chatbot.js

//Copyright Oscar Stausholm 2022
//
//chatbot for js, by zhiftyDK
//Utilizing brain.js & Porters stemmer algorithm
//

//Brain.js
(function (global, factory) {
    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('gpu.js')) :
    typeof define === 'function' && define.amd ? define(['exports', 'gpu.js'], factory) :
    (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.brain = {}, global.GPU));
}(this, (function (exports, gpu_js) { 'use strict';

    /**
     * Relu Activation, aka Rectified Linear Unit Activation
     * @description https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
     */
    function activate$3(weight) {
        return Math.max(0, weight);
    }
    /**
     * Relu derivative
     */
    function measure$3(weight, delta) {
        if (weight <= 0) {
            return 0;
        }
        return delta;
    }

    var relu$2 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        activate: activate$3,
        measure: measure$3
    });

    /**
     * sigmoid activation
     */
    function activate$2(value) {
        return 1 / (1 + Math.exp(-value));
    }
    /**
     * sigmoid derivative
     */
    function measure$2(weight, error) {
        return weight * (1 - weight) * error;
    }

    var sigmoid$2 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        activate: activate$2,
        measure: measure$2
    });

    /**
     * Hyperbolic tan
     */
    function activate$1(weight) {
        return Math.tanh(weight);
    }
    /**
     * @description grad for z = tanh(x) is (1 - z^2)
     */
    function measure$1(weight, error) {
        return (1 - weight * weight) * error;
    }

    var tanh$2 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        activate: activate$1,
        measure: measure$1
    });

    /**
     * Leaky Relu Activation, aka Leaky Rectified Linear Unit Activation
     * @description https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
     */
    function activate(weight) {
        return weight > 0 ? weight : 0.01 * weight;
    }
    /**
     * Leaky Relu derivative
     */
    function measure(weight, error) {
        return weight > 0 ? error : 0.01 * error;
    }

    var leakyRelu$1 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        activate: activate,
        measure: measure
    });

    var index$1 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        relu: relu$2,
        sigmoid: sigmoid$2,
        tanh: tanh$2,
        leakyRelu: leakyRelu$1
    });

    class CrossValidate {
        constructor(initClassifier) {
            this.json = {
                avgs: {
                    error: 0,
                    iterations: 0,
                    testTime: 0,
                    trainTime: 0,
                },
                stats: {
                    total: 0,
                    testSize: 0,
                    trainSize: 0,
                },
                sets: [],
            };
            this.initClassifier = initClassifier;
        }
        testPartition(trainOpts, trainSet, testSet) {
            const classifier = this.initClassifier();
            const beginTrain = Date.now();
            const trainingStats = classifier.train(trainSet, trainOpts);
            const beginTest = Date.now();
            const testStats = classifier.test(testSet);
            const endTest = Date.now();
            return {
                ...testStats,
                trainTime: beginTest - beginTrain,
                testTime: endTest - beginTest,
                iterations: trainingStats.iterations,
                error: trainingStats.error,
                total: testStats.total,
                network: classifier.toJSON(),
            };
        }
        /**
         * Randomize array element order in-place.
         * Using Durstenfeld shuffle algorithm.
         * source: http://stackoverflow.com/a/12646864/1324039
         */
        shuffleArray(array) {
            for (let i = array.length - 1; i > 0; i--) {
                const j = Math.floor(Math.random() * (i + 1));
                const temp = array[i];
                array[i] = array[j];
                array[j] = temp;
            }
            return array;
        }
        train(data, trainOpts = {}, k = 4) {
            if (data.length < k) {
                throw new Error(`Training set size is too small for ${data.length} k folds of ${k}`);
            }
            this.shuffleArray(data);
            const size = data.length / k;
            const avgs = {
                trainTime: 0,
                testTime: 0,
                iterations: 0,
                error: 0,
            };
            const stats = {
                total: 0,
                testSize: 0,
                trainSize: 0,
            };
            const binaryStats = {
                total: 0,
                testSize: 0,
                trainSize: 0,
                truePos: 0,
                trueNeg: 0,
                falsePos: 0,
                falseNeg: 0,
                precision: 0,
                recall: 0,
                accuracy: 0,
            };
            const results = [];
            let isBinary = null;
            for (let i = 0; i < k; i++) {
                const dclone = data.slice(0);
                const testSet = dclone.splice(i * size, size);
                const trainSet = dclone;
                const result = this.testPartition(trainOpts, trainSet, testSet);
                if (isBinary === null) {
                    isBinary =
                        result.hasOwnProperty('falseNeg') &&
                            result.hasOwnProperty('falsePos') &&
                            result.hasOwnProperty('trueNeg') &&
                            result.hasOwnProperty('truePos');
                    if (isBinary) {
                        Object.assign(stats, binaryStats);
                    }
                }
                avgs.iterations += result.iterations;
                avgs.testTime += result.testTime;
                avgs.trainTime += result.trainTime;
                avgs.error += result.error;
                stats.total += result.total;
                if (CrossValidate.isBinaryStats(stats) &&
                    CrossValidate.isBinaryPartitionResults(result)) {
                    stats.accuracy += result.accuracy;
                    stats.falseNeg += result.falseNeg;
                    stats.falsePos += result.falsePos;
                    stats.precision += result.precision;
                    stats.recall += result.recall;
                    stats.trueNeg += result.trueNeg;
                    stats.truePos += result.truePos;
                }
                results.push(result);
            }
            avgs.error /= k;
            avgs.iterations /= k;
            avgs.testTime /= k;
            avgs.trainTime /= k;
            if (CrossValidate.isBinaryStats(stats)) {
                stats.precision = stats.truePos / (stats.truePos + stats.falsePos);
                stats.recall = stats.truePos / (stats.truePos + stats.falseNeg);
                stats.accuracy = (stats.trueNeg + stats.truePos) / stats.total;
            }
            stats.testSize = size;
            stats.trainSize = data.length - size;
            this.json = {
                avgs: avgs,
                stats: stats,
                sets: results,
            };
            return this.json;
        }
        toNeuralNetwork() {
            return this.fromJSON(this.json);
        }
        toJSON() {
            return this.json;
        }
        fromJSON(crossValidateJson) {
            const winningJSON = crossValidateJson.sets.reduce((prev, cur) => (prev.error < cur.error ? prev : cur));
            return this.initClassifier().fromJSON(winningJSON.network);
        }
    }
    CrossValidate.isBinaryStats = (stats) => {
        return (stats.accuracy !== undefined);
    };
    CrossValidate.isBinaryResults = (stats) => stats.stats.accuracy !== undefined;
    CrossValidate.isBinaryPartitionResults = (stats) => stats.accuracy !==
        undefined;

    let gpuInstance = null;
    /**
     * Sets up the gpu.js instance
     */
    function setup(value) {
        gpuInstance = value;
    }
    function makeKernel(fn, settings) {
        let _gpuInstance = gpuInstance;
        if (_gpuInstance === null) {
            _gpuInstance = new gpu_js.GPU({ mode: 'gpu' });
            setup(_gpuInstance);
        }
        return _gpuInstance
            .createKernel(fn, settings)
            .setPipeline(true);
    }
    function makeKernelMap(map, fn, settings) {
        let _gpuInstance = gpuInstance;
        if (_gpuInstance === null) {
            _gpuInstance = new gpu_js.GPU({ mode: 'gpu' });
            setup(_gpuInstance);
        }
        return _gpuInstance
            .createKernelMap(map, fn, settings)
            .setPipeline(true);
    }
    /**
     * Compiles a function into a gpu.js dev mode kernel
     */
    // export function makeDevKernel(
    //   fn: ThreadFunction,
    //   settings: makeKernelSettings
    // ): IKernelRunShortcut {
    //   if ('map' in settings) {
    //     throw new Error('map kernels are not supported by dev kernels');
    //   }
    //   const gpu = new GPU({ mode: 'dev' });
    //   return gpu.createKernel(fn, settings);
    // }
    function kernelInput(value, size) {
        return new gpu_js.Input(value, size);
    }
    /**
     * Deletes a gpu.js texture and frees VRAM
     */
    function release(possibleTexture) {
        if (possibleTexture instanceof gpu_js.Texture) {
            possibleTexture.delete();
        }
    }
    /**
     * Cleans ie sets all elements to 0 of a Texture or a js array
     */
    function clear(value) {
        if (value instanceof gpu_js.Texture) {
            value.clear();
            return;
        }
        // array
        if (Array.isArray(value)) {
            if (typeof value[0] === 'number') {
                value.fill(0);
            }
            else if (typeof value[0][0] === 'number') {
                for (let x = 0; x < value.length; x++) {
                    value[x].fill(0);
                }
                return;
            }
            else if (typeof value[0][0][0] === 'number') {
                // cube
                for (let y = 0; y < value.length; y++) {
                    const row = value[y];
                    for (let x = 0; x < row.length; x++) {
                        row[x].fill(0);
                    }
                }
                return;
            }
        }
        if (value instanceof Float32Array) {
            value.fill(0);
            return;
        }
        throw new Error('unhandled value');
    }
    /**
     * Clones a value
     */
    function clone(value) {
        if (value instanceof gpu_js.Texture) {
            return value.clone();
        }
        if (value instanceof Float32Array) {
            return value.slice(0);
        }
        if (Array.isArray(value)) {
            if (typeof value[0] === 'number') {
                return value.slice(0);
            }
            else if (typeof value[0][0] === 'number') {
                const matrix = new Array(value.length);
                for (let x = 0; x < value.length; x++) {
                    matrix[x] = value[x].slice(0);
                }
                return matrix;
            }
            else if (typeof value[0][0][0] === 'number') {
                const cube = new Array(value.length);
                for (let y = 0; y < value.length; y++) {
                    const row = value[y];
                    const matrix = new Array(row.length);
                    for (let x = 0; x < row.length; x++) {
                        matrix[x] = row[x].slice(0);
                    }
                }
                return cube;
            }
        }
        throw new Error('unhandled value');
    }

    /**
     * 2D Mean Squared Error
     */
    function mse2d(errors) {
        let sum = 0;
        for (let y = 0; y < this.constants.height; y++) {
            for (let x = 0; x < this.constants.width; x++) {
                sum += errors[y][x] ** 2;
            }
        }
        return sum / this.constants.length;
    }
    class MeanSquaredError {
        constructor({ width, height }) {
            this.calculate = makeKernel(mse2d, {
                output: [1],
                constants: {
                    width,
                    height,
                    length: width * height,
                },
                immutable: true,
            });
            this.addAbsolute = makeKernel(function (prevError, prevLayerErrors) {
                return prevError[0] + Math.abs(prevLayerErrors[0][0]);
            }, {
                output: [1],
                immutable: true,
            });
            this.add = makeKernel(function (value1, value2) {
                return value1[0] + value2[0];
            }, {
                output: [1],
                immutable: true,
            });
            this.divide = makeKernel(function (length, mseSum) {
                const value = mseSum[0];
                if (value > 0) {
                    return value / length;
                }
                return 0;
            }, {
                output: [1],
                immutable: true,
            });
        }
    }

    const baseLayerDefaultSettings = {
        width: 1,
        height: 1,
        depth: null,
        weights: null,
        deltas: null,
        praxis: null,
        praxisOpts: null,
        cleanupDeltas: true,
    };
    class BaseLayer {
        constructor(settings) {
            this.praxis = null;
            this.predictKernel = null;
            this.compareKernel = null;
            if (settings) {
                this.settings = { ...baseLayerDefaultSettings, ...settings };
            }
            else {
                this.settings = { ...baseLayerDefaultSettings };
            }
            this.setupPraxis();
        }
        get width() {
            var _a;
            return (_a = this.settings.width) !== null && _a !== void 0 ? _a : 0;
        }
        get height() {
            var _a;
            return (_a = this.settings.height) !== null && _a !== void 0 ? _a : 0;
        }
        get depth() {
            var _a;
            return (_a = this.settings.depth) !== null && _a !== void 0 ? _a : 0;
        }
        get weights() {
            return this.settings.weights;
        }
        set weights(weights) {
            this.settings.weights = weights;
            if (this.settings.cleanupDeltas && this.deltas) {
                clear(this.deltas);
            }
        }
        get deltas() {
            return this.settings.deltas;
        }
        set deltas(deltas) {
            this.settings.deltas = deltas;
        }
        get id() {
            var _a;
            return (_a = this.settings.id) !== null && _a !== void 0 ? _a : '';
        }
        set id(title) {
            this.settings.id = title;
        }
        setupPraxis() {
            const { initPraxis, praxis, praxisOpts } = this.settings;
            if (!this.praxis) {
                if (initPraxis) {
                    if (praxisOpts) {
                        this.praxis = initPraxis(this, praxisOpts);
                    }
                    else {
                        this.praxis = initPraxis(this);
                    }
                }
                else if (praxis) {
                    this.praxis = praxis;
                }
            }
        }
        /*
        get weights() {
          return this._weights;
        }
      
        set weights(value) {
          if (value) {
            if (value.dimensions) {
              if (value.dimensions[0] !== this.width) {
                throw new Error(`${this.constructor.name}.weights being set with improper value width`);
              }
              if (value.dimensions[1] !== this.height) {
                throw new Error(`${this.constructor.name}.weights being set with improper value height`);
              }
            } else {
              if (value[0].length !== this.width) {
                throw new Error(`${this.constructor.name}.weights being set with improper value width`);
              }
              if (value.length !== this.height) {
                throw new Error(`${this.constructor.name}.weights being set with improper value height`);
              }
            }
          }
          this._weights = value;
        }
      
        get deltas() {
          return this._deltas;
        }
      
        set deltas(value) {
          if (value) {
            if (value.dimensions) {
              if (value.dimensions[0] !== this.width) {
                throw new Error(`${this.constructor.name}.deltas being set with improper value width`);
              }
              if (value.dimensions[1] !== this.height) {
                throw new Error(`${this.constructor.name}.deltas being set with improper value height`);
              }
            } else {
              if (value[0].length !== this.width) {
                throw new Error(`${this.constructor.name}.deltas being set with improper value width`);
              }
              if (value.length !== this.height) {
                throw new Error(`${this.constructor.name}.deltas being set with improper value height`);
              }
            }
          }
          this._deltas = value;
        } */
        validate() {
            if (Number.isNaN(this.height)) {
                throw new Error(`${this.constructor.name} layer height is not a number`);
            }
            if (Number.isNaN(this.width)) {
                throw new Error(`${this.constructor.name} layer width is not a number`);
            }
            if (this.height < 1) {
                throw new Error(`${this.constructor.name} layer height is less than 1`);
            }
            if (this.width < 1) {
                throw new Error(`${this.constructor.name} layer width is less than 1`);
            }
        }
        setupKernels(isTraining) { }
        reuseKernels(layer) {
            if (layer.width !== this.width) {
                throw new Error(`${this.constructor.name} kernel width mismatch ${layer.width} is not ${this.width}`);
            }
            if (layer.height !== this.height) {
                throw new Error(`${this.constructor.name} kernel width mismatch ${layer.height} is not ${this.height}`);
            }
            if (layer.hasOwnProperty('predictKernel') && layer.predictKernel !== null) {
                if (!layer.predictKernel.immutable) {
                    throw new Error(`${layer.constructor.name}.predictKernel is not reusable, set kernel.immutable = true`);
                }
                this.predictKernel = layer.predictKernel;
            }
            if (layer.hasOwnProperty('compareKernel') && layer.compareKernel !== null) {
                if (!layer.compareKernel.immutable) {
                    throw new Error(`${layer.constructor.name}.compareKernel is not reusable, set kernel.immutable = true`);
                }
                this.compareKernel = layer.compareKernel;
            }
            this.praxis = layer.praxis;
        }
        predict(inputs) { }
        compare(targetValues) { }
        learn(learningRate) { }
        toArray() {
            return Array.isArray(this.weights)
                ? this.weights
                : this.weights.toArray();
        }
        toJSON() {
            return BaseLayer.toJSON(this);
        }
        static toJSON(layer) {
            const { weights } = layer;
            return {
                width: layer.width,
                height: layer.height,
                depth: layer.depth,
                weights: toUntypedArray((weights && weights instanceof gpu_js.Texture
                    ? weights.toArray()
                    : weights)),
                type: layer.constructor.name,
                praxisOpts: layer.praxis ? layer.praxis.toJSON() : null,
            };
        }
    }
    function toUntypedArray(weights) {
        if (weights === null)
            return null;
        if (Array.isArray(weights)) {
            if (typeof weights[0] === 'number') {
                return weights;
            }
            else if (Array.isArray(weights[0]) && typeof weights[0][0] === 'number') {
                return weights;
            }
            else if (Array.isArray(weights[0][0]) &&
                typeof weights[0][0][0] === 'number') {
                return weights;
            }
            else if (weights[0] instanceof Float32Array) {
                const matrix = weights;
                return matrix.map((row) => {
                    return Array.from(row);
                });
            }
            else if (weights[0][0] instanceof Float32Array) {
                const cube = weights;
                return cube.map((matrix) => {
                    return matrix.map((row) => {
                        return Array.from(row);
                    });
                });
            }
        }
        else if (weights) {
            return Array.from(weights);
        }
        throw new Error('unexpected value');
    }

    /**
     * Returns an array of zeros
     */
    function zeros$1(size) {
        return new Float32Array(size);
    }

    /**
     * Returns a 2D tensor(matrix) of zeros
     */
    function zeros2D(width, height) {
        const result = new Array(height);
        for (let y = 0; y < height; y++) {
            result[y] = zeros$1(width);
        }
        return result;
    }

    /**
     * Returns a 3D tensor of arrays
     */
    function zeros3D(width, height, depth) {
        const result = new Array(depth);
        for (let z = 0; z < depth; z++) {
            result[z] = zeros2D(width, height);
        }
        return result;
    }

    class Activation extends BaseLayer {
        constructor(inputLayer, settings) {
            super(settings);
            this.inputLayer = inputLayer;
            const { width, height, depth } = this;
            this.predictKernel = null;
            this.compareKernel = null;
            this.validate();
            if (depth > 0) {
                this.weights = zeros3D(width, height, depth);
                this.deltas = zeros3D(width, height, depth);
            }
            else if (height > 0) {
                this.weights = zeros2D(width, height);
                this.deltas = zeros2D(width, height);
            }
            this.setupPraxis();
        }
        get width() {
            return this.inputLayer.width;
        }
        get height() {
            return this.inputLayer.height;
        }
        get depth() {
            return this.inputLayer.depth;
        }
    }

    class Filter extends BaseLayer {
        constructor(settings, inputLayer) {
            super();
            this.settings = settings;
            this.inputLayer = inputLayer;
        }
        get width() {
            return this.inputLayer.width;
        }
        get height() {
            return this.inputLayer.height;
        }
        get depth() {
            return this.inputLayer.depth;
        }
        get filterCount() {
            return this.settings.filterCount;
        }
        get filterWidth() {
            return this.settings.filterWidth;
        }
        get filterHeight() {
            return this.settings.filterHeight;
        }
        get filters() {
            return this.settings.filters;
        }
        set filters(filters) {
            this.settings.filters = filters;
        }
        get filterDeltas() {
            return this.settings.filterDeltas;
        }
        set filterDeltas(filterDeltas) {
            this.settings.filterDeltas = filterDeltas;
        }
    }

    class Internal {
        constructor() {
            this.predictKernel = null;
            this.compareKernel = null;
            this.praxis = null;
        }
        get width() {
            return this.settings.width;
        }
        get height() {
            return this.settings.height;
        }
        get depth() {
            return this.settings.depth;
        }
        get weights() {
            return this.settings.weights;
        }
        set weights(weights) {
            this.settings.weights = weights;
        }
        get deltas() {
            return this.settings.deltas;
        }
        set deltas(deltas) {
            this.settings.deltas = deltas;
        }
        toJSON() {
            return BaseLayer.toJSON(this);
        }
    }

    class Modifier extends BaseLayer {
        constructor(inputLayer, settings) {
            super({
                ...settings,
                width: inputLayer.width,
                height: inputLayer.height,
                depth: inputLayer.depth,
            });
            this.inputLayer = inputLayer;
        }
        validate() {
            var _a;
            super.validate();
            if (this.width !== this.inputLayer.width) {
                throw new Error(`width of ${this.width} does not match inputLayer.width of ${this.inputLayer.width}`);
            }
            if (this.height !== this.inputLayer.height) {
                throw new Error(`height of ${this.height} does not match inputLayer.height of ${this.inputLayer.height}`);
            }
            if (this.depth !== ((_a = this.inputLayer.depth) !== null && _a !== void 0 ? _a : 0)) {
                throw new Error(`depth of ${this.depth} does not match inputLayer.depth of ${this.inputLayer.depth}`);
            }
        }
    }

    class Operator extends BaseLayer {
        constructor(inputLayer1, inputLayer2, settings) {
            super(settings);
            this.inputLayer1 = inputLayer1;
            this.inputLayer2 = inputLayer2;
            this.validate();
            this.weights = zeros2D(this.width, this.height);
            this.deltas = zeros2D(this.width, this.height);
            this.setupPraxis();
        }
    }

    function compare1D(weights, targetValues) {
        return weights[this.thread.y][this.thread.x] - targetValues[this.thread.x];
    }
    function compare2D$5(weights, targetValues) {
        return (weights[this.thread.y][this.thread.x] -
            targetValues[this.thread.y][this.thread.x]);
    }
    class Target extends BaseLayer {
        constructor(settings, inputLayer) {
            super(settings);
            this.inputLayer = inputLayer;
            this.validate();
            if (this.depth) {
                throw new Error('Target layer not implemented for depth');
            }
            else if (this.height) {
                this.weights = zeros2D(this.width, this.height);
                this.deltas = zeros2D(this.width, this.height);
                this.errors = zeros2D(this.width, this.height);
            }
            else {
                this.weights = zeros$1(this.width);
                this.deltas = zeros$1(this.width);
                this.errors = zeros$1(this.width);
            }
        }
        setupKernels() {
            if (this.width === 1) {
                this.compareKernel = makeKernel(compare1D, {
                    output: [this.width, this.height],
                    immutable: true,
                });
            }
            else {
                this.compareKernel = makeKernel(compare2D$5, {
                    output: [this.width, this.height],
                    immutable: true,
                });
            }
        }
        predict() {
            // TODO: should we clone here?
            // NOTE: this looks like it shouldn't be, but the weights are immutable, and this is where they are reused.
            release(this.weights);
            this.weights = clone(this.inputLayer.weights);
        }
        compare(targetValues) {
            // this is where weights attach to deltas
            // deltas will be zero on learn, so save it in error for comparing to mse later
            release(this.deltas);
            release(this.errors);
            release(this.inputLayer.deltas);
            this.deltas = this.compareKernel(this.weights, targetValues);
            this.inputLayer.deltas = clone(this.deltas);
            this.errors = clone(this.deltas);
        }
        setupPraxis() { }
    }
    function target(settings, inputLayer) {
        return new Target(settings, inputLayer);
    }

    // eslint-disable-next-line @typescript-eslint/no-extraneous-class
    class InternalModel {
    }
    // eslint-disable-next-line @typescript-eslint/no-extraneous-class
    class EntryPoint extends BaseLayer {
    }
    // eslint-disable-next-line @typescript-eslint/no-extraneous-class
    class Model extends BaseLayer {
        learn(learningRate) {
            // TODO: do we need to release here?
            const { weights: oldWeights } = this;
            if (!this.praxis)
                throw new Error('this.praxis not defined');
            this.weights = this.praxis.run(this, learningRate);
            release(oldWeights);
        }
    }

