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Posted 16 Oct 2012

JavaScript Machine Learning and Neural Networks with Encog

, 16 Oct 2012
Use Encog genetic algorithms, simulated annealing, neural networks and more with HTML5 Javascript.
/**
 * This is the top level global variable that this example is stored under.
 * For this example, it is MYAPP.
 *
 * @module MYAPP
 */
var TESTLIB = TESTLIB || {};

/**
 * The namespace function, used to define new namespaces.
 * @param ns_string The namespace that is to be defined.
 * @method namespace
 * @return {Object} The newly created namespace, or existing one.
 */
TESTLIB.namespace = function (ns_string) {
    'use strict';
    var parts = ns_string.split('.'),
        parent = TESTLIB,
        i;

    if (parts[0] === "TESTLIB") {
        parts = parts.slice(1);
    }

    for (i = 0; i < parts.length; i += 1) {
        // create a property if it doesn't exist
        if (parent[parts[i]] === "undefined") {
            parent[parts[i]] = {};
        }
        parent = parent[parts[i]];
    }

    return parent;
};

TESTLIB.namespace('TESTLIB.Vector');
TESTLIB.namespace('TESTLIB.VectorUtil');

/**
 * The vector class.
 * @class Vector
 * @constructor
 */
TESTLIB.Vector = function () {
    'use strict';
};

TESTLIB.Vector.prototype = {


    /**
     * The vector's data.
     *
     * @property data
     * @type Array
     * @default {}
     **/
    data: {},

    /**
     * Sum all of the values in the vector.
     * @method sum
     * @return {Number} The sum of all of the vector values.
     */
    sum: function () {
        'use strict';
        var result = 0, i;
        for (i = 0; i < this.data.length; i += 1) {
            result += this.data[i];
        }
        return result;
    },

    /**
     * The number of elements in this vector.
     * @method sum
     * @return {Number} The number of elements in this vector.
     */
    count: function () {
        'use strict';
        return this.data.length;
    },

    /**
     * The vector length.
     *
     * @method length
     * @return {Number} The vector length.
     */
    length: function () {
        'use strict';
        var i, result;

        result = 0;

        for (i = 0; i < this.data.length; i += 1) {
            result += Math.pow(this.data[i], 2);
        }

        return Math.sqrt(result);
    },

    /**
     * Clear all of the vector elements to zero.
     *
     * @method clear
     * @param len The length, if omitted, length stays the same.
     */
    clear: function (len) {
        'use strict';
        var i, len2;

        this.data = [];
        len2 = len || this.data.length;
        for (i = 0; i < len2; i += 1) {
            this.data[i] = 0;
        }
    },
    /**
     * Format the vector as a string.
     * @method toString
     * @return {String} The string.
     */
    toString: function () {
        'use strict';
        return '[' + this.data.join(',') + ']';
    }
};

/**
 * Create a new vector.
 * @param a If a number, then the length of the vector.  If an array, the initial value of the vector.
 * @method create
 * @static
 * @return {TESTLIB.Vector} The newly created vector.
 */
TESTLIB.Vector.create = function (a) {
    'use strict';
    var result = new TESTLIB.Vector();

    if (a instanceof Array) {
        result.data = a.splice(0);
    } else {
        result.clear(a || 0);
    }


    return result;
};

/**
 * A class that provides utilities for vectors.
 * @class VectorUtil
 * @constructor
 */
TESTLIB.VectorUtil = function () {
    'use strict';
};

/**
 * Calculate the distance between two vectors.
 * @param a The first vector.
 * @param b The second vector.
 * @method distance
 * @return {Number} The distance.
 */
TESTLIB.VectorUtil.distance = function (a, b) {
    'use strict';
    var result = 0, i, d;

    if (!(a instanceof TESTLIB.Vector) || !(b instanceof  TESTLIB.Vector)) {
        throw new Error("Only Vector objects can have have their distance compared.");
    }

    if (a.count() !== b.count()) {
        throw new Error("Only Vector objects of the same size can calculate a distance.");
    }

    for (i = 0; i < a.data.length; i += 1) {
        d = a.data[i] - b.data[i];
        result += d * d;
    }

    return Math.sqrt(result);
};

/**
 * Calculate the distance between two vectors.
 * @param a The first vector to add.
 * @param b The second vector to add.
 * @method distance
 * @return {TESTLIB.Vector} The distance.
 */
TESTLIB.VectorUtil.add = function (a, b) {
    'use strict';
    var result, i;

    if (!(a instanceof TESTLIB.Vector) || !(b instanceof  TESTLIB.Vector)) {
        throw new Error("Only Vector objects can be added.");
    }

    if (a.count() !== b.count()) {
        throw new Error("Only Vector objects of the same size can be added.");
    }

    result = TESTLIB.Vector.create(a.count());

    for (i = 0; i < a.data.length; i += 1) {
        result.data[i] = a.data[i] + b.data[i];
    }

    return result;
};

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License

This article, along with any associated source code and files, is licensed under The Apache License, Version 2.0

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About the Author

JeffHeaton
Publisher
United States United States
Jeff Heaton, Ph.D., is a data scientist, an adjunct instructor for the Sever Institute at Washington University, and the author of several books about artificial intelligence. Jeff holds a Master of Information Management (MIM) from Washington University and a PhD in computer science from Nova Southeastern University. Over twenty years of experience in all aspects of software development allows Jeff to bridge the gap between complex data science problems and proven software development. Working primarily with the Python, R, Java/C#, and JavaScript programming languages he leverages frameworks such as TensorFlow, Scikit-Learn, Numpy, and Theano to implement deep learning, random forests, gradient boosting machines, support vector machines, T-SNE, and generalized linear models (GLM). Jeff holds numerous certifications and credentials, such as the Johns Hopkins Data Science certification, Fellow of the Life Management Institute (FLMI), ACM Upsilon Pi Epsilon (UPE), a senior membership with IEEE. He has published his research through peer reviewed papers with the Journal of Machine Learning Research and IEEE.

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