+                if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
+                    p = i;
+                }
+            }
+
+            if (p !== j) {
+                for (k = 0; k < columns; k++) {
+                    t = lu[p][k];
+                    lu[p][k] = lu[j][k];
+                    lu[j][k] = t;
+                }
+
+                v = pivotVector[p];
+                pivotVector[p] = pivotVector[j];
+                pivotVector[j] = v;
+
+                pivotSign = -pivotSign;
+            }
+
+            if (j < rows && lu[j][j] !== 0) {
+                for (i = j + 1; i < rows; i++) {
+                    lu[i][j] /= lu[j][j];
+                }
+            }
+        }
+
+        this.LU = lu;
+        this.pivotVector = pivotVector;
+        this.pivotSign = pivotSign;
+    }
+
+    LuDecomposition.prototype = {
+        isSingular: function () {
+            var data = this.LU,
+                col = data.columns;
+            for (var j = 0; j < col; j++) {
+                if (data[j][j] === 0) {
+                    return true;
+                }
+            }
+            return false;
+        },
+        get determinant() {
+            var data = this.LU;
+            if (!data.isSquare()) {
+                throw new Error('Matrix must be square');
+            }
+            var determinant = this.pivotSign, col = data.columns;
+            for (var j = 0; j < col; j++) {
+                determinant *= data[j][j];
+            }
+            return determinant;
+        },
+        get lowerTriangularMatrix() {
+            var data = this.LU,
+                rows = data.rows,
+                columns = data.columns,
+                X = new Matrix.Matrix(rows, columns);
+            for (var i = 0; i < rows; i++) {
+                for (var j = 0; j < columns; j++) {
+                    if (i > j) {
+                        X[i][j] = data[i][j];
+                    } else if (i === j) {
+                        X[i][j] = 1;
+                    } else {
+                        X[i][j] = 0;
+                    }
+                }
+            }
+            return X;
+        },
+        get upperTriangularMatrix() {
+            var data = this.LU,
+                rows = data.rows,
+                columns = data.columns,
+                X = new Matrix.Matrix(rows, columns);
+            for (var i = 0; i < rows; i++) {
+                for (var j = 0; j < columns; j++) {
+                    if (i <= j) {
+                        X[i][j] = data[i][j];
+                    } else {
+                        X[i][j] = 0;
+                    }
+                }
+            }
+            return X;
+        },
+        get pivotPermutationVector() {
+            return this.pivotVector.slice();
+        },
+        solve: function (value) {
+            value = Matrix.Matrix.checkMatrix(value);
+
+            var lu = this.LU,
+                rows = lu.rows;
+
+            if (rows !== value.rows) {
+                throw new Error('Invalid matrix dimensions');
+            }
+            if (this.isSingular()) {
+                throw new Error('LU matrix is singular');
+            }
+
+            var count = value.columns;
+            var X = value.subMatrixRow(this.pivotVector, 0, count - 1);
+            var columns = lu.columns;
+            var i, j, k;
+
+            for (k = 0; k < columns; k++) {
+                for (i = k + 1; i < columns; i++) {
+                    for (j = 0; j < count; j++) {
+                        X[i][j] -= X[k][j] * lu[i][k];
+                    }
+                }
+            }
+            for (k = columns - 1; k >= 0; k--) {
+                for (j = 0; j < count; j++) {
+                    X[k][j] /= lu[k][k];
+                }
+                for (i = 0; i < k; i++) {
+                    for (j = 0; j < count; j++) {
+                        X[i][j] -= X[k][j] * lu[i][k];
+                    }
+                }
+            }
+            return X;
+        }
+    };
+
+    MLMatrixDCLU = LuDecomposition;
+}
+
+
+// ml-matrix src/dc/svd.js
+let MLMatrixDCSVD = {};
+{
+    let Matrix = MLMatrixMatrix;
+    let util = MLMatrixDCUtil;
+    let hypotenuse = util.hypotenuse;
+    let getFilled2DArray = util.getFilled2DArray;
+
+    // https://github.com/lutzroeder/Mapack/blob/master/Source/SingularValueDecomposition.cs
+    function SingularValueDecomposition(value, options) {
+        if (!(this instanceof SingularValueDecomposition)) {
+            return new SingularValueDecomposition(value, options);
+        }
+        value = Matrix.Matrix.checkMatrix(value);
+
+        options = options || {};
+
+        var m = value.rows,
+            n = value.columns,
+            nu = Math.min(m, n);
+
+        var wantu = true, wantv = true;
+        if (options.computeLeftSingularVectors === false) wantu = false;
+        if (options.computeRightSingularVectors === false) wantv = false;
+        var autoTranspose = options.autoTranspose === true;
+
+        var swapped = false;
+        var a;
+        if (m < n) {
+            if (!autoTranspose) {
+                a = value.clone();
+                // eslint-disable-next-line no-console
+                console.warn('Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose');
+            } else {
+                a = value.transpose();
+                m = a.rows;
+                n = a.columns;
+                swapped = true;
+                var aux = wantu;
+                wantu = wantv;
+                wantv = aux;
+            }
+        } else {
+            a = value.clone();
+        }
+
+        var s = new Array(Math.min(m + 1, n)),
+            U = getFilled2DArray(m, nu, 0),
+            V = getFilled2DArray(n, n, 0),
+            e = new Array(n),
+            work = new Array(m);
+
+        var nct = Math.min(m - 1, n);
+        var nrt = Math.max(0, Math.min(n - 2, m));
+
+        var i, j, k, p, t, ks, f, cs, sn, max, kase,
+            scale, sp, spm1, epm1, sk, ek, b, c, shift, g;
+
+        for (k = 0, max = Math.max(nct, nrt); k < max; k++) {
+            if (k < nct) {
+                s[k] = 0;
+                for (i = k; i < m; i++) {
+                    s[k] = hypotenuse(s[k], a[i][k]);
+                }
+                if (s[k] !== 0) {
+                    if (a[k][k] < 0) {
+                        s[k] = -s[k];
+                    }
+                    for (i = k; i < m; i++) {
+                        a[i][k] /= s[k];
+                    }
+                    a[k][k] += 1;
+                }
+                s[k] = -s[k];
+            }
+
+            for (j = k + 1; j < n; j++) {
+                if ((k < nct) && (s[k] !== 0)) {
+                    t = 0;
+                    for (i = k; i < m; i++) {
+                        t += a[i][k] * a[i][j];
+                    }
+                    t = -t / a[k][k];
+                    for (i = k; i < m; i++) {
+                        a[i][j] += t * a[i][k];
+                    }
+                }
+                e[j] = a[k][j];
+            }
+
+            if (wantu && (k < nct)) {
+                for (i = k; i < m; i++) {
+                    U[i][k] = a[i][k];
+                }
+            }
+
+            if (k < nrt) {
+                e[k] = 0;
+                for (i = k + 1; i < n; i++) {
+                    e[k] = hypotenuse(e[k], e[i]);
+                }
+                if (e[k] !== 0) {
+                    if (e[k + 1] < 0) {
+                        e[k] = 0 - e[k];
+                    }
+                    for (i = k + 1; i < n; i++) {
+                        e[i] /= e[k];
+                    }
+                    e[k + 1] += 1;
+                }
+                e[k] = -e[k];
+                if ((k + 1 < m) && (e[k] !== 0)) {
+                    for (i = k + 1; i < m; i++) {
+                        work[i] = 0;
+                    }
+                    for (j = k + 1; j < n; j++) {
+                        for (i = k + 1; i < m; i++) {
+                            work[i] += e[j] * a[i][j];
+                        }
+                    }
+                    for (j = k + 1; j < n; j++) {
+                        t = -e[j] / e[k + 1];
+                        for (i = k + 1; i < m; i++) {
+                            a[i][j] += t * work[i];
+                        }
+                    }
+                }
+                if (wantv) {
+                    for (i = k + 1; i < n; i++) {
+                        V[i][k] = e[i];
+                    }
+                }
+            }
+        }
+
+        p = Math.min(n, m + 1);
+        if (nct < n) {
+            s[nct] = a[nct][nct];
+        }
+        if (m < p) {
+            s[p - 1] = 0;
+        }
+        if (nrt + 1 < p) {
+            e[nrt] = a[nrt][p - 1];
+        }
+        e[p - 1] = 0;
+
+        if (wantu) {
+            for (j = nct; j < nu; j++) {
+                for (i = 0; i < m; i++) {
+                    U[i][j] = 0;
+                }
+                U[j][j] = 1;
+            }
+            for (k = nct - 1; k >= 0; k--) {
+                if (s[k] !== 0) {
+                    for (j = k + 1; j < nu; j++) {
+                        t = 0;
+                        for (i = k; i < m; i++) {
+                            t += U[i][k] * U[i][j];
+                        }
+                        t = -t / U[k][k];
+                        for (i = k; i < m; i++) {
+                            U[i][j] += t * U[i][k];
+                        }
+                    }
+                    for (i = k; i < m; i++) {
+                        U[i][k] = -U[i][k];
+                    }
+                    U[k][k] = 1 + U[k][k];
+                    for (i = 0; i < k - 1; i++) {
+                        U[i][k] = 0;
+                    }
+                } else {
+                    for (i = 0; i < m; i++) {
+                        U[i][k] = 0;
+                    }
+                    U[k][k] = 1;
+                }
+            }
+        }
+
+        if (wantv) {
+            for (k = n - 1; k >= 0; k--) {
+                if ((k < nrt) && (e[k] !== 0)) {
+                    for (j = k + 1; j < n; j++) {
+                        t = 0;
+                        for (i = k + 1; i < n; i++) {
+                            t += V[i][k] * V[i][j];
+                        }
+                        t = -t / V[k + 1][k];
+                        for (i = k + 1; i < n; i++) {
+                            V[i][j] += t * V[i][k];
+                        }
+                    }
+                }
+                for (i = 0; i < n; i++) {
+                    V[i][k] = 0;
+                }
+                V[k][k] = 1;
+            }
+        }
+
+        var pp = p - 1,
+            iter = 0,
+            eps = Math.pow(2, -52);
+        while (p > 0) {
+            for (k = p - 2; k >= -1; k--) {
+                if (k === -1) {
+                    break;
+                }
+                if (Math.abs(e[k]) <= eps * (Math.abs(s[k]) + Math.abs(s[k + 1]))) {
+                    e[k] = 0;
+                    break;
+                }
+            }
+            if (k === p - 2) {
+                kase = 4;
+            } else {
+                for (ks = p - 1; ks >= k; ks--) {
+                    if (ks === k) {
+                        break;
+                    }
+                    t = (ks !== p ? Math.abs(e[ks]) : 0) + (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);
+                    if (Math.abs(s[ks]) <= eps * t) {
+                        s[ks] = 0;
+                        break;
+                    }
+                }
+                if (ks === k) {
+                    kase = 3;
+                } else if (ks === p - 1) {
+                    kase = 1;
+                } else {
+                    kase = 2;
+                    k = ks;
+                }
+            }
+
+            k++;
+
+            switch (kase) {
+                case 1: {
+                    f = e[p - 2];
+                    e[p - 2] = 0;
+                    for (j = p - 2; j >= k; j--) {
+                        t = hypotenuse(s[j], f);
+                        cs = s[j] / t;
+                        sn = f / t;
+                        s[j] = t;
+                        if (j !== k) {
+                            f = -sn * e[j - 1];
+                            e[j - 1] = cs * e[j - 1];
+                        }
+                        if (wantv) {
+                            for (i = 0; i < n; i++) {
+                                t = cs * V[i][j] + sn * V[i][p - 1];
+                                V[i][p - 1] = -sn * V[i][j] + cs * V[i][p - 1];
+                                V[i][j] = t;
+                            }
+                        }
+                    }
+                    break;
+                }
+                case 2 : {
+                    f = e[k - 1];
+                    e[k - 1] = 0;
+                    for (j = k; j < p; j++) {
+                        t = hypotenuse(s[j], f);
+                        cs = s[j] / t;
+                        sn = f / t;
+                        s[j] = t;
+                        f = -sn * e[j];
+                        e[j] = cs * e[j];
+                        if (wantu) {
+                            for (i = 0; i < m; i++) {
+                                t = cs * U[i][j] + sn * U[i][k - 1];
+                                U[i][k - 1] = -sn * U[i][j] + cs * U[i][k - 1];
+                                U[i][j] = t;
+                            }
+                        }
+                    }
+                    break;
+                }
+                case 3 : {
+                    scale = Math.max(Math.max(Math.max(Math.max(Math.abs(s[p - 1]), Math.abs(s[p - 2])), Math.abs(e[p - 2])), Math.abs(s[k])), Math.abs(e[k]));
+                    sp = s[p - 1] / scale;
+                    spm1 = s[p - 2] / scale;
+                    epm1 = e[p - 2] / scale;
+                    sk = s[k] / scale;
+                    ek = e[k] / scale;
+                    b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
+                    c = (sp * epm1) * (sp * epm1);
+                    shift = 0;
+                    if ((b !== 0) || (c !== 0)) {
+                        shift = Math.sqrt(b * b + c);
+                        if (b < 0) {
+                            shift = -shift;
+                        }
+                        shift = c / (b + shift);
+                    }
+                    f = (sk + sp) * (sk - sp) + shift;
+                    g = sk * ek;
+                    for (j = k; j < p - 1; j++) {
+                        t = hypotenuse(f, g);
+                        cs = f / t;
+                        sn = g / t;
+                        if (j !== k) {
+                            e[j - 1] = t;
+                        }
+                        f = cs * s[j] + sn * e[j];
+                        e[j] = cs * e[j] - sn * s[j];
+                        g = sn * s[j + 1];
+                        s[j + 1] = cs * s[j + 1];
+                        if (wantv) {
+                            for (i = 0; i < n; i++) {
+                                t = cs * V[i][j] + sn * V[i][j + 1];
+                                V[i][j + 1] = -sn * V[i][j] + cs * V[i][j + 1];
+                                V[i][j] = t;
+                            }
+                        }
+                        t = hypotenuse(f, g);
+                        cs = f / t;
+                        sn = g / t;
+                        s[j] = t;
+                        f = cs * e[j] + sn * s[j + 1];
+                        s[j + 1] = -sn * e[j] + cs * s[j + 1];
+                        g = sn * e[j + 1];
+                        e[j + 1] = cs * e[j + 1];
+                        if (wantu && (j < m - 1)) {
+                            for (i = 0; i < m; i++) {
+                                t = cs * U[i][j] + sn * U[i][j + 1];
+                                U[i][j + 1] = -sn * U[i][j] + cs * U[i][j + 1];
+                                U[i][j] = t;
+                            }
+                        }
+                    }
+                    e[p - 2] = f;
+                    iter = iter + 1;
+                    break;
+                }
+                case 4: {
+                    if (s[k] <= 0) {
+                        s[k] = (s[k] < 0 ? -s[k] : 0);
+                        if (wantv) {
+                            for (i = 0; i <= pp; i++) {
+                                V[i][k] = -V[i][k];
+                            }
+                        }
+                    }
+                    while (k < pp) {
+                        if (s[k] >= s[k + 1]) {
+                            break;
+                        }
+                        t = s[k];
+                        s[k] = s[k + 1];
+                        s[k + 1] = t;
+                        if (wantv && (k < n - 1)) {
+                            for (i = 0; i < n; i++) {
+                                t = V[i][k + 1];
+                                V[i][k + 1] = V[i][k];
+                                V[i][k] = t;
+                            }
+                        }
+                        if (wantu && (k < m - 1)) {
+                            for (i = 0; i < m; i++) {
+                                t = U[i][k + 1];
+                                U[i][k + 1] = U[i][k];
+                                U[i][k] = t;
+                            }
+                        }
+                        k++;
+                    }
+                    iter = 0;
+                    p--;
+                    break;
+                }
+                // no default
+            }
+        }
+
+        if (swapped) {
+            var tmp = V;
+            V = U;
+            U = tmp;
+        }
+
+        this.m = m;
+        this.n = n;
+        this.s = s;
+        this.U = U;
+        this.V = V;
+    }
+
+    SingularValueDecomposition.prototype = {
+        get condition() {
+            return this.s[0] / this.s[Math.min(this.m, this.n) - 1];
+        },
+        get norm2() {
+            return this.s[0];
+        },
+        get rank() {
+            var eps = Math.pow(2, -52),
+                tol = Math.max(this.m, this.n) * this.s[0] * eps,
+                r = 0,
+                s = this.s;
+            for (var i = 0, ii = s.length; i < ii; i++) {
+                if (s[i] > tol) {
+                    r++;
+                }
+            }