    /* Functions for turning sparse hashes into arrays and vice versa */
    const lookup = {
        /**
         * Performs `[{a: 1}, {b: 6, c: 7}] -> {a: 0, b: 1, c: 2}`
         * @param {Object} hashes
         * @returns {Object}
         */
        toTable(hashes) {
            const hash = hashes.reduce((memo, hash) => {
                return Object.assign(memo, hash);
            }, {});
            return lookup.toHash(hash);
        },
        /**
         * Performs `[{a: 1}, {b: 6, c: 7}] -> {a: 0, b: 1, c: 2}`
         */
        toTable2D(objects2D) {
            const table = {};
            let valueIndex = 0;
            for (let i = 0; i < objects2D.length; i++) {
                const objects = objects2D[i];
                for (let j = 0; j < objects.length; j++) {
                    const object = objects[j];
                    for (const p in object) {
                        if (object.hasOwnProperty(p) && !table.hasOwnProperty(p)) {
                            table[p] = valueIndex++;
                        }
                    }
                }
            }
            return table;
        },
        toInputTable2D(data) {
            const table = {};
            let tableIndex = 0;
            for (let dataIndex = 0; dataIndex < data.length; dataIndex++) {
                const input = data[dataIndex].input;
                for (let i = 0; i < input.length; i++) {
                    const object = input[i];
                    for (const p in object) {
                        if (!object.hasOwnProperty(p))
                            continue;
                        if (!table.hasOwnProperty(p)) {
                            table[p] = tableIndex++;
                        }
                    }
                }
            }
            return table;
        },
        toOutputTable2D(data) {
            const table = {};
            let tableIndex = 0;
            for (let dataIndex = 0; dataIndex < data.length; dataIndex++) {
                const output = data[dataIndex].output;
                for (let i = 0; i < output.length; i++) {
                    const object = output[i];
                    for (const p in object) {
                        if (!object.hasOwnProperty(p))
                            continue;
                        if (!table.hasOwnProperty(p)) {
                            table[p] = tableIndex++;
                        }
                    }
                }
            }
            return table;
        },
        /**
         * performs `{a: 6, b: 7} -> {a: 0, b: 1}`
         */
        toHash(hash) {
            const lookup = {};
            let index = 0;
            const keys = Object.keys(hash);
            for (let i = 0; i < keys.length; i++) {
                lookup[keys[i]] = index++;
            }
            return lookup;
        },
        /**
         * performs `{a: 0, b: 1}, {a: 6} -> [6, 0]`
         */
        toArray(lookup, object, arrayLength) {
            const result = new Float32Array(arrayLength);
            for (const p in lookup) {
                if (!lookup.hasOwnProperty(p))
                    continue;
                result[lookup[p]] = object.hasOwnProperty(p) ? object[p] : 0;
            }
            return result;
        },
        toArrayShort(lookup, object) {
            const result = [];
            for (const p in lookup) {
                if (!lookup.hasOwnProperty(p))
                    continue;
                if (!object.hasOwnProperty(p))
                    break;
                result[lookup[p]] = object[p];
            }
            return Float32Array.from(result);
        },
        toArrays(lookup, objects, arrayLength) {
            const result = [];
            for (let i = 0; i < objects.length; i++) {
                result.push(this.toArray(lookup, objects[i], arrayLength));
            }
            return result;
        },
        /**
         * performs `{a: 0, b: 1}, [6, 7] -> {a: 6, b: 7}`
         * @param {Object} lookup
         * @param {Array} array
         * @returns {Object}
         */
        toObject(lookup, array) {
            const object = {};
            for (const p in lookup) {
                if (!lookup.hasOwnProperty(p))
                    continue;
                object[p] = array[lookup[p]];
            }
            return object;
        },
        toObjectPartial(lookup, array, offset = 0, limit = 0) {
            const object = {};
            let i = 0;
            for (const p in lookup) {
                if (!lookup.hasOwnProperty(p))
                    continue;
                if (offset > 0) {
                    if (i++ < offset)
                        continue;
                }
                if (limit > 0) {
                    if (i++ >= limit)
                        continue;
                }
                object[p] = array[lookup[p] - offset];
            }
            return object;
        },
        dataShape(data) {
            const shape = [];
            let lastData;
            if (data.hasOwnProperty('input')) {
                shape.push('datum');
                lastData = data.input;
            }
            else if (Array.isArray(data)) {
                if (data[0] &&
                    data[0].input) {
                    shape.push('array', 'datum');
                    lastData = data[0].input;
                }
                else if (Array.isArray(data[0])) {
                    shape.push('array');
                    lastData = data[0];
                }
                else {
                    lastData = data;
                }
            }
            else {
                lastData = data;
            }
            let p;
            while (lastData) {
                p = Object.keys(lastData)[0];
                if (Array.isArray(lastData) ||
                    typeof lastData.buffer === 'object') {
                    shape.push('array');
                    const possibleNumber = lastData[parseInt(p)];
                    if (typeof possibleNumber === 'number') {
                        shape.push('number');
                        break;
                    }
                    else {
                        lastData = possibleNumber;
                    }
                }
                else if (typeof lastData === 'object' &&
                    typeof lastData.buffer !== 'object') {
                    shape.push('object');
                    const possibleNumber = lastData[p];
                    if (typeof possibleNumber === 'number') {
                        shape.push('number');
                        break;
                    }
                    else {
                        lastData = possibleNumber;
                    }
                }
                else {
                    throw new Error('unhandled signature');
                }
            }
            return shape;
        },
        addKeys(value, table) {
            if (Array.isArray(value))
                return table;
            let i = Object.keys(table).length;
            for (const p in value) {
                if (!value.hasOwnProperty(p))
                    continue;
                if (table.hasOwnProperty(p))
                    continue;
                table[p] = i++;
            }
            return table;
        },
    };

    class BasePraxis {
        constructor(layerTemplate, settings = {}) {
            this.layerTemplate = layerTemplate;
            this.settings = { ...settings };
            this.kernel = null;
        }
        get width() {
            return this.layerTemplate.width;
        }
        get height() {
            return this.layerTemplate.height;
        }
        get depth() {
            return this.layerTemplate.depth;
        }
        setupKernels() { }
        reuseKernels(praxis) {
            if (praxis.width !== this.width) {
                throw new Error(`${this.constructor.name} kernel width mismatch ${praxis.width} is not ${this.width}`);
            }
            if (praxis.height !== this.height) {
                throw new Error(`${this.constructor.name} kernel width mismatch ${praxis.height} is not ${this.height}`);
            }
            if (praxis.hasOwnProperty('kernel')) {
                this.kernel = praxis.kernel;
            }
        }
        toJSON() {
            return { ...this.settings };
        }
    }

    function update$2(weights, deltas) {
        return (weights[this.thread.y][this.thread.x] +
            this.constants.learningRate * deltas[this.thread.y][this.thread.x]);
    }
    const defaultSettings$1 = {
        learningRate: 0.3,
    };
    class ArthurDeviationBiases extends BasePraxis {
        constructor(layer, settings) {
            super(layer);
            this.settings = { ...defaultSettings$1, ...settings };
            this.kernel = null;
        }
        run(layer) {
            return this.kernel(layer.weights, layer.deltas);
        }
        setupKernels() {
            this.kernel = makeKernel(update$2, {
                output: [this.width, this.height],
                constants: {
                    learningRate: this.settings.learningRate,
                },
            });
        }
    }
    function arthurDeviationBiases(layer, settings) {
        return new ArthurDeviationBiases(layer, settings);
    }

    function updateChange(value) {
        return value;
    }
    function update$1(changes, weights, incomingWeights, inputDeltas) {
        const lastChange = changes[this.thread.y][this.thread.x];
        const inputDelta = inputDeltas[this.thread.y][0];
        const weight = weights[this.thread.y][this.thread.x];
        const incoming = incomingWeights[this.thread.x][0];
        const change = this.constants.learningRate * inputDelta * incoming +
            this.constants.momentum * lastChange;
        return weight + change;
    }
    const defaultSettings = {
        learningRate: 0.3,
        momentum: 0.1,
        weightsLayer: null,
        incomingLayer: null,
        deltaLayer: null,
    };
    class ArthurDeviationWeights extends BasePraxis {
        constructor(layer, settings) {
            super(layer);
            this.kernelMap = null;
            this.settings = { ...defaultSettings, ...settings };
            this.changes = zeros2D(layer.width, layer.height);
        }
        get learningRate() {
            return this.settings.learningRate;
        }
        get momentum() {
            return this.settings.momentum;
        }
        get weightsLayer() {
            return this.settings.weightsLayer;
        }
        set weightsLayer(layer) {
            this.settings.weightsLayer = layer;
        }
        get deltaLayer() {
            return this.settings.deltaLayer;
        }
        set deltaLayer(layer) {
            this.settings.deltaLayer = layer;
        }
        get incomingLayer() {
            return this.settings.incomingLayer;
        }
        set incomingLayer(layer) {
            this.settings.incomingLayer = layer;
        }
        run() {
            const output = this.kernelMap(this.changes, this.weightsLayer.weights, this.incomingLayer.weights, this.deltaLayer.deltas);
            this.changes = output.changes;
            return output.result;
        }
        setupKernels() {
            this.kernelMap = makeKernelMap({
                changes: updateChange,
            }, update$1, {
                output: [this.width, this.height],
                constants: {
                    learningRate: this.learningRate,
                    momentum: this.momentum,
                },
            });
        }
    }
    function arthurDeviationWeights(layer, settings) {
        return new ArthurDeviationWeights(layer, settings);
    }

    function getMomentum(delta, decay, previousMomentum) {
        return previousMomentum * decay + (1 - decay) * delta * delta;
    }
    function clipByValue(value, max, min) {
        if (value > max) {
            return max;
        }
        if (value < min) {
            return min;
        }
        return value;
    }
    /**
     * @description Momentum Root Mean Square Propagation Function
     */
    function update(weights, deltas, previousMomenta) {
        const delta = deltas[this.thread.y][this.thread.x];
        const clippedDelta = clipByValue(delta, this.constants.clipValue, -this.constants.clipValue);
        const weight = weights[this.thread.y][this.thread.x];
        const previousMomentum = previousMomenta[this.thread.y][this.thread.x];
        const momentum = getMomentum(delta, this.constants.decayRate, previousMomentum);
        return (weight +
            (-this.constants.learningRate * clippedDelta) /
                Math.sqrt(momentum + this.constants.smoothEps) -
            this.constants.regularizationStrength * weight);
    }
    const defaults$8 = {
        decayRate: 0.999,
        regularizationStrength: 0.000001,
        learningRate: 0.01,
        smoothEps: 1e-8,
        clipValue: 5,
    };
    class MomentumRootMeanSquaredPropagation extends BasePraxis {
        constructor(layerTemplate, settings = {}) {
            super(layerTemplate);
            this.kernelMap = null;
            this.settings = { ...defaults$8, ...settings };
            this.momenta = zeros2D(layerTemplate.width, layerTemplate.height);
        }
        get clipValue() {
            return this.settings.clipValue;
        }
        get decayRate() {
            return this.settings.decayRate;
        }
        get learningRate() {
            return this.settings.learningRate;
        }
        get regularizationStrength() {
            return this.settings.regularizationStrength;
        }
        get smoothEps() {
            return this.settings.smoothEps;
        }
        run(layer) {
            const { momenta, result } = this.kernelMap(layer.weights, layer.deltas, this.momenta);
            release(this.momenta);
            this.momenta = momenta;
            return result;
        }
        setupKernels() {
            this.kernelMap = makeKernelMap({
                momenta: getMomentum,
            }, update, {
                output: [this.width, this.height],
                constants: {
                    clipValue: this.clipValue,
                    decayRate: this.decayRate,
                    learningRate: this.learningRate,
                    regularizationStrength: this.regularizationStrength,
                    smoothEps: this.smoothEps,
                },
                functions: [clipByValue],
                immutable: true,
            });
        }
    }
    function momentumRootMeanSquaredPropagation(layer, settings) {
        return new MomentumRootMeanSquaredPropagation(layer, settings);
    }
    /**
     * @description Mathematician friendly name of MomentumRootMeanSquaredPropagation class. For those that are not mere mortals
     */
    const MRmsProp = MomentumRootMeanSquaredPropagation;
    const mRmsProp = momentumRootMeanSquaredPropagation;

    var index = /*#__PURE__*/Object.freeze({
        __proto__: null,
        ArthurDeviationBiases: ArthurDeviationBiases,
        arthurDeviationBiases: arthurDeviationBiases,
        ArthurDeviationWeights: ArthurDeviationWeights,
        arthurDeviationWeights: arthurDeviationWeights,
        MomentumRootMeanSquaredPropagation: MomentumRootMeanSquaredPropagation,
        momentumRootMeanSquaredPropagation: momentumRootMeanSquaredPropagation,
        MRmsProp: MRmsProp,
        mRmsProp: mRmsProp
    });

    function traverseLayersFrom(layer, cb) {
        if (layer.hasOwnProperty('inputLayer')) {
            traverseLayersFrom(layer.inputLayer, cb);
        }
        else {
            if (layer.hasOwnProperty('inputLayer1')) {
                traverseLayersFrom(layer.inputLayer1, cb);
            }
            if (layer.hasOwnProperty('inputLayer2')) {
                traverseLayersFrom(layer.inputLayer2, cb);
            }
        }
        cb(layer);
    }

    function flattenLayers(layers) {
        const result = layers.slice(0);
        for (let i = 0; i < result.length; i++) {
            let offset = 0;
            traverseLayersFrom(result[i], (layer) => {
                if (!result.includes(layer)) {
                    result.splice(i + offset, 0, layer);
                    offset++;
                }
            });
        }
        return result;
    }

    function checkSameSize(layer1, layer2) {
        if (layer1.width !== layer2.width) {
            throw new Error(`Layer width mismatch of ${layer1.width} and ${layer2.width}`);
        }
        if (layer1.height !== layer2.height) {
            throw new Error(`Layer height mismatch of ${layer1.height} and ${layer2.height}`);
        }
    }

    function predict$8(inputWeights1, inputWeights2) {
        return (inputWeights1[this.thread.y][this.thread.x] +
            inputWeights2[this.thread.y][this.thread.x]);
    }
    class Add extends Operator {
        get width() {
            return this.inputLayer1.width;
        }
        get height() {
            return this.inputLayer1.height;
        }
        get depth() {
            return this.inputLayer1.depth;
        }
        validate() {
            super.validate();
            checkSameSize(this.inputLayer1, this.inputLayer2);
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict$8, {
                output: [this.width, this.height],
                immutable: true,
            });
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer1.weights, this.inputLayer2.weights);
        }
        compare() {
            // TODO: Do we need release and clone here?
            release(this.inputLayer1.deltas);
            release(this.inputLayer2.deltas);
            this.inputLayer1.deltas = clone(this.deltas);
            this.inputLayer2.deltas = clone(this.deltas);
        }
    }
    function add$1(inputLayer1, inputLayer2, settings) {
        return new Add(inputLayer1, inputLayer2, settings);
    }

    function randomWeight() {
        return Math.random() * 0.4 - 0.2;
    }

    /**
     * Returns a random float between given min and max bounds (inclusive)
     * @param min Minimum value of the ranfom float
     * @param max Maximum value of the random float
     */
    function randomFloat(min, max) {
        return Math.random() * (max - min) + min;
    }
    /**
     * Complicated math. All you need to know is that it returns a random number.
     * More info: https://en.wikipedia.org/wiki/Normal_distribution
     */
    function gaussRandom() {
        if (gaussRandom.returnV) {
            gaussRandom.returnV = false;
            return gaussRandom.vVal;
        }
        const u = 2 * Math.random() - 1;
        const v = 2 * Math.random() - 1;
        const r = u * u + v * v;
        if (r === 0 || r > 1) {
            return gaussRandom();
        }
        const c = Math.sqrt((-2 * Math.log(r)) / r);
        gaussRandom.vVal = v * c; // cache this
        gaussRandom.returnV = true;
        return u * c;
    }
    /**
     * Returns a random integer between given min and max bounds
     * @param min Minimum value of the random integer
     * @param max Maximum value of the random integer
     */
    function randomInteger(min, max) {
        return Math.floor(Math.random() * (max - min) + min);
    }
    /**
     * If you know what this is: https://en.wikipedia.org/wiki/Normal_distribution
     * @param mu
     * @param std
     */
    function randomN(mu, std) {
        return mu + gaussRandom() * std;
    }
    gaussRandom.returnV = false;
    gaussRandom.vVal = 0;

    var random$1 = /*#__PURE__*/Object.freeze({
        __proto__: null,
        randomFloat: randomFloat,
        gaussRandom: gaussRandom,
        randomInteger: randomInteger,
        randomN: randomN
    });

    /**
     * Returns an array of given size, full of randomness
     */
    function randos(size, std = null) {
        const array = new Float32Array(size);
        if (std === null) {
            for (let i = 0; i < size; i++) {
                array[i] = randomWeight();
            }
        }
        else {
            for (let i = 0; i < size; i++) {
                array[i] = randomFloat(-std, std);
            }
        }
        return array;
    }
    /**
     * Returns a 2D matrix of given size, full of randomness
     */
    function randos2D(width, height, std) {
        const result = new Array(height);
        for (let y = 0; y < height; y++) {
            result[y] = randos(width, std);
        }
        return result;
    }
    /**
     * Returns a 3D tensor of given size, full of randomness
     */
    function randos3D(width, height, depth, std) {
        const result = new Array(depth);
        for (let z = 0; z < depth; z++) {
            result[z] = randos2D(width, height, std);
        }
        return result;
    }

    const defaults$7 = {
        ...baseLayerDefaultSettings,
        std: null,
    };
    class Random extends Model {
        constructor(settings) {
            super();
            this.settings = { ...defaults$7, ...settings };
            this.setupPraxis();
            this.validate();
            if (!this.weights) {
                this.weights = randos2D(this.width, this.height, settings.std);
            }
            if (!this.deltas) {
                this.deltas = zeros2D(this.width, this.height);
            }
        }
        predict() { }
        compare() { }
    }
    function random(settings) {
        return new Random(settings);
    }

    function predict$7(weights1, weights2) {
        let sum = 0;
        for (let i = 0; i < this.constants.size; i++) {
            sum += weights1[this.thread.y][i] * weights2[i][this.thread.x];
        }
        return sum;
    }
    function compareFromX(deltas, inputDeltas, inputWeights) {
        let sum = inputDeltas[this.thread.y][this.thread.x];
        for (let i = 0; i < this.constants.size; i++) {
            sum += deltas[this.thread.y][i] * inputWeights[this.thread.x][i];
        }
        return sum;
    }
    function compareFromY(deltas, inputDeltas, inputWeights) {
        let sum = inputDeltas[this.thread.y][this.thread.x];
        for (let i = 0; i < this.constants.size; i++) {
            sum += deltas[i][this.thread.x] * inputWeights[i][this.thread.y];
        }
        return sum;
    }
    class Multiply extends Operator {
        constructor() {
            super(...arguments);
            this.compareKernel1 = null;
            this.compareKernel2 = null;
        }
        get width() {
            return this.inputLayer2.width;
        }
        set width(width) {
            throw new Error('Cannot set width on Multiply');
        }
        get height() {
            return this.inputLayer1.height;
        }
        set height(height) {
            throw new Error('Cannot set height on Multiply');
        }
        get depth() {
            return this.inputLayer1.depth;
        }
        set depth(depth) {
            throw new Error('Cannot set depth on Multiply');
        }
        validate() {
            super.validate();
            if (this.inputLayer1.width !== this.inputLayer2.height) {
                throw new Error(`Layer width mismatch of ${this.inputLayer1.width} and ${this.inputLayer2.height}`);
            }
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict$7, {
                output: [this.width, this.height],
                constants: {
                    size: this.inputLayer2.height,
                },
                immutable: true,
            });
            this.compareKernel1 = makeKernel(compareFromX, {
                output: [this.inputLayer1.width, this.inputLayer1.height],
                constants: {
                    size: this.inputLayer2.width,
                },
                immutable: true,
            });
            this.compareKernel2 = makeKernel(compareFromY, {
                output: [this.inputLayer2.width, this.inputLayer2.height],
                constants: {
                    size: this.inputLayer1.height,
                },
                immutable: true,
            });
        }
        reuseKernels(layer) {
            super.reuseKernels(layer);
            this.compareKernel1 = layer.compareKernel1;
            this.compareKernel2 = layer.compareKernel2;
        }
        predict() {
            release(this.weights);
            if (!this.predictKernel)
                throw new Error('this.predictKernel is not set');
            this.weights = this.predictKernel(this.inputLayer1.weights, this.inputLayer2.weights);
        }
        compare() {
            if (!this.compareKernel1)
                throw new Error('this.compareKernel1 not set');
            if (!this.compareKernel2)
                throw new Error('this.compareKernel2 not set');
            const inputLayer1Deltas = this.inputLayer1.deltas;
            const inputLayer2Deltas = this.inputLayer2.deltas;
            const newDeltas1 = this.compareKernel1(this.deltas, this.inputLayer1.deltas, this.inputLayer2.weights);
            const newDeltas2 = this.compareKernel2(this.deltas, this.inputLayer2.deltas, this.inputLayer1.weights);
            this.inputLayer2.deltas = newDeltas2;
            this.inputLayer1.deltas = newDeltas1;
            release(inputLayer1Deltas);
            release(inputLayer2Deltas);
        }
        setupPraxis() { }
        toJSON() {
            return {
                ...super.toJSON(),
                width: this.width,
                height: this.height,
            };
        }
    }
    function multiply$1(inputLayer1, inputLayer2, settings) {
        return new Multiply(inputLayer1, inputLayer2, settings);
    }

    function predict2D$4(inputs) {
        return 1 / (1 + Math.exp(-inputs[this.thread.y][this.thread.x]));
    }
    function predict3D$5(inputs) {
        return (1 / (1 + Math.exp(-inputs[this.thread.z][this.thread.y][this.thread.x])));
    }
    function compare2D$4(weights, deltas) {
        const weight = weights[this.thread.y][this.thread.x];
        const delta = deltas[this.thread.y][this.thread.x];
        return weight * (1 - weight) * delta;
    }
    function compare3D$4(weights, deltas) {
        const weight = weights[this.thread.z][this.thread.y][this.thread.x];
        const delta = deltas[this.thread.z][this.thread.y][this.thread.x];
        return weight * (1 - weight) * delta;
    }
    class Sigmoid extends Activation {
        setupKernels() {
            if (this.depth > 0) {
                this.predictKernel = makeKernel(predict3D$5, {
                    output: [this.width, this.height, this.depth],
                    functions: [activate$2],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare3D$4, {
                    output: [this.width, this.height, this.depth],
                    functions: [measure$2],
                    immutable: true,
                });
            }
            else {
                this.predictKernel = makeKernel(predict2D$4, {
                    output: [this.width, this.height],
                    functions: [activate$2],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare2D$4, {
                    output: [this.width, this.height],
                    functions: [measure$2],
                    immutable: true,
                });
            }
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
        compare() {
            release(this.inputLayer.deltas);
            this.inputLayer.deltas = this.compareKernel(this.weights, this.deltas);
        }
        learn(learningRate) { }
    }
    function sigmoid$1(inputLayer, settings) {
        return new Sigmoid(inputLayer, settings);
    }

    function arthurFeedForward(settings, inputLayer) {
        const { height } = settings;
        function initWeightsPraxis(layerTemplate, settings) {
            const praxis = arthurDeviationWeights(layerTemplate, settings);
            praxis.setupKernels();
            return praxis;
        }
        function initBiasesPraxis(layerTemplate, settings) {
            const praxis = arthurDeviationBiases(layerTemplate, settings);
            praxis.setupKernels();
            return praxis;
        }
        const weightsLayer = random({
            id: 'weights',
            height,
            width: inputLayer.height,
            initPraxis: initWeightsPraxis,
        });
        const biasesLayer = random({
            id: 'biases',
            height,
            initPraxis: initBiasesPraxis,
        });
        const multiplyLayer = multiply$1(weightsLayer, inputLayer);
        const addLayer = add$1(multiplyLayer, biasesLayer);
        const sigmoidLayer = sigmoid$1(addLayer);
        const weightsPraxis = weightsLayer.praxis;
        weightsPraxis.weightsLayer = weightsLayer;
        weightsPraxis.incomingLayer = inputLayer;
        weightsPraxis.deltaLayer = sigmoidLayer;
        return sigmoidLayer;
    }

    function getStride(settings, defaults) {
        if (typeof settings.stride === 'number') {
            return { strideX: settings.stride, strideY: settings.stride };
        }
        else {
            let strideX = defaults.stride;
            let strideY = defaults.stride;
            if (typeof settings.strideX === 'number') {
                strideX = settings.strideX;
            }
            if (typeof settings.strideY === 'number') {
                strideY = settings.strideY;
            }
            return { strideX, strideY };
        }
    }
    function getPadding(settings, defaults) {
        if (typeof settings.padding === 'number') {
            return { paddingX: settings.padding, paddingY: settings.padding };
        }
        else {
            let paddingX = defaults.padding;
            let paddingY = defaults.padding;
            if (typeof settings.paddingX === 'number') {
                paddingX = settings.paddingX;
            }
            if (typeof settings.paddingY === 'number') {
                paddingY = settings.paddingY;
            }
            return { paddingX, paddingY };
        }
    }

    /**
     * Returns an array of a given size with each element filled with a single value
     */
    function values(size, value) {
        return new Float32Array(size).fill(value);
    }

    function predict$6(inputs, filters, biases) {
        const startFilterX = this.constants.paddingX - this.thread.x * this.constants.strideX;
        const startInputX = this.thread.x * this.constants.strideX - this.constants.paddingX;
        const endFilterX = Math.min(this.constants.filterWidth, startFilterX + this.constants.inputWidth);
        const startFilterY = this.constants.paddingY - this.thread.y * this.constants.strideY;
        const startInputY = this.thread.y * this.constants.strideY - this.constants.paddingY;
        const endFilterY = Math.min(this.constants.filterHeight, startFilterY + this.constants.inputHeight);
        let sum = 0;
        for (let z = 0; z < this.constants.inputDepth; z++) {
            for (let filterY = Math.max(0, startFilterY), inputY = Math.max(0, startInputY); filterY < endFilterY; filterY++, inputY++) {
                for (let filterX = Math.max(0, startFilterX), inputX = Math.max(0, startInputX); filterX < endFilterX; filterX++, inputX++) {
                    sum += filters[z][filterY][filterX] * inputs[z][inputY][inputX];
                }
            }
        }
        return sum + biases[this.thread.z];
    }
    function compareFilterDeltas$1(filterDeltas, inputs, deltas) {
        const startDeltaX = Math.max(0, Math.ceil((this.constants.paddingX - this.thread.x) / this.constants.strideX));
        const startInputX = startDeltaX * this.constants.strideX +
            this.thread.x -
            this.constants.paddingX;
        const endDeltaX = Math.min(this.constants.deltaWidth, Math.floor((this.constants.inputWidth -
            1 -
            this.thread.x +
            this.constants.paddingX) /
            this.constants.strideX) + 1);
        const startDeltaY = Math.max(0, Math.ceil((this.constants.paddingY - this.thread.y) / this.constants.strideY));
        const startInputY = startDeltaY * this.constants.strideY +
            this.thread.y -
            this.constants.paddingY;
        const endDeltaY = Math.min(this.constants.deltaHeight, Math.floor((this.constants.inputHeight -
            1 -
            this.thread.y +
            this.constants.paddingY) /
            this.constants.strideY) + 1);
        let sum = filterDeltas[this.thread.z][this.thread.y][this.thread.x];
        for (let deltaY = startDeltaY, inputY = startInputY; deltaY < endDeltaY; deltaY++, inputY += this.constants.strideY) {
            for (let deltaX = startDeltaX, inputX = startInputX; deltaX < endDeltaX; deltaX++, inputX += this.constants.strideX) {
                sum +=
                    inputs[this.thread.z][inputY][inputX] *
                        deltas[this.constants.deltaZ][deltaY][deltaX];
            }
        }
        return sum;
    }
    function compareInputDeltas$1(inputDeltas, filters, deltas) {
        const x = this.thread.x + this.constants.paddingX;
        const startDeltaX = x < this.constants.filterWidth
            ? 0
            : Math.floor((x - this.constants.filterWidth + this.constants.strideX) /
                this.constants.strideX);
        const startFilterX = x - startDeltaX * this.constants.strideX;
        const endDeltaX = Math.min(startDeltaX + Math.floor(startFilterX / this.constants.strideX) + 1, this.constants.deltaWidth);
        const y = this.thread.y + this.constants.paddingY;
        const startDeltaY = y < this.constants.filterHeight
            ? 0
            : Math.floor((y - this.constants.filterHeight + this.constants.strideY) /
                this.constants.strideY);
        const startFilterY = y - startDeltaY * this.constants.strideY;
        const endDeltaY = Math.min(startDeltaY + Math.floor(startFilterY / this.constants.strideY) + 1, this.constants.deltaHeight);
        let sum = inputDeltas[this.thread.z][this.thread.y][this.thread.x];
        let deltaY = startDeltaY;
        for (let filterY = startFilterY; deltaY < endDeltaY; filterY -= this.constants.strideY, deltaY++) {
            let deltaX = startDeltaX;
            for (let filterX = startFilterX; deltaX < endDeltaX; filterX -= this.constants.strideX, deltaX++) {
                sum +=
                    filters[this.thread.z][filterY][filterX] *
                        deltas[this.constants.deltaZ][deltaY][deltaX];
            }
        }
        return sum;
    }
    function compareBiases$1(biasDeltas, deltas) {
        let sum = 0;
        for (let y = 0; y < this.constants.deltaHeight; y++) {
            for (let x = 0; x < this.constants.deltaWidth; x++) {
                sum += deltas[this.thread.z][y][x];
            }
        }
        return biasDeltas[this.thread.z][this.thread.y][this.thread.x] + sum;
    }
    const defaults$6 = {
        stride: 0,
        padding: 0,
        bias: 0.1,
        filterCount: 1,
        filterWidth: 0,
        filterHeight: 0,
    };
    class Convolution extends Filter {
        constructor(settings, inputLayer) {
            var _a, _b, _c;
            super(settings, inputLayer);
            this.compareFilterDeltasKernel = null;
            this.compareInputDeltasKernel = null;
            this.compareBiasesKernel = null;
            this.settings = {
                ...defaults$6,
                ...settings,
                ...getPadding(settings, defaults$6),
                ...getStride(settings, defaults$6),
            };
            this.weights = (_a = settings.weights) !== null && _a !== void 0 ? _a : randos3D(this.width, this.height, this.depth);
            this.deltas = zeros3D(this.width, this.height, this.depth);
            this.biases = values(this.depth, this.bias);
            this.biasDeltas = (_b = settings.biasDeltas) !== null && _b !== void 0 ? _b : randos(this.depth);
            this.filters = (_c = settings.filters) !== null && _c !== void 0 ? _c : randos3D(this.filterWidth, this.filterHeight, this.filterCount);
            this.filterDeltas = zeros3D(this.filterWidth, this.filterHeight, this.filterCount);
            this.validate();
        }
        get strideX() {
            return this.settings.strideX;
        }
        get strideY() {
            return this.settings.strideY;
        }
        get paddingX() {
            return this.settings.paddingX;
        }
        get paddingY() {
            return this.settings.paddingX;
        }
        get width() {
            return Math.floor((this.inputLayer.width + this.paddingX * 2 - this.filterWidth) /
                this.strideX +
                1);
        }
        get height() {
            return Math.floor((this.inputLayer.height + this.paddingY * 2 - this.filterHeight) /
                this.strideY +
                1);
        }
        get bias() {
            return this.settings.bias;
        }
        get depth() {
            return this.filterCount;
        }
        get biases() {
            return this.settings.biases;
        }
        set biases(biases) {
            this.settings.biases = biases;
        }
        get biasDeltas() {
            return this.settings.biasDeltas;
        }
        set biasDeltas(weights) {
            this.settings.biasDeltas = weights;
        }
        get filters() {
            return this.settings.filters;
        }
        set filters(filters) {
            this.settings.filters = filters;
        }
        get filterDeltas() {
            return this.settings.filterDeltas;
        }
        set filterDeltas(filterDeltas) {
            this.settings.filterDeltas = filterDeltas;
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict$6, {
                constants: {
                    inputWidth: this.inputLayer.width,
                    inputHeight: this.inputLayer.height,
                    inputDepth: this.inputLayer.depth,
                    strideX: this.strideX,
                    strideY: this.strideY,
                    paddingX: this.paddingX,
                    paddingY: this.paddingY,
                    filterWidth: this.filterWidth,
                    filterHeight: this.filterHeight,
                },
                output: [this.width, this.height, this.depth],
                immutable: true,
            });
            this.compareFilterDeltasKernel = makeKernel(compareFilterDeltas$1, {
                constants: {
                    deltasWidth: this.width,
                    deltasHeight: this.height,
                    deltasDepth: this.depth,
                    inputWidth: this.inputLayer.width,
                    inputHeight: this.inputLayer.height,
                    inputDepth: this.inputLayer.depth,
                    strideX: this.strideX,
                    strideY: this.strideY,
                    paddingX: this.paddingX,
                    paddingY: this.paddingY,
                    filterWidth: this.filterWidth,
                    filterHeight: this.filterHeight,
                },
                output: [this.width, this.height, this.depth],
                immutable: true,
            });
            this.compareInputDeltasKernel = makeKernel(compareInputDeltas$1, {
                constants: {
                    filterCount: this.filterCount,
                },
                output: [
                    this.inputLayer.width,
                    this.inputLayer.height,
                    this.inputLayer.depth,
                ],
                immutable: true,
            });
            this.compareBiasesKernel = makeKernel(compareBiases$1, {
                output: [1, 1, this.depth],
                constants: {
                    deltaWidth: this.width,
                    deltaHeight: this.height,
                },
                immutable: true,
            });
        }
        predict() {
            this.weights = this.predictKernel(this.inputLayer.weights, this.filters, this.biases);
        }
        compare() {
            const { filterDeltas, biasDeltas } = this;
            this.filterDeltas = this.compareFilterDeltasKernel(filterDeltas, this.inputLayer.weights, this.deltas);
            release(filterDeltas);
            this.biasDeltas = this.compareBiasesKernel(biasDeltas, this.deltas);
            release(biasDeltas);
            release(this.deltas);
            this.deltas = this.compareInputDeltasKernel(this.filters, this.inputLayer.deltas);
            release(this.inputLayer.deltas);
            // TODO: do we need to clone here?
            this.inputLayer.deltas = clone(this.deltas);
        }
        learn(learningRate) {
            // TODO: handle filters
            // TODO: do we need to release here?
            const { weights: oldWeights } = this;
            this.weights = this.praxis.run(this, learningRate);
            release(oldWeights);
        }
    }
    function convolution(settings, inputLayer) {
        return new Convolution(settings, inputLayer);
    }

    function setDropout(dropout) {
        return dropout;
    }
    function trainingPredict(inputs) {
        if (setDropout(Math.random()) < this.constants.probability) {
            return 0;
        }
        return inputs[this.thread.y][this.thread.x];
    }
    function predict$5(inputs) {
        return inputs[this.thread.y][this.thread.x] * this.constants.probability;
    }
    function compare$3(dropouts, deltas) {
        if (dropouts[this.thread.y][this.thread.x] === 0) {
            return 0;
        }
        return deltas[this.thread.y][this.thread.x];
    }
    const dropoutDefaults = {
        ...baseLayerDefaultSettings,
        probability: 0.5,
    };
    class Dropout extends Filter {
        constructor(inputLayer, settings) {
            super(settings, inputLayer);
            this.predictKernelMap = null;
            this.settings = { ...dropoutDefaults, ...settings };
            this.dropouts = null;
            this.validate();
        }
        setupKernels(isTraining) {
            const output = [this.width, this.height];
            if (isTraining) {
                this.predictKernelMap = makeKernelMap({ dropouts: setDropout }, trainingPredict, {
                    output,
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare$3, { output, immutable: true });
            }
            else {
                this.predictKernelMap = makeKernelMap({}, predict$5, { output, immutable: true });
            }
        }
        predict() {
            release(this.weights);
            if (this.dropouts) {
                release(this.dropouts);
            }
            const { result, dropouts } = this
                .predictKernelMap(this.inputLayer.weights);
            this.weights = result;
            this.dropouts = dropouts;
        }
        compare() {
            release(this.deltas);
            this.deltas = this.compareKernel(this.dropouts, this.inputLayer.deltas);
        }
    }
    function dropout(inputLayer, settings) {
        return new Dropout(inputLayer, settings);
    }

    function feedForward(settings, input) {
        const { height, praxisOpts = null } = settings;
        const weights = random({
            id: 'weights',
            height,
            width: input.height,
            praxisOpts,
        });
        const biases = random({ id: 'biases', height, praxisOpts });
        return sigmoid$1(add$1(multiply$1(weights, input, { praxisOpts }), biases, { praxisOpts }), { praxisOpts });
    }

    function predict$4(inputs, filters, biases) {
        let output = 0;
        let i = 0;
        for (let y = 0; y < this.constants.inputHeight; y++) {
            for (let x = 0; x < this.constants.inputWidth; x++) {
                output += inputs[y][x] * filters[this.thread.x][i];
                i++;
            }
        }
        return output + biases[this.thread.x];
    }
    function predict3D$4(inputs, filters, biases) {
        let output = 0;
        let i = 0;
        for (let z = 0; z < this.constants.inputDepth; z++) {
            for (let y = 0; y < this.constants.inputHeight; y++) {
                for (let x = 0; x < this.constants.inputWidth; x++) {
                    output += inputs[z][y][x] * filters[this.thread.x][i];
                    i++;
                }
            }
        }
        return output + biases[this.thread.x];
    }
    function compareInputDeltas(inputDeltas, deltas, filters) {
        let sum = 0;
        const filterX = this.thread.x + this.thread.y * this.output.x;
        for (let filterY = 0; filterY < this.constants.filterCount; filterY++) {
            sum += filters[filterY][filterX] * deltas[0][filterY];
        }
        return sum + inputDeltas[this.thread.y][this.thread.x];
    }
    function compareInputDeltas3D(inputDeltas, deltas, filters) {
        let sum = 0;
        const filterX = this.thread.x + this.thread.y * this.output.x;
        for (let filterY = 0; filterY < this.constants.filterCount; filterY++) {
            sum += filters[filterY][filterX] * deltas[0][filterY];
        }
        return sum + inputDeltas[this.thread.z][this.thread.y][this.thread.x];
    }
    function compareBiases(biases, deltas) {
        return biases[this.thread.x] + deltas[this.thread.y][this.thread.x];
    }
    function compareFilterDeltas(filterDeltas, inputWeights, deltas) {
        return (filterDeltas[this.thread.y][this.thread.x] +
            inputWeights[this.thread.y][this.thread.x] *
                deltas[this.constants.deltaY][this.constants.deltaX]);
    }
    function compareFilterDeltas3D(filterDeltas, inputWeights, deltas) {
        const inputZ = Math.floor(this.thread.x / (this.constants.inputWidth * this.constants.inputHeight));
        const inputY = Math.floor((this.thread.x -
            inputZ * this.constants.inputWidth * this.constants.inputHeight) /
            this.constants.inputWidth);
        const inputX = this.thread.x -
            this.constants.inputWidth * (inputY + this.constants.inputHeight * inputZ);
        return (filterDeltas[this.thread.y][this.thread.x] +
            inputWeights[inputZ][inputY][inputX] * deltas[0][this.thread.y]);
    }
    class FullyConnected extends Filter {
        constructor(settings, inputLayer) {
            super(settings, inputLayer);
            this.compareFilterDeltasKernel = null;
            this.compareInputDeltasKernel = null;
            this.compareBiasesKernel = null;
            this.settings = { ...settings };
            this.validate();
            const connectionCount = inputLayer.width * inputLayer.height * inputLayer.depth;
            this.biases = values(this.height, this.bias);
            this.biasDeltas = zeros$1(this.height);
            this.filters = randos2D(connectionCount, this.height);
            this.filterDeltas = zeros2D(connectionCount, this.height);
            if (this.depth > 0) {
                this.weights = randos3D(this.width, this.height, this.depth);
                this.deltas = zeros3D(this.width, this.height, this.depth);
            }
            else if (this.height > 0) {
                this.weights = randos2D(this.width, this.height);
                this.deltas = zeros2D(this.width, this.height);
            }
        }
        get bias() {
            return this.settings.bias;
        }
        get biases() {
            return this.settings.biases;
        }
        set biases(biases) {
            this.settings.biases = biases;
        }
        get biasDeltas() {
            return this.settings.biases;
        }
        set biasDeltas(biasDeltas) {
            this.settings.biasDeltas = biasDeltas;
        }
        validate() {
            super.validate();
            if (this.depth > 0)
                throw new Error('depth not supported');
        }
        setupKernels() {
            const { inputLayer } = this;
            const connectionCount = inputLayer.width * inputLayer.height * inputLayer.depth;
            if (inputLayer.depth > 0) {
                this.predictKernel = makeKernel(predict3D$4, {
                    output: [this.width, this.height],
                    constants: {
                        inputHeight: inputLayer.height,
                        inputWidth: inputLayer.width,
                        inputDepth: inputLayer.depth,
                    },
                });
                this.compareFilterDeltasKernel = makeKernel(compareFilterDeltas3D, {
                    output: [connectionCount, this.height],
                    constants: {
                        inputWidth: inputLayer.width,
                        inputHeight: inputLayer.height,
                    },
                    immutable: true,
                });
                this.compareInputDeltasKernel = makeKernel(compareInputDeltas3D, {
                    output: [inputLayer.width, inputLayer.height, inputLayer.depth],
                    constants: {
                        filterCount: this.height,
                    },
                    immutable: true,
                });
            }
            else {
                this.predictKernel = makeKernel(predict$4, {
                    output: [this.width, this.height],
                    constants: {
                        inputHeight: inputLayer.height,
                        inputWidth: inputLayer.width,
                    },
                });
                this.compareFilterDeltasKernel = makeKernel(compareFilterDeltas, {
                    output: [connectionCount, this.height],
                    constants: {
                        inputWidth: inputLayer.width,
                    },
                });
                this.compareInputDeltasKernel = makeKernel(compareInputDeltas, {
                    output: [inputLayer.width, inputLayer.height],
                    constants: {
                        filterCount: this.height,
                    },
                });
            }
            this.compareBiasesKernel = makeKernel(compareBiases, {
                output: [this.width, this.height],
            });
        }
        predict() {
            this.weights = this.predictKernel(this.inputLayer.weights, this.filters, this.biases);
        }
        compare() {
            const inputLayerDeltas = this.inputLayer.deltas;
            this.inputLayer.deltas = this
                .compareInputDeltasKernel(inputLayerDeltas, this.deltas, this.filters);
            release(inputLayerDeltas);
            const { biasDeltas, filterDeltas } = this;
            // TODO: handle biasDeltas learn
            this.biasDeltas = this.compareBiasesKernel(this.biases, this.deltas);
            // TODO: handle filterDeltas learn
            this.filterDeltas = this.compareFilterDeltasKernel(filterDeltas, this.inputLayer.weights, this.deltas);
            release(biasDeltas);
            release(filterDeltas);
        }
    }
    function fullyConnected(settings, inputLayer) {
        return new FullyConnected(settings, inputLayer);
    }

    function predict$3(weights) {
        return -weights[this.thread.y][this.thread.x];
    }
    class Negative extends Modifier {
        constructor(inputLayer, settings) {
            super(inputLayer, settings);
            this.validate();
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict$3, {
                output: [this.width, this.height],
            });
        }
        predict() {
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
    }
    function negative(inputLayer, settings) {
        return new Negative(inputLayer, settings);
    }

    function predict$2(inputLayerWeights1, inputLayerWeights2) {
        return (inputLayerWeights1[this.thread.y][this.thread.x] *
            inputLayerWeights2[this.thread.y][this.thread.x]);
    }
    function compare$2(weights, deltas) {
        return (weights[this.thread.y][this.thread.x] * deltas[this.thread.y][this.thread.x]);
    }
    class MultiplyElement extends Operator {
        get width() {
            return this.inputLayer1.width;
        }
        get height() {
            return this.inputLayer1.height;
        }
        get depth() {
            return this.inputLayer1.depth;
        }
        validate() {
            super.validate();
            checkSameSize(this.inputLayer1, this.inputLayer2);
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict$2, {
                output: [this.width, this.height],
                immutable: true,
            });
            this.compareKernel = makeKernel(compare$2, {
                output: [this.width, this.height],
                immutable: true,
            });
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer1.weights, this.inputLayer2.weights);
        }
        compare() {
            release(this.inputLayer1.deltas);
            release(this.inputLayer2.deltas);
            this.inputLayer1.deltas = this.compareKernel(this.inputLayer2.weights, this.deltas);
            this.inputLayer2.deltas = this.compareKernel(this.inputLayer1.weights, this.deltas);
        }
    }
    function multiplyElement$1(inputLayer1, inputLayer2, settings) {
        return new MultiplyElement(inputLayer1, inputLayer2, settings);
    }

    function ones$1(size) {
        return new Float32Array(size).fill(1);
    }
    function ones2D(width, height) {
        const result = new Array(height);
        for (let y = 0; y < height; y++) {
            result[y] = ones$1(width);
        }
        return result;
    }

    class Ones extends Model {
        constructor(settings) {
            super(settings);
            this.validate();
            this.weights = ones2D(this.width, this.height);
            this.deltas = zeros2D(this.width, this.height);
        }
    }
    function ones(settings) {
        return new Ones(settings);
    }

    function predict2D$3(inputs) {
        return activate$1(inputs[this.thread.y][this.thread.x]);
    }
    function predict3D$3(inputs) {
        return activate$1(inputs[this.thread.z][this.thread.y][this.thread.x]);
    }
    function compare2D$3(weights, errors) {
        return measure$1(weights[this.thread.y][this.thread.x], errors[this.thread.y][this.thread.x]);
    }
    function compare3D$3(weights, errors) {
        return measure$1(weights[this.thread.z][this.thread.y][this.thread.x], errors[this.thread.z][this.thread.y][this.thread.x]);
    }
    class Tanh extends Activation {
        setupKernels() {
            if (this.depth > 0) {
                this.predictKernel = makeKernel(predict3D$3, {
                    output: [this.width, this.height, this.depth],
                    functions: [activate$1],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare3D$3, {
                    output: [this.width, this.height, this.depth],
                    functions: [measure$1],
                    immutable: true,
                });
            }
            else {
                this.predictKernel = makeKernel(predict2D$3, {
                    output: [this.width, this.height],
                    functions: [activate$1],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare2D$3, {
                    output: [this.width, this.height],
                    functions: [measure$1],
                    immutable: true,
                });
            }
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
        compare() {
            release(this.inputLayer.deltas);
            this.inputLayer.deltas = this.compareKernel(this.weights, this.deltas);
        }
    }
    function tanh$1(inputLayer, settings) {
        return new Tanh(inputLayer, settings);
    }

    class Zeros extends Model {
        constructor(settings) {
            super(settings);
            this.validate();
            this.weights = zeros2D(this.width, this.height);
            this.deltas = zeros2D(this.width, this.height);
        }
        predict() {
            // throw new Error(`${this.constructor.name}-predict is not yet implemented`)
        }
        compare() {
            // throw new Error(`${this.constructor.name}-compare is not yet implemented`)
        }
    }
    function zeros(settings) {
        return new Zeros(settings);
    }

    function gru(settings, recurrentInput, input) {
        const { height } = settings;
        const updateGateWeights = random({ height, width: input.height });
        const updateGatePeepholes = random({ width: height, height });
        const updateGateBias = zeros({ height });
        const updateGate = sigmoid$1(add$1(add$1(multiply$1(updateGateWeights, input), multiply$1(updateGatePeepholes, recurrentInput)), updateGateBias));
        const resetGateWeights = random({ height, width: input.height });
        const resetGatePeepholes = random({ width: height, height });
        const resetGateBias = zeros({ height });
        const resetGate = sigmoid$1(add$1(add$1(multiply$1(resetGateWeights, input), multiply$1(resetGatePeepholes, recurrentInput)), resetGateBias));
        const cellWeights = random({ height, width: input.height });
        const cellPeepholes = random({ width: height, height });
        const cellBias = zeros({ height });
        const cell = tanh$1(add$1(add$1(multiply$1(cellWeights, input), multiply$1(cellPeepholes, multiplyElement$1(resetGate, recurrentInput))), cellBias));
        // compute hidden state as gated, saturated cell activations
        // negate updateGate
        return add$1(multiplyElement$1(add$1(ones({ width: updateGate.width, height: updateGate.height }), negative(updateGate)), cell), multiplyElement$1(recurrentInput, updateGate));
    }

    const defaults$5 = {
        weights: null,
    };
    class Input extends EntryPoint {
        constructor(settings) {
            super({ ...defaults$5, ...settings });
            this.reshapeInput = null;
            this.validate();
            this.reshapeInput = null;
            this.deltas = zeros2D(this.width, this.height);
        }
        setupKernels() {
            if (this.width === 1) {
                this.predict = this.predict1D;
                this.reshapeInput = makeKernel(function (value) {
                    return value[this.thread.y];
                }, {
                    output: [1, this.height],
                    immutable: true,
                });
            }
        }
        reuseKernels(layer) {
            // super.reuseKernels(layer);
            this.reshapeInput = layer.reshapeInput;
        }
        predict(inputs) {
            if ((Array.isArray(inputs) || inputs instanceof Float32Array) &&
                typeof inputs[0] === 'number' &&
                inputs.length === this.height * this.width) {
                release(this.weights);
                this.weights = kernelInput(inputs, [this.width, this.height]);
            }
            else if (Array.isArray(inputs) &&
                inputs.length === this.height &&
                (Array.isArray(inputs[0]) || inputs[0] instanceof Float32Array) &&
                inputs[0].length === this.width) {
                this.weights = clone(inputs);
            }
            else {
                throw new Error('Inputs are not of sized correctly');
            }
        }
        predict1D(inputs) {
            if (this.weights)
                release(this.weights);
            if (this.reshapeInput) {
                this.weights = this.reshapeInput(inputs);
            }
            else {
                this.weights = inputs;
            }
        }
        compare() {
            // throw new Error(`${this.constructor.name}-compare is not yet implemented`)
        }
        learn() { }
    }
    function input(settings) {
        return new Input(settings);
    }