+            return r;
+        },
+        get diagonal() {
+            return this.s;
+        },
+        // https://github.com/accord-net/framework/blob/development/Sources/Accord.Math/Decompositions/SingularValueDecomposition.cs
+        get threshold() {
+            return (Math.pow(2, -52) / 2) * Math.max(this.m, this.n) * this.s[0];
+        },
+        get leftSingularVectors() {
+            if (!Matrix.Matrix.isMatrix(this.U)) {
+                this.U = new Matrix.Matrix(this.U);
+            }
+            return this.U;
+        },
+        get rightSingularVectors() {
+            if (!Matrix.Matrix.isMatrix(this.V)) {
+                this.V = new Matrix.Matrix(this.V);
+            }
+            return this.V;
+        },
+        get diagonalMatrix() {
+            return Matrix.Matrix.diag(this.s);
+        },
+        solve: function (value) {
+
+            var Y = value,
+                e = this.threshold,
+                scols = this.s.length,
+                Ls = Matrix.Matrix.zeros(scols, scols),
+                i;
+
+            for (i = 0; i < scols; i++) {
+                if (Math.abs(this.s[i]) <= e) {
+                    Ls[i][i] = 0;
+                } else {
+                    Ls[i][i] = 1 / this.s[i];
+                }
+            }
+
+            var U = this.U;
+            var V = this.rightSingularVectors;
+
+            var VL = V.mmul(Ls),
+                vrows = V.rows,
+                urows = U.length,
+                VLU = Matrix.Matrix.zeros(vrows, urows),
+                j, k, sum;
+
+            for (i = 0; i < vrows; i++) {
+                for (j = 0; j < urows; j++) {
+                    sum = 0;
+                    for (k = 0; k < scols; k++) {
+                        sum += VL[i][k] * U[j][k];
+                    }
+                    VLU[i][j] = sum;
+                }
+            }
+
+            return VLU.mmul(Y);
+        },
+        solveForDiagonal: function (value) {
+            return this.solve(Matrix.Matrix.diag(value));
+        },
+        inverse: function () {
+            var V = this.V;
+            var e = this.threshold,
+                vrows = V.length,
+                vcols = V[0].length,
+                X = new Matrix.Matrix(vrows, this.s.length),
+                i, j;
+
+            for (i = 0; i < vrows; i++) {
+                for (j = 0; j < vcols; j++) {
+                    if (Math.abs(this.s[j]) > e) {
+                        X[i][j] = V[i][j] / this.s[j];
+                    } else {
+                        X[i][j] = 0;
+                    }
+                }
+            }
+
+            var U = this.U;
+
+            var urows = U.length,
+                ucols = U[0].length,
+                Y = new Matrix.Matrix(vrows, urows),
+                k, sum;
+
+            for (i = 0; i < vrows; i++) {
+                for (j = 0; j < urows; j++) {
+                    sum = 0;
+                    for (k = 0; k < ucols; k++) {
+                        sum += X[i][k] * U[j][k];
+                    }
+                    Y[i][j] = sum;
+                }
+            }
+
+            return Y;
+        }
+    };
+
+    MLMatrixDCSVD = SingularValueDecomposition;
+}
+
+// ml-matrix src/abstractMatrix.js
+let MLMatrixAbstractMatrix;
+{
+    let LuDecomposition = MLMatrixDCLU;
+    let SvDecomposition = MLMatrixDCSVD;
+    let arrayUtils = MLArrayUtils;
+    let util = MLMatrixUtil;
+
+    MLMatrixAbstractMatrix = function abstractMatrix(superCtor) {
+        if (superCtor === undefined) superCtor = Object;
+
+        /**
+         * Real matrix
+         * @class Matrix
+         * @param {number|Array|Matrix} nRows - Number of rows of the new matrix,
+         * 2D array containing the data or Matrix instance to clone
+         * @param {number} [nColumns] - Number of columns of the new matrix
+         */
+        class Matrix extends superCtor {
+            static get [Symbol.species]() {
+                return this;
+            }
+
+            /**
+             * Constructs a Matrix with the chosen dimensions from a 1D array
+             * @param {number} newRows - Number of rows
+             * @param {number} newColumns - Number of columns
+             * @param {Array} newData - A 1D array containing data for the matrix
+             * @return {Matrix} - The new matrix
+             */
+            static from1DArray(newRows, newColumns, newData) {
+                var length = newRows * newColumns;
+                if (length !== newData.length) {
+                    throw new RangeError('Data length does not match given dimensions');
+                }
+                var newMatrix = new this(newRows, newColumns);
+                for (var row = 0; row < newRows; row++) {
+                    for (var column = 0; column < newColumns; column++) {
+                        newMatrix.set(row, column, newData[row * newColumns + column]);
+                    }
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Creates a row vector, a matrix with only one row.
+             * @param {Array} newData - A 1D array containing data for the vector
+             * @return {Matrix} - The new matrix
+             */
+            static rowVector(newData) {
+                var vector = new this(1, newData.length);
+                for (var i = 0; i < newData.length; i++) {
+                    vector.set(0, i, newData[i]);
+                }
+                return vector;
+            }
+
+            /**
+             * Creates a column vector, a matrix with only one column.
+             * @param {Array} newData - A 1D array containing data for the vector
+             * @return {Matrix} - The new matrix
+             */
+            static columnVector(newData) {
+                var vector = new this(newData.length, 1);
+                for (var i = 0; i < newData.length; i++) {
+                    vector.set(i, 0, newData[i]);
+                }
+                return vector;
+            }
+
+            /**
+             * Creates an empty matrix with the given dimensions. Values will be undefined. Same as using new Matrix(rows, columns).
+             * @param {number} rows - Number of rows
+             * @param {number} columns - Number of columns
+             * @return {Matrix} - The new matrix
+             */
+            static empty(rows, columns) {
+                return new this(rows, columns);
+            }
+
+            /**
+             * Creates a matrix with the given dimensions. Values will be set to zero.
+             * @param {number} rows - Number of rows
+             * @param {number} columns - Number of columns
+             * @return {Matrix} - The new matrix
+             */
+            static zeros(rows, columns) {
+                return this.empty(rows, columns).fill(0);
+            }
+
+            /**
+             * Creates a matrix with the given dimensions. Values will be set to one.
+             * @param {number} rows - Number of rows
+             * @param {number} columns - Number of columns
+             * @return {Matrix} - The new matrix
+             */
+            static ones(rows, columns) {
+                return this.empty(rows, columns).fill(1);
+            }
+
+            /**
+             * Creates a matrix with the given dimensions. Values will be randomly set.
+             * @param {number} rows - Number of rows
+             * @param {number} columns - Number of columns
+             * @param {function} [rng=Math.random] - Random number generator
+             * @return {Matrix} The new matrix
+             */
+            static rand(rows, columns, rng) {
+                if (rng === undefined) rng = Math.random;
+                var matrix = this.empty(rows, columns);
+                for (var i = 0; i < rows; i++) {
+                    for (var j = 0; j < columns; j++) {
+                        matrix.set(i, j, rng());
+                    }
+                }
+                return matrix;
+            }
+
+            /**
+             * Creates a matrix with the given dimensions. Values will be random integers.
+             * @param {number} rows - Number of rows
+             * @param {number} columns - Number of columns
+             * @param {number} [maxValue=1000] - Maximum value
+             * @param {function} [rng=Math.random] - Random number generator
+             * @return {Matrix} The new matrix
+             */
+            static randInt(rows, columns, maxValue, rng) {
+                if (maxValue === undefined) maxValue = 1000;
+                if (rng === undefined) rng = Math.random;
+                var matrix = this.empty(rows, columns);
+                for (var i = 0; i < rows; i++) {
+                    for (var j = 0; j < columns; j++) {
+                        var value = Math.floor(rng() * maxValue);
+                        matrix.set(i, j, value);
+                    }
+                }
+                return matrix;
+            }
+
+            /**
+             * Creates an identity matrix with the given dimension. Values of the diagonal will be 1 and others will be 0.
+             * @param {number} rows - Number of rows
+             * @param {number} [columns=rows] - Number of columns
+             * @param {number} [value=1] - Value to fill the diagonal with
+             * @return {Matrix} - The new identity matrix
+             */
+            static eye(rows, columns, value) {
+                if (columns === undefined) columns = rows;
+                if (value === undefined) value = 1;
+                var min = Math.min(rows, columns);
+                var matrix = this.zeros(rows, columns);
+                for (var i = 0; i < min; i++) {
+                    matrix.set(i, i, value);
+                }
+                return matrix;
+            }
+
+            /**
+             * Creates a diagonal matrix based on the given array.
+             * @param {Array} data - Array containing the data for the diagonal
+             * @param {number} [rows] - Number of rows (Default: data.length)
+             * @param {number} [columns] - Number of columns (Default: rows)
+             * @return {Matrix} - The new diagonal matrix
+             */
+            static diag(data, rows, columns) {
+                var l = data.length;
+                if (rows === undefined) rows = l;
+                if (columns === undefined) columns = rows;
+                var min = Math.min(l, rows, columns);
+                var matrix = this.zeros(rows, columns);
+                for (var i = 0; i < min; i++) {
+                    matrix.set(i, i, data[i]);
+                }
+                return matrix;
+            }
+
+            /**
+             * Returns a matrix whose elements are the minimum between matrix1 and matrix2
+             * @param {Matrix} matrix1
+             * @param {Matrix} matrix2
+             * @return {Matrix}
+             */
+            static min(matrix1, matrix2) {
+                matrix1 = this.checkMatrix(matrix1);
+                matrix2 = this.checkMatrix(matrix2);
+                var rows = matrix1.rows;
+                var columns = matrix1.columns;
+                var result = new this(rows, columns);
+                for (var i = 0; i < rows; i++) {
+                    for (var j = 0; j < columns; j++) {
+                        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));
+                    }
+                }
+                return result;
+            }
+
+            /**
+             * Returns a matrix whose elements are the maximum between matrix1 and matrix2
+             * @param {Matrix} matrix1
+             * @param {Matrix} matrix2
+             * @return {Matrix}
+             */
+            static max(matrix1, matrix2) {
+                matrix1 = this.checkMatrix(matrix1);
+                matrix2 = this.checkMatrix(matrix2);
+                var rows = matrix1.rows;
+                var columns = matrix1.columns;
+                var result = new this(rows, columns);
+                for (var i = 0; i < rows; i++) {
+                    for (var j = 0; j < columns; j++) {
+                        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));
+                    }
+                }
+                return result;
+            }
+
+            /**
+             * Check that the provided value is a Matrix and tries to instantiate one if not
+             * @param {*} value - The value to check
+             * @return {Matrix}
+             */
+            static checkMatrix(value) {
+                return Matrix.isMatrix(value) ? value : new this(value);
+            }
+
+            /**
+             * Returns true if the argument is a Matrix, false otherwise
+             * @param {*} value - The value to check
+             * @return {boolean}
+             */
+            static isMatrix(value) {
+                return (value != null) && (value.klass === 'Matrix');
+            }
+
+            /**
+             * @prop {number} size - The number of elements in the matrix.
+             */
+            get size() {
+                return this.rows * this.columns;
+            }
+
+            /**
+             * Applies a callback for each element of the matrix. The function is called in the matrix (this) context.
+             * @param {function} callback - Function that will be called with two parameters : i (row) and j (column)
+             * @return {Matrix} this
+             */
+            apply(callback) {
+                if (typeof callback !== 'function') {
+                    throw new TypeError('callback must be a function');
+                }
+                var ii = this.rows;
+                var jj = this.columns;
+                for (var i = 0; i < ii; i++) {
+                    for (var j = 0; j < jj; j++) {
+                        callback.call(this, i, j);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Returns a new 1D array filled row by row with the matrix values
+             * @return {Array}
+             */
+            to1DArray() {
+                var array = new Array(this.size);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        array[i * this.columns + j] = this.get(i, j);
+                    }
+                }
+                return array;
+            }
+
+            /**
+             * Returns a 2D array containing a copy of the data
+             * @return {Array}
+             */
+            to2DArray() {
+                var copy = new Array(this.rows);
+                for (var i = 0; i < this.rows; i++) {
+                    copy[i] = new Array(this.columns);
+                    for (var j = 0; j < this.columns; j++) {
+                        copy[i][j] = this.get(i, j);
+                    }
+                }
+                return copy;
+            }
+
+            /**
+             * @return {boolean} true if the matrix has one row
+             */
+            isRowVector() {
+                return this.rows === 1;
+            }
+
+            /**
+             * @return {boolean} true if the matrix has one column
+             */
+            isColumnVector() {
+                return this.columns === 1;
+            }
+
+            /**
+             * @return {boolean} true if the matrix has one row or one column
+             */
+            isVector() {
+                return (this.rows === 1) || (this.columns === 1);
+            }
+
+            /**
+             * @return {boolean} true if the matrix has the same number of rows and columns
+             */
+            isSquare() {
+                return this.rows === this.columns;
+            }
+
+            /**
+             * @return {boolean} true if the matrix is square and has the same values on both sides of the diagonal
+             */
+            isSymmetric() {
+                if (this.isSquare()) {
+                    for (var i = 0; i < this.rows; i++) {
+                        for (var j = 0; j <= i; j++) {
+                            if (this.get(i, j) !== this.get(j, i)) {
+                                return false;
+                            }
+                        }
+                    }
+                    return true;
+                }
+                return false;
+            }
+
+            /**
+             * Sets a given element of the matrix. mat.set(3,4,1) is equivalent to mat[3][4]=1
+             * @abstract
+             * @param {number} rowIndex - Index of the row
+             * @param {number} columnIndex - Index of the column
+             * @param {number} value - The new value for the element
+             * @return {Matrix} this
+             */
+            set(rowIndex, columnIndex, value) { // eslint-disable-line no-unused-vars
+                throw new Error('set method is unimplemented');
+            }
+
+            /**
+             * Returns the given element of the matrix. mat.get(3,4) is equivalent to matrix[3][4]
+             * @abstract
+             * @param {number} rowIndex - Index of the row
+             * @param {number} columnIndex - Index of the column
+             * @return {number}
+             */
+            get(rowIndex, columnIndex) { // eslint-disable-line no-unused-vars
+                throw new Error('get method is unimplemented');
+            }
+
+            /**
+             * Creates a new matrix that is a repetition of the current matrix. New matrix has rowRep times the number of
+             * rows of the matrix, and colRep times the number of columns of the matrix
+             * @param {number} rowRep - Number of times the rows should be repeated
+             * @param {number} colRep - Number of times the columns should be re
+             * @return {Matrix}
+             * @example
+             * var matrix = new Matrix([[1,2]]);
+             * matrix.repeat(2); // [[1,2],[1,2]]
+             */
+            repeat(rowRep, colRep) {
+                rowRep = rowRep || 1;
+                colRep = colRep || 1;
+                var matrix = new this.constructor[Symbol.species](this.rows * rowRep, this.columns * colRep);
+                for (var i = 0; i < rowRep; i++) {
+                    for (var j = 0; j < colRep; j++) {
+                        matrix.setSubMatrix(this, this.rows * i, this.columns * j);
+                    }
+                }
+                return matrix;
+            }
+
+            /**
+             * Fills the matrix with a given value. All elements will be set to this value.