    function predict2D$2(inputs) {
        return activate(inputs[this.thread.y][this.thread.x]);
    }
    function predict3D$2(inputs) {
        return activate(inputs[this.thread.z][this.thread.y][this.thread.x]);
    }
    function compare2D$2(weights, deltas) {
        return measure(weights[this.thread.y][this.thread.x], deltas[this.thread.y][this.thread.x]);
    }
    function compare3D$2(weights, deltas) {
        return measure(weights[this.thread.z][this.thread.y][this.thread.x], deltas[this.thread.z][this.thread.y][this.thread.x]);
    }
    class LeakyRelu extends Activation {
        setupKernels() {
            const { width, height, depth } = this.inputLayer;
            if (this.depth > 0) {
                this.predictKernel = makeKernel(predict3D$2, {
                    output: [width, height, depth],
                    functions: [activate],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare3D$2, {
                    output: [width, height, depth],
                    functions: [measure],
                    immutable: true,
                });
            }
            else {
                this.predictKernel = makeKernel(predict2D$2, {
                    output: [width, height],
                    functions: [activate],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare2D$2, {
                    output: [width, height],
                    functions: [measure],
                    immutable: true,
                });
            }
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
        compare() {
            const { deltas } = this;
            this.deltas = this.compareKernel(this.weights, deltas);
            release(deltas);
        }
    }
    function leakyRelu(inputLayer, settings) {
        return new LeakyRelu(inputLayer, settings);
    }

    function lstmCell(settings, input, recurrentInput) {
        const { height } = settings;
        if (typeof height !== 'number') {
            throw new Error('no settings.height given');
        }
        if (recurrentInput.setDimensions) {
            recurrentInput.setDimensions(1, height);
        }
        const inputGateWeights = random({
            width: input.height,
            height,
            std: 0.08,
            id: 'inputGateWeights',
        });
        const inputGatePeepholes = random({
            width: height,
            height,
            std: 0.08,
            id: 'inputGatePeepholes',
        });
        const inputGateBias = zeros({ width: 1, height, id: 'inputGateBias' });
        const inputGate = sigmoid$1(add$1(add$1(multiply$1(inputGateWeights, input), multiply$1(inputGatePeepholes, recurrentInput)), inputGateBias), { id: 'inputGate' });
        const forgetGateWeights = random({
            width: input.height,
            height,
            std: 0.08,
            id: 'forgetGateWeights',
        });
        const forgetGatePeepholes = random({
            width: height,
            height,
            std: 0.08,
            id: 'forgetGatePeepholes',
        });
        const forgetGateBias = zeros({ width: 1, height, id: 'forgetGateBias' });
        const forgetGate = sigmoid$1(add$1(add$1(multiply$1(forgetGateWeights, input), multiply$1(forgetGatePeepholes, recurrentInput)), forgetGateBias), { id: 'forgetGate' });
        const outputGateWeights = random({
            width: input.height,
            height,
            std: 0.08,
            id: 'outputGateWeights',
        });
        const outputGatePeepholes = random({
            width: height,
            height,
            std: 0.08,
            id: 'outputGatePeepholes',
        });
        const outputGateBias = zeros({ width: 1, height, id: 'outputGateBias' });
        const outputGate = sigmoid$1(add$1(add$1(multiply$1(outputGateWeights, input), multiply$1(outputGatePeepholes, recurrentInput)), outputGateBias), { id: 'outputGate' });
        const memoryWeights = random({
            width: input.height,
            height,
            std: 0.08,
            id: 'memoryWeights',
        });
        const memoryPeepholes = random({
            width: height,
            height,
            std: 0.08,
            id: 'memoryPeepholes',
        });
        const memoryBias = zeros({ width: 1, height, id: 'memoryBias' });
        const memory = tanh$1(add$1(add$1(multiply$1(memoryWeights, input), multiply$1(memoryPeepholes, recurrentInput)), memoryBias), { id: 'memory' });
        // compute new cell activation
        const retainCell = multiplyElement$1(forgetGate, recurrentInput, {
            id: 'retainCell',
        }); // what do we keep from cell
        const writeCell = multiplyElement$1(inputGate, memory, { id: 'writeCell' }); // what do we write to cell
        const cell = add$1(retainCell, writeCell, { id: 'cell' }); // new cell contents
        // compute hidden state as gated, saturated cell activations
        return multiplyElement$1(outputGate, tanh$1(cell), { id: 'activations' });
    }

    function output(settings, inputLayer) {
        const { height } = settings;
        const outputGate = random({
            height,
            width: inputLayer.height,
            id: 'outputGate',
            std: 0.08,
        });
        const output = random({ height, id: 'output', std: 0.08 });
        const outputGateConnector = multiply$1(outputGate, inputLayer, {
            id: 'outputGateConnected',
        });
        return target({ id: 'target', ...settings }, add$1(outputGateConnector, output));
    }

    function setSwitchY(value) {
        return value;
    }
    function setSwitchX(value) {
        return value;
    }
    function predict$1(inputs) {
        const startFilterX = this.constants.paddingX - this.thread.x * this.constants.strideX;
        const startInputX = this.thread.x * this.constants.strideX - this.constants.paddingX;
        const endFilterX = Math.min(this.constants.filterWidth, startFilterX + this.constants.inputWidth);
        const startFilterY = this.constants.paddingY - this.thread.y * this.constants.strideY;
        const startInputY = this.thread.y * this.constants.strideY - this.constants.paddingY;
        const endFilterY = Math.min(this.constants.filterHeight, startFilterY + this.constants.inputHeight);
        let largestValue = -99999;
        // convolve centered at this particular location
        for (let filterY = Math.max(0, startFilterY), inputY = Math.max(0, startInputY); filterY < endFilterY; filterY++, inputY++) {
            for (let filterX = Math.max(0, startFilterX), inputX = Math.max(0, startInputX); filterX < endFilterX; filterX++, inputX++) {
                if (inputY >= 0 &&
                    inputY < this.constants.inputHeight &&
                    inputX >= 0 &&
                    inputX < this.constants.inputWidth) {
                    const input = inputs[this.thread.z][inputY][inputX];
                    if (input > largestValue) {
                        largestValue = input;
                    }
                }
            }
        }
        return largestValue;
    }
    function compare$1(deltas, switchY, switchX) {
        const x = Math.floor((this.thread.x / this.output.x) * this.constants.outputWidth);
        const y = Math.floor((this.thread.y / this.output.y) * this.constants.outputHeight);
        let value = 0;
        for (let deltasY = 0; deltasY < this.constants.inputHeight; deltasY++) {
            for (let deltasX = 0; deltasX < this.constants.inputWidth; deltasX++) {
                const switchXValue = switchX[deltasY][deltasX];
                const switchYValue = switchY[deltasY][deltasX];
                if (switchXValue === x && switchYValue === y) {
                    value += deltas[deltasY][deltasX];
                }
            }
        }
        return value;
    }
    const defaults$4 = {
        padding: 0,
        stride: 0,
        filterWidth: 0,
        filterHeight: 0,
        filterCount: 0,
    };
    class Pool extends Filter {
        constructor(settings, inputLayer) {
            super(settings, inputLayer);
            this.predictKernelMap = null;
            this.settings = {
                ...settings,
                ...getStride(settings, defaults$4),
                ...getPadding(settings, defaults$4),
            };
            this.weights = randos3D(this.width, this.height, this.depth);
            this.deltas = zeros3D(this.width, this.height, this.depth);
            this.filters = randos3D(this.filterWidth, this.filterHeight, this.filterCount);
            this.filterDeltas = zeros3D(this.filterWidth, this.filterHeight, this.filterCount);
            this.validate();
        }
        get strideX() {
            return this.settings.strideX;
        }
        get strideY() {
            return this.settings.strideY;
        }
        get paddingX() {
            return this.settings.paddingX;
        }
        get paddingY() {
            return this.settings.paddingY;
        }
        get width() {
            return Math.floor((this.inputLayer.width + this.paddingX * 2 - this.filterWidth) /
                this.strideX +
                1);
        }
        get height() {
            return Math.floor((this.inputLayer.height + this.paddingY * 2 - this.filterHeight) /
                this.strideY +
                1);
        }
        get depth() {
            return this.settings.filterCount;
        }
        get filterCount() {
            // TODO: handle 1 depth?
            return this.settings.filterCount;
        }
        get switchX() {
            return this.settings.switchX;
        }
        set switchX(switchX) {
            this.settings.switchX = switchX;
        }
        get switchY() {
            return this.settings.switchY;
        }
        set switchY(switchY) {
            this.settings.switchY = switchY;
        }
        setupKernels() {
            this.predictKernelMap = makeKernelMap({
                switchX: setSwitchX,
                switchY: setSwitchY,
            }, predict$1, {
                output: [this.width, this.height, this.depth],
                constants: {
                    inputWidth: this.inputLayer.width,
                    inputHeight: this.inputLayer.height,
                    paddingX: this.paddingX,
                    paddingY: this.paddingY,
                    filterHeight: this.filterHeight,
                    filterWidth: this.filterWidth,
                },
            });
            this.compareKernel = makeKernel(compare$1, {
                output: [
                    this.inputLayer.width,
                    this.inputLayer.height,
                    this.inputLayer.depth,
                ],
                constants: {
                    inputWidth: this.inputLayer.width,
                    inputHeight: this.inputLayer.height,
                    outputWidth: this.width,
                    outputHeight: this.height,
                },
            });
        }
        predict() {
            const { result: weights, switchX, switchY } = this
                .predictKernelMap(this.inputLayer.weights);
            this.switchX = switchX;
            this.switchY = switchY;
            this.weights = weights;
        }
        compare() {
            // debugger;
            // const depth = this.inputLayer.deltas.length;
            // const height = this.inputLayer.deltas[0].length;
            // const width = this.inputLayer.deltas[0][0].length;
            // const type = typeof this.inputLayer.deltas[0][0][0];
            const inputLayerDeltas = this.inputLayer.deltas;
            this.inputLayer.deltas = this.compareKernel(this.deltas, this.switchX, this.switchY);
            release(inputLayerDeltas);
            // debugger;
            // if (depth !== this.inputLayer.deltas.length) debugger;
            // if (height !== this.inputLayer.deltas[0].length) debugger;
            // if (width !== this.inputLayer.deltas[0][0].length) debugger;
            // if (type !== typeof this.inputLayer.deltas[0][0][0]) debugger;
        }
    }
    function pool(settings, inputLayer) {
        return new Pool(settings, inputLayer);
    }

    class RecurrentInput extends Internal {
        constructor(recurrentInput) {
            super();
            this.praxis = null;
            this.predictKernel = null;
            this.compareKernel = null;
            this.settings = {};
            this.recurrentInput = recurrentInput;
            this.validate();
        }
        get width() {
            return this.recurrentInput.width;
        }
        get height() {
            return this.recurrentInput.height;
        }
        get depth() {
            return this.recurrentInput.depth;
        }
        get deltas() {
            return this.recurrentInput.deltas;
        }
        set deltas(deltas) {
            const recurrentInputDeltas = this.recurrentInput.deltas;
            this.recurrentInput.deltas = deltas;
            release(recurrentInputDeltas);
        }
        get weights() {
            return this.recurrentInput.weights;
        }
        set weights(weights) {
            const recurrentInputWeights = this.recurrentInput.weights;
            this.recurrentInput.weights = weights;
            release(recurrentInputWeights);
        }
        validate() {
            BaseLayer.prototype.validate.call(this);
            if (this.width !== this.recurrentInput.width) {
                throw new Error(`${this.constructor.name} layer width ${this.width} and ${this.recurrentInput.constructor.name} width (${this.recurrentInput.width}) are not same`);
            }
            if (this.height !== this.recurrentInput.height) {
                throw new Error(`${this.constructor.name} layer height ${this.height} and ${this.recurrentInput.constructor.name} width (${this.recurrentInput.height}) are not same`);
            }
        }
        setDimensions(width, height) {
            this.recurrentInput.width = width;
            this.recurrentInput.height = height;
        }
        predict() {
            // throw new Error(`${this.constructor.name}-predict is not yet implemented`)
        }
        compare() {
            // throw new Error(`${this.constructor.name}-compare is not yet implemented`)
        }
        learn() {
            // throw new Error(`${this.constructor.name}-learn is not yet implemented`)
        }
        setupKernels() {
            // throw new Error(
            //   `${this.constructor.name}-setupKernels is not yet implemented`
            // )
        }
        reuseKernels() {
            // throw new Error(
            //   `${this.constructor.name}-reuseKernels is not yet implemented`
            // )
        }
    }

    class RecurrentZeros extends Internal {
        constructor(settings) {
            super();
            this.praxis = null;
            this.settings = {};
            this.predictKernel = null;
            this.compareKernel = null;
            if (settings) {
                this.settings = { ...settings };
            }
        }
        setDimensions(width, height) {
            this.praxis = null;
            this.settings = {
                ...this.settings,
                width,
                height,
                weights: zeros2D(width, height),
                deltas: zeros2D(width, height),
            };
        }
        setupKernels() {
            // throw new Error(
            //   `${this.constructor.name}-setupKernels is not yet implemented`
            // )
        }
        reuseKernels() {
            // throw new Error(
            //   `${this.constructor.name}-reuseKernels is not yet implemented`
            // )
        }
        predict() {
            // throw new Error(`${this.constructor.name}-predict is not yet implemented`)
        }
        compare() {
            // throw new Error(`${this.constructor.name}-compare is not yet implemented`)
        }
        learn(learningRate) {
            const { weights: oldWeights } = this;
            this.weights = this.praxis.run(this, learningRate);
            // this.deltas = deltas;
            release(oldWeights);
        }
    }
    function recurrentZeros() {
        return new RecurrentZeros();
    }

    function predict2D$1(inputs) {
        return activate$3(inputs[this.thread.y][this.thread.x]);
    }
    function compare2D$1(weights, deltas) {
        return measure$3(weights[this.thread.y][this.thread.x], deltas[this.thread.y][this.thread.x]);
    }
    function predict3D$1(inputs) {
        return activate$3(inputs[this.thread.z][this.thread.y][this.thread.x]);
    }
    function compare3D$1(weights, deltas) {
        return measure$3(weights[this.thread.z][this.thread.y][this.thread.x], deltas[this.thread.z][this.thread.y][this.thread.x]);
    }
    class Relu extends Activation {
        setupKernels() {
            const { width, height, depth } = this.inputLayer;
            if (depth > 0) {
                this.predictKernel = makeKernel(predict3D$1, {
                    output: [width, height, depth],
                    functions: [activate$3],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare3D$1, {
                    output: [width, height, depth],
                    functions: [measure$3],
                    immutable: true,
                });
            }
            else {
                this.predictKernel = makeKernel(predict2D$1, {
                    output: [width, height],
                    functions: [activate$3],
                    immutable: true,
                });
                this.compareKernel = makeKernel(compare2D$1, {
                    output: [width, height],
                    functions: [measure$3],
                    immutable: true,
                });
            }
        }
        predict() {
            release(this.weights);
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
        compare() {
            release(this.inputLayer.deltas);
            this.inputLayer.deltas = this.compareKernel(this.weights, this.deltas);
        }
    }
    function relu$1(inputLayer, settings) {
        return new Relu(inputLayer, settings);
    }

    function rnnCell(settings, input, recurrentInput) {
        const { height } = settings;
        if (typeof height !== 'number')
            throw new Error('height not set');
        if (recurrentInput.setDimensions) {
            recurrentInput.setDimensions(1, height);
        }
        // wxh
        const weight = random({
            id: 'weight',
            height,
            width: input.height,
            std: 0.08,
        });
        // whh
        const transition = random({
            id: 'transition',
            height,
            width: height,
            std: 0.08,
        });
        // bhh
        const bias = zeros({ id: 'bias', height });
        return relu$1(add$1(add$1(multiply$1(weight, input), multiply$1(transition, recurrentInput)), bias));
    }

    class Regression extends BaseLayer {
        constructor(settings, inputLayer) {
            super(settings);
            this.inputLayer = inputLayer;
            this.validate();
        }
        predict() {
            release(this.weights);
            this.weights = clone(this.inputLayer.weights);
        }
        learn() {
            // throw new Error(`${this.constructor.name}-learn is not yet implemented`)
        }
    }
    // TODO: handle `loss += 0.5*dy*dy;` total and sum in learn
    function regression(settings, inputLayer) {
        return new Regression(settings, inputLayer);
    }

    function getMaxValue2D(inputs) {
        let maxInput = -Infinity;
        for (let y = 0; y < this.constants.inputHeight; y++) {
            for (let x = 0; x < this.constants.inputWidth; x++) {
                const input = inputs[y][x];
                if (input > maxInput) {
                    maxInput = input;
                }
            }
        }
        return maxInput;
    }
    function getMaxValue3D(inputs) {
        let maxInput = -Infinity;
        for (let z = 0; z < this.constants.inputDepth; z++) {
            for (let y = 0; y < this.constants.inputHeight; y++) {
                for (let x = 0; x < this.constants.inputWidth; x++) {
                    const input = inputs[z][y][x];
                    if (input > maxInput) {
                        maxInput = input;
                    }
                }
            }
        }
        return maxInput;
    }
    function getSum2D(inputs) {
        let sum = 0;
        for (let y = 0; y < this.constants.inputHeight; y++) {
            for (let x = 0; x < this.constants.inputWidth; x++) {
                sum += inputs[y][x];
            }
        }
        return sum;
    }
    function getSum3D(inputs) {
        let sum = 0;
        for (let z = 0; z < this.constants.inputDepth; z++) {
            for (let y = 0; y < this.constants.inputHeight; y++) {
                for (let x = 0; x < this.constants.inputWidth; x++) {
                    sum += inputs[z][y][x];
                }
            }
        }
        return sum;
    }
    function getExponentials(inputs, maxInput) {
        return Math.exp(inputs[this.thread.x] - maxInput[0]);
    }
    function getExponentials3D(inputs, maxInput) {
        return Math.exp(inputs[this.thread.z][this.thread.y][this.thread.x] - maxInput[0]);
    }
    function predict2D(exponentials, exponentialsSum) {
        return exponentials[this.thread.y][this.thread.x] / exponentialsSum[0];
    }
    function predict3D(exponentials, exponentialsSum) {
        return (exponentials[this.thread.z][this.thread.y][this.thread.x] /
            exponentialsSum[0]);
    }
    function compare2D(target, exponentials) {
        let indicator = 0;
        const index = this.thread.x + this.thread.y * this.output.x;
        if (index === target) {
            indicator = 1;
        }
        return -(indicator - exponentials[this.thread.y][this.thread.x]);
    }
    function compare3D(target, exponentials) {
        let indicator = 0;
        const index = this.thread.x +
            this.thread.y * this.output.x +
            this.thread.z * this.output.x * this.output.y;
        if (index === target) {
            indicator = 1;
        }
        return -(indicator - exponentials[this.thread.z][this.thread.y][this.thread.x]);
    }
    // TODO: handle: `return -Math.log(this.es[y]);` in learn
    class SoftMax extends Modifier {
        constructor(inputLayer, settings) {
            super(inputLayer, settings);
            this.errors = null;
            this.getExponentialsKernel = null;
            this.getMaxValueKernel = null;
            this.getSumKernel = null;
            this.validate();
            if (this.depth > 0) {
                this.weights = randos3D(this.width, this.height, this.depth);
                this.deltas = zeros3D(this.width, this.height, this.depth);
            }
            else if (this.height > 0) {
                this.weights = randos2D(this.width, this.height);
                this.deltas = zeros2D(this.width, this.height);
            }
            else {
                this.weights = randos(this.width);
                this.deltas = zeros$1(this.width);
            }
        }
        setupKernels() {
            const { width, height, depth } = this;
            if (depth > 0) {
                this.getExponentialsKernel = makeKernel(getExponentials3D, {
                    output: [width, height, depth],
                });
                this.getMaxValueKernel = makeKernel(getMaxValue3D, {
                    output: [1, 1, 1],
                    constants: {
                        inputWidth: width,
                        inputHeight: height,
                        inputDepth: depth,
                    },
                });
                this.getSumKernel = makeKernel(getSum3D, {
                    output: [1, 1, 1],
                    constants: {
                        inputWidth: width,
                        inputHeight: height,
                        inputDepth: depth,
                    },
                });
                this.predictKernel = makeKernel(predict3D, {
                    output: [width, height, depth],
                });
                this.compareKernel = makeKernel(compare3D, {
                    output: [width, height, depth],
                    immutable: true,
                });
            }
            else {
                this.getExponentialsKernel = makeKernel(getExponentials, {
                    output: [width, height],
                });
                this.getMaxValueKernel = makeKernel(getMaxValue2D, {
                    output: [1, 1],
                    constants: {
                        inputWidth: width,
                        inputHeight: height,
                    },
                });
                this.getSumKernel = makeKernel(getSum2D, {
                    output: [1, 1],
                    constants: {
                        inputWidth: width,
                        inputHeight: height,
                    },
                });
                this.predictKernel = makeKernel(predict2D, {
                    output: [width, height],
                });
                this.compareKernel = makeKernel(compare2D, {
                    output: [width, height],
                    immutable: true,
                });
            }
        }
        predict() {
            const maxValue = this.getMaxValueKernel(this.inputLayer.weights);
            const exponentials = this.getExponentialsKernel(this.inputLayer.weights, maxValue);
            const exponentialsSum = this.getSumKernel(exponentials);
            this.weights = this.predictKernel(exponentials, exponentialsSum);
        }
        compare(targetValues) {
            const { deltas, errors } = this;
            this.errors = this.compareKernel(targetValues[0], deltas);
            this.deltas = clone(this.errors);
            release(deltas);
            release(errors);
            const inputLayerDeltas = this.inputLayer.deltas;
            this.inputLayer.deltas = clone(this.deltas);
            release(inputLayerDeltas);
        }
    }
    function softMax(inputLayer, settings) {
        return new SoftMax(inputLayer, settings);
    }

    class SVM extends BaseLayer {
        constructor(inputLayer, settings) {
            super(settings);
            this.inputLayer = inputLayer;
        }
        predict() {
            release(this.weights);
            this.weights = clone(this.inputLayer.weights);
            this.validate();
        }
        learn() {
            // throw new Error(`${this.constructor.name}-learn is not yet implemented`)
        }
    }
    // function learn(target) {
    //   if (y === i) {
    //     continue;
    //   }
    //   const ydiff = -yscore + x.w[i] + margin;
    //   if (ydiff > 0) {
    //     // violating dimension, apply loss
    //     x.dw[i] += 1;
    //     x.dw[y] -= 1;
    //     loss += ydiff;
    //   }
    // }
    function svm(inputLayer, settings) {
        return new SVM(inputLayer, settings);
    }

    function predict(value) {
        return value[this.thread.x][this.thread.y];
    }
    const compare = predict;
    class Transpose extends Modifier {
        get width() {
            return this.inputLayer.height;
        }
        get height() {
            return this.inputLayer.width;
        }
        constructor(inputLayer) {
            super(inputLayer);
            this.validate();
        }
        setupKernels() {
            this.predictKernel = makeKernel(predict, {
                output: [this.height, this.width],
            });
            this.compareKernel = makeKernel(compare, {
                output: [this.width, this.height],
            });
        }
        predict() {
            this.weights = this.predictKernel(this.inputLayer.weights);
        }
        compare() {
            this.inputLayer.deltas = this.compareKernel(this.deltas);
        }
    }
    function transpose(inputLayer) {
        return new Transpose(inputLayer);
    }

    const layerTypes = {
        Activation,
        Internal,
        InternalModel,
        EntryPoint,
        Filter,
        Model,
        Modifier,
        Operator,
        Target,
    };

    var layer = /*#__PURE__*/Object.freeze({
        __proto__: null,
        layerTypes: layerTypes,
        Add: Add,
        add: add$1,
        arthurFeedForward: arthurFeedForward,
        BaseLayer: BaseLayer,
        baseLayerDefaultSettings: baseLayerDefaultSettings,
        Convolution: Convolution,
        convolution: convolution,
        Dropout: Dropout,
        dropout: dropout,
        feedForward: feedForward,
        FullyConnected: FullyConnected,
        fullyConnected: fullyConnected,
        gru: gru,
        Input: Input,
        input: input,
        LeakyRelu: LeakyRelu,
        leakyRelu: leakyRelu,
        lstmCell: lstmCell,
        Multiply: Multiply,
        multiply: multiply$1,
        MultiplyElement: MultiplyElement,
        multiplyElement: multiplyElement$1,
        Negative: Negative,
        negative: negative,
        Ones: Ones,
        ones: ones,
        output: output,
        Pool: Pool,
        pool: pool,
        Random: Random,
        random: random,
        RecurrentInput: RecurrentInput,
        RecurrentZeros: RecurrentZeros,
        rnnCell: rnnCell,
        Regression: Regression,
        regression: regression,
        Relu: Relu,
        relu: relu$1,
        Sigmoid: Sigmoid,
        sigmoid: sigmoid$1,
        SoftMax: SoftMax,
        softMax: softMax,
        SVM: SVM,
        svm: svm,
        Tanh: Tanh,
        tanh: tanh$1,
        Target: Target,
        target: target,
        Transpose: Transpose,
        transpose: transpose,
        Zeros: Zeros,
        zeros: zeros
    });