+             * @param {number} value - New value
+             * @return {Matrix} this
+             */
+            fill(value) {
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, value);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Negates the matrix. All elements will be multiplied by (-1)
+             * @return {Matrix} this
+             */
+            neg() {
+                return this.mulS(-1);
+            }
+
+            /**
+             * Returns a new array from the given row index
+             * @param {number} index - Row index
+             * @return {Array}
+             */
+            getRow(index) {
+                util.checkRowIndex(this, index);
+                var row = new Array(this.columns);
+                for (var i = 0; i < this.columns; i++) {
+                    row[i] = this.get(index, i);
+                }
+                return row;
+            }
+
+            /**
+             * Returns a new row vector from the given row index
+             * @param {number} index - Row index
+             * @return {Matrix}
+             */
+            getRowVector(index) {
+                return this.constructor.rowVector(this.getRow(index));
+            }
+
+            /**
+             * Sets a row at the given index
+             * @param {number} index - Row index
+             * @param {Array|Matrix} array - Array or vector
+             * @return {Matrix} this
+             */
+            setRow(index, array) {
+                util.checkRowIndex(this, index);
+                array = util.checkRowVector(this, array);
+                for (var i = 0; i < this.columns; i++) {
+                    this.set(index, i, array[i]);
+                }
+                return this;
+            }
+
+            /**
+             * Swaps two rows
+             * @param {number} row1 - First row index
+             * @param {number} row2 - Second row index
+             * @return {Matrix} this
+             */
+            swapRows(row1, row2) {
+                util.checkRowIndex(this, row1);
+                util.checkRowIndex(this, row2);
+                for (var i = 0; i < this.columns; i++) {
+                    var temp = this.get(row1, i);
+                    this.set(row1, i, this.get(row2, i));
+                    this.set(row2, i, temp);
+                }
+                return this;
+            }
+
+            /**
+             * Returns a new array from the given column index
+             * @param {number} index - Column index
+             * @return {Array}
+             */
+            getColumn(index) {
+                util.checkColumnIndex(this, index);
+                var column = new Array(this.rows);
+                for (var i = 0; i < this.rows; i++) {
+                    column[i] = this.get(i, index);
+                }
+                return column;
+            }
+
+            /**
+             * Returns a new column vector from the given column index
+             * @param {number} index - Column index
+             * @return {Matrix}
+             */
+            getColumnVector(index) {
+                return this.constructor.columnVector(this.getColumn(index));
+            }
+
+            /**
+             * Sets a column at the given index
+             * @param {number} index - Column index
+             * @param {Array|Matrix} array - Array or vector
+             * @return {Matrix} this
+             */
+            setColumn(index, array) {
+                util.checkColumnIndex(this, index);
+                array = util.checkColumnVector(this, array);
+                for (var i = 0; i < this.rows; i++) {
+                    this.set(i, index, array[i]);
+                }
+                return this;
+            }
+
+            /**
+             * Swaps two columns
+             * @param {number} column1 - First column index
+             * @param {number} column2 - Second column index
+             * @return {Matrix} this
+             */
+            swapColumns(column1, column2) {
+                util.checkColumnIndex(this, column1);
+                util.checkColumnIndex(this, column2);
+                for (var i = 0; i < this.rows; i++) {
+                    var temp = this.get(i, column1);
+                    this.set(i, column1, this.get(i, column2));
+                    this.set(i, column2, temp);
+                }
+                return this;
+            }
+
+            /**
+             * Adds the values of a vector to each row
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            addRowVector(vector) {
+                vector = util.checkRowVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) + vector[j]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Subtracts the values of a vector from each row
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            subRowVector(vector) {
+                vector = util.checkRowVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) - vector[j]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Multiplies the values of a vector with each row
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            mulRowVector(vector) {
+                vector = util.checkRowVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) * vector[j]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Divides the values of each row by those of a vector
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            divRowVector(vector) {
+                vector = util.checkRowVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) / vector[j]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Adds the values of a vector to each column
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            addColumnVector(vector) {
+                vector = util.checkColumnVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) + vector[i]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Subtracts the values of a vector from each column
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            subColumnVector(vector) {
+                vector = util.checkColumnVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) - vector[i]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Multiplies the values of a vector with each column
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            mulColumnVector(vector) {
+                vector = util.checkColumnVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) * vector[i]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Divides the values of each column by those of a vector
+             * @param {Array|Matrix} vector - Array or vector
+             * @return {Matrix} this
+             */
+            divColumnVector(vector) {
+                vector = util.checkColumnVector(this, vector);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        this.set(i, j, this.get(i, j) / vector[i]);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Multiplies the values of a row with a scalar
+             * @param {number} index - Row index
+             * @param {number} value
+             * @return {Matrix} this
+             */
+            mulRow(index, value) {
+                util.checkRowIndex(this, index);
+                for (var i = 0; i < this.columns; i++) {
+                    this.set(index, i, this.get(index, i) * value);
+                }
+                return this;
+            }
+
+            /**
+             * Multiplies the values of a column with a scalar
+             * @param {number} index - Column index
+             * @param {number} value
+             * @return {Matrix} this
+             */
+            mulColumn(index, value) {
+                util.checkColumnIndex(this, index);
+                for (var i = 0; i < this.rows; i++) {
+                    this.set(i, index, this.get(i, index) * value);
+                }
+                return this;
+            }
+
+            /**
+             * Returns the maximum value of the matrix
+             * @return {number}
+             */
+            max() {
+                var v = this.get(0, 0);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        if (this.get(i, j) > v) {
+                            v = this.get(i, j);
+                        }
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the maximum value
+             * @return {Array}
+             */
+            maxIndex() {
+                var v = this.get(0, 0);
+                var idx = [0, 0];
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        if (this.get(i, j) > v) {
+                            v = this.get(i, j);
+                            idx[0] = i;
+                            idx[1] = j;
+                        }
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns the minimum value of the matrix
+             * @return {number}
+             */
+            min() {
+                var v = this.get(0, 0);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        if (this.get(i, j) < v) {
+                            v = this.get(i, j);
+                        }
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the minimum value
+             * @return {Array}
+             */
+            minIndex() {
+                var v = this.get(0, 0);
+                var idx = [0, 0];
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        if (this.get(i, j) < v) {
+                            v = this.get(i, j);
+                            idx[0] = i;
+                            idx[1] = j;
+                        }
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns the maximum value of one row
+             * @param {number} row - Row index
+             * @return {number}
+             */
+            maxRow(row) {
+                util.checkRowIndex(this, row);
+                var v = this.get(row, 0);
+                for (var i = 1; i < this.columns; i++) {
+                    if (this.get(row, i) > v) {
+                        v = this.get(row, i);
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the maximum value of one row
+             * @param {number} row - Row index
+             * @return {Array}
+             */
+            maxRowIndex(row) {
+                util.checkRowIndex(this, row);
+                var v = this.get(row, 0);
+                var idx = [row, 0];
+                for (var i = 1; i < this.columns; i++) {
+                    if (this.get(row, i) > v) {
+                        v = this.get(row, i);
+                        idx[1] = i;
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns the minimum value of one row
+             * @param {number} row - Row index
+             * @return {number}
+             */
+            minRow(row) {
+                util.checkRowIndex(this, row);
+                var v = this.get(row, 0);
+                for (var i = 1; i < this.columns; i++) {
+                    if (this.get(row, i) < v) {
+                        v = this.get(row, i);
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the maximum value of one row
+             * @param {number} row - Row index
+             * @return {Array}
+             */
+            minRowIndex(row) {
+                util.checkRowIndex(this, row);
+                var v = this.get(row, 0);
+                var idx = [row, 0];
+                for (var i = 1; i < this.columns; i++) {
+                    if (this.get(row, i) < v) {
+                        v = this.get(row, i);
+                        idx[1] = i;
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns the maximum value of one column
+             * @param {number} column - Column index
+             * @return {number}
+             */
+            maxColumn(column) {
+                util.checkColumnIndex(this, column);
+                var v = this.get(0, column);
+                for (var i = 1; i < this.rows; i++) {
+                    if (this.get(i, column) > v) {
+                        v = this.get(i, column);
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the maximum value of one column
+             * @param {number} column - Column index
+             * @return {Array}
+             */
+            maxColumnIndex(column) {
+                util.checkColumnIndex(this, column);
+                var v = this.get(0, column);
+                var idx = [0, column];
+                for (var i = 1; i < this.rows; i++) {
+                    if (this.get(i, column) > v) {
+                        v = this.get(i, column);
+                        idx[0] = i;
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns the minimum value of one column
+             * @param {number} column - Column index
+             * @return {number}
+             */
+            minColumn(column) {
+                util.checkColumnIndex(this, column);
+                var v = this.get(0, column);
+                for (var i = 1; i < this.rows; i++) {
+                    if (this.get(i, column) < v) {
+                        v = this.get(i, column);
+                    }
+                }
+                return v;
+            }
+
+            /**
+             * Returns the index of the minimum value of one column
+             * @param {number} column - Column index
+             * @return {Array}
+             */
+            minColumnIndex(column) {
+                util.checkColumnIndex(this, column);
+                var v = this.get(0, column);
+                var idx = [0, column];
+                for (var i = 1; i < this.rows; i++) {
+                    if (this.get(i, column) < v) {
+                        v = this.get(i, column);
+                        idx[0] = i;
+                    }
+                }
+                return idx;
+            }
+
+            /**
+             * Returns an array containing the diagonal values of the matrix
+             * @return {Array}
+             */
+            diag() {
+                var min = Math.min(this.rows, this.columns);
+                var diag = new Array(min);
+                for (var i = 0; i < min; i++) {
+                    diag[i] = this.get(i, i);
+                }
+                return diag;
+            }
+
+            /**
+             * Returns the sum by the argument given, if no argument given,
+             * it returns the sum of all elements of the matrix.
+             * @param {string} by - sum by 'row' or 'column'.
+             * @return {Matrix|number}
+             */
+            sum(by) {
+                switch (by) {
+                    case 'row':
+                        return util.sumByRow(this);
+                    case 'column':
+                        return util.sumByColumn(this);
+                    default:
+                        return util.sumAll(this);
+                }
+            }
+
+            /**
+             * Returns the mean of all elements of the matrix
+             * @return {number}
+             */
+            mean() {
+                return this.sum() / this.size;
+            }
+
+            /**
+             * Returns the product of all elements of the matrix
+             * @return {number}
+             */
+            prod() {
+                var prod = 1;
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        prod *= this.get(i, j);
+                    }
+                }
+                return prod;
+            }
+
+            /**
+             * Computes the cumulative sum of the matrix elements (in place, row by row)
+             * @return {Matrix} this
+             */
+            cumulativeSum() {
+                var sum = 0;
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        sum += this.get(i, j);
+                        this.set(i, j, sum);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Computes the dot (scalar) product between the matrix and another
+             * @param {Matrix} vector2 vector
+             * @return {number}
+             */
+            dot(vector2) {
+                if (Matrix.isMatrix(vector2)) vector2 = vector2.to1DArray();
+                var vector1 = this.to1DArray();
+                if (vector1.length !== vector2.length) {
+                    throw new RangeError('vectors do not have the same size');
+                }
+                var dot = 0;
+                for (var i = 0; i < vector1.length; i++) {
+                    dot += vector1[i] * vector2[i];
+                }
+                return dot;
+            }
+
+            /**
+             * Returns the matrix product between this and other
+             * @param {Matrix} other
+             * @return {Matrix}
+             */
+            mmul(other) {
+                other = this.constructor.checkMatrix(other);
+                if (this.columns !== other.rows) {
+                    // eslint-disable-next-line no-console
+                    console.warn('Number of columns of left matrix are not equal to number of rows of right matrix.');
+                }
+
+                var m = this.rows;
+                var n = this.columns;
+                var p = other.columns;
+
+                var result = new this.constructor[Symbol.species](m, p);
+
+                var Bcolj = new Array(n);
+                for (var j = 0; j < p; j++) {
+                    for (var k = 0; k < n; k++) {
+                        Bcolj[k] = other.get(k, j);
+                    }
+
+                    for (var i = 0; i < m; i++) {
+                        var s = 0;
+                        for (k = 0; k < n; k++) {
+                            s += this.get(i, k) * Bcolj[k];
+                        }
+
+                        result.set(i, j, s);
+                    }
+                }
+                return result;
+            }
+
+            strassen2x2(other) {
+                var result = new this.constructor[Symbol.species](2, 2);
+                const a11 = this.get(0, 0);
+                const b11 = other.get(0, 0);
+                const a12 = this.get(0, 1);
+                const b12 = other.get(0, 1);
+                const a21 = this.get(1, 0);
+                const b21 = other.get(1, 0);
+                const a22 = this.get(1, 1);
+                const b22 = other.get(1, 1);
+
+                // Compute intermediate values.
+                const m1 = (a11 + a22) * (b11 + b22);
+                const m2 = (a21 + a22) * b11;
+                const m3 = a11 * (b12 - b22);
+                const m4 = a22 * (b21 - b11);
+                const m5 = (a11 + a12) * b22;
+                const m6 = (a21 - a11) * (b11 + b12);
+                const m7 = (a12 - a22) * (b21 + b22);
+
+                // Combine intermediate values into the output.
+                const c00 = m1 + m4 - m5 + m7;
+                const c01 = m3 + m5;
+                const c10 = m2 + m4;
+                const c11 = m1 - m2 + m3 + m6;
+
+                result.set(0, 0, c00);
+                result.set(0, 1, c01);
+                result.set(1, 0, c10);
+                result.set(1, 1, c11);
+                return result;
+            }
+
+            strassen3x3(other) {
+                var result = new this.constructor[Symbol.species](3, 3);
+
+                const a00 = this.get(0, 0);
+                const a01 = this.get(0, 1);
+                const a02 = this.get(0, 2);
+                const a10 = this.get(1, 0);
+                const a11 = this.get(1, 1);
+                const a12 = this.get(1, 2);
+                const a20 = this.get(2, 0);
+                const a21 = this.get(2, 1);
+                const a22 = this.get(2, 2);
+
+                const b00 = other.get(0, 0);
+                const b01 = other.get(0, 1);
+                const b02 = other.get(0, 2);
+                const b10 = other.get(1, 0);
+                const b11 = other.get(1, 1);
+                const b12 = other.get(1, 2);
+                const b20 = other.get(2, 0);
+                const b21 = other.get(2, 1);
+                const b22 = other.get(2, 2);
+
+                const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;
+                const m2 = (a00 - a10) * (-b01 + b11);
+                const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);
+                const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);
+                const m5 = (a10 + a11) * (-b00 + b01);
+                const m6 = a00 * b00;
+                const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);
+                const m8 = (-a00 + a20) * (b02 - b12);
+                const m9 = (a20 + a21) * (-b00 + b02);
+                const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;
+                const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);
+                const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);
+                const m13 = (a02 - a22) * (b11 - b21);
+                const m14 = a02 * b20;
+                const m15 = (a21 + a22) * (-b20 + b21);
+                const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);
+                const m17 = (a02 - a12) * (b12 - b22);
+                const m18 = (a11 + a12) * (-b20 + b22);
+                const m19 = a01 * b10;
+                const m20 = a12 * b21;
+                const m21 = a10 * b02;
+                const m22 = a20 * b01;
+                const m23 = a22 * b22;
+
+                const c00 = m6 + m14 + m19;
+                const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;
+                const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;
+                const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;
+                const c11 = m2 + m4 + m5 + m6 + m20;
+                const c12 = m14 + m16 + m17 + m18 + m21;
+                const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;
+                const c21 = m12 + m13 + m14 + m15 + m22;
+                const c22 = m6 + m7 + m8 + m9 + m23;
+
+                result.set(0, 0, c00);
+                result.set(0, 1, c01);
+                result.set(0, 2, c02);
+                result.set(1, 0, c10);
+                result.set(1, 1, c11);
+                result.set(1, 2, c12);
+                result.set(2, 0, c20);
+                result.set(2, 1, c21);
+                result.set(2, 2, c22);
+                return result;
+            }
+
+            /**
+             * Returns the matrix product between x and y. More efficient than mmul(other) only when we multiply squared matrix and when the size of the matrix is > 1000.
+             * @param {Matrix} y
+             * @return {Matrix}
+             */
+            mmulStrassen(y) {
+                var x = this.clone();
+                var r1 = x.rows;
+                var c1 = x.columns;
+                var r2 = y.rows;
+                var c2 = y.columns;
+                if (c1 !== r2) {
+                    // eslint-disable-next-line no-console
+                    console.warn(`Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`);
+                }
+
+                // Put a matrix into the top left of a matrix of zeros.
+                // `rows` and `cols` are the dimensions of the output matrix.
+                function embed(mat, rows, cols) {
+                    var r = mat.rows;
+                    var c = mat.columns;
+                    if ((r === rows) && (c === cols)) {
+                        return mat;
+                    } else {
+                        var resultat = Matrix.zeros(rows, cols);
+                        resultat = resultat.setSubMatrix(mat, 0, 0);
+                        return resultat;
+                    }
+                }
+
+
+                // Make sure both matrices are the same size.