    const layerNameTypes = Object.keys(layer);
    function layerFromJSON(jsonLayer, inputLayer1, inputLayer2) {
        if (!layerNameTypes.find((layerNameType) => layerNameType === jsonLayer.type)) {
            return null;
        }
        const Layer = layer[jsonLayer.type];
        if (Layer.prototype instanceof layerTypes.Filter) {
            if (!inputLayer1)
                throw new Error('inputLayer missing');
            return new Layer(jsonLayer, inputLayer1);
        }
        else if (Layer.prototype instanceof layerTypes.Activation ||
            Layer.prototype instanceof layerTypes.Modifier) {
            if (!inputLayer1)
                throw new Error('inputLayer missing');
            return new Layer(inputLayer1, jsonLayer);
        }
        else if (Layer.prototype instanceof layerTypes.Internal) {
            return new Layer(jsonLayer);
        }
        else if (Layer.prototype instanceof layerTypes.Operator) {
            if (!inputLayer1)
                throw new Error('inputLayer1 missing');
            if (!inputLayer2)
                throw new Error('inputLayer2 missing');
            return new Layer(inputLayer1, inputLayer2, jsonLayer);
        }
        else if (Layer.prototype instanceof layerTypes.InternalModel ||
            Layer.prototype instanceof layerTypes.EntryPoint ||
            Layer.prototype instanceof layerTypes.Model) {
            return new Layer(jsonLayer);
        }
        else if (Layer === Target) {
            if (!inputLayer1)
                throw new Error('inputLayer missing');
            return new Layer(jsonLayer, inputLayer1);
        }
        return null;
    }

    class LookupTable {
        constructor(data, prop) {
            this.prop = null;
            this.table = {};
            this.length = 0;
            const table = this.table;
            if (prop) {
                this.prop = prop;
                for (let i = 0; i < data.length; i++) {
                    const datum = data[i];
                    const object = datum[prop];
                    for (const p in object) {
                        if (!object.hasOwnProperty(p))
                            continue;
                        if (table.hasOwnProperty(p))
                            continue;
                        table[p] = this.length++;
                    }
                }
            }
            else if (Array.isArray(data) && Array.isArray(data[0])) {
                for (let i = 0; i < data.length; i++) {
                    const array = data[i];
                    for (let j = 0; j < array.length; j++) {
                        const object = array[j];
                        for (const p in object) {
                            if (!object.hasOwnProperty(p))
                                continue;
                            if (table.hasOwnProperty(p))
                                continue;
                            table[p] = this.length++;
                        }
                    }
                }
            }
            else {
                for (let i = 0; i < data.length; i++) {
                    const object = data[i];
                    for (const p in object) {
                        if (!object.hasOwnProperty(p))
                            continue;
                        if (table.hasOwnProperty(p))
                            continue;
                        table[p] = this.length++;
                    }
                }
            }
        }
    }

    var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};

    function createCommonjsModule(fn, basedir, module) {
    	return module = {
    		path: basedir,
    		exports: {},
    		require: function (path, base) {
    			return commonjsRequire(path, (base === undefined || base === null) ? module.path : base);
    		}
    	}, fn(module, module.exports), module.exports;
    }

    function commonjsRequire () {
    	throw new Error('Dynamic requires are not currently supported by @rollup/plugin-commonjs');
    }

    var browser = createCommonjsModule(function (module, exports) {
    var __assign = (commonjsGlobal && commonjsGlobal.__assign) || function () {
        __assign = Object.assign || function(t) {
            for (var s, i = 1, n = arguments.length; i < n; i++) {
                s = arguments[i];
                for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
                    t[p] = s[p];
            }
            return t;
        };
        return __assign.apply(this, arguments);
    };
    Object.defineProperty(exports, "__esModule", { value: true });
    exports.thaw = exports.Block = exports.Thaw = void 0;
    /**
     * thaw an array of items
     */
    var Thaw = /** @class */ (function () {
        function Thaw(items, options) {
            var _this = this;
            if (options === void 0) { options = {}; }
            var _a = __assign(__assign({}, Thaw.defaultSettings), options), each = _a.each, done = _a.done;
            this.i = 0;
            this.isStopped = false;
            this.items = items;
            this.options = options;
            this.tick = function () {
                if (_this.isStopped)
                    return;
                _this.timeout = setTimeout(_this.tick, 0);
                if (Thaw.thawing)
                    return;
                var item = _this.items[_this.i];
                if (_this.i >= _this.items.length) {
                    if (done !== null) {
                        Thaw.thawing = true;
                        done();
                        Thaw.thawing = false;
                    }
                    _this.isStopped = true;
                    clearTimeout(_this.timeout);
                    return;
                }
                if (each !== null) {
                    Thaw.thawing = true;
                    each(item, _this.i);
                    Thaw.thawing = false;
                }
                else if (item !== undefined) {
                    item();
                }
                _this.i++;
            };
            Thaw.thaws.push(this);
            if (!options.delay) {
                this.tick();
            }
        }
        Object.defineProperty(Thaw, "isThawing", {
            /**
             * returns if Thaw.js is thawing
             */
            get: function () {
                return Thaw.thawing;
            },
            enumerable: false,
            configurable: true
        });
        /**
         * Stops all Thaw instances
         */
        Thaw.stopAll = function () {
            for (var i = 0; i < Thaw.thaws.length; i++) {
                Thaw.thaws[i].stop();
            }
        };
        /**
         * readies thaw to continue
         */
        Thaw.prototype.makeReady = function () {
            if (this.isStopped) {
                this.isStopped = false;
                return true;
            }
            return false;
        };
        /**
         * Adds an item to the end of this instance of Thaw and readies Thaw to process it
         */
        Thaw.prototype.add = function (item) {
            this.items.push(item);
            if (this.makeReady()) {
                this.tick();
            }
            return this;
        };
        /**
         * Inserts an item just after the current item being processed in Thaw and readies Thaw to process it
         */
        Thaw.prototype.insert = function (item) {
            this.items.splice(this.i, 0, item);
            if (this.makeReady()) {
                this.tick();
            }
            return this;
        };
        /**
         * Adds an Array to the end of this instance of Thaw and readies Thaw to process it
         */
        Thaw.prototype.addArray = function (items) {
            this.items = this.items.concat(items);
            if (this.makeReady()) {
                this.tick();
            }
            return this;
        };
        /**
         * Inserts an Array just after the current item being processed in Thaw and readies Thaw to process them
         */
        Thaw.prototype.insertArray = function (items) {
            var before = this.items.splice(0, this.i);
            var after = this.items;
            this.items = before.concat(items, after);
            if (this.makeReady()) {
                this.tick();
            }
            return this;
        };
        /**
         * Stops this instance of Thaw
         */
        Thaw.prototype.stop = function () {
            this.isStopped = true;
            clearTimeout(this.timeout);
            if (this.options.done) {
                this.options.done();
            }
            return this;
        };
        Thaw.thawing = false;
        Thaw.thaws = [];
        Thaw.defaultSettings = {
            each: null,
            done: null
        };
        return Thaw;
    }());
    exports.Thaw = Thaw;
    /**
     * simple thaw
     */
    function thaw(items, options) {
        return new Thaw(items, options);
    }
    exports.thaw = thaw;
    var Block = /** @class */ (function () {
        function Block(options, count) {
            if (count === void 0) { count = 200; }
            this.index = 0;
            this.thaws = [];
            this.count = count;
            this.options = options;
        }
        /**
         * add an item to the end of items
         */
        Block.prototype.add = function (item) {
            var next = this.next();
            next.add(item);
            return this;
        };
        /**
         * add an Array to the end of items
         */
        Block.prototype.addArray = function (items) {
            var next = this.next();
            next.addArray(items);
            return this;
        };
        /**
         * insert an item into items @ current position
         */
        Block.prototype.insert = function (item) {
            var next = this.next();
            next.insert(item);
            return this;
        };
        /**
         * insert and array into items @ current position
         */
        Block.prototype.insertArray = function (items) {
            var next = this.next();
            next.insertArray(items);
            return this;
        };
        /**
         * Stops all thaws in this block
         */
        Block.prototype.stop = function () {
            for (var i = 0; i < this.thaws.length; i++) {
                this.thaws[i].stop();
            }
            return this;
        };
        /**
         * Get next available in block
         */
        Block.prototype.next = function () {
            var thaw;
            var thaws = this.thaws;
            if (thaws.length < this.count) {
                thaw = new Thaw([], this.options);
                thaws.push(thaw);
            }
            else {
                thaw = thaws[this.index] || null;
            }
            this.index++;
            if (this.index >= this.count) {
                this.index = 0;
            }
            return thaw;
        };
        return Block;
    }());
    exports.Block = Block;
    if (typeof window !== 'undefined') {
        // @ts-ignore
        window.Thaw = Thaw;
        // @ts-ignore
        window.thaw = thaw;
        // @ts-ignore
        window.Thaw.Block = Block;
    }
    });

    const defaults$3 = {
        learningRate: 0.3,
        binaryThresh: 0.5,
        initPraxis: (layerTemplate, settings) => {
            var _a;
            return momentumRootMeanSquaredPropagation(layerTemplate, (_a = layerTemplate.settings.praxisOpts) !== null && _a !== void 0 ? _a : settings);
        },
    };
    const trainDefaults$3 = {
        iterations: 20000,
        errorThresh: 0.005,
        log: false,
        logPeriod: 10,
        learningRate: 0.3,
        callbackPeriod: 10,
        errorCheckInterval: 100,
        timeout: Infinity,
    };
    class FeedForward {
        constructor(options = {}) {
            this.trainOpts = {};
            this.layers = null;
            this._inputLayer = null;
            this._hiddenLayers = null;
            this._outputLayer = null;
            this._model = null;
            this.meanSquaredError = null;
            this.inputLookup = null;
            this.inputLookupLength = null;
            this.outputLookup = null;
            this.outputLookupLength = null;
            this.options = { ...defaults$3, ...options };
            this._updateTrainingOptions({
                ...trainDefaults$3,
                ...options,
            });
        }
        static _validateTrainingOptions(options) {
            const { iterations, errorThresh, log, logPeriod, learningRate, callback, callbackPeriod, timeout, } = options;
            const validations = {
                iterations: () => typeof iterations === 'number' && iterations > 0,
                errorThresh: () => typeof errorThresh === 'number' && errorThresh > 0 && errorThresh < 1,
                log: () => typeof log === 'function' || typeof log === 'boolean',
                logPeriod: () => typeof logPeriod === 'number' && logPeriod > 0,
                learningRate: () => typeof learningRate === 'number' &&
                    learningRate > 0 &&
                    learningRate < 1,
                callback: () => typeof callback === 'function' || callback === null,
                callbackPeriod: () => typeof callbackPeriod === 'number' && callbackPeriod > 0,
                timeout: () => typeof timeout === 'number' && timeout > 0,
            };
            Object.keys(trainDefaults$3).forEach((key) => {
                if (validations.hasOwnProperty(key) && !validations[key]()) {
                    const val = options[key];
                    throw new Error(`[${key}, ${(val !== null && val !== void 0 ? val : 'undefined').toString()}] is out of normal training range, your network will probably not train.`);
                }
            });
        }
        /**
         * if a method is passed in method is used
         * if false passed in nothing is logged
         */
        _setLogMethod(log) {
            if (typeof log === 'function') {
                this.trainOpts.log = log;
            }
            else if (log) {
                // eslint-disable-next-line
                this.trainOpts.log = console.log;
            }
            else {
                this.trainOpts.log = false;
            }
        }
        _updateTrainingOptions(opts) {
            var _a;
            this.trainOpts = { ...trainDefaults$3, ...this.trainOpts, ...opts };
            FeedForward._validateTrainingOptions(this.trainOpts);
            this._setLogMethod((_a = opts.log) !== null && _a !== void 0 ? _a : this.trainOpts.log);
            const { callback, callbackPeriod, errorCheckInterval } = this.trainOpts;
            if (callback && callbackPeriod !== errorCheckInterval) {
                console.warn(`options.callbackPeriod with value of ${(callbackPeriod !== null && callbackPeriod !== void 0 ? callbackPeriod : 'undefined').toString()} does not match options.errorCheckInterval with value of ${(errorCheckInterval !== null && errorCheckInterval !== void 0 ? errorCheckInterval : 'undefined').toString()}, if logging error, it will repeat.  These values may need to match`);
            }
        }
        _connectOptionsLayers() {
            const { inputLayerIndex, outputLayerIndex, layers } = this.options;
            if (!layers)
                throw new Error('this.options.layers in unexpected state');
            if (typeof inputLayerIndex !== 'number')
                throw new Error('inputLayerIndex not a number');
            if (typeof outputLayerIndex !== 'number')
                throw new Error('inputLayerIndex not a number');
            const inputLayer = layers[inputLayerIndex];
            if (!inputLayer) {
                throw new Error('inputLayer not found in this.options.layers');
            }
            const outputLayer = layers[outputLayerIndex];
            if (!outputLayer) {
                throw new Error('outputLayer not found in this.options.layers');
            }
            this._inputLayer = inputLayer;
            this._hiddenLayers = layers.slice(inputLayerIndex, outputLayerIndex - inputLayerIndex);
            this._outputLayer = outputLayer;
            return layers;
        }
        _connectNewLayers() {
            const { inputLayer, outputLayer } = this.options;
            if (!inputLayer)
                throw new Error('inputLayer not defined');
            const layers = [];
            this._inputLayer = inputLayer();
            const hiddenLayers = this._connectHiddenLayers(this._inputLayer);
            if (!outputLayer)
                throw new Error('outputLayer not defined');
            this._outputLayer = outputLayer(hiddenLayers[hiddenLayers.length - 1], hiddenLayers.length);
            layers.push(this._inputLayer);
            layers.push(...hiddenLayers);
            layers.push(this._outputLayer);
            return flattenLayers(layers);
        }
        _connectHiddenLayers(previousLayer) {
            this._hiddenLayers = [];
            const result = [];
            const { hiddenLayers } = this.options;
            if (!hiddenLayers)
                throw new Error('hiddenLayers not defined');
            for (let i = 0; i < hiddenLayers.length; i++) {
                const hiddenLayer = hiddenLayers[i](previousLayer, i);
                result.push(hiddenLayer);
                this._hiddenLayers.push(hiddenLayer);
                previousLayer = hiddenLayer;
            }
            return result;
        }
        initialize() {
            this.layers = this.options.layers
                ? this._connectOptionsLayers()
                : this._connectNewLayers();
            this.initializeLayers(this.layers);
            this._model = this.layers.filter((l) => l instanceof Model);
        }
        initializeLayers(layers) {
            var _a, _b;
            for (let i = 0; i < layers.length; i++) {
                const layer = layers[i];
                // TODO: optimize for when training or just running
                layer.setupKernels(true);
                if (layer instanceof Model &&
                    layer.praxis === null &&
                    typeof this.options.initPraxis === 'function') {
                    layer.praxis = this.options.initPraxis(layer, (_b = (_a = layer.settings.praxisOpts) !== null && _a !== void 0 ? _a : this.options.praxisOpts) !== null && _b !== void 0 ? _b : {});
                    layer.praxis.setupKernels();
                }
            }
            const lastLayer = layers[layers.length - 1];
            this.meanSquaredError = new MeanSquaredError({
                width: lastLayer.width,
                height: lastLayer.height,
            });
        }
        run(input) {
            let typeSafeInput;
            if (Array.isArray(input) || input.buffer) {
                typeSafeInput = input;
            }
            else {
                if (this.inputLookup) {
                    typeSafeInput = lookup.toArray(this.inputLookup, input, this.inputLookupLength);
                }
                else {
                    throw new Error('input is incompatible with net');
                }
            }
            let output = this.runInput(typeSafeInput);
            if (output instanceof gpu_js.Texture) {
                output = output.toArray();
            }
            if (this.outputLookup) {
                return lookup.toObject(this.outputLookup, output);
            }
            return output;
        }
        runInput(input) {
            if (!this.layers)
                throw new Error('not initialized');
            this.layers[0].predict(input);
            for (let i = 1; i < this.layers.length; i++) {
                this.layers[i].predict();
            }
            return this.layers[this.layers.length - 1].weights;
        }
        train(data, options = {}) {
            const { preparedData, status, endTime } = this._prepTraining(data, options);
            let continueTicking = true;
            const calculateError = () => this._calculateTrainingError(preparedData);
            const trainPatterns = () => this._trainPatterns(preparedData);
            while (continueTicking) {
                continueTicking = this._trainingTick(status, endTime, calculateError, trainPatterns);
            }
            return status;
        }
        async trainAsync(data, options = {}) {
            const { preparedData, status, endTime } = this._prepTraining(data, options);
            return await new Promise((resolve, reject) => {
                try {
                    const calculateError = () => this._calculateTrainingError(preparedData);
                    const trainPatterns = () => this._trainPatterns(preparedData);
                    const thawedTrain = new browser.Thaw(new Array(this.trainOpts.iterations), {
                        delay: true,
                        each: () => this._trainingTick(status, endTime, calculateError, trainPatterns) || thawedTrain.stop(),
                        done: () => resolve(status),
                    });
                    thawedTrain.tick();
                }
                catch (trainError) {
                    reject(trainError);
                }
            });
        }
        _trainingTick(status, endTime, calculateError, trainPatterns) {
            const { trainOpts } = this;
            if (status.iterations >= trainOpts.iterations ||
                status.error <= trainOpts.errorThresh ||
                Date.now() >= endTime) {
                return false;
            }
            if (typeof trainOpts.log === 'function' &&
                status.iterations % trainOpts.logPeriod === 0) {
                status.error = calculateError();
                trainOpts.log(`iterations: ${status.iterations}, training error: ${status.error}`);
            }
            else if (status.iterations % trainOpts.errorCheckInterval ===
                0) {
                status.error = calculateError();
            }
            else {
                trainPatterns();
            }
            if (trainOpts.callback &&
                status.iterations % trainOpts.callbackPeriod === 0) {
                trainOpts.callback(Object.assign(status));
            }
            status.iterations++;
            return true;
        }
        _prepTraining(data, options) {
            this._updateTrainingOptions(options);
            const formattedData = this.formatData(data);
            const endTime = this.trainOpts.timeout
                ? Date.now() + this.trainOpts.timeout
                : 0;
            const status = {
                error: 1,
                iterations: 0,
            };
            this.verifyIsInitialized();
            return {
                preparedData: this.transferData(formattedData),
                status,
                endTime,
            };
        }
        verifyIsInitialized() {
            if (!this._model) {
                this.initialize();
            }
        }
        _calculateTrainingError(preparedData) {
            let sum = new Float32Array([0]);
            const meanSquaredError = this.meanSquaredError;
            for (let i = 0; i < preparedData.length; ++i) {
                const prevSum = sum;
                const error = this._trainPattern(preparedData[i].input, preparedData[i].output, true);
                sum = meanSquaredError.add(sum, error);
                release(error);
                release(prevSum);
            }
            const result = meanSquaredError.divide(preparedData.length, sum);
            release(sum);
            if (result instanceof gpu_js.Texture) {
                const resultArray = result.toArray();
                release(result);
                return resultArray[0];
            }
            return result[0];
        }
        /**
         * @param data
         * @private
         */
        _trainPatterns(data) {
            for (let i = 0; i < data.length; ++i) {
                this._trainPattern(data[i].input, data[i].output, false);
            }
        }
        _trainPattern(input, target, logErrorRate) {
            var _a;
            // forward propagate
            this.runInput(input);
            // back propagate
            this._calculateDeltas(target);
            this.adjustWeights();
            if (logErrorRate) {
                if (!((_a = this._outputLayer) === null || _a === void 0 ? void 0 : _a.errors)) {
                    throw new Error('outputLayer.errors not defined');
                }
                return this.meanSquaredError.calculate(this._outputLayer.errors);
            }
            return null;
        }
        _calculateDeltas(target) {
            const layers = this.layers;
            for (let i = layers.length - 1; i > -1; i--) {
                layers[i].compare(target);
            }
        }
        /**
         *
         */
        adjustWeights() {
            const _model = this._model;
            for (let i = 0; i < _model.length; i++) {
                _model[i].learn(this.trainOpts.learningRate);
            }
        }
        /**
         *
         * @param data
         * @returns {*}
         */
        formatData(data) {
            if (!Array.isArray(data)) {
                // turn stream datum into array
                const tmp = [];
                tmp.push(data);
                data = tmp;
            }
            // turn sparse hash input into arrays with 0s as filler
            const inputDatumCheck = data[0].input;
            let formattedData;
            if (Array.isArray(data) &&
                !Array.isArray(inputDatumCheck) &&
                !(inputDatumCheck instanceof Float32Array)) {
                if (!this.inputLookup) {
                    const lookupTable = new LookupTable(data, 'input');
                    this.inputLookup = lookupTable.table;
                    this.inputLookupLength = lookupTable.length;
                }
                formattedData = data.map((datumParam) => {
                    const array = lookup.toArray(this.inputLookup, datumParam.input, this.inputLookupLength);
                    return { input: array };
                }, this);
            }
            else {
                formattedData = data;
            }
            const outputDatumCheck = data[0].output;
            if (!Array.isArray(outputDatumCheck) &&
                !(outputDatumCheck instanceof Float32Array)) {
                if (!this.outputLookup) {
                    const lookupTable = new LookupTable(data, 'output');
                    this.outputLookup = lookupTable.table;
                    this.outputLookupLength = lookupTable.length;
                }
                formattedData = data.map((datumParam, index) => {
                    const array = lookup.toArray(this.outputLookup, datumParam.output, this.inputLookupLength);
                    return {
                        input: formattedData[index].input,
                        output: array,
                    };
                }, this);
            }
            return formattedData;
        }
        transferData(formattedData) {
            const transferredData = new Array(formattedData.length);
            const transferInput = makeKernel(function (value) {
                return value[this.thread.x];
            }, {
                output: [formattedData[0].input.length],
                immutable: true,
            });
            const transferOutput = makeKernel(function (value) {
                return value[this.thread.x];
            }, {
                output: [formattedData[0].output.length],
                immutable: true,
            });
            for (let i = 0; i < formattedData.length; i++) {
                const formattedDatum = formattedData[i];
                transferredData[i] = {
                    input: transferInput(formattedDatum.input),
                    output: transferOutput(formattedDatum.output),
                };
            }
            return transferredData;
        }
        /**
         *
         * @param data
         * @returns {
         *  {
         *    error: number,
         *    misclasses: Array
         *  }
         * }
         */
        test() {
            throw new Error(`${this.constructor.name}-test is not yet implemented`);
        }
        /**
         *
         */
        toJSON() {
            var _a;
            if (!this.layers) {
                this.initialize();
            }
            if (!this._model ||
                !this.layers ||
                !this._inputLayer ||
                !this._hiddenLayers ||
                !this._outputLayer) {
                throw new Error('network is not initialized');
            }
            const jsonLayers = [];
            for (let i = 0; i < this.layers.length; i++) {
                const layer = this.layers[i];
                const jsonLayer = layer.toJSON();
                if (layer.hasOwnProperty('inputLayer')) {
                    jsonLayer.inputLayerIndex = this.layers.indexOf(layer.inputLayer);
                }
                else if (layer.hasOwnProperty('inputLayer1') &&
                    layer.hasOwnProperty('inputLayer2')) {
                    jsonLayer.inputLayer1Index = this.layers.indexOf(layer.inputLayer1);
                    jsonLayer.inputLayer2Index = this.layers.indexOf(layer.inputLayer2);
                }
                jsonLayers.push(jsonLayer);
            }
            return {
                type: this.constructor.name,
                sizes: (_a = this.options.sizes) !== null && _a !== void 0 ? _a : [this._inputLayer.height]
                    .concat(this._hiddenLayers.map((l) => l.height))
                    .concat([this._outputLayer.height]),
                outputLayerIndex: this.layers.indexOf(this._outputLayer),
                layers: jsonLayers,
                inputLayerIndex: this.layers.indexOf(this._inputLayer),
            };
        }
        static fromJSON(json, getLayer) {
            var _a, _b, _c, _d;
            const jsonLayers = json.layers;
            const layers = [];
            const inputLayer = getLayer
                ? (_a = layerFromJSON(jsonLayers[0])) !== null && _a !== void 0 ? _a : getLayer(jsonLayers[0]) : layerFromJSON(jsonLayers[0]);
            if (!inputLayer)
                throw new Error('unable to find layer');
            layers.push(inputLayer);
            for (let i = 1; i < jsonLayers.length; i++) {
                const jsonLayer = jsonLayers[i];
                if (typeof jsonLayer.inputLayerIndex === 'undefined' &&
                    typeof jsonLayer.inputLayer1Index === 'undefined' &&
                    typeof jsonLayer.inputLayer2Index === 'undefined') {
                    const layer = getLayer
                        ? (_b = layerFromJSON(jsonLayer)) !== null && _b !== void 0 ? _b : getLayer(jsonLayer) : layerFromJSON(jsonLayer);
                    if (!layer)
                        throw new Error('unable to find layer');
                    layers.push(layer);
                }
                else if (typeof jsonLayer.inputLayerIndex === 'number') {
                    const inputLayer = layers[jsonLayer.inputLayerIndex];
                    if (!inputLayer) {
                        throw new Error('inputLayer1 not found');
                    }
                    const layer = getLayer
                        ? (_c = layerFromJSON(jsonLayer, inputLayer)) !== null && _c !== void 0 ? _c : getLayer(jsonLayer, inputLayer) : layerFromJSON(jsonLayer, inputLayer);
                    if (!layer)
                        throw new Error('unable to find layer');
                    layers.push(layer);
                }
                else {
                    if (typeof jsonLayer.inputLayer1Index !== 'number') {
                        throw new Error('Cannot create network from provided JSON. inputLayer1Index not defined.');
                    }
                    if (typeof jsonLayer.inputLayer2Index !== 'number') {
                        throw new Error('Cannot create network from provided JSON. inputLayer2Index not defined.');
                    }
                    const inputLayer1 = layers[jsonLayer.inputLayer1Index];
                    const inputLayer2 = layers[jsonLayer.inputLayer2Index];
                    if (inputLayer1 === undefined)
                        throw new Error(`Cannot create network from provided JSON. layer of index ${jsonLayer.inputLayer1Index} not found.`);
                    if (inputLayer2 === undefined)
                        throw new Error(`Cannot create network from provided JSON. layer of index ${jsonLayer.inputLayer2Index} not found.`);
                    const layer = getLayer
                        ? (_d = layerFromJSON(jsonLayer, inputLayer1, inputLayer2)) !== null && _d !== void 0 ? _d : getLayer(jsonLayer, inputLayer1, inputLayer2) : layerFromJSON(jsonLayer, inputLayer1, inputLayer2);
                    if (!layer)
                        throw new Error('unable to find layer');
                    layers.push(layer);
                }
            }
            return new this({ ...json, layers });
        }
        /**
         *
         * @returns {Function}
         */
        toFunction() {
            throw new Error(`${this.constructor.name}-toFunction is not yet implemented`);
        }
        /**
         * This will create a TrainStream (WriteStream) for us to send the training data to.
         * @param opts training options
         * @returns {TrainStream|*}
         */
        createTrainStream() {
            throw new Error(`${this.constructor.name}-createTrainStream is not yet implemented`);
        }
    }