+                // This is exclusively for simplicity:
+                // this algorithm can be implemented with matrices of different sizes.
+
+                var r = Math.max(r1, r2);
+                var c = Math.max(c1, c2);
+                x = embed(x, r, c);
+                y = embed(y, r, c);
+
+                // Our recursive multiplication function.
+                function blockMult(a, b, rows, cols) {
+                    // For small matrices, resort to naive multiplication.
+                    if (rows <= 512 || cols <= 512) {
+                        return a.mmul(b); // a is equivalent to this
+                    }
+
+                    // Apply dynamic padding.
+                    if ((rows % 2 === 1) && (cols % 2 === 1)) {
+                        a = embed(a, rows + 1, cols + 1);
+                        b = embed(b, rows + 1, cols + 1);
+                    } else if (rows % 2 === 1) {
+                        a = embed(a, rows + 1, cols);
+                        b = embed(b, rows + 1, cols);
+                    } else if (cols % 2 === 1) {
+                        a = embed(a, rows, cols + 1);
+                        b = embed(b, rows, cols + 1);
+                    }
+
+                    var halfRows = parseInt(a.rows / 2);
+                    var halfCols = parseInt(a.columns / 2);
+                    // Subdivide input matrices.
+                    var a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);
+                    var b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);
+
+                    var a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);
+                    var b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);
+
+                    var a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);
+                    var b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);
+
+                    var a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);
+                    var b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);
+
+                    // Compute intermediate values.
+                    var m1 = blockMult(Matrix.add(a11, a22), Matrix.add(b11, b22), halfRows, halfCols);
+                    var m2 = blockMult(Matrix.add(a21, a22), b11, halfRows, halfCols);
+                    var m3 = blockMult(a11, Matrix.sub(b12, b22), halfRows, halfCols);
+                    var m4 = blockMult(a22, Matrix.sub(b21, b11), halfRows, halfCols);
+                    var m5 = blockMult(Matrix.add(a11, a12), b22, halfRows, halfCols);
+                    var m6 = blockMult(Matrix.sub(a21, a11), Matrix.add(b11, b12), halfRows, halfCols);
+                    var m7 = blockMult(Matrix.sub(a12, a22), Matrix.add(b21, b22), halfRows, halfCols);
+
+                    // Combine intermediate values into the output.
+                    var c11 = Matrix.add(m1, m4);
+                    c11.sub(m5);
+                    c11.add(m7);
+                    var c12 = Matrix.add(m3, m5);
+                    var c21 = Matrix.add(m2, m4);
+                    var c22 = Matrix.sub(m1, m2);
+                    c22.add(m3);
+                    c22.add(m6);
+
+                    //Crop output to the desired size (undo dynamic padding).
+                    var resultat = Matrix.zeros(2 * c11.rows, 2 * c11.columns);
+                    resultat = resultat.setSubMatrix(c11, 0, 0);
+                    resultat = resultat.setSubMatrix(c12, c11.rows, 0);
+                    resultat = resultat.setSubMatrix(c21, 0, c11.columns);
+                    resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);
+                    return resultat.subMatrix(0, rows - 1, 0, cols - 1);
+                }
+                return blockMult(x, y, r, c);
+            }
+
+            /**
+             * Returns a row-by-row scaled matrix
+             * @param {number} [min=0] - Minimum scaled value
+             * @param {number} [max=1] - Maximum scaled value
+             * @return {Matrix} - The scaled matrix
+             */
+            scaleRows(min, max) {
+                min = min === undefined ? 0 : min;
+                max = max === undefined ? 1 : max;
+                if (min >= max) {
+                    throw new RangeError('min should be strictly smaller than max');
+                }
+                var newMatrix = this.constructor.empty(this.rows, this.columns);
+                for (var i = 0; i < this.rows; i++) {
+                    var scaled = arrayUtils.scale(this.getRow(i), {min, max});
+                    newMatrix.setRow(i, scaled);
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Returns a new column-by-column scaled matrix
+             * @param {number} [min=0] - Minimum scaled value
+             * @param {number} [max=1] - Maximum scaled value
+             * @return {Matrix} - The new scaled matrix
+             * @example
+             * var matrix = new Matrix([[1,2],[-1,0]]);
+             * var scaledMatrix = matrix.scaleColumns(); // [[1,1],[0,0]]
+             */
+            scaleColumns(min, max) {
+                min = min === undefined ? 0 : min;
+                max = max === undefined ? 1 : max;
+                if (min >= max) {
+                    throw new RangeError('min should be strictly smaller than max');
+                }
+                var newMatrix = this.constructor.empty(this.rows, this.columns);
+                for (var i = 0; i < this.columns; i++) {
+                    var scaled = arrayUtils.scale(this.getColumn(i), {
+                        min: min,
+                        max: max
+                    });
+                    newMatrix.setColumn(i, scaled);
+                }
+                return newMatrix;
+            }
+
+
+            /**
+             * Returns the Kronecker product (also known as tensor product) between this and other
+             * See https://en.wikipedia.org/wiki/Kronecker_product
+             * @param {Matrix} other
+             * @return {Matrix}
+             */
+            kroneckerProduct(other) {
+                other = this.constructor.checkMatrix(other);
+
+                var m = this.rows;
+                var n = this.columns;
+                var p = other.rows;
+                var q = other.columns;
+
+                var result = new this.constructor[Symbol.species](m * p, n * q);
+                for (var i = 0; i < m; i++) {
+                    for (var j = 0; j < n; j++) {
+                        for (var k = 0; k < p; k++) {
+                            for (var l = 0; l < q; l++) {
+                                result[p * i + k][q * j + l] = this.get(i, j) * other.get(k, l);
+                            }
+                        }
+                    }
+                }
+                return result;
+            }
+
+            /**
+             * Transposes the matrix and returns a new one containing the result
+             * @return {Matrix}
+             */
+            transpose() {
+                var result = new this.constructor[Symbol.species](this.columns, this.rows);
+                for (var i = 0; i < this.rows; i++) {
+                    for (var j = 0; j < this.columns; j++) {
+                        result.set(j, i, this.get(i, j));
+                    }
+                }
+                return result;
+            }
+
+            /**
+             * Sorts the rows (in place)
+             * @param {function} compareFunction - usual Array.prototype.sort comparison function
+             * @return {Matrix} this
+             */
+            sortRows(compareFunction) {
+                if (compareFunction === undefined) compareFunction = compareNumbers;
+                for (var i = 0; i < this.rows; i++) {
+                    this.setRow(i, this.getRow(i).sort(compareFunction));
+                }
+                return this;
+            }
+
+            /**
+             * Sorts the columns (in place)
+             * @param {function} compareFunction - usual Array.prototype.sort comparison function
+             * @return {Matrix} this
+             */
+            sortColumns(compareFunction) {
+                if (compareFunction === undefined) compareFunction = compareNumbers;
+                for (var i = 0; i < this.columns; i++) {
+                    this.setColumn(i, this.getColumn(i).sort(compareFunction));
+                }
+                return this;
+            }
+
+            /**
+             * Returns a subset of the matrix
+             * @param {number} startRow - First row index
+             * @param {number} endRow - Last row index
+             * @param {number} startColumn - First column index
+             * @param {number} endColumn - Last column index
+             * @return {Matrix}
+             */
+            subMatrix(startRow, endRow, startColumn, endColumn) {
+                util.checkRange(this, startRow, endRow, startColumn, endColumn);
+                var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, endColumn - startColumn + 1);
+                for (var i = startRow; i <= endRow; i++) {
+                    for (var j = startColumn; j <= endColumn; j++) {
+                        newMatrix[i - startRow][j - startColumn] = this.get(i, j);
+                    }
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Returns a subset of the matrix based on an array of row indices
+             * @param {Array} indices - Array containing the row indices
+             * @param {number} [startColumn = 0] - First column index
+             * @param {number} [endColumn = this.columns-1] - Last column index
+             * @return {Matrix}
+             */
+            subMatrixRow(indices, startColumn, endColumn) {
+                if (startColumn === undefined) startColumn = 0;
+                if (endColumn === undefined) endColumn = this.columns - 1;
+                if ((startColumn > endColumn) || (startColumn < 0) || (startColumn >= this.columns) || (endColumn < 0) || (endColumn >= this.columns)) {
+                    throw new RangeError('Argument out of range');
+                }
+
+                var newMatrix = new this.constructor[Symbol.species](indices.length, endColumn - startColumn + 1);
+                for (var i = 0; i < indices.length; i++) {
+                    for (var j = startColumn; j <= endColumn; j++) {
+                        if (indices[i] < 0 || indices[i] >= this.rows) {
+                            throw new RangeError('Row index out of range: ' + indices[i]);
+                        }
+                        newMatrix.set(i, j - startColumn, this.get(indices[i], j));
+                    }
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Returns a subset of the matrix based on an array of column indices
+             * @param {Array} indices - Array containing the column indices
+             * @param {number} [startRow = 0] - First row index
+             * @param {number} [endRow = this.rows-1] - Last row index
+             * @return {Matrix}
+             */
+            subMatrixColumn(indices, startRow, endRow) {
+                if (startRow === undefined) startRow = 0;
+                if (endRow === undefined) endRow = this.rows - 1;
+                if ((startRow > endRow) || (startRow < 0) || (startRow >= this.rows) || (endRow < 0) || (endRow >= this.rows)) {
+                    throw new RangeError('Argument out of range');
+                }
+
+                var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, indices.length);
+                for (var i = 0; i < indices.length; i++) {
+                    for (var j = startRow; j <= endRow; j++) {
+                        if (indices[i] < 0 || indices[i] >= this.columns) {
+                            throw new RangeError('Column index out of range: ' + indices[i]);
+                        }
+                        newMatrix.set(j - startRow, i, this.get(j, indices[i]));
+                    }
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Set a part of the matrix to the given sub-matrix
+             * @param {Matrix|Array< Array >} matrix - The source matrix from which to extract values.
+             * @param {number} startRow - The index of the first row to set
+             * @param {number} startColumn - The index of the first column to set
+             * @return {Matrix}
+             */
+            setSubMatrix(matrix, startRow, startColumn) {
+                matrix = this.constructor.checkMatrix(matrix);
+                var endRow = startRow + matrix.rows - 1;
+                var endColumn = startColumn + matrix.columns - 1;
+                util.checkRange(this, startRow, endRow, startColumn, endColumn);
+                for (var i = 0; i < matrix.rows; i++) {
+                    for (var j = 0; j < matrix.columns; j++) {
+                        this[startRow + i][startColumn + j] = matrix.get(i, j);
+                    }
+                }
+                return this;
+            }
+
+            /**
+             * Return a new matrix based on a selection of rows and columns
+             * @param {Array<number>} rowIndices - The row indices to select. Order matters and an index can be more than once.
+             * @param {Array<number>} columnIndices - The column indices to select. Order matters and an index can be use more than once.
+             * @return {Matrix} The new matrix
+             */
+            selection(rowIndices, columnIndices) {
+                var indices = util.checkIndices(this, rowIndices, columnIndices);
+                var newMatrix = new this.constructor[Symbol.species](rowIndices.length, columnIndices.length);
+                for (var i = 0; i < indices.row.length; i++) {
+                    var rowIndex = indices.row[i];
+                    for (var j = 0; j < indices.column.length; j++) {
+                        var columnIndex = indices.column[j];
+                        newMatrix[i][j] = this.get(rowIndex, columnIndex);
+                    }
+                }
+                return newMatrix;
+            }
+
+            /**
+             * Returns the trace of the matrix (sum of the diagonal elements)
+             * @return {number}
+             */
+            trace() {
+                var min = Math.min(this.rows, this.columns);
+                var trace = 0;
+                for (var i = 0; i < min; i++) {
+                    trace += this.get(i, i);
+                }
+                return trace;
+            }
+
+            /*
+             Matrix views
+             */
+
+            /**
+             * Returns a view of the transposition of the matrix
+             * @return {MatrixTransposeView}
+             */
+            transposeView() {
+                return new MLMatrixTransposeView(this);
+            }
+
+            /**
+             * Returns a view of the row vector with the given index
+             * @param {number} row - row index of the vector
+             * @return {MatrixRowView}
+             */
+            rowView(row) {
+                util.checkRowIndex(this, row);
+                return new MLMatrixRowView(this, row);
+            }
+
+            /**
+             * Returns a view of the column vector with the given index
+             * @param {number} column - column index of the vector
+             * @return {MatrixColumnView}
+             */
+            columnView(column) {
+                util.checkColumnIndex(this, column);
+                return new MLMatrixColumnView(this, column);
+            }
+
+            /**
+             * Returns a view of the matrix flipped in the row axis
+             * @return {MatrixFlipRowView}
+             */
+            flipRowView() {
+                return new MLMatrixFlipRowView(this);
+            }
+
+            /**
+             * Returns a view of the matrix flipped in the column axis
+             * @return {MatrixFlipColumnView}
+             */
+            flipColumnView() {
+                return new MLMatrixFlipColumnView(this);
+            }
+
+            /**
+             * Returns a view of a submatrix giving the index boundaries
+             * @param {number} startRow - first row index of the submatrix
+             * @param {number} endRow - last row index of the submatrix
+             * @param {number} startColumn - first column index of the submatrix
+             * @param {number} endColumn - last column index of the submatrix
+             * @return {MatrixSubView}
+             */
+            subMatrixView(startRow, endRow, startColumn, endColumn) {
+                return new MLMatrixSubView(this, startRow, endRow, startColumn, endColumn);
+            }
+
+            /**
+             * Returns a view of the cross of the row indices and the column indices
+             * @example
+             * // resulting vector is [[2], [2]]
+             * var matrix = new Matrix([[1,2,3], [4,5,6]]).selectionView([0, 0], [1])
+             * @param {Array<number>} rowIndices
+             * @param {Array<number>} columnIndices
+             * @return {MatrixSelectionView}
+             */
+            selectionView(rowIndices, columnIndices) {
+                return new MLMatrixSelectionView(this, rowIndices, columnIndices);
+            }
+
+
+            /**
+            * Calculates and returns the determinant of a matrix as a Number
+            * @example
+            *   new Matrix([[1,2,3], [4,5,6]]).det()
+            * @return {number}
+            */
+            det() {
+                if (this.isSquare()) {
+                    var a, b, c, d;
+                    if (this.columns === 2) {
+                        // 2 x 2 matrix
+                        a = this.get(0, 0);
+                        b = this.get(0, 1);
+                        c = this.get(1, 0);
+                        d = this.get(1, 1);
+
+                        return a * d - (b * c);
+                    } else if (this.columns === 3) {
+                        // 3 x 3 matrix
+                        var subMatrix0, subMatrix1, subMatrix2;
+                        subMatrix0 = this.selectionView([1, 2], [1, 2]);
+                        subMatrix1 = this.selectionView([1, 2], [0, 2]);
+                        subMatrix2 = this.selectionView([1, 2], [0, 1]);
+                        a = this.get(0, 0);
+                        b = this.get(0, 1);
+                        c = this.get(0, 2);
+
+                        return a * subMatrix0.det() - b * subMatrix1.det() + c * subMatrix2.det();
+                    } else {
+                        // general purpose determinant using the LU decomposition
+                        return new LuDecomposition(this).determinant;
+                    }
+
+                } else {
+                    throw Error('Determinant can only be calculated for a square matrix.');
+                }
+            }
+
+            /**
+             * Returns inverse of a matrix if it exists or the pseudoinverse
+             * @param {number} threshold - threshold for taking inverse of singular values (default = 1e-15)
+             * @return {Matrix} the (pseudo)inverted matrix.