    function likely(input, net) {
        if (!net) {
            throw new TypeError(`Required parameter 'net' is of type ${typeof net}. Must be of type 'brain.NeuralNetwork'`);
        }
        const output = net.run(input);
        let maxProp = null;
        let maxValue = -1;
        Object.entries(output).forEach(([key, value]) => {
            if (typeof value !== 'undefined' &&
                typeof value === 'number' &&
                value > maxValue) {
                maxProp = key;
                maxValue = value;
            }
        });
        return maxProp;
    }

    function arraysToFloat32Arrays(arrays) {
        const result = [];
        for (let i = 0; i < arrays.length; i++) {
            result.push(Float32Array.from(arrays[i]));
        }
        return result;
    }
    function inputOutputArraysToFloat32Arrays(input, output) {
        const result = [];
        for (let i = 0; i < input.length; i++) {
            result.push(Float32Array.from(input[i]));
        }
        for (let i = 0; i < output.length; i++) {
            result.push(Float32Array.from(output[i]));
        }
        return result;
    }
    function arrayToFloat32Arrays(array) {
        const result = [];
        for (let i = 0; i < array.length; i++) {
            result.push(Float32Array.from([array[i]]));
        }
        return result;
    }
    function inputOutputArrayToFloat32Arrays(input, output) {
        const result = [];
        for (let i = 0; i < input.length; i++) {
            result.push(Float32Array.from([input[i]]));
        }
        for (let i = 0; i < output.length; i++) {
            result.push(Float32Array.from([output[i]]));
        }
        return result;
    }
    function arrayToFloat32Array(array) {
        return Float32Array.from(array);
    }
    function inputOutputObjectsToFloat32Arrays(input, output, inputTable, outputTable, inputLength, outputLength) {
        const results = [];
        for (let i = 0; i < input.length; i++) {
            const object = input[i];
            const result = new Float32Array(inputLength);
            for (const p in object) {
                if (object.hasOwnProperty(p)) {
                    result[inputTable[p]] = object[p];
                }
            }
            results.push(result);
        }
        for (let i = 0; i < output.length; i++) {
            const object = output[i];
            const result = new Float32Array(outputLength);
            for (const p in object) {
                if (object.hasOwnProperty(p)) {
                    result[outputTable[p]] = object[p];
                }
            }
            results.push(result);
        }
        return results;
    }
    function objectToFloat32Arrays(object) {
        const result = [];
        for (const p in object) {
            if (!object.hasOwnProperty(p))
                continue;
            result.push(Float32Array.from([object[p]]));
        }
        return result;
    }
    function inputOutputObjectToFloat32Arrays(input, output) {
        const result = [];
        for (const p in input) {
            if (!input.hasOwnProperty(p))
                continue;
            result.push(Float32Array.from([input[p]]));
        }
        for (const p in output) {
            if (!output.hasOwnProperty(p))
                continue;
            result.push(Float32Array.from([output[p]]));
        }
        return result;
    }
    function objectToFloat32Array(object, table, length) {
        const result = new Float32Array(length);
        for (const p in object) {
            if (object.hasOwnProperty(p)) {
                result[table[p]] = object[p];
            }
        }
        return result;
    }

    function max(values) {
        if (Array.isArray(values) || values instanceof Float32Array) {
            return Math.max(...values);
        }
        else {
            return Math.max(...Object.values(values));
        }
    }

    function mse$1(errors) {
        // mean squared error
        let sum = 0;
        for (let i = 0; i < errors.length; i++) {
            sum += errors[i] ** 2;
        }
        return sum / errors.length;
    }

    function getTypedArrayFn(value, table) {
        if (value.buffer instanceof ArrayBuffer) {
            return null;
        }
        if (Array.isArray(value)) {
            return arrayToFloat32Array;
        }
        if (!table)
            throw new Error('table is not Object');
        const { length } = Object.keys(table);
        return (v) => {
            const array = new Float32Array(length);
            for (const p in table) {
                if (!table.hasOwnProperty(p))
                    continue;
                if (typeof v[p] !== 'number')
                    continue;
                array[table[p]] = v[p] || 0;
            }
            return array;
        };
    }
    function defaults$2() {
        return {
            inputSize: 0,
            outputSize: 0,
            binaryThresh: 0.5,
        };
    }
    function trainDefaults$2() {
        return {
            activation: 'sigmoid',
            iterations: 20000,
            errorThresh: 0.005,
            log: false,
            logPeriod: 10,
            leakyReluAlpha: 0.01,
            learningRate: 0.3,
            momentum: 0.1,
            callbackPeriod: 10,
            timeout: Infinity,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        };
    }
    class NeuralNetwork {
        constructor(options = {}) {
            this.options = defaults$2();
            this.trainOpts = trainDefaults$2();
            this.sizes = [];
            this.outputLayer = -1;
            this.biases = [];
            this.weights = []; // weights for bias nodes
            this.outputs = [];
            // state for training
            this.deltas = [];
            this.changes = []; // for momentum
            this.errors = [];
            this.errorCheckInterval = 1;
            this.inputLookup = null;
            this.inputLookupLength = 0;
            this.outputLookup = null;
            this.outputLookupLength = 0;
            this._formatInput = null;
            this._formatOutput = null;
            this.runInput = (input) => {
                this.setActivation();
                return this.runInput(input);
            };
            this.calculateDeltas = (output) => {
                this.setActivation();
                return this.calculateDeltas(output);
            };
            // adam
            this.biasChangesLow = [];
            this.biasChangesHigh = [];
            this.changesLow = [];
            this.changesHigh = [];
            this.iterations = 0;
            this.options = { ...this.options, ...options };
            this.updateTrainingOptions(options);
            const { inputSize, hiddenLayers, outputSize } = this.options;
            if (inputSize && outputSize) {
                this.sizes = [inputSize].concat(hiddenLayers !== null && hiddenLayers !== void 0 ? hiddenLayers : []).concat([outputSize]);
            }
        }
        /**
         *
         * Expects this.sizes to have been set
         */
        initialize() {
            if (!this.sizes.length) {
                throw new Error('Sizes must be set before initializing');
            }
            this.outputLayer = this.sizes.length - 1;
            this.biases = new Array(this.outputLayer); // weights for bias nodes
            this.weights = new Array(this.outputLayer);
            this.outputs = new Array(this.outputLayer);
            // state for training
            this.deltas = new Array(this.outputLayer);
            this.changes = new Array(this.outputLayer); // for momentum
            this.errors = new Array(this.outputLayer);
            for (let layerIndex = 0; layerIndex <= this.outputLayer; layerIndex++) {
                const size = this.sizes[layerIndex];
                this.deltas[layerIndex] = zeros$1(size);
                this.errors[layerIndex] = zeros$1(size);
                this.outputs[layerIndex] = zeros$1(size);
                if (layerIndex > 0) {
                    this.biases[layerIndex] = randos(size);
                    this.weights[layerIndex] = new Array(size);
                    this.changes[layerIndex] = new Array(size);
                    for (let nodeIndex = 0; nodeIndex < size; nodeIndex++) {
                        const prevSize = this.sizes[layerIndex - 1];
                        this.weights[layerIndex][nodeIndex] = randos(prevSize);
                        this.changes[layerIndex][nodeIndex] = zeros$1(prevSize);
                    }
                }
            }
            this.setActivation();
            if (this.trainOpts.praxis === 'adam') {
                this._setupAdam();
            }
        }
        setActivation(activation) {
            const value = activation !== null && activation !== void 0 ? activation : this.trainOpts.activation;
            switch (value) {
                case 'sigmoid':
                    this.runInput = this._runInputSigmoid;
                    this.calculateDeltas = this._calculateDeltasSigmoid;
                    break;
                case 'relu':
                    this.runInput = this._runInputRelu;
                    this.calculateDeltas = this._calculateDeltasRelu;
                    break;
                case 'leaky-relu':
                    this.runInput = this._runInputLeakyRelu;
                    this.calculateDeltas = this._calculateDeltasLeakyRelu;
                    break;
                case 'tanh':
                    this.runInput = this._runInputTanh;
                    this.calculateDeltas = this._calculateDeltasTanh;
                    break;
                default:
                    throw new Error(`Unknown activation ${value}. Available activations are: 'sigmoid', 'relu', 'leaky-relu', 'tanh'`);
            }
        }
        get isRunnable() {
            return this.sizes.length > 0;
        }
        run(input) {
            if (!this.isRunnable) {
                throw new Error('network not runnable');
            }
            let formattedInput;
            if (this.inputLookup) {
                formattedInput = lookup.toArray(this.inputLookup, input, this.inputLookupLength);
            }
            else {
                formattedInput = input;
            }
            this.validateInput(formattedInput);
            const output = this.runInput(formattedInput).slice(0);
            if (this.outputLookup) {
                return lookup.toObject(this.outputLookup, output);
            }
            return output;
        }
        _runInputSigmoid(input) {
            this.outputs[0] = input; // set output state of input layer
            let output = null;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const activeLayer = this.sizes[layer];
                const activeWeights = this.weights[layer];
                const activeBiases = this.biases[layer];
                const activeOutputs = this.outputs[layer];
                for (let node = 0; node < activeLayer; node++) {
                    const weights = activeWeights[node];
                    let sum = activeBiases[node];
                    for (let k = 0; k < weights.length; k++) {
                        sum += weights[k] * input[k];
                    }
                    // sigmoid
                    activeOutputs[node] = 1 / (1 + Math.exp(-sum));
                }
                output = input = activeOutputs;
            }
            if (!output) {
                throw new Error('output was empty');
            }
            return output;
        }
        _runInputRelu(input) {
            this.outputs[0] = input; // set output state of input layer
            let output = null;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const activeSize = this.sizes[layer];
                const activeWeights = this.weights[layer];
                const activeBiases = this.biases[layer];
                const activeOutputs = this.outputs[layer];
                for (let node = 0; node < activeSize; node++) {
                    const weights = activeWeights[node];
                    let sum = activeBiases[node];
                    for (let k = 0; k < weights.length; k++) {
                        sum += weights[k] * input[k];
                    }
                    // relu
                    activeOutputs[node] = sum < 0 ? 0 : sum;
                }
                output = input = activeOutputs;
            }
            if (!output) {
                throw new Error('output was empty');
            }
            return output;
        }
        _runInputLeakyRelu(input) {
            this.outputs[0] = input; // set output state of input layer
            const { leakyReluAlpha } = this.trainOpts;
            let output = null;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const activeSize = this.sizes[layer];
                const activeWeights = this.weights[layer];
                const activeBiases = this.biases[layer];
                const activeOutputs = this.outputs[layer];
                for (let node = 0; node < activeSize; node++) {
                    const weights = activeWeights[node];
                    let sum = activeBiases[node];
                    for (let k = 0; k < weights.length; k++) {
                        sum += weights[k] * input[k];
                    }
                    // leaky relu
                    activeOutputs[node] = Math.max(sum, leakyReluAlpha * sum);
                }
                output = input = activeOutputs;
            }
            if (!output) {
                throw new Error('output was empty');
            }
            return output;
        }
        _runInputTanh(input) {
            this.outputs[0] = input; // set output state of input layer
            let output = null;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const activeSize = this.sizes[layer];
                const activeWeights = this.weights[layer];
                const activeBiases = this.biases[layer];
                const activeOutputs = this.outputs[layer];
                for (let node = 0; node < activeSize; node++) {
                    const weights = activeWeights[node];
                    let sum = activeBiases[node];
                    for (let k = 0; k < weights.length; k++) {
                        sum += weights[k] * input[k];
                    }
                    // tanh
                    activeOutputs[node] = Math.tanh(sum);
                }
                output = input = activeOutputs;
            }
            if (!output) {
                throw new Error('output was empty');
            }
            return output;
        }
        /**
         *
         * Verifies network sizes are initialized
         * If they are not it will initialize them based off the data set.
         */
        verifyIsInitialized(preparedData) {
            if (this.sizes.length && this.outputLayer > 0)
                return;
            this.sizes = [];
            this.sizes.push(preparedData[0].input.length);
            if (!this.options.hiddenLayers) {
                this.sizes.push(Math.max(3, Math.floor(preparedData[0].input.length / 2)));
            }
            else {
                this.options.hiddenLayers.forEach((size) => {
                    this.sizes.push(size);
                });
            }
            this.sizes.push(preparedData[0].output.length);
            this.initialize();
        }
        updateTrainingOptions(trainOpts) {
            const merged = { ...this.trainOpts, ...trainOpts };
            this.validateTrainingOptions(merged);
            this.trainOpts = merged;
            this.setLogMethod(this.trainOpts.log);
        }
        validateTrainingOptions(options) {
            const validations = {
                activation: () => {
                    return ['sigmoid', 'relu', 'leaky-relu', 'tanh'].includes(options.activation);
                },
                iterations: () => {
                    const val = options.iterations;
                    return typeof val === 'number' && val > 0;
                },
                errorThresh: () => {
                    const val = options.errorThresh;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                log: () => {
                    const val = options.log;
                    return typeof val === 'function' || typeof val === 'boolean';
                },
                logPeriod: () => {
                    const val = options.logPeriod;
                    return typeof val === 'number' && val > 0;
                },
                leakyReluAlpha: () => {
                    const val = options.leakyReluAlpha;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                learningRate: () => {
                    const val = options.learningRate;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                momentum: () => {
                    const val = options.momentum;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                callback: () => {
                    const val = options.callback;
                    return typeof val === 'function' || val === undefined;
                },
                callbackPeriod: () => {
                    const val = options.callbackPeriod;
                    return typeof val === 'number' && val > 0;
                },
                timeout: () => {
                    const val = options.timeout;
                    return typeof val === 'number' && val > 0;
                },
                praxis: () => {
                    const val = options.praxis;
                    return !val || val === 'adam';
                },
                beta1: () => {
                    const val = options.beta1;
                    return val > 0 && val < 1;
                },
                beta2: () => {
                    const val = options.beta2;
                    return val > 0 && val < 1;
                },
                epsilon: () => {
                    const val = options.epsilon;
                    return val > 0 && val < 1;
                },
            };
            for (const p in validations) {
                const v = options;
                if (!validations[p]()) {
                    throw new Error(`[${p}, ${v[p]}] is out of normal training range, your network will probably not train.`);
                }
            }
        }
        /**
         *
         *  Gets JSON of trainOpts object
         *    NOTE: Activation is stored directly on JSON object and not in the training options
         */
        getTrainOptsJSON() {
            const { activation, iterations, errorThresh, log, logPeriod, leakyReluAlpha, learningRate, momentum, callbackPeriod, timeout, praxis, beta1, beta2, epsilon, } = this.trainOpts;
            return {
                activation,
                iterations,
                errorThresh,
                log: typeof log === 'function'
                    ? true
                    : typeof log === 'boolean'
                        ? log
                        : false,
                logPeriod,
                leakyReluAlpha,
                learningRate,
                momentum,
                callbackPeriod,
                timeout: timeout === Infinity ? 'Infinity' : timeout,
                praxis,
                beta1,
                beta2,
                epsilon,
            };
        }
        setLogMethod(log) {
            if (typeof log === 'function') {
                this.trainOpts.log = log;
            }
            else if (log) {
                this.trainOpts.log = this.logTrainingStatus;
            }
            else {
                this.trainOpts.log = false;
            }
        }
        logTrainingStatus(status) {
            console.log(`iterations: ${status.iterations}, training error: ${status.error}`);
        }
        calculateTrainingError(data) {
            let sum = 0;
            for (let i = 0; i < data.length; ++i) {
                sum += this.trainPattern(data[i], true);
            }
            return sum / data.length;
        }
        trainPatterns(data) {
            for (let i = 0; i < data.length; ++i) {
                this.trainPattern(data[i]);
            }
        }
        trainingTick(data, status, endTime) {
            const { callback, callbackPeriod, errorThresh, iterations, log, logPeriod, } = this.trainOpts;
            if (status.iterations >= iterations ||
                status.error <= errorThresh ||
                Date.now() >= endTime) {
                return false;
            }
            status.iterations++;
            if (log && status.iterations % logPeriod === 0) {
                status.error = this.calculateTrainingError(data);
                log(status);
            }
            else if (status.iterations % this.errorCheckInterval === 0) {
                status.error = this.calculateTrainingError(data);
            }
            else {
                this.trainPatterns(data);
            }
            if (callback && status.iterations % callbackPeriod === 0) {
                callback({
                    iterations: status.iterations,
                    error: status.error,
                });
            }
            return true;
        }
        prepTraining(data, options = {}) {
            this.updateTrainingOptions(options);
            const preparedData = this.formatData(data);
            const endTime = Date.now() + this.trainOpts.timeout;
            const status = {
                error: 1,
                iterations: 0,
            };
            this.verifyIsInitialized(preparedData);
            this.validateData(preparedData);
            return {
                preparedData,
                status,
                endTime,
            };
        }
        train(data, options = {}) {
            const { preparedData, status, endTime } = this.prepTraining(data, options);
            while (true) {
                if (!this.trainingTick(preparedData, status, endTime)) {
                    break;
                }
            }
            return status;
        }
        async trainAsync(data, options = {}) {
            const { preparedData, status, endTime } = this.prepTraining(data, options);
            return await new Promise((resolve, reject) => {
                try {
                    const thawedTrain = new browser.Thaw(new Array(this.trainOpts.iterations), {
                        delay: true,
                        each: () => this.trainingTick(preparedData, status, endTime) ||
                            thawedTrain.stop(),
                        done: () => resolve(status),
                    });
                    thawedTrain.tick();
                }
                catch (trainError) {
                    reject(trainError);
                }
            });
        }
        trainPattern(value, logErrorRate) {
            // forward propagate
            this.runInput(value.input);
            // back propagate
            this.calculateDeltas(value.output);
            this.adjustWeights();
            if (logErrorRate) {
                return mse$1(this.errors[this.outputLayer]);
            }
            return null;
        }
        _calculateDeltasSigmoid(target) {
            for (let layer = this.outputLayer; layer >= 0; layer--) {
                const activeSize = this.sizes[layer];
                const activeOutput = this.outputs[layer];
                const activeError = this.errors[layer];
                const activeDeltas = this.deltas[layer];
                const nextLayer = this.weights[layer + 1];
                for (let node = 0; node < activeSize; node++) {
                    const output = activeOutput[node];
                    let error = 0;
                    if (layer === this.outputLayer) {
                        error = target[node] - output;
                    }
                    else {
                        const deltas = this.deltas[layer + 1];
                        for (let k = 0; k < deltas.length; k++) {
                            error += deltas[k] * nextLayer[k][node];
                        }
                    }
                    activeError[node] = error;
                    activeDeltas[node] = error * output * (1 - output);
                }
            }
        }
        _calculateDeltasRelu(target) {
            for (let layer = this.outputLayer; layer >= 0; layer--) {
                const currentSize = this.sizes[layer];
                const currentOutputs = this.outputs[layer];
                const nextWeights = this.weights[layer + 1];
                const nextDeltas = this.deltas[layer + 1];
                const currentErrors = this.errors[layer];
                const currentDeltas = this.deltas[layer];
                for (let node = 0; node < currentSize; node++) {
                    const output = currentOutputs[node];
                    let error = 0;
                    if (layer === this.outputLayer) {
                        error = target[node] - output;
                    }
                    else {
                        for (let k = 0; k < nextDeltas.length; k++) {
                            error += nextDeltas[k] * nextWeights[k][node];
                        }
                    }
                    currentErrors[node] = error;
                    currentDeltas[node] = output > 0 ? error : 0;
                }
            }
        }
        _calculateDeltasLeakyRelu(target) {
            const alpha = this.trainOpts.leakyReluAlpha;
            for (let layer = this.outputLayer; layer >= 0; layer--) {
                const currentSize = this.sizes[layer];
                const currentOutputs = this.outputs[layer];
                const nextDeltas = this.deltas[layer + 1];
                const nextWeights = this.weights[layer + 1];
                const currentErrors = this.errors[layer];
                const currentDeltas = this.deltas[layer];
                for (let node = 0; node < currentSize; node++) {
                    const output = currentOutputs[node];
                    let error = 0;
                    if (layer === this.outputLayer) {
                        error = target[node] - output;
                    }
                    else {
                        for (let k = 0; k < nextDeltas.length; k++) {
                            error += nextDeltas[k] * nextWeights[k][node];
                        }
                    }
                    currentErrors[node] = error;
                    currentDeltas[node] = output > 0 ? error : alpha * error;
                }
            }
        }
        _calculateDeltasTanh(target) {
            for (let layer = this.outputLayer; layer >= 0; layer--) {
                const currentSize = this.sizes[layer];
                const currentOutputs = this.outputs[layer];
                const nextDeltas = this.deltas[layer + 1];
                const nextWeights = this.weights[layer + 1];
                const currentErrors = this.errors[layer];
                const currentDeltas = this.deltas[layer];
                for (let node = 0; node < currentSize; node++) {
                    const output = currentOutputs[node];
                    let error = 0;
                    if (layer === this.outputLayer) {
                        error = target[node] - output;
                    }
                    else {
                        for (let k = 0; k < nextDeltas.length; k++) {
                            error += nextDeltas[k] * nextWeights[k][node];
                        }
                    }
                    currentErrors[node] = error;
                    currentDeltas[node] = (1 - output * output) * error;
                }
            }
        }
        /**
         *
         * Changes weights of networks
         */
        adjustWeights() {
            const { learningRate, momentum } = this.trainOpts;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const incoming = this.outputs[layer - 1];
                const activeSize = this.sizes[layer];
                const activeDelta = this.deltas[layer];
                const activeChanges = this.changes[layer];
                const activeWeights = this.weights[layer];
                const activeBiases = this.biases[layer];
                for (let node = 0; node < activeSize; node++) {
                    const delta = activeDelta[node];
                    for (let k = 0; k < incoming.length; k++) {
                        let change = activeChanges[node][k];
                        change = learningRate * delta * incoming[k] + momentum * change;
                        activeChanges[node][k] = change;
                        activeWeights[node][k] += change;
                    }
                    activeBiases[node] += learningRate * delta;
                }
            }
        }
        _setupAdam() {
            this.biasChangesLow = [];
            this.biasChangesHigh = [];
            this.changesLow = [];
            this.changesHigh = [];
            this.iterations = 0;
            for (let layer = 0; layer <= this.outputLayer; layer++) {
                const size = this.sizes[layer];
                if (layer > 0) {
                    this.biasChangesLow[layer] = zeros$1(size);
                    this.biasChangesHigh[layer] = zeros$1(size);
                    this.changesLow[layer] = new Array(size);
                    this.changesHigh[layer] = new Array(size);
                    for (let node = 0; node < size; node++) {
                        const prevSize = this.sizes[layer - 1];
                        this.changesLow[layer][node] = zeros$1(prevSize);
                        this.changesHigh[layer][node] = zeros$1(prevSize);
                    }
                }
            }
            this.adjustWeights = this._adjustWeightsAdam;
        }
        _adjustWeightsAdam() {
            this.iterations++;
            const { iterations } = this;
            const { beta1, beta2, epsilon, learningRate } = this.trainOpts;
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const incoming = this.outputs[layer - 1];
                const currentSize = this.sizes[layer];
                const currentDeltas = this.deltas[layer];
                const currentChangesLow = this.changesLow[layer];
                const currentChangesHigh = this.changesHigh[layer];
                const currentWeights = this.weights[layer];
                const currentBiases = this.biases[layer];
                const currentBiasChangesLow = this.biasChangesLow[layer];
                const currentBiasChangesHigh = this.biasChangesHigh[layer];
                for (let node = 0; node < currentSize; node++) {
                    const delta = currentDeltas[node];
                    for (let k = 0; k < incoming.length; k++) {
                        const gradient = delta * incoming[k];
                        const changeLow = currentChangesLow[node][k] * beta1 + (1 - beta1) * gradient;
                        const changeHigh = currentChangesHigh[node][k] * beta2 +
                            (1 - beta2) * gradient * gradient;
                        const momentumCorrection = changeLow / (1 - Math.pow(beta1, iterations));
                        const gradientCorrection = changeHigh / (1 - Math.pow(beta2, iterations));
                        currentChangesLow[node][k] = changeLow;
                        currentChangesHigh[node][k] = changeHigh;
                        currentWeights[node][k] +=
                            (learningRate * momentumCorrection) /
                                (Math.sqrt(gradientCorrection) + epsilon);
                    }
                    const biasGradient = currentDeltas[node];
                    const biasChangeLow = currentBiasChangesLow[node] * beta1 + (1 - beta1) * biasGradient;
                    const biasChangeHigh = currentBiasChangesHigh[node] * beta2 +
                        (1 - beta2) * biasGradient * biasGradient;
                    const biasMomentumCorrection = currentBiasChangesLow[node] / (1 - Math.pow(beta1, iterations));
                    const biasGradientCorrection = currentBiasChangesHigh[node] / (1 - Math.pow(beta2, iterations));
                    currentBiasChangesLow[node] = biasChangeLow;
                    currentBiasChangesHigh[node] = biasChangeHigh;
                    currentBiases[node] +=
                        (learningRate * biasMomentumCorrection) /
                            (Math.sqrt(biasGradientCorrection) + epsilon);
                }
            }
        }
        validateData(data) {
            const inputSize = this.sizes[0];
            const outputSize = this.sizes[this.sizes.length - 1];
            const { length } = data;
            for (let i = 0; i < length; i++) {
                const { input, output } = data[i];
                if (input.length !== inputSize) {
                    throw new Error(`input at index ${i} length ${input.length} must be ${inputSize}`);
                }
                if (data[i].output.length !== outputSize) {
                    throw new Error(`output at index ${i} length ${output.length} must be ${outputSize}`);
                }
            }
        }
        validateInput(formattedInput) {
            const inputSize = this.sizes[0];
            if (formattedInput.length !== inputSize) {
                throw new Error(`input length ${formattedInput.length} must match options.inputSize of ${inputSize}`);
            }
        }
        formatData(data) {
            if (!Array.isArray(data[0].input)) {
                if (this.inputLookup) {
                    this.inputLookupLength = Object.keys(this.inputLookup).length;
                }
                else {
                    const inputLookup = new LookupTable(data, 'input');
                    this.inputLookup = inputLookup.table;
                    this.inputLookupLength = inputLookup.length;
                }
            }
            if (!Array.isArray(data[0].output)) {
                if (this.outputLookup) {
                    this.outputLookupLength = Object.keys(this.outputLookup).length;
                }
                else {
                    const lookup = new LookupTable(data, 'output');
                    this.outputLookup = lookup.table;
                    this.outputLookupLength = lookup.length;
                }
            }
            if (!this._formatInput) {
                this._formatInput = getTypedArrayFn(data[0].input, this.inputLookup);
            }
            if (!this._formatOutput) {
                this._formatOutput = getTypedArrayFn(data[0].output, this.outputLookup);
            }
            // turn sparse hash input into arrays with 0s as filler
            if (this._formatInput && this._formatOutput) {
                const result = [];
                for (let i = 0; i < data.length; i++) {
                    result.push({
                        input: this._formatInput(data[i].input),
                        output: this._formatOutput(data[i].output),
                    });
                }
                return result;
            }
            if (this._formatInput) {
                const result = [];
                for (let i = 0; i < data.length; i++) {
                    result.push({
                        input: this._formatInput(data[i].input),
                        output: data[i].output,
                    });
                }
                return result;
            }
            if (this._formatOutput) {
                const result = [];
                for (let i = 0; i < data.length; i++) {
                    result.push({
                        input: data[i].input,
                        output: this._formatOutput(data[i].output),
                    });
                }
                return result;
            }
            return data;
        }
        addFormat(data) {
            var _a, _b;
            if (!Array.isArray(data.input) || typeof data.input[0] !== 'number') {
                this.inputLookup = lookup.addKeys(data.input, (_a = this.inputLookup) !== null && _a !== void 0 ? _a : {});
                if (this.inputLookup) {
                    this.inputLookupLength = Object.keys(this.inputLookup).length;
                }
            }
            if (!Array.isArray(data.output) || typeof data.output[0] !== 'number') {
                this.outputLookup = lookup.addKeys(data.output, (_b = this.outputLookup) !== null && _b !== void 0 ? _b : {});
                if (this.outputLookup) {
                    this.outputLookupLength = Object.keys(this.outputLookup).length;
                }
            }
        }
        test(data) {
            const { preparedData } = this.prepTraining(data);
            // for binary classification problems with one output node
            const isBinary = preparedData[0].output.length === 1;
            // for classification problems
            const misclasses = [];
            // run each pattern through the trained network and collect
            // error and misclassification statistics
            let errorSum = 0;
            if (isBinary) {
                let falsePos = 0;
                let falseNeg = 0;
                let truePos = 0;
                let trueNeg = 0;
                for (let i = 0; i < preparedData.length; i++) {
                    const output = this.runInput(preparedData[i].input);
                    const target = preparedData[i].output;
                    const actual = output[0] > this.options.binaryThresh ? 1 : 0;
                    const expected = target[0];
                    if (actual !== expected) {
                        const misclass = preparedData[i];
                        misclasses.push({
                            input: misclass.input,
                            output: misclass.output,
                            actual,
                            expected,
                        });
                    }
                    if (actual === 0 && expected === 0) {
                        trueNeg++;
                    }
                    else if (actual === 1 && expected === 1) {
                        truePos++;
                    }
                    else if (actual === 0 && expected === 1) {
                        falseNeg++;
                    }
                    else if (actual === 1 && expected === 0) {
                        falsePos++;
                    }
                    errorSum += mse$1(output.map((value, i) => {
                        return target[i] - value;
                    }));
                }
                return {
                    error: errorSum / preparedData.length,
                    misclasses,
                    total: preparedData.length,
                    trueNeg,
                    truePos,
                    falseNeg,
                    falsePos,
                    precision: truePos > 0 ? truePos / (truePos + falsePos) : 0,
                    recall: truePos > 0 ? truePos / (truePos + falseNeg) : 0,
                    accuracy: (trueNeg + truePos) / preparedData.length,
                };
            }
            for (let i = 0; i < preparedData.length; i++) {
                const output = this.runInput(preparedData[i].input);
                const target = preparedData[i].output;
                const actual = output.indexOf(max(output));
                const expected = target.indexOf(max(target));
                if (actual !== expected) {
                    const misclass = preparedData[i];
                    misclasses.push({
                        input: misclass.input,
                        output: misclass.output,
                        actual,
                        expected,
                    });
                }
                errorSum += mse$1(output.map((value, i) => {
                    return target[i] - value;
                }));
            }
            return {
                error: errorSum / preparedData.length,
                misclasses,
                total: preparedData.length,
            };
        }
        toJSON() {
            var _a, _b;
            if (!this.isRunnable) {
                this.initialize();
            }
            // use Array.from, keeping json small
            const jsonLayerWeights = this.weights.map((layerWeights) => {
                return layerWeights.map((layerWeights) => Array.from(layerWeights));
            });
            const jsonLayerBiases = this.biases.map((layerBiases) => Array.from(layerBiases));
            const jsonLayers = [];
            const outputLength = this.sizes.length - 1;
            for (let i = 0; i <= outputLength; i++) {
                jsonLayers.push({
                    weights: (_a = jsonLayerWeights[i]) !== null && _a !== void 0 ? _a : [],
                    biases: (_b = jsonLayerBiases[i]) !== null && _b !== void 0 ? _b : [],
                });
            }
            return {
                type: 'NeuralNetwork',
                sizes: [...this.sizes],
                layers: jsonLayers,
                inputLookup: this.inputLookup ? { ...this.inputLookup } : null,
                inputLookupLength: this.inputLookupLength,
                outputLookup: this.outputLookup ? { ...this.outputLookup } : null,
                outputLookupLength: this.outputLookupLength,
                options: { ...this.options },
                trainOpts: this.getTrainOptsJSON(),
            };
        }
        fromJSON(json) {
            this.options = { ...defaults$2(), ...json.options };
            if (json.hasOwnProperty('trainOpts')) {
                const trainOpts = {
                    ...json.trainOpts,
                    timeout: json.trainOpts.timeout === 'Infinity'
                        ? Infinity
                        : json.trainOpts.timeout,
                };
                this.updateTrainingOptions(trainOpts);
            }
            this.sizes = json.sizes;
            this.initialize();
            this.inputLookup = json.inputLookup ? { ...json.inputLookup } : null;
            this.inputLookupLength = json.inputLookupLength;
            this.outputLookup = json.outputLookup ? { ...json.outputLookup } : null;
            this.outputLookupLength = json.outputLookupLength;
            const jsonLayers = json.layers;
            const layerWeights = this.weights.map((layerWeights, layerIndex) => {
                return jsonLayers[layerIndex].weights.map((layerWeights) => Float32Array.from(layerWeights));
            });
            const layerBiases = this.biases.map((layerBiases, layerIndex) => Float32Array.from(jsonLayers[layerIndex].biases));
            for (let i = 0; i <= this.outputLayer; i++) {
                this.weights[i] = layerWeights[i] || [];
                this.biases[i] = layerBiases[i] || [];
            }
            return this;
        }
        toFunction(cb) {
            const { activation, leakyReluAlpha } = this.trainOpts;
            let needsVar = false;
            const nodeHandle = (layerIndex, nodeIndex) => {
                if (layerIndex === 0) {
                    return `(input[${nodeIndex}]||0)`;
                }
                const weights = this.weights[layerIndex][nodeIndex];
                const bias = this.biases[layerIndex][nodeIndex];
                if (!weights) {
                    throw new Error(`weights at layerIndex ${layerIndex} & nodeIndex ${nodeIndex} not found`);
                }
                if (!bias) {
                    throw new Error(`bias as layerIndex ${layerIndex} & nodeIndex ${nodeIndex} not found`);
                }
                const weightsArray = [];
                weights.forEach((weight, subNodeIndex) => {
                    if (weight < 0) {
                        weightsArray.push(`${weight}*${nodeHandle(layerIndex - 1, subNodeIndex)}`);
                    }
                    else {
                        weightsArray.push(`+${weight}*${nodeHandle(layerIndex - 1, subNodeIndex)}`);
                    }
                });
                const result = `(${bias.toString()}${weightsArray.join('')})`;
                switch (activation) {
                    case 'sigmoid':
                        return `1/(1+1/Math.exp(${result}))`;
                    case 'relu': {
                        needsVar = true;
                        return `((v=${result})<0?0:v)`;
                    }
                    case 'leaky-relu': {
                        needsVar = true;
                        return `Math.max((v=${result}),${leakyReluAlpha}*v)`;
                    }
                    case 'tanh':
                        return `Math.tanh(${result})`;
                    default:
                        throw new Error(`Unknown activation ${activation}. Available activations are: 'sigmoid', 'relu', 'leaky-relu', 'tanh'`);
                }
            };
            function checkKeys(keys) {
                if (keys.find((v) => v.includes('"'))) {
                    throw new Error(`key contains '"', which is not compatible`);
                }
            }
            const layersAsMath = [];
            let result;
            let inputLookup = '';
            if (this.inputLookup) {
                const keys = Object.keys(this.inputLookup);
                checkKeys(keys);
                inputLookup = `input = new Float32Array([${Object.keys(this.inputLookup)
                .map((key) => `input["${key}"]`)
                .join(',')}]);`;
            }
            if (this.sizes.length < 1)
                throw new Error('No layers');
            for (let nodeIndex = 0; nodeIndex < this.sizes[this.outputLayer]; nodeIndex++) {
                layersAsMath.push(nodeHandle(this.outputLayer, nodeIndex));
            }
            if (this.outputLookup) {
                const keys = Object.keys(this.outputLookup);
                checkKeys(keys);
                const values = keys
                    .map((key, i) => `"${key}":${layersAsMath[i]}`)
                    .join(',');
                result = `{${values}}`;
            }
            else {
                result = `[${layersAsMath.join(',')}]`;
            }
            const source = `${inputLookup}${needsVar ? 'var v;' : ''}return ${result};`;
            // eslint-disable-next-line @typescript-eslint/no-implied-eval,no-new-func
            return new Function('input', cb ? cb(source) : source);
        }
    }

    function weightedSumSigmoid(weights, biases, inputs) {
        let sum = biases[this.thread.x];
        for (let k = 0; k < this.constants.size; k++) {
            sum += weights[this.thread.x][k] * inputs[k];
        }
        // sigmoid
        return 1 / (1 + Math.exp(-sum));
    }
    function weightedSumRelu(weights, biases, inputs) {
        let sum = biases[this.thread.x];
        for (let k = 0; k < this.constants.size; k++) {
            sum += weights[this.thread.x][k] * inputs[k];
        }
        // relu
        return sum < 0 ? 0 : sum;
    }
    function weightedSumLeakyRelu(weights, biases, inputs) {
        let sum = biases[this.thread.x];
        for (let k = 0; k < this.constants.size; k++) {
            sum += weights[this.thread.x][k] * inputs[k];
        }
        // leaky relu
        return sum < 0 ? 0 : 0.01 * sum;
    }
    function weightedSumTanh(weights, biases, inputs) {
        let sum = biases[this.thread.x];
        for (let k = 0; k < this.constants.size; k++) {
            sum += weights[this.thread.x][k] * inputs[k];
        }
        // tanh
        return Math.tanh(sum);
    }
    function calcErrorOutput(output, target) {
        return target - output;
    }
    function calcDeltasSigmoid(error, output) {
        // sigmoid derivative
        return error * output * (1 - output);
    }
    function calcDeltasRelu(error, output) {
        // relu derivative
        return output > 0 ? error : 0;
    }
    function calcDeltasLeakyRelu(error, output) {
        // leaky relu derivative
        return output > 0 ? error : 0.01 * error;
    }
    function calcDeltasTanh(error, output) {
        // tanh derivative
        return (1 - output * output) * error;
    }
    function calcError(x, size, nextWeights, nextDeltas) {
        let error = 0;
        for (let k = 0; k < size; k++) {
            error += nextDeltas[k] * nextWeights[k][x];
        }
        return error;
    }
    function calcChanges(learningRate, momentum, previousChange, delta, previousOutput) {
        return learningRate * delta * previousOutput + momentum * previousChange;
    }
    function addWeights(change, weight) {
        return change + weight;
    }
    function addBiases(biases, deltas) {
        return (biases[this.thread.x] + deltas[this.thread.x] * this.constants.learningRate);
    }
    // mean squared error, reimplemented for GPU
    function mse(errors) {
        let sum = 0;
        for (let i = 0; i < this.constants.size; i++) {
            sum += errors[i] ** 2;
        }
        return sum / this.constants.size;
    }
    class NeuralNetworkGPU extends NeuralNetwork {
        constructor(options = {}) {
            super(options);
            this.texturizeInputData = () => {
                throw new Error('not yet setup');
            };
            this.forwardPropagate = [];
            this.backwardPropagate = [];
            this.changesPropagate = [];
            this.biasesPropagate = [];
            this.getMSE = () => {
                throw new Error('not yet setup');
            };
            this._addMSE = () => {
                throw new Error('not yet setup');
            };
            this._divideMSESum = () => {
                throw new Error('not yet setup');
            };
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.outputs = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.deltas = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.errors = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.weights = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.changes = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.biases = [];
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.runInput = (input) => {
                let output;
                this.outputs[0] = input;
                for (let layer = 1; layer <= this.outputLayer; layer++) {
                    release(this.outputs[layer]);
                    this.outputs[layer] = this.forwardPropagate[layer](this.weights[layer], this.biases[layer], input);
                    output = input = this.outputs[layer];
                }
                return output;
            };
            this.calculateDeltas = (target) => {
                for (let layer = this.outputLayer; layer > 0; layer--) {
                    release(this.deltas[layer]);
                    release(this.errors[layer]);
                    let output;
                    if (layer === this.outputLayer) {
                        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                        // @ts-expect-error
                        output = this.backwardPropagate[layer](this.outputs[layer], target);
                    }
                    else {
                        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                        // @ts-expect-error
                        output = this.backwardPropagate[layer](this.weights[layer + 1], this.outputs[layer], this.deltas[layer + 1]);
                    }
                    this.deltas[layer] = output.result;
                    this.errors[layer] = output.error;
                }
            };
            this.errorCheckInterval = 100;
            this.gpu = new gpu_js.GPU({ mode: options.mode });
        }
        initialize() {
            super.initialize();
            this.buildRunInput();
            this.buildCalculateDeltas();
            this.buildGetChanges();
            this.buildChangeBiases();
            this.buildGetMSE();
        }
        setActivation() { }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        trainPattern(value, logErrorRate) {
            // forward propagate
            this.runInput(value.input);
            // back propagate
            this.calculateDeltas(value.output);
            this.adjustWeights();
            if (logErrorRate) {
                return this.getMSE(this.errors[this.outputLayer]);
            }
            return null;
        }
        calculateTrainingError(data) {
            let sum = new Float32Array([0]);
            for (let i = 0; i < data.length; ++i) {
                const prevSum = sum;
                const error = this.trainPattern(data[i], true);
                sum = this._addMSE(sum, error);
                release(error);
                release(prevSum);
            }
            const result = this._divideMSESum(data.length, sum);
            release(sum);
            return (result instanceof gpu_js.Texture
                ? result.toArray()
                : result)[0];
        }
        adjustWeights() {
            this.getChanges();
            this.changeBiases();
        }
        buildRunInput() {
            let weightedSum = null;
            switch (this.trainOpts.activation) {
                case 'sigmoid':
                    weightedSum = weightedSumSigmoid;
                    break;
                case 'relu':
                    weightedSum = weightedSumRelu;
                    break;
                case 'leaky-relu':
                    weightedSum = weightedSumLeakyRelu;
                    break;
                case 'tanh':
                    weightedSum = weightedSumTanh;
                    break;
                default:
                    throw new Error(`Unknown activation ${this.trainOpts.activation}. Available activations are: 'sigmoid', 'relu', 'leaky-relu', 'tanh'`);
            }
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                this.forwardPropagate[layer] = this.gpu.createKernel(weightedSum, {
                    output: [this.sizes[layer]],
                    pipeline: true,
                    constants: {
                        size: this.sizes[layer - 1],
                    },
                    immutable: true,
                });
            }
            this.texturizeInputData = this.gpu.createKernel(function (value) {
                return value[this.thread.x];
            }, {
                output: [this.sizes[1]],
                pipeline: true,
                immutable: true,
            });
        }
        buildCalculateDeltas() {
            let calcDeltas;
            switch (this.trainOpts.activation) {
                case 'sigmoid':
                    calcDeltas = calcDeltasSigmoid;
                    break;
                case 'relu':
                    calcDeltas = calcDeltasRelu;
                    break;
                case 'leaky-relu':
                    calcDeltas = calcDeltasLeakyRelu;
                    break;
                case 'tanh':
                    calcDeltas = calcDeltasTanh;
                    break;
                default:
                    throw new Error(`Unknown activation ${this.trainOpts.activation}. Available activations are: 'sigmoid', 'relu', 'leaky-relu', 'tanh'`);
            }
            calcDeltas = gpu_js.alias(gpu_js.utils.getMinifySafeName(() => calcDeltas), calcDeltas);
            this.gpu.addFunction(calcDeltas);
            for (let layer = this.outputLayer; layer > 0; layer--) {
                if (layer === this.outputLayer) {
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    this.backwardPropagate[this.outputLayer] = this.gpu.createKernelMap({
                        error: calcErrorOutput,
                    }, function (outputs, targets) {
                        const output = outputs[this.thread.x];
                        const target = targets[this.thread.x];
                        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                        // @ts-expect-error
                        return calcDeltas(calcErrorOutput(output, target), output);
                    }, {
                        output: [this.sizes[this.outputLayer]],
                        pipeline: true,
                        immutable: true,
                    });
                }
                else {
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    this.backwardPropagate[layer] = this.gpu.createKernelMap({
                        error: calcError,
                    }, function (nextWeights, outputs, nextDeltas) {
                        const output = outputs[this.thread.x];
                        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                        // @ts-expect-error
                        return calcDeltas(calcError(this.thread.x, this.constants.size, nextWeights, nextDeltas), output);
                    }, {
                        output: [this.sizes[layer]],
                        pipeline: true,
                        constants: {
                            size: this.sizes[layer + 1],
                        },
                        immutable: true,
                    });
                }
            }
        }
        buildGetChanges() {
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                // @ts-expect-error
                this.changesPropagate[layer] = this.gpu.createKernelMap({
                    weights: addWeights,
                    changes: calcChanges,
                }, function (previousOutputs, deltas, weights, previousChanges) {
                    const change = calcChanges(this.constants.learningRate, this.constants.momentum, previousChanges[this.thread.y][this.thread.x], deltas[this.thread.y], previousOutputs[this.thread.x]);
                    return addWeights(change, weights[this.thread.y][this.thread.x]);
                }, {
                    output: [this.sizes[layer - 1], this.sizes[layer]],
                    pipeline: true,
                    constants: {
                        size: this.sizes[layer - 1],
                        learningRate: this.trainOpts.learningRate,
                        momentum: this.trainOpts.momentum,
                    },
                    immutable: true,
                });
            }
        }
        getChanges() {
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const weights = this.weights[layer];
                const changes = this.changes[layer];
                const output = this.changesPropagate[layer](this.outputs[layer - 1], this.deltas[layer], weights, changes);
                release(weights);
                release(changes);
                this.weights[layer] = output.weights;
                this.changes[layer] = output.changes;
                release(output.result);
            }
        }
        buildChangeBiases() {
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                this.biasesPropagate[layer] = this.gpu.createKernel(addBiases, {
                    output: [this.sizes[layer]],
                    pipeline: true,
                    constants: {
                        learningRate: this.trainOpts.learningRate,
                    },
                    immutable: true,
                });
            }
        }
        changeBiases() {
            for (let layer = 1; layer <= this.outputLayer; layer++) {
                const biases = this.biases[layer];
                this.biases[layer] = this.biasesPropagate[layer](biases, this.deltas[layer]);
                release(biases);
            }
        }
        buildGetMSE() {
            this.getMSE = this.gpu.createKernel(mse, {
                output: [1],
                constants: {
                    size: this.sizes[this.outputLayer],
                },
                pipeline: true,
                immutable: true,
            });
            this._addMSE = this.gpu.createKernel(function (value1, value2) {
                return value1[0] + value2[0];
            }, {
                output: [1],
                pipeline: true,
                immutable: true,
            });
            this._divideMSESum = this.gpu.createKernel(function (length, mseSum) {
                const value = mseSum[0];
                if (value > 0) {
                    return value / length;
                }
                return 0;
            }, {
                output: [1],
            });
        }
        run(input) {
            if (!this.isRunnable) {
                throw new Error('network not runnable');
            }
            let formattedInput;
            if (this.inputLookup) {
                formattedInput = lookup.toArray(this.inputLookup, input, this.inputLookupLength);
            }
            else {
                formattedInput = input;
            }
            this.validateInput(formattedInput);
            const outputTextures = this.runInput(formattedInput);
            const output = outputTextures instanceof gpu_js.Texture
                ? outputTextures.toArray()
                : outputTextures;
            if (this.outputLookup) {
                return lookup.toObject(this.outputLookup, output);
            }
            return output;
        }
        // @ts-expect-error the underlying network works as normal, but we are working on the GPU
        prepTraining(data, options = {}) {
            this.updateTrainingOptions(options);
            const preparedData = this.formatData(data);
            const endTime = Date.now() + this.trainOpts.timeout;
            const status = {
                error: 1,
                iterations: 0,
            };
            this.verifyIsInitialized(preparedData);
            const texturizeOutputData = this.gpu.createKernel(function (value) {
                return value[this.thread.x];
            }, {
                output: [preparedData[0].output.length],
                pipeline: true,
                immutable: true,
            });
            return {
                preparedData: preparedData.map((set) => ({
                    input: this.texturizeInputData(set.input),
                    output: texturizeOutputData(set.output),
                })),
                status,
                endTime,
            };
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        toFunction() {
            throw new Error(`${this.constructor.name}-toFunction is not yet implemented`);
        }
        toJSON() {
            var _a, _b;
            if (this.sizes === null) {
                this.initialize();
            }
            // use Array.from, keeping json small
            const jsonLayerWeights = this.weights.map((layerWeights) => {
                return (layerWeights instanceof gpu_js.Texture
                    ? layerWeights.toArray()
                    : layerWeights).map((layerWeights) => Array.from(layerWeights));
            });
            const jsonLayerBiases = this.biases.map((layerBiases) => Array.from(layerBiases instanceof gpu_js.Texture
                ? layerBiases.toArray()
                : layerBiases));
            const jsonLayers = [];
            for (let i = 0; i <= this.outputLayer; i++) {
                jsonLayers.push({
                    weights: (_a = jsonLayerWeights[i]) !== null && _a !== void 0 ? _a : [],
                    biases: (_b = jsonLayerBiases[i]) !== null && _b !== void 0 ? _b : [],
                });
            }
            return {
                type: 'NeuralNetworkGPU',
                sizes: [...this.sizes],
                layers: jsonLayers,
                inputLookup: this.inputLookup ? { ...this.inputLookup } : null,
                inputLookupLength: this.inputLookupLength,
                outputLookup: this.outputLookup ? { ...this.outputLookup } : null,
                outputLookupLength: this.outputLookupLength,
                options: { ...this.options },
                trainOpts: this.getTrainOptsJSON(),
            };
        }
    }