+             */
+            pseudoInverse(threshold) {
+                if (threshold === undefined) threshold = Number.EPSILON;
+                var svdSolution = new SvDecomposition(this, {autoTranspose: true});
+
+                var U = svdSolution.leftSingularVectors;
+                var V = svdSolution.rightSingularVectors;
+                var s = svdSolution.diagonal;
+
+                for (var i = 0; i < s.length; i++) {
+                    if (Math.abs(s[i]) > threshold) {
+                        s[i] = 1.0 / s[i];
+                    } else {
+                        s[i] = 0.0;
+                    }
+                }
+
+                // convert list to diagonal
+                s = this.constructor[Symbol.species].diag(s);
+                return V.mmul(s.mmul(U.transposeView()));
+            }
+        }
+
+        Matrix.prototype.klass = 'Matrix';
+
+        /**
+         * @private
+         * Check that two matrices have the same dimensions
+         * @param {Matrix} matrix
+         * @param {Matrix} otherMatrix
+         */
+        function checkDimensions(matrix, otherMatrix) { // eslint-disable-line no-unused-vars
+            if (matrix.rows !== otherMatrix.rows ||
+                matrix.columns !== otherMatrix.columns) {
+                throw new RangeError('Matrices dimensions must be equal');
+            }
+        }
+
+        function compareNumbers(a, b) {
+            return a - b;
+        }
+
+        /*
+         Synonyms
+         */
+
+        Matrix.random = Matrix.rand;
+        Matrix.diagonal = Matrix.diag;
+        Matrix.prototype.diagonal = Matrix.prototype.diag;
+        Matrix.identity = Matrix.eye;
+        Matrix.prototype.negate = Matrix.prototype.neg;
+        Matrix.prototype.tensorProduct = Matrix.prototype.kroneckerProduct;
+        Matrix.prototype.determinant = Matrix.prototype.det;
+
+        /*
+         Add dynamically instance and static methods for mathematical operations
+         */
+
+        var inplaceOperator = `
+    (function %name%(value) {
+        if (typeof value === 'number') return this.%name%S(value);
+        return this.%name%M(value);
+    })
+    `;
+
+        var inplaceOperatorScalar = `
+    (function %name%S(value) {
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, this.get(i, j) %op% value);
+            }
+        }
+        return this;
+    })
+    `;
+
+        var inplaceOperatorMatrix = `
+    (function %name%M(matrix) {
+        matrix = this.constructor.checkMatrix(matrix);
+        checkDimensions(this, matrix);
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, this.get(i, j) %op% matrix.get(i, j));
+            }
+        }
+        return this;
+    })
+    `;
+
+        var staticOperator = `
+    (function %name%(matrix, value) {
+        var newMatrix = new this[Symbol.species](matrix);
+        return newMatrix.%name%(value);
+    })
+    `;
+
+        var inplaceMethod = `
+    (function %name%() {
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, %method%(this.get(i, j)));
+            }
+        }
+        return this;
+    })
+    `;
+
+        var staticMethod = `
+    (function %name%(matrix) {
+        var newMatrix = new this[Symbol.species](matrix);
+        return newMatrix.%name%();
+    })
+    `;
+
+        var inplaceMethodWithArgs = `
+    (function %name%(%args%) {
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, %method%(this.get(i, j), %args%));
+            }
+        }
+        return this;
+    })
+    `;
+
+        var staticMethodWithArgs = `
+    (function %name%(matrix, %args%) {
+        var newMatrix = new this[Symbol.species](matrix);
+        return newMatrix.%name%(%args%);
+    })
+    `;
+
+
+        var inplaceMethodWithOneArgScalar = `
+    (function %name%S(value) {
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, %method%(this.get(i, j), value));
+            }
+        }
+        return this;
+    })
+    `;
+        var inplaceMethodWithOneArgMatrix = `
+    (function %name%M(matrix) {
+        matrix = this.constructor.checkMatrix(matrix);
+        checkDimensions(this, matrix);
+        for (var i = 0; i < this.rows; i++) {
+            for (var j = 0; j < this.columns; j++) {
+                this.set(i, j, %method%(this.get(i, j), matrix.get(i, j)));
+            }
+        }
+        return this;
+    })
+    `;
+
+        var inplaceMethodWithOneArg = `
+    (function %name%(value) {
+        if (typeof value === 'number') return this.%name%S(value);
+        return this.%name%M(value);
+    })
+    `;
+
+        var staticMethodWithOneArg = staticMethodWithArgs;
+
+        var operators = [
+            // Arithmetic operators
+            ['+', 'add'],
+            ['-', 'sub', 'subtract'],
+            ['*', 'mul', 'multiply'],
+            ['/', 'div', 'divide'],
+            ['%', 'mod', 'modulus'],
+            // Bitwise operators
+            ['&', 'and'],
+            ['|', 'or'],
+            ['^', 'xor'],
+            ['<<', 'leftShift'],
+            ['>>', 'signPropagatingRightShift'],
+            ['>>>', 'rightShift', 'zeroFillRightShift']
+        ];
+
+        var i;
+
+        for (var operator of operators) {
+            var inplaceOp = eval(fillTemplateFunction(inplaceOperator, {name: operator[1], op: operator[0]}));
+            var inplaceOpS = eval(fillTemplateFunction(inplaceOperatorScalar, {name: operator[1] + 'S', op: operator[0]}));
+            var inplaceOpM = eval(fillTemplateFunction(inplaceOperatorMatrix, {name: operator[1] + 'M', op: operator[0]}));
+            var staticOp = eval(fillTemplateFunction(staticOperator, {name: operator[1]}));
+            for (i = 1; i < operator.length; i++) {
+                Matrix.prototype[operator[i]] = inplaceOp;
+                Matrix.prototype[operator[i] + 'S'] = inplaceOpS;
+                Matrix.prototype[operator[i] + 'M'] = inplaceOpM;
+                Matrix[operator[i]] = staticOp;
+            }
+        }
+
+        var methods = [
+            ['~', 'not']
+        ];
+
+        [
+            'abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cbrt', 'ceil',
+            'clz32', 'cos', 'cosh', 'exp', 'expm1', 'floor', 'fround', 'log', 'log1p',
+            'log10', 'log2', 'round', 'sign', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'
+        ].forEach(function (mathMethod) {
+            methods.push(['Math.' + mathMethod, mathMethod]);
+        });
+
+        for (var method of methods) {
+            var inplaceMeth = eval(fillTemplateFunction(inplaceMethod, {name: method[1], method: method[0]}));
+            var staticMeth = eval(fillTemplateFunction(staticMethod, {name: method[1]}));
+            for (i = 1; i < method.length; i++) {
+                Matrix.prototype[method[i]] = inplaceMeth;
+                Matrix[method[i]] = staticMeth;
+            }
+        }
+
+        var methodsWithArgs = [
+            ['Math.pow', 1, 'pow']
+        ];
+
+        for (var methodWithArg of methodsWithArgs) {
+            var args = 'arg0';
+            for (i = 1; i < methodWithArg[1]; i++) {
+                args += `, arg${i}`;
+            }
+            if (methodWithArg[1] !== 1) {
+                var inplaceMethWithArgs = eval(fillTemplateFunction(inplaceMethodWithArgs, {
+                    name: methodWithArg[2],
+                    method: methodWithArg[0],
+                    args: args
+                }));
+                var staticMethWithArgs = eval(fillTemplateFunction(staticMethodWithArgs, {name: methodWithArg[2], args: args}));
+                for (i = 2; i < methodWithArg.length; i++) {
+                    Matrix.prototype[methodWithArg[i]] = inplaceMethWithArgs;
+                    Matrix[methodWithArg[i]] = staticMethWithArgs;
+                }
+            } else {
+                var tmplVar = {
+                    name: methodWithArg[2],
+                    args: args,
+                    method: methodWithArg[0]
+                };
+                var inplaceMethod2 = eval(fillTemplateFunction(inplaceMethodWithOneArg, tmplVar));
+                var inplaceMethodS = eval(fillTemplateFunction(inplaceMethodWithOneArgScalar, tmplVar));
+                var inplaceMethodM = eval(fillTemplateFunction(inplaceMethodWithOneArgMatrix, tmplVar));
+                var staticMethod2 = eval(fillTemplateFunction(staticMethodWithOneArg, tmplVar));
+                for (i = 2; i < methodWithArg.length; i++) {
+                    Matrix.prototype[methodWithArg[i]] = inplaceMethod2;
+                    Matrix.prototype[methodWithArg[i] + 'M'] = inplaceMethodM;
+                    Matrix.prototype[methodWithArg[i] + 'S'] = inplaceMethodS;
+                    Matrix[methodWithArg[i]] = staticMethod2;
+                }
+            }
+        }
+
+        function fillTemplateFunction(template, values) {
+            for (var value in values) {
+                template = template.replace(new RegExp('%' + value + '%', 'g'), values[value]);
+            }
+            return template;
+        }
+
+        return Matrix;
+    }
+}
+
+
+// ml-matrix src/views/base
+let MLMatrixBaseView;
+{
+    let abstractMatrix = MLMatrixAbstractMatrix;
+    let Matrix = MLMatrixMatrix;
+
+    class BaseView extends abstractMatrix() {
+        constructor(matrix, rows, columns) {
+            super();
+            this.matrix = matrix;
+            this.rows = rows;
+            this.columns = columns;
+        }
+
+        static get [Symbol.species]() {
+            return Matrix.Matrix;
+        }
+    }
+
+    MLMatrixBaseView = BaseView;
+}
+
+
+// ml-matrix src/views/column.js
+let MLMatrixColumnView;
+{
+    let BaseView = MLMatrixBaseView;
+
+    class MatrixColumnView extends BaseView {
+        constructor(matrix, column) {
+            super(matrix, matrix.rows, 1);
+            this.column = column;
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(rowIndex, this.column, value);
+            return this;
+        }
+
+        get(rowIndex) {
+            return this.matrix.get(rowIndex, this.column);
+        }
+    }
+
+    MLMatrixColumnView = MatrixColumnView;
+}
+
+
+// ml-matrix src/views/flipColumn.js
+let MLMatrixFlipColumnView;
+{
+    let BaseView = MLMatrixBaseView
+
+    class MatrixFlipColumnView extends BaseView {
+        constructor(matrix) {
+            super(matrix, matrix.rows, matrix.columns);
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(rowIndex, this.columns - columnIndex - 1);
+        }
+    }
+
+    MLMatrixFlipColumnView = MatrixFlipColumnView;
+}
+
+
+// ml-matrix src/views/flipRow.js
+let MLMatrixFlipRowView;
+{
+    let BaseView = MLMatrixBaseView
+
+    class MatrixFlipRowView extends BaseView {
+        constructor(matrix) {
+            super(matrix, matrix.rows, matrix.columns);
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(this.rows - rowIndex - 1, columnIndex);
+        }
+    }
+
+    MLMatrixFlipRowView = MatrixFlipRowView;
+}
+
+// ml-matrix src/views/row.js
+let MLMatrixRowView;
+{
+    let BaseView = MLMatrixBaseView;
+
+    class MatrixRowView extends BaseView {
+        constructor(matrix, row) {
+            super(matrix, 1, matrix.columns);
+            this.row = row;
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(this.row, columnIndex, value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(this.row, columnIndex);
+        }
+    }
+
+    MLMatrixRowView = MatrixRowView;
+}
+
+
+// ml-matrix src/views/selection.js
+let MLMatrixSelectionView;
+{
+    let BaseView = MLMatrixBaseView;
+    let util = MLMatrixUtil;
+
+    class MatrixSelectionView extends BaseView {
+        constructor(matrix, rowIndices, columnIndices) {
+            var indices = util.checkIndices(matrix, rowIndices, columnIndices);
+            super(matrix, indices.row.length, indices.column.length);
+            this.rowIndices = indices.row;
+            this.columnIndices = indices.column;
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(this.rowIndices[rowIndex], this.columnIndices[columnIndex], value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(this.rowIndices[rowIndex], this.columnIndices[columnIndex]);
+        }
+    }
+
+    MLMatrixSelectionView = MatrixSelectionView;
+}
+
+// ml-matrix src/views/sub.js
+let MLMatrixSubView;
+{
+    let BaseView = MLMatrixBaseView;
+    let util = MLMatrixUtil;
+
+    class MatrixSubView extends BaseView {
+        constructor(matrix, startRow, endRow, startColumn, endColumn) {
+            util.checkRange(matrix, startRow, endRow, startColumn, endColumn);
+            super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);
+            this.startRow = startRow;
+            this.startColumn = startColumn;
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(this.startRow + rowIndex, this.startColumn + columnIndex, value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(this.startRow + rowIndex, this.startColumn + columnIndex);
+        }
+    }
+
+    MLMatrixSubView = MatrixSubView;
+}
+
+// ml-matrix src/views/transpose.js
+let MLMatrixTransposeView;
+{
+    let BaseView = MLMatrixBaseView;
+
+    class MatrixTransposeView extends BaseView {
+        constructor(matrix) {
+            super(matrix, matrix.columns, matrix.rows);
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this.matrix.set(columnIndex, rowIndex, value);
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this.matrix.get(columnIndex, rowIndex);
+        }
+    }
+
+    MLMatrixTransposeView = MatrixTransposeView;
+}
+
+// mlmatrix src/matrix.js
+{
+    let abstractMatrix = MLMatrixAbstractMatrix;
+    let util = MLMatrixUtil;
+
+    class Matrix extends abstractMatrix(Array) {
+        constructor(nRows, nColumns) {
+            var i;
+            if (arguments.length === 1 && typeof nRows === 'number') {
+                return new Array(nRows);
+            }
+            if (Matrix.isMatrix(nRows)) {
+                return nRows.clone();
+            } else if (Number.isInteger(nRows) && nRows > 0) { // Create an empty matrix
+                super(nRows);
+                if (Number.isInteger(nColumns) && nColumns > 0) {
+                    for (i = 0; i < nRows; i++) {
+                        this[i] = new Array(nColumns);
+                    }
+                } else {
+                    throw new TypeError('nColumns must be a positive integer');
+                }
+            } else if (Array.isArray(nRows)) { // Copy the values from the 2D array
+                const matrix = nRows;
+                nRows = matrix.length;
+                nColumns = matrix[0].length;
+                if (typeof nColumns !== 'number' || nColumns === 0) {
+                    throw new TypeError('Data must be a 2D array with at least one element');
+                }
+                super(nRows);
+                for (i = 0; i < nRows; i++) {
+                    if (matrix[i].length !== nColumns) {
+                        throw new RangeError('Inconsistent array dimensions');
+                    }
+                    this[i] = [].concat(matrix[i]);
+                }
+            } else {
+                throw new TypeError('First argument must be a positive number or an array');
+            }
+            this.rows = nRows;
+            this.columns = nColumns;
+            return this;
+        }
+
+        set(rowIndex, columnIndex, value) {
+            this[rowIndex][columnIndex] = value;
+            return this;
+        }
+
+        get(rowIndex, columnIndex) {
+            return this[rowIndex][columnIndex];
+        }
+
+        /**
+         * Creates an exact and independent copy of the matrix
+         * @return {Matrix}
+         */
+        clone() {
+            var newMatrix = new this.constructor[Symbol.species](this.rows, this.columns);
+            for (var row = 0; row < this.rows; row++) {
+                for (var column = 0; column < this.columns; column++) {
+                    newMatrix.set(row, column, this.get(row, column));
+                }
+            }
+            return newMatrix;
+        }
+
+        /**
+         * Removes a row from the given index
+         * @param {number} index - Row index
+         * @return {Matrix} this
+         */
+        removeRow(index) {
+            util.checkRowIndex(this, index);
+            if (this.rows === 1) {
+                throw new RangeError('A matrix cannot have less than one row');
+            }
+            this.splice(index, 1);
+            this.rows -= 1;
+            return this;
+        }
+
+        /**
+         * Adds a row at the given index
+         * @param {number} [index = this.rows] - Row index
+         * @param {Array|Matrix} array - Array or vector
+         * @return {Matrix} this
+         */
+        addRow(index, array) {
+            if (array === undefined) {
+                array = index;
+                index = this.rows;
+            }
+            util.checkRowIndex(this, index, true);
+            array = util.checkRowVector(this, array, true);
+            this.splice(index, 0, array);
+            this.rows += 1;
+            return this;
+        }
+
+        /**
+         * Removes a column from the given index
+         * @param {number} index - Column index
+         * @return {Matrix} this
+         */
+        removeColumn(index) {
+            util.checkColumnIndex(this, index);
+            if (this.columns === 1) {
+                throw new RangeError('A matrix cannot have less than one column');
+            }
+            for (var i = 0; i < this.rows; i++) {
+                this[i].splice(index, 1);
+            }
+            this.columns -= 1;
+            return this;
+        }
+
+        /**
+         * Adds a column at the given index
+         * @param {number} [index = this.columns] - Column index
+         * @param {Array|Matrix} array - Array or vector
+         * @return {Matrix} this
+         */
+        addColumn(index, array) {
+            if (typeof array === 'undefined') {
+                array = index;
+                index = this.columns;
+            }
+            util.checkColumnIndex(this, index, true);
+            array = util.checkColumnVector(this, array);
+            for (var i = 0; i < this.rows; i++) {
+                this[i].splice(index, 0, array[i]);
+            }
+            this.columns += 1;
+            return this;
+        }
+    }
+
+    MLMatrixMatrix.Matrix = Matrix;
+    Matrix.abstractMatrix = abstractMatrix;
+}
+
+
+// ml-matrix src/dc/cholesky.js
+let MLMatrixDCCholesky = {};
+{
+    let Matrix = MLMatrixMatrix.Matrix;
+
+    // https://github.com/lutzroeder/Mapack/blob/master/Source/CholeskyDecomposition.cs
+    function CholeskyDecomposition(value) {
+        if (!(this instanceof CholeskyDecomposition)) {
+            return new CholeskyDecomposition(value);
+        }
+        value = Matrix.checkMatrix(value);
+        if (!value.isSymmetric()) {
+            throw new Error('Matrix is not symmetric');
+        }
+
+        var a = value,
+            dimension = a.rows,
+            l = new Matrix(dimension, dimension),
+            positiveDefinite = true,
+            i, j, k;
+
+        for (j = 0; j < dimension; j++) {
+            var Lrowj = l[j];
+            var d = 0;
+            for (k = 0; k < j; k++) {
+                var Lrowk = l[k];
+                var s = 0;
+                for (i = 0; i < k; i++) {
+                    s += Lrowk[i] * Lrowj[i];
+                }
+                Lrowj[k] = s = (a[j][k] - s) / l[k][k];
+                d = d + s * s;
+            }
+
+            d = a[j][j] - d;
+
+            positiveDefinite &= (d > 0);
+            l[j][j] = Math.sqrt(Math.max(d, 0));
+            for (k = j + 1; k < dimension; k++) {
+                l[j][k] = 0;
+            }
+        }
+
+        if (!positiveDefinite) {
+            throw new Error('Matrix is not positive definite');
+        }
+
+        this.L = l;
+    }
+
+    CholeskyDecomposition.prototype = {
+        get lowerTriangularMatrix() {
+            return this.L;
+        },
+        solve: function (value) {
+            value = Matrix.checkMatrix(value);
+
+            var l = this.L,
+                dimension = l.rows;
+
+            if (value.rows !== dimension) {
+                throw new Error('Matrix dimensions do not match');
+            }
+
+            var count = value.columns,
+                B = value.clone(),
+                i, j, k;
+
+            for (k = 0; k < dimension; k++) {
+                for (j = 0; j < count; j++) {
+                    for (i = 0; i < k; i++) {
+                        B[k][j] -= B[i][j] * l[k][i];
+                    }
+                    B[k][j] /= l[k][k];
+                }
+            }
+
+            for (k = dimension - 1; k >= 0; k--) {
+                for (j = 0; j < count; j++) {
+                    for (i = k + 1; i < dimension; i++) {
+                        B[k][j] -= B[i][j] * l[i][k];
+                    }
+                    B[k][j] /= l[k][k];
+                }
+            }
+
+            return B;
+        }
+    };
+
+    MLMatrixDCCholesky = CholeskyDecomposition;
+}
+
+
+// ml-matrix src/dc/evd.js
+let MLMatrixDCEVD;
+{
+    const Matrix = MLMatrixMatrix.Matrix;
+    const util = MLMatrixDCUtil;
+    const hypotenuse = util.hypotenuse;
+    const getFilled2DArray = util.getFilled2DArray;
+
+    const defaultOptions = {
+        assumeSymmetric: false
+    };
+
+    // https://github.com/lutzroeder/Mapack/blob/master/Source/EigenvalueDecomposition.cs
+    function EigenvalueDecomposition(matrix, options) {
+        options = Object.assign({}, defaultOptions, options);
+        if (!(this instanceof EigenvalueDecomposition)) {
+            return new EigenvalueDecomposition(matrix, options);
+        }
+        matrix = Matrix.checkMatrix(matrix);
+        if (!matrix.isSquare()) {
+            throw new Error('Matrix is not a square matrix');
+        }
+
+        var n = matrix.columns,
+            V = getFilled2DArray(n, n, 0),
+            d = new Array(n),
+            e = new Array(n),
+            value = matrix,
+            i, j;
+
+        var isSymmetric = false;
+        if (options.assumeSymmetric) {
+            isSymmetric = true;
+        } else {
+            isSymmetric = matrix.isSymmetric();
+        }
+
+        if (isSymmetric) {
+            for (i = 0; i < n; i++) {
+                for (j = 0; j < n; j++) {
+                    V[i][j] = value.get(i, j);
+                }
+            }
+            tred2(n, e, d, V);
+            tql2(n, e, d, V);
+        } else {
+            var H = getFilled2DArray(n, n, 0),
+                ort = new Array(n);
+            for (j = 0; j < n; j++) {
+                for (i = 0; i < n; i++) {
+                    H[i][j] = value.get(i, j);
+                }
+            }
+            orthes(n, H, ort, V);
+            hqr2(n, e, d, V, H);
+        }
+
+        this.n = n;
+        this.e = e;
+        this.d = d;
+        this.V = V;
+    }
+
+    EigenvalueDecomposition.prototype = {
+        get realEigenvalues() {
+            return this.d;
+        },
+        get imaginaryEigenvalues() {
+            return this.e;
+        },
+        get eigenvectorMatrix() {
+            if (!Matrix.isMatrix(this.V)) {
+                this.V = new Matrix(this.V);
+            }
+            return this.V;
+        },
+        get diagonalMatrix() {
+            var n = this.n,
+                e = this.e,
+                d = this.d,
+                X = new Matrix(n, n),
+                i, j;
+            for (i = 0; i < n; i++) {
+                for (j = 0; j < n; j++) {
+                    X[i][j] = 0;
+                }
+                X[i][i] = d[i];
+                if (e[i] > 0) {
+                    X[i][i + 1] = e[i];
+                } else if (e[i] < 0) {
+                    X[i][i - 1] = e[i];
+                }
+            }
+            return X;
+        }
+    };
+
+    function tred2(n, e, d, V) {
+
+        var f, g, h, i, j, k,
+            hh, scale;
+
+        for (j = 0; j < n; j++) {
+            d[j] = V[n - 1][j];
+        }
+
+        for (i = n - 1; i > 0; i--) {
+            scale = 0;
+            h = 0;
+            for (k = 0; k < i; k++) {
+                scale = scale + Math.abs(d[k]);
+            }
+
+            if (scale === 0) {
+                e[i] = d[i - 1];
+                for (j = 0; j < i; j++) {
+                    d[j] = V[i - 1][j];
+                    V[i][j] = 0;
+                    V[j][i] = 0;
+                }
+            } else {
+                for (k = 0; k < i; k++) {
+                    d[k] /= scale;
+                    h += d[k] * d[k];
+                }
+
+                f = d[i - 1];
+                g = Math.sqrt(h);
+                if (f > 0) {
+                    g = -g;
+                }
+
+                e[i] = scale * g;
+                h = h - f * g;
+                d[i - 1] = f - g;
+                for (j = 0; j < i; j++) {
+                    e[j] = 0;
+                }
+
+                for (j = 0; j < i; j++) {
+                    f = d[j];
+                    V[j][i] = f;
+                    g = e[j] + V[j][j] * f;
+                    for (k = j + 1; k <= i - 1; k++) {
+                        g += V[k][j] * d[k];
+                        e[k] += V[k][j] * f;
+                    }
+                    e[j] = g;
+                }
+
+                f = 0;
+                for (j = 0; j < i; j++) {
+                    e[j] /= h;
+                    f += e[j] * d[j];
+                }
+
+                hh = f / (h + h);
+                for (j = 0; j < i; j++) {
+                    e[j] -= hh * d[j];
+                }
+
+                for (j = 0; j < i; j++) {
+                    f = d[j];
+                    g = e[j];
+                    for (k = j; k <= i - 1; k++) {
+                        V[k][j] -= (f * e[k] + g * d[k]);
+                    }
+                    d[j] = V[i - 1][j];
+                    V[i][j] = 0;
+                }
+            }
+            d[i] = h;
+        }
+
+        for (i = 0; i < n - 1; i++) {
+            V[n - 1][i] = V[i][i];
+            V[i][i] = 1;
+            h = d[i + 1];
+            if (h !== 0) {
+                for (k = 0; k <= i; k++) {
+                    d[k] = V[k][i + 1] / h;
+                }
+
+                for (j = 0; j <= i; j++) {
+                    g = 0;
+                    for (k = 0; k <= i; k++) {
+                        g += V[k][i + 1] * V[k][j];
+                    }
+                    for (k = 0; k <= i; k++) {
+                        V[k][j] -= g * d[k];
+                    }
+                }
+            }
+
+            for (k = 0; k <= i; k++) {
+                V[k][i + 1] = 0;
+            }
+        }
+
+        for (j = 0; j < n; j++) {
+            d[j] = V[n - 1][j];
+            V[n - 1][j] = 0;
+        }
+
+        V[n - 1][n - 1] = 1;
+        e[0] = 0;
+    }
+
+    function tql2(n, e, d, V) {
+
+        var g, h, i, j, k, l, m, p, r,
+            dl1, c, c2, c3, el1, s, s2,
+            iter;
+
+        for (i = 1; i < n; i++) {
+            e[i - 1] = e[i];
+        }
+
+        e[n - 1] = 0;
+
+        var f = 0,
+            tst1 = 0,
+            eps = Math.pow(2, -52);
+
+        for (l = 0; l < n; l++) {
+            tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));
+            m = l;
+            while (m < n) {
+                if (Math.abs(e[m]) <= eps * tst1) {
+                    break;
+                }
+                m++;
+            }
+
+            if (m > l) {
+                iter = 0;
+                do {
+                    iter = iter + 1;
+
+                    g = d[l];
+                    p = (d[l + 1] - g) / (2 * e[l]);
+                    r = hypotenuse(p, 1);
+                    if (p < 0) {
+                        r = -r;
+                    }
+
+                    d[l] = e[l] / (p + r);
+                    d[l + 1] = e[l] * (p + r);
+                    dl1 = d[l + 1];
+                    h = g - d[l];
+                    for (i = l + 2; i < n; i++) {
+                        d[i] -= h;
+                    }
+
+                    f = f + h;
+
+                    p = d[m];
+                    c = 1;
+                    c2 = c;
+                    c3 = c;
+                    el1 = e[l + 1];
+                    s = 0;
+                    s2 = 0;
+                    for (i = m - 1; i >= l; i--) {
+                        c3 = c2;
+                        c2 = c;
+                        s2 = s;
+                        g = c * e[i];
+                        h = c * p;
+                        r = hypotenuse(p, e[i]);
+                        e[i + 1] = s * r;
+                        s = e[i] / r;
+                        c = p / r;
+                        p = c * d[i] - s * g;
+                        d[i + 1] = h + s * (c * g + s * d[i]);
+
+                        for (k = 0; k < n; k++) {
+                            h = V[k][i + 1];
+                            V[k][i + 1] = s * V[k][i] + c * h;
+                            V[k][i] = c * V[k][i] - s * h;
+                        }
+                    }
+
+                    p = -s * s2 * c3 * el1 * e[l] / dl1;
+                    e[l] = s * p;
+                    d[l] = c * p;
+
+                }
+                while (Math.abs(e[l]) > eps * tst1);
+            }
+            d[l] = d[l] + f;
+            e[l] = 0;
+        }
+
+        for (i = 0; i < n - 1; i++) {
+            k = i;
+            p = d[i];
+            for (j = i + 1; j < n; j++) {
+                if (d[j] < p) {
+                    k = j;
+                    p = d[j];
+                }
+            }
+
+            if (k !== i) {
+                d[k] = d[i];
+                d[i] = p;
+                for (j = 0; j < n; j++) {
+                    p = V[j][i];
+                    V[j][i] = V[j][k];
+                    V[j][k] = p;
+                }
+            }
+        }
+    }
+
+    function orthes(n, H, ort, V) {
+
+        var low = 0,
+            high = n - 1,
+            f, g, h, i, j, m,
+            scale;
+
+        for (m = low + 1; m <= high - 1; m++) {
+            scale = 0;
+            for (i = m; i <= high; i++) {
+                scale = scale + Math.abs(H[i][m - 1]);
+            }
+
+            if (scale !== 0) {
+                h = 0;
+                for (i = high; i >= m; i--) {
+                    ort[i] = H[i][m - 1] / scale;
+                    h += ort[i] * ort[i];
+                }
+
+                g = Math.sqrt(h);
+                if (ort[m] > 0) {
+                    g = -g;
+                }
+
+                h = h - ort[m] * g;
+                ort[m] = ort[m] - g;
+
+                for (j = m; j < n; j++) {
+                    f = 0;
+                    for (i = high; i >= m; i--) {
+                        f += ort[i] * H[i][j];
+                    }
+
+                    f = f / h;
+                    for (i = m; i <= high; i++) {
+                        H[i][j] -= f * ort[i];
+                    }
+                }
+
+                for (i = 0; i <= high; i++) {
+                    f = 0;
+                    for (j = high; j >= m; j--) {
+                        f += ort[j] * H[i][j];
+                    }
+
+                    f = f / h;
+                    for (j = m; j <= high; j++) {
+                        H[i][j] -= f * ort[j];
+                    }
+                }
+
+                ort[m] = scale * ort[m];
+                H[m][m - 1] = scale * g;
+            }
+        }
+
+        for (i = 0; i < n; i++) {
+            for (j = 0; j < n; j++) {
+                V[i][j] = (i === j ? 1 : 0);
+            }
+        }
+
+        for (m = high - 1; m >= low + 1; m--) {
+            if (H[m][m - 1] !== 0) {
+                for (i = m + 1; i <= high; i++) {
+                    ort[i] = H[i][m - 1];
+                }
+
+                for (j = m; j <= high; j++) {
+                    g = 0;
+                    for (i = m; i <= high; i++) {
+                        g += ort[i] * V[i][j];
+                    }
+
+                    g = (g / ort[m]) / H[m][m - 1];
+                    for (i = m; i <= high; i++) {
+                        V[i][j] += g * ort[i];
+                    }
+                }
+            }
+        }
+    }
+
+    function hqr2(nn, e, d, V, H) {
+        var n = nn - 1,
+            low = 0,
+            high = nn - 1,
+            eps = Math.pow(2, -52),
+            exshift = 0,
+            norm = 0,
+            p = 0,
+            q = 0,
+            r = 0,
+            s = 0,
+            z = 0,
+            iter = 0,
+            i, j, k, l, m, t, w, x, y,
+            ra, sa, vr, vi,
+            notlast, cdivres;
+
+        for (i = 0; i < nn; i++) {
+            if (i < low || i > high) {
+                d[i] = H[i][i];
+                e[i] = 0;
+            }
+
+            for (j = Math.max(i - 1, 0); j < nn; j++) {
+                norm = norm + Math.abs(H[i][j]);
+            }
+        }
+
+        while (n >= low) {
+            l = n;
+            while (l > low) {
+                s = Math.abs(H[l - 1][l - 1]) + Math.abs(H[l][l]);
+                if (s === 0) {
+                    s = norm;
+                }
+                if (Math.abs(H[l][l - 1]) < eps * s) {
+                    break;
+                }
+                l--;
+            }
+
+            if (l === n) {
+                H[n][n] = H[n][n] + exshift;
+                d[n] = H[n][n];
+                e[n] = 0;
+                n--;
+                iter = 0;
+            } else if (l === n - 1) {
+                w = H[n][n - 1] * H[n - 1][n];
+                p = (H[n - 1][n - 1] - H[n][n]) / 2;
+                q = p * p + w;
+                z = Math.sqrt(Math.abs(q));
+                H[n][n] = H[n][n] + exshift;
+                H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
+                x = H[n][n];
+
+                if (q >= 0) {
+                    z = (p >= 0) ? (p + z) : (p - z);
+                    d[n - 1] = x + z;
+                    d[n] = d[n - 1];
+                    if (z !== 0) {
+                        d[n] = x - w / z;
+                    }
+                    e[n - 1] = 0;
+                    e[n] = 0;
+                    x = H[n][n - 1];
+                    s = Math.abs(x) + Math.abs(z);
+                    p = x / s;
+                    q = z / s;
+                    r = Math.sqrt(p * p + q * q);
+                    p = p / r;
+                    q = q / r;
+
+                    for (j = n - 1; j < nn; j++) {
+                        z = H[n - 1][j];
+                        H[n - 1][j] = q * z + p * H[n][j];
+                        H[n][j] = q * H[n][j] - p * z;
+                    }
+
+                    for (i = 0; i <= n; i++) {
+                        z = H[i][n - 1];
+                        H[i][n - 1] = q * z + p * H[i][n];
+                        H[i][n] = q * H[i][n] - p * z;
+                    }
+
+                    for (i = low; i <= high; i++) {
+                        z = V[i][n - 1];
+                        V[i][n - 1] = q * z + p * V[i][n];
+                        V[i][n] = q * V[i][n] - p * z;
+                    }
+                } else {
+                    d[n - 1] = x + p;
+                    d[n] = x + p;
+                    e[n - 1] = z;
+                    e[n] = -z;
+                }
+
+                n = n - 2;
+                iter = 0;
+            } else {
+                x = H[n][n];
+                y = 0;
+                w = 0;
+                if (l < n) {
+                    y = H[n - 1][n - 1];
+                    w = H[n][n - 1] * H[n - 1][n];
+                }
+
+                if (iter === 10) {
+                    exshift += x;
+                    for (i = low; i <= n; i++) {
+                        H[i][i] -= x;
+                    }
+                    s = Math.abs(H[n][n - 1]) + Math.abs(H[n - 1][n - 2]);
+                    x = y = 0.75 * s;
+                    w = -0.4375 * s * s;
+                }
+
+                if (iter === 30) {
+                    s = (y - x) / 2;
+                    s = s * s + w;
+                    if (s > 0) {
+                        s = Math.sqrt(s);
+                        if (y < x) {
+                            s = -s;
+                        }
+                        s = x - w / ((y - x) / 2 + s);
+                        for (i = low; i <= n; i++) {
+                            H[i][i] -= s;
+                        }
+                        exshift += s;
+                        x = y = w = 0.964;
+                    }
+                }
+
+                iter = iter + 1;
+
+                m = n - 2;
+                while (m >= l) {
+                    z = H[m][m];
+                    r = x - z;
+                    s = y - z;
+                    p = (r * s - w) / H[m + 1][m] + H[m][m + 1];
+                    q = H[m + 1][m + 1] - z - r - s;
+                    r = H[m + 2][m + 1];
+                    s = Math.abs(p) + Math.abs(q) + Math.abs(r);
+                    p = p / s;
+                    q = q / s;
+                    r = r / s;
+                    if (m === l) {
+                        break;
+                    }
+                    if (Math.abs(H[m][m - 1]) * (Math.abs(q) + Math.abs(r)) < eps * (Math.abs(p) * (Math.abs(H[m - 1][m - 1]) + Math.abs(z) + Math.abs(H[m + 1][m + 1])))) {
+                        break;
+                    }
+                    m--;
+                }
+
+                for (i = m + 2; i <= n; i++) {
+                    H[i][i - 2] = 0;
+                    if (i > m + 2) {
+                        H[i][i - 3] = 0;
+                    }
+                }
+
+                for (k = m; k <= n - 1; k++) {
+                    notlast = (k !== n - 1);
+                    if (k !== m) {
+                        p = H[k][k - 1];
+                        q = H[k + 1][k - 1];
+                        r = (notlast ? H[k + 2][k - 1] : 0);
+                        x = Math.abs(p) + Math.abs(q) + Math.abs(r);
+                        if (x !== 0) {
+                            p = p / x;
+                            q = q / x;
+                            r = r / x;
+                        }
+                    }
+
+                    if (x === 0) {
+                        break;
+                    }
+
+                    s = Math.sqrt(p * p + q * q + r * r);
+                    if (p < 0) {
+                        s = -s;
+                    }
+
+                    if (s !== 0) {
+                        if (k !== m) {
+                            H[k][k - 1] = -s * x;
+                        } else if (l !== m) {
+                            H[k][k - 1] = -H[k][k - 1];
+                        }
+
+                        p = p + s;
+                        x = p / s;
+                        y = q / s;
+                        z = r / s;
+                        q = q / p;
+                        r = r / p;
+
+                        for (j = k; j < nn; j++) {
+                            p = H[k][j] + q * H[k + 1][j];
+                            if (notlast) {
+                                p = p + r * H[k + 2][j];
+                                H[k + 2][j] = H[k + 2][j] - p * z;
+                            }
+
+                            H[k][j] = H[k][j] - p * x;
+                            H[k + 1][j] = H[k + 1][j] - p * y;
+                        }
+
+                        for (i = 0; i <= Math.min(n, k + 3); i++) {
+                            p = x * H[i][k] + y * H[i][k + 1];
+                            if (notlast) {
+                                p = p + z * H[i][k + 2];
+                                H[i][k + 2] = H[i][k + 2] - p * r;
+                            }
+
+                            H[i][k] = H[i][k] - p;
+                            H[i][k + 1] = H[i][k + 1] - p * q;
+                        }
+
+                        for (i = low; i <= high; i++) {
+                            p = x * V[i][k] + y * V[i][k + 1];
+                            if (notlast) {
+                                p = p + z * V[i][k + 2];
+                                V[i][k + 2] = V[i][k + 2] - p * r;
+                            }
+
+                            V[i][k] = V[i][k] - p;
+                            V[i][k + 1] = V[i][k + 1] - p * q;
+                        }
+                    }
+                }
+            }
+        }
+
+        if (norm === 0) {
+            return;
+        }
+
+        for (n = nn - 1; n >= 0; n--) {
+            p = d[n];
+            q = e[n];
+
+            if (q === 0) {
+                l = n;
+                H[n][n] = 1;
+                for (i = n - 1; i >= 0; i--) {
+                    w = H[i][i] - p;
+                    r = 0;
+                    for (j = l; j <= n; j++) {
+                        r = r + H[i][j] * H[j][n];
+                    }
+
+                    if (e[i] < 0) {
+                        z = w;
+                        s = r;
+                    } else {
+                        l = i;
+                        if (e[i] === 0) {
+                            H[i][n] = (w !== 0) ? (-r / w) : (-r / (eps * norm));
+                        } else {
+                            x = H[i][i + 1];
+                            y = H[i + 1][i];
+                            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
+                            t = (x * s - z * r) / q;
+                            H[i][n] = t;
+                            H[i + 1][n] = (Math.abs(x) > Math.abs(z)) ? ((-r - w * t) / x) : ((-s - y * t) / z);
+                        }
+
+                        t = Math.abs(H[i][n]);
+                        if ((eps * t) * t > 1) {
+                            for (j = i; j <= n; j++) {
+                                H[j][n] = H[j][n] / t;
+                            }
+                        }
+                    }
+                }
+            } else if (q < 0) {
+                l = n - 1;
+
+                if (Math.abs(H[n][n - 1]) > Math.abs(H[n - 1][n])) {
+                    H[n - 1][n - 1] = q / H[n][n - 1];
+                    H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
+                } else {
+                    cdivres = cdiv(0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
+                    H[n - 1][n - 1] = cdivres[0];
+                    H[n - 1][n] = cdivres[1];
+                }
+
+                H[n][n - 1] = 0;
+                H[n][n] = 1;
+                for (i = n - 2; i >= 0; i--) {
+                    ra = 0;
+                    sa = 0;
+                    for (j = l; j <= n; j++) {
+                        ra = ra + H[i][j] * H[j][n - 1];
+                        sa = sa + H[i][j] * H[j][n];
+                    }
+
+                    w = H[i][i] - p;
+
+                    if (e[i] < 0) {
+                        z = w;
+                        r = ra;
+                        s = sa;
+                    } else {
+                        l = i;
+                        if (e[i] === 0) {
+                            cdivres = cdiv(-ra, -sa, w, q);
+                            H[i][n - 1] = cdivres[0];
+                            H[i][n] = cdivres[1];
+                        } else {
+                            x = H[i][i + 1];
+                            y = H[i + 1][i];
+                            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
+                            vi = (d[i] - p) * 2 * q;
+                            if (vr === 0 && vi === 0) {
+                                vr = eps * norm * (Math.abs(w) + Math.abs(q) + Math.abs(x) + Math.abs(y) + Math.abs(z));
+                            }
+                            cdivres = cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi);
+                            H[i][n - 1] = cdivres[0];
+                            H[i][n] = cdivres[1];
+                            if (Math.abs(x) > (Math.abs(z) + Math.abs(q))) {
+                                H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q * H[i][n]) / x;
+                                H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n - 1]) / x;
+                            } else {
+                                cdivres = cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, q);
+                                H[i + 1][n - 1] = cdivres[0];
+                                H[i + 1][n] = cdivres[1];
+                            }
+                        }
+
+                        t = Math.max(Math.abs(H[i][n - 1]), Math.abs(H[i][n]));
+                        if ((eps * t) * t > 1) {
+                            for (j = i; j <= n; j++) {
+                                H[j][n - 1] = H[j][n - 1] / t;
+                                H[j][n] = H[j][n] / t;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        for (i = 0; i < nn; i++) {
+            if (i < low || i > high) {
+                for (j = i; j < nn; j++) {
+                    V[i][j] = H[i][j];
+                }
+            }
+        }
+
+        for (j = nn - 1; j >= low; j--) {
+            for (i = low; i <= high; i++) {
+                z = 0;
+                for (k = low; k <= Math.min(j, high); k++) {
+                    z = z + V[i][k] * H[k][j];
+                }
+                V[i][j] = z;
+            }
+        }
+    }
+
+    function cdiv(xr, xi, yr, yi) {
+        var r, d;
+        if (Math.abs(yr) > Math.abs(yi)) {
+            r = yi / yr;
+            d = yr + r * yi;
+            return [(xr + r * xi) / d, (xi - r * xr) / d];
+        } else {
+            r = yr / yi;
+            d = yi + r * yr;
+            return [(r * xr + xi) / d, (r * xi - xr) / d];
+        }
+    }
+
+    MLMatrixDCEVD = EigenvalueDecomposition;
+}
+
+
+// ml-matrix src/dc/qr.js
+let MLMatrixDCQR;
+{
+    let Matrix = MLMatrixMatrix.Matrix;
+    let hypotenuse = MLMatrixDCUtil.hypotenuse;
+
+    //https://github.com/lutzroeder/Mapack/blob/master/Source/QrDecomposition.cs
+    function QrDecomposition(value) {
+        if (!(this instanceof QrDecomposition)) {
+            return new QrDecomposition(value);
+        }
+        value = Matrix.checkMatrix(value);
+
+        var qr = value.clone(),
+            m = value.rows,
+            n = value.columns,
+            rdiag = new Array(n),
+            i, j, k, s;
+
+        for (k = 0; k < n; k++) {
+            var nrm = 0;
+            for (i = k; i < m; i++) {
+                nrm = hypotenuse(nrm, qr[i][k]);
+            }
+            if (nrm !== 0) {
+                if (qr[k][k] < 0) {
+                    nrm = -nrm;
+                }
+                for (i = k; i < m; i++) {
+                    qr[i][k] /= nrm;
+                }
+                qr[k][k] += 1;
+                for (j = k + 1; j < n; j++) {
+                    s = 0;
+                    for (i = k; i < m; i++) {
+                        s += qr[i][k] * qr[i][j];
+                    }
+                    s = -s / qr[k][k];
+                    for (i = k; i < m; i++) {
+                        qr[i][j] += s * qr[i][k];
+                    }
+                }
+            }
+            rdiag[k] = -nrm;
+        }
+
+        this.QR = qr;
+        this.Rdiag = rdiag;
+    }
+
+    QrDecomposition.prototype = {
+        solve: function (value) {
+            value = Matrix.checkMatrix(value);
+
+            var qr = this.QR,
+                m = qr.rows;
+
+            if (value.rows !== m) {
+                throw new Error('Matrix row dimensions must agree');
+            }
+            if (!this.isFullRank()) {
+                throw new Error('Matrix is rank deficient');
+            }
+
+            var count = value.columns;
+            var X = value.clone();
+            var n = qr.columns;
+            var i, j, k, s;
+
+            for (k = 0; k < n; k++) {
+                for (j = 0; j < count; j++) {
+                    s = 0;
+                    for (i = k; i < m; i++) {
+                        s += qr[i][k] * X[i][j];
+                    }
+                    s = -s / qr[k][k];
+                    for (i = k; i < m; i++) {
+                        X[i][j] += s * qr[i][k];
+                    }
+                }
+            }
+            for (k = n - 1; k >= 0; k--) {
+                for (j = 0; j < count; j++) {
+                    X[k][j] /= this.Rdiag[k];
+                }
+                for (i = 0; i < k; i++) {
+                    for (j = 0; j < count; j++) {
+                        X[i][j] -= X[k][j] * qr[i][k];
+                    }
+                }
+            }
+
+            return X.subMatrix(0, n - 1, 0, count - 1);
+        },
+        isFullRank: function () {
+            var columns = this.QR.columns;
+            for (var i = 0; i < columns; i++) {
+                if (this.Rdiag[i] === 0) {
+                    return false;
+                }
+            }
+            return true;
+        },
+        get upperTriangularMatrix() {
+            var qr = this.QR,
+                n = qr.columns,
+                X = new Matrix(n, n),
+                i, j;
+            for (i = 0; i < n; i++) {
+                for (j = 0; j < n; j++) {
+                    if (i < j) {
+                        X[i][j] = qr[i][j];
+                    } else if (i === j) {
+                        X[i][j] = this.Rdiag[i];
+                    } else {
+                        X[i][j] = 0;
+                    }
+                }
+            }
+            return X;
+        },
+        get orthogonalMatrix() {
+            var qr = this.QR,
+                rows = qr.rows,
+                columns = qr.columns,
+                X = new Matrix(rows, columns),
+                i, j, k, s;
+
+            for (k = columns - 1; k >= 0; k--) {
+                for (i = 0; i < rows; i++) {
+                    X[i][k] = 0;
+                }
+                X[k][k] = 1;
+                for (j = k; j < columns; j++) {
+                    if (qr[k][k] !== 0) {
+                        s = 0;
+                        for (i = k; i < rows; i++) {
+                            s += qr[i][k] * X[i][j];
+                        }
+
+                        s = -s / qr[k][k];
+
+                        for (i = k; i < rows; i++) {
+                            X[i][j] += s * qr[i][k];
+                        }
+                    }
+                }
+            }
+            return X;
+        }
+    };
+
+    MLMatrixDCQR = QrDecomposition;
+}
+
+// ml-matric src/decompositions.js
+let MLMatrixDecompositions = {};
+{
+    let Matrix = MLMatrixMatrix.Matrix;
+
+    let SingularValueDecomposition = MLMatrixDCSVD;
+    let EigenvalueDecomposition = MLMatrixDCEVD;
+    let LuDecomposition = MLMatrixDCLU;
+    let QrDecomposition = MLMatrixDCQR
+    let CholeskyDecomposition = MLMatrixDCCholesky;
+
+    function inverse(matrix) {
+        matrix = Matrix.checkMatrix(matrix);
+        return solve(matrix, Matrix.eye(matrix.rows));
+    }
+
+    /**
+     * Returns the inverse
+     * @memberOf Matrix
+     * @static
+     * @param {Matrix} matrix
+     * @return {Matrix} matrix
+     * @alias inv
+     */
+    Matrix.inverse = Matrix.inv = inverse;
+
+    /**
+     * Returns the inverse
+     * @memberOf Matrix
+     * @static
+     * @param {Matrix} matrix
+     * @return {Matrix} matrix
+     * @alias inv
+     */
+    Matrix.prototype.inverse = Matrix.prototype.inv = function () {
+        return inverse(this);
+    };
+
+    function solve(leftHandSide, rightHandSide) {
+        leftHandSide = Matrix.checkMatrix(leftHandSide);
+        rightHandSide = Matrix.checkMatrix(rightHandSide);
+        return leftHandSide.isSquare() ? new LuDecomposition(leftHandSide).solve(rightHandSide) : new QrDecomposition(leftHandSide).solve(rightHandSide);
+    }
+
+    Matrix.solve = solve;
+    Matrix.prototype.solve = function (other) {
+        return solve(this, other);
+    };
+
+    MLMatrixDecompositions = {
+        SingularValueDecomposition: SingularValueDecomposition,
+        SVD: SingularValueDecomposition,
+        EigenvalueDecomposition: EigenvalueDecomposition,
+        EVD: EigenvalueDecomposition,
+        LuDecomposition: LuDecomposition,
+        LU: LuDecomposition,
+        QrDecomposition: QrDecomposition,
+        QR: QrDecomposition,
+        CholeskyDecomposition: CholeskyDecomposition,
+        CHO: CholeskyDecomposition,
+        inverse: inverse,
+        solve: solve
+    };
+}
+
+// ml-matrix src/index.js
+let MLMatrix = {};
+{
+    MLMatrix = MLMatrixMatrix.Matrix;
+    MLMatrix.Decompositions = MLMatrix.DC = MLMatrixDecompositions;
+}
+
+// feedforward-neural-networks utils.js
+let FeedforwardNeuralNetworksUtils;
+{
+    let Matrix = MLMatrix;
+
+    /**
+     * @private
+     * Retrieves the sum at each row of the given matrix.
+     * @param {Matrix} matrix
+     * @return {Matrix}
+     */
+    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;
+    }
+
+    /**
+     * @private
+     * Retrieves the sum at each column of the given matrix.
+     * @param {Matrix} matrix
+     * @return {Matrix}
+     */
+    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;
+    }
+
+    /**
+     * @private
+     * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to
+     * numbers and other in the reverse way
+     * @param {Array} array
+     * @return {object}
+     */
+    function dictOutputs(array) {
+        var inputs = {}, outputs = {}, l = array.length, index = 0;
+        for (var i = 0; i < l; i += 1) {
+            if (inputs[array[i]] === undefined) {
+                inputs[array[i]] = index;
+                outputs[index] = array[i];
+                index++;
+            }
+        }
+
+        return {
+            inputs: inputs,
+            outputs: outputs
+        };
+    }
+
+    FeedforwardNeuralNetworksUtils = {
+        dictOutputs: dictOutputs,
+        sumCol: sumCol,
+        sumRow: sumRow
+    };
+}
+
+// feedforward-neural-networks activationFunctions.js
+let FeedforwardNeuralNetworksActivationFunctions;
+{
+    function logistic(val) {
+        return 1 / (1 + Math.exp(-val));
+    }
+
+    function expELU(val, param) {
+        return val < 0 ? param * (Math.exp(val) - 1) : val;
+    }
+
+    function softExponential(val, param) {
+        if (param < 0) {
+            return -Math.log(1 - param * (val + param)) / param;
+        }
+        if (param > 0) {
+            return ((Math.exp(param * val) - 1) / param) + param;
+        }
+        return val;
+    }
+
+    function softExponentialPrime(val, param) {
+        if (param < 0) {
+            return 1 / (1 - param * (param + val));
+        } else {
+            return Math.exp(param * val);
+        }
+    }
+
+    const ACTIVATION_FUNCTIONS = {
+        'tanh': {
+            activation: Math.tanh,
+            derivate: val => 1 - (val * val)
+        },
+        'identity': {
+            activation: val => val,
+            derivate: () => 1
+        },
+        'logistic': {
+            activation: logistic,
+            derivate: val => logistic(val) * (1 - logistic(val))
+        },
+        'arctan': {
+            activation: Math.atan,
+            derivate: val => 1 / (val * val + 1)
+        },
+        'softsign': {
+            activation: val => val / (1 + Math.abs(val)),
+            derivate: val => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))
+        },
+        'relu': {
+            activation: val => val < 0 ? 0 : val,
+            derivate: val => val < 0 ? 0 : 1
+        },
+        'softplus': {
+            activation: val => Math.log(1 + Math.exp(val)),
+            derivate: val => 1 / (1 + Math.exp(-val))
+        },
+        'bent': {
+            activation: val => ((Math.sqrt(val * val + 1) - 1) / 2) + val,
+            derivate: val => (val / (2 * Math.sqrt(val * val + 1))) + 1
+        },
+        'sinusoid': {
+            activation: Math.sin,
+            derivate: Math.cos
+        },
+        'sinc': {
+            activation: val => val === 0 ? 1 : Math.sin(val) / val,
+            derivate: val => val === 0 ? 0 : (Math.cos(val) / val) - (Math.sin(val) / (val * val))
+        },
+        'gaussian': {
+            activation: val => Math.exp(-(val * val)),
+            derivate: val => -2 * val * Math.exp(-(val * val))
+        },
+        'parametric-relu': {
+            activation: (val, param) => val < 0 ? param * val : val,
+            derivate: (val, param) => val < 0 ? param : 1
+        },
+        'exponential-elu': {
+            activation: expELU,
+            derivate: (val, param) => val < 0 ? expELU(val, param) + param : 1
+        },
+        'soft-exponential': {
+            activation: softExponential,
+            derivate: softExponentialPrime
+        }
+    };
+
+    FeedforwardNeuralNetworksActivationFunctions = ACTIVATION_FUNCTIONS;
+}
+
+// feedforward-neural-networks Layer.js
+let FeedforwardNeuralNetworksLayer;
+{
+    let Matrix = MLMatrix;
+
+    let Utils = FeedforwardNeuralNetworksUtils;
+    const ACTIVATION_FUNCTIONS = FeedforwardNeuralNetworksActivationFunctions;
+
+    class Layer {
+        /**
+         * @private
+         * Create a new layer with the given options
+         * @param {object} options
+         * @param {number} [options.inputSize] - Number of conections that enter the neurons.
+         * @param {number} [options.outputSize] - Number of conections that leave the neurons.
+         * @param {number} [options.regularization] - Regularization parameter.
+         * @param {number} [options.epsilon] - Learning rate parameter.
+         * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.
+         * @param {number} [options.activationParam] - Activation parameter if needed.
+         */
+        constructor(options) {
+            this.inputSize = options.inputSize;
+            this.outputSize = options.outputSize;
+            this.regularization = options.regularization;
+            this.epsilon = options.epsilon;
+            this.activation = options.activation;
+            this.activationParam = options.activationParam;
+
+            var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];
+            var params = selectedFunction.activation.length;
+
+            var actFunction = params > 1 ? val => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;
+            var derFunction = params > 1 ? val => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;
+
+            this.activationFunction = function (i, j) {
+                this[i][j] = actFunction(this[i][j]);
+            };
+            this.derivate = function (i, j) {
+                this[i][j] = derFunction(this[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[i][j] /= Math.sqrt(options.inputSize);
+                });
+            }
+        }
+
+        /**
+         * @private
+         * propagate the given input through the current layer.
+         * @param {Matrix} X - input.
+         * @return {Matrix} output at the current layer.
+         */
+        forward(X) {
+            var z = X.mmul(this.W).addRowVector(this.b);
+            z.apply(this.activationFunction);
+            this.a = z.clone();
+            return z;
+        }
+
+        /**
+         * @private
+         * apply backpropagation algorithm at the current layer
+         * @param {Matrix} delta - delta values estimated at the following layer.
+         * @param {Matrix} a - 'a' values from the following layer.
+         * @return {Matrix} the new delta values for the next layer.
+         */
+        backpropagation(delta, a) {
+            this.dW = a.transposeView().mmul(delta);
+            this.db = Utils.sumCol(delta);
+
+            var aCopy = a.clone();
+            return delta.mmul(this.W.transposeView()).mul(aCopy.apply(this.derivate));
+        }
+
+        /**
+         * @private
+         * Function that updates the weights at the current layer with the derivatives.
+         */
+        update() {
+            this.dW.add(this.W.clone().mul(this.regularization));
+            this.W.add(this.dW.mul(-this.epsilon));
+            this.b.add(this.db.mul(-this.epsilon));
+        }
+
+        /**
+         * @private
+         * Export the current layer to JSON.
+         * @return {object} model
+         */
+        toJSON() {
+            return {
+                model: 'Layer',
+                inputSize: this.inputSize,
+                outputSize: this.outputSize,
+                regularization: this.regularization,
+                epsilon: this.epsilon,
+                activation: this.activation,
+                W: this.W,
+                b: this.b
+            };
+        }
+
+        /**
+         * @private
+         * Creates a new Layer with the given model.
+         * @param {object} model
+         * @return {Layer}
+         */
+        static load(model) {
+            if (model.model !== 'Layer') {
+                throw new RangeError('the current model is not a Layer model');
+            }
+            return new Layer(model);
+        }
+
+    }
+
+    FeedforwardNeuralNetworksLayer = Layer;
+}
+
+// feedforward-neural-networks OutputLayer.js
+let FeedforwardNeuralNetworksOutputLayer;
+{
+    let Layer = FeedforwardNeuralNetworksLayer;
+
+    class OutputLayer extends Layer {
+        constructor(options) {
+            super(options);
+
+            this.activationFunction = function (i, j) {
+                this[i][j] = Math.exp(this[i][j]);
+            };
+        }
+
+        static load(model) {
+            if (model.model !== 'Layer') {
+                throw new RangeError('the current model is not a Layer model');
+            }
+
+            return new OutputLayer(model);
+        }
+    }
+
+    FeedforwardNeuralNetworksOutputLayer = OutputLayer;
+}
+
+// feedforward-neural-networks FeedForwardNeuralNetwork.js
+let FeedforwardNeuralNetwork;
+{
+    const Matrix = MLMatrix;
+
+    const Layer = FeedforwardNeuralNetworksLayer;
+    const OutputLayer = FeedforwardNeuralNetworksOutputLayer;
+    const Utils = FeedforwardNeuralNetworksUtils;
+    const ACTIVATION_FUNCTIONS = FeedforwardNeuralNetworksActivationFunctions;
+
+    class FeedForwardNeuralNetworks {
+
+        /**
+         * Create a new Feedforword neural network model.
+         * @param {object} options
+         * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.
+         * @oaram {number} [options.iterations=50] - Number of iterations at the training step.
+         * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).
+         * @poram {number} [options.regularization=0.01] - Regularization parameter af the neural net.
+         * @poram {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),
+         * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').
+         * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').
+         * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.
+         */
+        constructor(options) {
+            options = options || {};
+            if (options.model) {
+                // load network
+                this.hiddenLayers = options.hiddenLayers;
+                this.iterations = options.iterations;
+                this.learningRate = options.learningRate;
+                this.regularization = options.regularization;
+                this.dicts = options.dicts;
+                this.activation = options.activation;
+                this.activationParam = options.activationParam;
+                this.model = new Array(options.layers.length);
+
+                for (var i = 0; i < this.model.length - 1; ++i) {
+                    this.model[i] = Layer.load(options.layers[i]);
+                }
+                this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);
+            } else {
+                // default constructor
+                this.hiddenLayers = options.hiddenLayers === undefined ? [10] : options.hiddenLayers;
+                this.iterations = options.iterations === undefined ? 50 : options.iterations;
+
+                this.learningRate = options.learningRate === undefined ? 0.01 : options.learningRate;
+                //this.momentum = options.momentum === undefined ? 0.1 : options.momentum;
+                this.regularization = options.regularization === undefined ? 0.01 : options.regularization;
+
+                this.activation = options.activation === undefined ? 'tanh' : options.activation;
+                this.activationParam = options.activationParam === undefined ? 1 : options.activationParam;
+                if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {
+                    this.activation = 'tanh';
+                }
+            }
+        }
+
+        /**
+         * @private
+         * Function that build and initialize the neural net.
+         * @param {number} inputSize - total of features to fit.
+         * @param {number} outputSize - total of labels of the prediction set.
+         */
+        buildNetwork(inputSize, outputSize) {
+            var size = 2 + (this.hiddenLayers.length - 1);
+            this.model = new Array(size);
+
+            // input layer
+            this.model[0] = new Layer({
+                inputSize: inputSize,
+                outputSize: this.hiddenLayers[0],
+                activation: this.activation,
+                activationParam: this.activationParam,
+                regularization: this.regularization,
+                epsilon: this.learningRate
+            });
+
+            // hidden layers
+            for (var i = 1; i < this.hiddenLayers.length; ++i) {
+                this.model[i] = new Layer({
+                    inputSize: this.hiddenLayers[i - 1],
+                    outputSize: this.hiddenLayers[i],
+                    activation: this.activation,
+                    activationParam: this.activationParam,
+                    regularization: this.regularization,
+                    epsilon: this.learningRate
+                });
+            }
+
+            // output layer
+            this.model[size - 1] = new OutputLayer({
+                inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],
+                outputSize: outputSize,
+                activation: this.activation,
+                activationParam: this.activationParam,
+                regularization: this.regularization,
+                epsilon: this.learningRate
+            });
+        }
+
+        /**
+         * Train the neural net with the given features and labels.
+         * @param {Matrix|Array} features
+         * @param {Matrix|Array} labels
+         */
+        train(features, labels) {
+            features = Matrix.checkMatrix(features);
+            this.dicts = Utils.dictOutputs(labels);
+
+            var inputSize = features.columns;
+            var outputSize = Object.keys(this.dicts.inputs).length;
+
+            this.buildNetwork(inputSize, outputSize);
+
+            for (var i = 0; i < this.iterations; ++i) {
+                var probabilities = this.propagate(features);
+                this.backpropagation(features, labels, probabilities);
+            }
+        }
+
+        /**
+         * @private
+         * Propagate the input(training set) and retrives the probabilities of each class.
+         * @param {Matrix} X
+         * @return {Matrix} probabilities of each class.
+         */
+        propagate(X) {
+            var input = X;
+            for (var i = 0; i < this.model.length; ++i) {
+                //console.log(i);
+                input = this.model[i].forward(input);
+            }
+
+            // get probabilities
+            return input.divColumnVector(Utils.sumRow(input));
+        }
+
+        /**
+         * @private
+         * Function that applies the backpropagation algorithm on each layer of the network
+         * in order to fit the features and labels.
+         * @param {Matrix} features
+         * @param {Array} labels
+         * @param {Matrix} probabilities - probabilities of each class of the feature set.
+         */
+        backpropagation(features, labels, probabilities) {
+            for (var i = 0; i < probabilities.length; ++i) {
+                probabilities[i][this.dicts.inputs[labels[i]]] -= 1;
+            }
+
+            // remember, the last delta doesn't matter
+            var delta = probabilities;
+            for (i = this.model.length - 1; i >= 0; --i) {
+                var a = i > 0 ? this.model[i - 1].a : features;
+                delta = this.model[i].backpropagation(delta, a);
+            }
+
+            for (i = 0; i < this.model.length; ++i) {
+                this.model[i].update();
+            }
+        }
+
+        /**
+         * Predict the output given the feature set.
+         * @param {Array|Matrix} features
+         * @return {Array}
+         */
+        predict(features) {
+            features = Matrix.checkMatrix(features);
+            var outputs = new Array(features.rows);
+            var probabilities = this.propagate(features);
+            for (var i = 0; i < features.rows; ++i) {
+                outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];
+            }
+
+            return outputs;
+        }
+
+        /**
+         * Export the current model to JSOM.
+         * @return {object} model
+         */
+        toJSON() {
+            var model = {
+                model: 'FNN',
+                hiddenLayers: this.hiddenLayers,
+                iterations: this.iterations,
+                learningRate: this.learningRate,
+                regularization: this.regularization,
+                activation: this.activation,
+                activationParam: this.activationParam,
+                dicts: this.dicts,
+                layers: new Array(this.model.length)
+            };
+
+            for (var i = 0; i < this.model.length; ++i) {
+                model.layers[i] = this.model[i].toJSON();
+            }
+
+            return model;
+        }
+
+        /**
+         * Load a Feedforward Neural Network with the current model.
+         * @param {object} model
+         * @return {FeedForwardNeuralNetworks}
+         */
+        static load(model) {
+            if (model.model !== 'FNN') {
+                throw new RangeError('the current model is not a feed forward network');
+            }
+
+            return new FeedForwardNeuralNetworks(model);
+        }
+    }
+
+    FeedforwardNeuralNetwork = FeedForwardNeuralNetworks;
+}