    class RecurrentConnection extends Internal {
        constructor() {
            super(...arguments);
            this.settings = {};
            this.layer = null;
        }
        setLayer(layer) {
            this.layer = layer;
        }
        get width() {
            if (!this.layer)
                throw new Error('layer not set');
            return this.layer.width;
        }
        set width(value) {
            throw new Error(`${this.constructor.name}-width is not yet implemented`);
        }
        get height() {
            if (!this.layer)
                throw new Error('layer not set');
            return this.layer.height;
        }
        set height(value) {
            throw new Error(`${this.constructor.name}-height is not yet implemented`);
        }
        get deltas() {
            if (!this.layer)
                throw new Error('layer not set');
            return this.layer.deltas;
        }
        set deltas(deltas) {
            if (!this.layer)
                throw new Error('layer not set');
            release(this.layer.deltas);
            this.layer.deltas = deltas;
        }
        get weights() {
            if (!this.layer)
                throw new Error('layer not set');
            return this.layer.weights;
        }
        set weights(weights) {
            if (!this.layer)
                throw new Error('layer not set');
            release(this.layer.weights);
            this.layer.weights = weights;
        }
        predict() {
            // throw new Error(`${this.constructor.name}-predict is not yet implemented`)
        }
        compare() {
            // throw new Error(`${this.constructor.name}-compare is not yet implemented`)
        }
        learn() {
            throw new Error('no longer using');
        }
        setupKernels() {
            // throw new Error(
            //   `${this.constructor.name}-setupKernels is not yet implemented`
            // )
        }
        reuseKernels() {
            // throw new Error(
            //   `${this.constructor.name}-reuseKernels is not yet implemented`
            // )
        }
    }

    class Recurrent extends FeedForward {
        // TODO: use generics in extend
        constructor(options = {}) {
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            super(options);
            this.trainOpts = {};
            this._outputConnection = null;
            this._layerSets = [];
            this._hiddenLayerOutputIndices = [];
            this._model = null;
        }
        _connectLayers() {
            if (!this.options.inputLayer) {
                throw new Error('inputLayer not found');
            }
            if (!this.options.outputLayer) {
                throw new Error('outputLayer not found');
            }
            const inputLayer = this.options.inputLayer();
            const hiddenLayers = this._connectHiddenLayers(inputLayer);
            const outputLayer = this.options.outputLayer(hiddenLayers[hiddenLayers.length - 1], -1);
            return {
                inputLayer,
                hiddenLayers,
                outputLayer,
            };
        }
        _connectLayersDeep() {
            const layers = [];
            const previousLayers = this._layerSets[this._layerSets.length - 1];
            let usedHiddenLayerOutputIndex = 0;
            function findInputLayer(inputLayer) {
                const index = previousLayers.indexOf(inputLayer);
                if (index < 0)
                    throw new Error('unable to find layer');
                return layers[index];
            }
            function layerSettings(layer) {
                return {
                    ...layer.settings,
                    weights: null,
                    deltas: null,
                    praxis: null,
                };
            }
            for (let i = 0; i < previousLayers.length; i++) {
                const previousLayer = previousLayers[i];
                let layer;
                if (previousLayer instanceof Activation) {
                    layer = new previousLayer.constructor(findInputLayer(previousLayer.inputLayer), layerSettings(previousLayer));
                }
                else if (previousLayer instanceof EntryPoint) {
                    layer = new previousLayer.constructor(layerSettings(previousLayer));
                }
                else if (previousLayer instanceof Filter) {
                    layer = new previousLayer.constructor(layerSettings(previousLayer.inputLayer), findInputLayer(previousLayer.inputLayer));
                }
                else if (previousLayer instanceof Internal) {
                    const previousHiddenLayerOutput = previousLayers[this._hiddenLayerOutputIndices[usedHiddenLayerOutputIndex++]];
                    if (previousLayer instanceof RecurrentConnection) {
                        throw new Error('unfinished');
                    }
                    else if (previousLayer instanceof RecurrentInput) {
                        layer = new RecurrentInput(previousHiddenLayerOutput);
                    }
                    else if (previousLayer instanceof RecurrentZeros) {
                        layer = new RecurrentInput(previousHiddenLayerOutput);
                    }
                    else {
                        throw new Error(`hidden layer ${previousLayer.constructor.name} extends unknown hidden layer`);
                    }
                }
                else if (previousLayer instanceof InternalModel ||
                    previousLayer instanceof Model) {
                    layer = previousLayer;
                }
                else if (previousLayer instanceof Modifier) {
                    layer = new previousLayer.constructor(findInputLayer(previousLayer.inputLayer), layerSettings(previousLayer.inputLayer));
                }
                else if (previousLayer instanceof Operator) {
                    layer = new previousLayer.constructor(findInputLayer(previousLayer.inputLayer1), findInputLayer(previousLayer.inputLayer2), layerSettings(previousLayer));
                }
                else if (previousLayer instanceof Target) {
                    layer = new previousLayer.constructor(layerSettings(previousLayer), findInputLayer(previousLayer.inputLayer));
                }
                else {
                    throw new Error(`hidden layer ${previousLayer.constructor.name} extends unknown hidden layer`);
                }
                layers.push(layer);
            }
            return layers;
        }
        _connectHiddenLayers(previousLayer) {
            const hiddenLayers = [];
            if (!this.options.hiddenLayers)
                throw new Error('hiddenLayers not defined');
            for (let i = 0; i < this.options.hiddenLayers.length; i++) {
                const recurrentInput = new RecurrentZeros();
                const hiddenLayer = this.options.hiddenLayers[i](previousLayer, recurrentInput, i);
                previousLayer = hiddenLayer;
                hiddenLayers.push(hiddenLayer);
            }
            return hiddenLayers;
        }
        initialize() {
            this._outputConnection = new RecurrentConnection();
            let layerSet;
            if (this.options.layers) {
                layerSet = this._connectOptionsLayers();
            }
            else {
                const { inputLayer, hiddenLayers, outputLayer } = this._connectLayers();
                layerSet = flattenLayers([inputLayer, ...hiddenLayers, outputLayer]);
                this._hiddenLayerOutputIndices = hiddenLayers.map((l) => layerSet.indexOf(l));
                this._inputLayer = inputLayer;
                this._hiddenLayers = hiddenLayers;
                this._outputLayer = outputLayer;
            }
            this.layers = layerSet;
            this._layerSets = [layerSet];
            this._model = layerSet.filter((l) => l instanceof Model || l instanceof InternalModel);
            this.initializeLayers(layerSet);
        }
        initializeDeep() {
            const layers = this._connectLayersDeep();
            for (let i = 0; i < layers.length; i++) {
                const layer = layers[i];
                layer.setupKernels(true);
                layer.reuseKernels(this._layerSets[0][i]);
            }
            this._layerSets.push(layers);
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        run(inputs) {
            while (this._layerSets.length <= inputs.length) {
                this.initializeDeep();
            }
            const result = this.runInputs(inputs);
            if (result instanceof gpu_js.Texture) {
                return result.toArray();
            }
            return result;
        }
        runInput(input) {
            throw new Error('use .runInputs()');
        }
        runInputs(inputs) {
            while (this._layerSets.length < inputs.length) {
                this.initializeDeep();
            }
            const max = inputs.length - 1; // last output will be compared with last index
            for (let x = 0; x <= max; x++) {
                const layerSet = this._layerSets[x];
                layerSet[0].predict(inputs[x]);
                for (let i = 1; i < layerSet.length; i++) {
                    layerSet[i].predict();
                }
            }
            const lastLayerUsed = this._layerSets[max];
            const result = lastLayerUsed[lastLayerUsed.length - 1].weights;
            this.end();
            return result;
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        train(data, options = {}) {
            const { preparedData, status, endTime } = this._prepTraining(data, options);
            let continueTicking = true;
            const calculateError = () => this._calculateTrainingError(preparedData);
            const trainPatters = () => this._trainPatterns(preparedData);
            while (continueTicking) {
                continueTicking = this._trainingTick(status, endTime, calculateError, trainPatters);
            }
            return status;
        }
        end() {
            const x = this._layerSets.length - 1;
            const lastLayerSet = this._layerSets[x];
            lastLayerSet[0].predict([new Float32Array([0])]);
            for (let i = 1; i < lastLayerSet.length; i++) {
                lastLayerSet[i].predict();
            }
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        transferData(formattedData) {
            return formattedData;
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        _prepTraining(data, options) {
            this._updateTrainingOptions(options);
            const endTime = this.trainOpts.timeout
                ? Date.now() + this.trainOpts.timeout
                : 0;
            const status = {
                error: 1,
                iterations: 0,
            };
            this.verifyIsInitialized();
            return {
                preparedData: this.transferData(data),
                status,
                endTime,
            };
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        _calculateTrainingError(data) {
            if (!this.meanSquaredError) {
                throw new Error('this.meanSquaredError not setup');
            }
            let sum = new Float32Array(1);
            for (let i = 0; i < data.length; ++i) {
                const prevSum = sum;
                const error = this._trainPattern(data[i], true);
                sum = this.meanSquaredError.add(sum, error);
                release(error);
                release(prevSum);
            }
            const result = this.meanSquaredError.divide(data.length, sum);
            release(sum);
            if (result instanceof gpu_js.Texture) {
                const resultArray = result.toArray();
                return resultArray[0];
            }
            return result[0];
        }
        // TODO: more types
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        formatData(data) {
            return data;
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        _calculateDeltas(target) {
            const lastLayerSet = this._layerSets[this._layerSets.length - 1];
            // Iterate from the second to last layer backwards, propagating 0's
            for (let i = lastLayerSet.length - 2; i >= 0; i--) {
                lastLayerSet[i].compare();
            }
            for (let x = target.length - 2; x >= 0; x--) {
                const layerSet = this._layerSets[x];
                layerSet[layerSet.length - 1].compare(target[x + 1]);
                for (let i = layerSet.length - 2; i >= 0; i--) {
                    layerSet[i].compare();
                }
            }
        }
        adjustWeights() {
            var _a;
            const _model = this._model;
            for (let i = 0; i < _model.length; i++) {
                _model[i].learn((_a = this.options.learningRate) !== null && _a !== void 0 ? _a : 0);
            }
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        _trainPatterns(data) {
            for (let i = 0; i < data.length; ++i) {
                this._trainPattern(data[i], false);
            }
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        _trainPattern(inputs, logErrorRate) {
            // forward propagate
            this.runInputs(inputs);
            // back propagate
            this._calculateDeltas(inputs);
            this.adjustWeights();
            if (logErrorRate) {
                if (!this.meanSquaredError) {
                    throw new Error('this.meanSquaredError not setup');
                }
                let error = new Float32Array(1);
                for (let i = 0, max = inputs.length - 2; i <= max; i++) {
                    const layerSet = this._layerSets[i];
                    const lastLayer = layerSet[layerSet.length - 1];
                    const prevError = error;
                    error = this.meanSquaredError.addAbsolute(prevError, lastLayer.errors);
                    release(prevError);
                }
                return clone(this.meanSquaredError.divide(inputs.length, error));
            }
            return null;
        }
    }

    /**
     * A matrix
     */
    class Matrix {
        constructor(rows, columns) {
            this.rows = 0;
            this.columns = 0;
            if (rows)
                this.rows = rows;
            if (columns)
                this.columns = columns;
            this.weights = zeros$1(this.rows * this.columns);
            this.deltas = zeros$1(this.rows * this.columns);
        }
        getWeight(row, col) {
            // slow but careful accessor function
            // we want row-major order
            const ix = this.columns * row + col;
            if (ix < 0 || ix >= this.weights.length) {
                throw new Error('get accessor is skewed');
            }
            return this.weights[ix];
        }
        setWeight(row, col, v) {
            // slow but careful accessor function
            const ix = this.columns * row + col;
            if (ix < 0 || ix >= this.weights.length) {
                throw new Error('set accessor is skewed');
            }
            this.weights[ix] = v;
            return this;
        }
        getDelta(row, col) {
            // slow but careful accessor function
            // we want row-major order
            const ix = this.columns * row + col;
            if (ix < 0 || ix >= this.deltas.length) {
                throw new Error('get accessor is skewed');
            }
            return this.deltas[ix];
        }
        setDelta(row, col, v) {
            // slow but careful accessor function
            const ix = this.columns * row + col;
            if (ix < 0 || ix >= this.weights.length) {
                throw new Error('set accessor is skewed');
            }
            this.deltas[ix] = v;
            return this;
        }
        toJSON() {
            return {
                rows: this.rows,
                columns: this.columns,
                weights: Array.from(this.weights.slice(0)),
            };
        }
        static fromJSON(json) {
            const matrix = new Matrix(json.rows, json.columns);
            for (let i = 0, max = json.rows * json.columns; i < max; i++) {
                matrix.weights[i] = json.weights[i]; // copy over weights
            }
            return matrix;
        }
        static fromArray(weights) {
            const matrix = new Matrix(weights.length, weights[0].length);
            matrix.fromArray(weights);
            return matrix;
        }
        deltasToArray() {
            return this.toArray('deltas');
        }
        weightsToArray() {
            return this.toArray('weights');
        }
        toArray(prop = 'weights') {
            const result = new Array(this.rows);
            this.iterate({
                row: (rowIndex) => {
                    result[rowIndex] = new Array(this.columns);
                },
                column: (rowIndex, columnIndex) => {
                    if (prop === 'weights') {
                        result[rowIndex][columnIndex] = this.getWeight(rowIndex, columnIndex);
                    }
                    else if (prop === 'deltas') {
                        result[rowIndex][columnIndex] = this.getDelta(rowIndex, columnIndex);
                    }
                },
            });
            return result;
        }
        fromArray(array, prop = 'weights') {
            if (array.length !== this.rows) {
                throw new Error('rows do not match');
            }
            if (array[0].length !== this.columns) {
                throw new Error('columns do not match');
            }
            this.iterate({
                column: (rowIndex, columnIndex) => {
                    const value = array[rowIndex][columnIndex];
                    if (typeof value !== 'number') {
                        throw new Error('value not number');
                    }
                    if (prop === 'weights') {
                        this.setWeight(rowIndex, columnIndex, value);
                    }
                    else if (prop === 'deltas') {
                        this.setDelta(rowIndex, columnIndex, value);
                    }
                },
            });
            return this;
        }
        iterate(callbacks) {
            const rows = this.rows;
            const columns = this.columns;
            for (let rowIndex = 0; rowIndex < rows; rowIndex++) {
                if (callbacks.row) {
                    callbacks.row(rowIndex);
                }
                for (let columnIndex = 0; columnIndex < columns; columnIndex++) {
                    if (callbacks.column) {
                        callbacks.column(rowIndex, columnIndex);
                    }
                }
            }
            return this;
        }
    }

    /** return Matrix but filled with random numbers from gaussian
     */
    class RandomMatrix extends Matrix {
        constructor(rows, columns, std) {
            super(rows, columns);
            this.std = std;
            for (let i = 0, max = this.weights.length; i < max; i++) {
                this.weights[i] = randomFloat(-std, std);
            }
        }
    }

    class DataFormatter {
        constructor(values, maxThreshold = 0) {
            this.values = values;
            this.indexTable = {};
            this.characterTable = {};
            this.characters = [];
            this.specialIndexes = [];
            this.isSetup = false;
            if (values === undefined)
                return;
            this.setup(values, maxThreshold);
        }
        setup(values, maxThreshold = 0) {
            if (this.isSetup)
                throw new Error('DataFormatter is already setup');
            this.values = values;
            // go over all characters and keep track of all unique ones seen
            // count up all characters
            this.buildCharactersFromIterable(values);
            this.buildTables(maxThreshold);
            if (values[0].input) {
                this.addInputOutput();
            }
            this.addUnrecognized();
            this.isSetup = true;
        }
        buildCharactersFromIterable(values) {
            const tempCharactersTable = {};
            for (let dataFormatterIndex = 0, dataFormatterLength = values.length; dataFormatterIndex < dataFormatterLength; dataFormatterIndex++) {
                const characters = values[dataFormatterIndex];
                // if (typeof characters === 'string') {
                //   const character = characters;
                //   if (tempCharactersTable.hasOwnProperty(character)) continue;
                //   tempCharactersTable[character] = true;
                //   this.characters.push(character);
                if (characters.hasOwnProperty('length')) {
                    const iteratable = characters;
                    for (let characterIndex = 0, charactersLength = iteratable.length; characterIndex < charactersLength; characterIndex++) {
                        const character = iteratable[characterIndex];
                        if (tempCharactersTable.hasOwnProperty(character))
                            continue;
                        tempCharactersTable[character] = true;
                        this.characters.push(character);
                    }
                }
                else if (typeof characters === 'number') {
                    if (tempCharactersTable.hasOwnProperty(characters))
                        continue;
                    tempCharactersTable[characters] = true;
                    this.characters.push(characters);
                }
                else if (typeof characters === 'boolean') {
                    const character = characters.toString();
                    if (tempCharactersTable.hasOwnProperty(character))
                        continue;
                    tempCharactersTable[character] = true;
                    this.characters.push(character);
                }
                else if (Array.isArray(characters) &&
                    typeof characters[0] === 'string') {
                    for (let i = 0; i < characters.length; i++) {
                        const character = characters[i];
                        if (tempCharactersTable.hasOwnProperty(character))
                            continue;
                        tempCharactersTable[character] = true;
                        this.characters.push(character);
                    }
                }
                else if (Array.isArray(characters) &&
                    (typeof characters[0] === 'number' ||
                        typeof characters[0] === 'boolean')) {
                    for (let i = 0; i < characters.length; i++) {
                        const character = characters[i].toString();
                        if (tempCharactersTable.hasOwnProperty(dataFormatterIndex))
                            continue;
                        tempCharactersTable[character] = true;
                        this.characters.push(character);
                    }
                }
                else if (characters.hasOwnProperty('input') &&
                    characters.hasOwnProperty('output')) {
                    const { input, output } = characters;
                    if (Array.isArray(input)) {
                        this.addCharacters(input, tempCharactersTable);
                    }
                    else {
                        this.addCharacters(input.toString(), tempCharactersTable);
                    }
                    if (Array.isArray(output)) {
                        this.addCharacters(output, tempCharactersTable);
                    }
                    else {
                        this.addCharacters(output.toString(), tempCharactersTable);
                    }
                }
                else {
                    throw new Error('Unhandled value');
                }
            }
        }
        addCharacters(characters, charactersTable) {
            for (let i = 0; i < characters.length; i++) {
                const character = characters[i].toString();
                if (charactersTable.hasOwnProperty(character))
                    continue;
                charactersTable[character] = true;
                this.characters.push(character);
            }
        }
        buildTables(maxThreshold) {
            // filter by count threshold and create pointers
            const charactersLength = this.characters.length;
            for (let characterIndex = 0; characterIndex < charactersLength; characterIndex++) {
                const character = this.characters[characterIndex];
                if (characterIndex >= maxThreshold) {
                    // add character to dataFormatter
                    this.indexTable[character] = characterIndex;
                    this.characterTable[characterIndex] = character;
                }
            }
        }
        toIndexes(value, maxThreshold = 0) {
            const result = [];
            const { indexTable } = this;
            switch (typeof value) {
                case 'number':
                case 'boolean':
                    value = value.toString();
            }
            for (let i = 0, max = value.length; i < max; i++) {
                const character = value[i].toString();
                let index = indexTable[character];
                if (index === undefined) {
                    if (indexTable.unrecognized) {
                        index = indexTable.unrecognized;
                    }
                    else {
                        throw new Error(`unrecognized character "${character}"`);
                    }
                }
                if (index < maxThreshold)
                    continue;
                result.push(index);
            }
            return result;
        }
        toIndexesInputOutput(input, output, maxThreshold = 0) {
            const result = this.toIndexesValue(input, maxThreshold, true);
            if (typeof output === 'undefined')
                return result;
            return result.concat(this.toIndexesValue(output, maxThreshold, false));
        }
        toIndexesValue(value, maxThreshold, isInput) {
            if (typeof value === 'string') {
                value = value.split('');
            }
            else if (typeof value === 'number' || typeof value === 'boolean') {
                value = value.toString().split('');
            }
            else if (Array.isArray(value) &&
                (typeof value[0] === 'number' ||
                    typeof value[0] === 'boolean' ||
                    typeof value[0] === 'string')) {
                value = value.map((v) => v.toString());
            }
            else {
                throw new Error('unrecognized value');
            }
            if (isInput) {
                value = value.concat(['stop-input', 'start-output']);
            }
            return this.toIndexes(value, maxThreshold);
        }
        toCharacters(indices, maxThreshold = 0) {
            const result = [];
            const { indexTable, characterTable } = this;
            for (let i = 0, max = indices.length; i < max; i++) {
                const index = indices[i];
                if (index < maxThreshold)
                    continue;
                let character = characterTable[index];
                if (character === undefined) {
                    if (indexTable.unrecognized) {
                        character = characterTable[indexTable.unrecognized];
                    }
                    else {
                        throw new Error(`unrecognized index "${index}"`);
                    }
                }
                else if (character !== null) {
                    result.push(character.toString());
                }
            }
            return result;
        }
        toString(indices, maxThreshold) {
            return this.toCharacters(indices, maxThreshold).join('');
        }
        addInputOutput() {
            this.addSpecial('stop-input');
            this.addSpecial('start-output');
        }
        addUnrecognized() {
            this.addSpecial('unrecognized');
        }
        static fromAllPrintable(maxThreshold, values = ['\n']) {
            for (let i = 32; i <= 126; i++) {
                values.push(String.fromCharCode(i));
            }
            return new DataFormatter(values, maxThreshold);
        }
        static fromAllPrintableInputOutput(maxThreshold, values = ['\n']) {
            const dataFormatter = DataFormatter.fromAllPrintable(maxThreshold, values);
            dataFormatter.addInputOutput();
            dataFormatter.addUnrecognized();
            return dataFormatter;
        }
        static fromStringInputOutput(string, maxThreshold) {
            const values = Array.from(new Set(string)).join('');
            const dataFormatter = new DataFormatter(values.split(''), maxThreshold);
            dataFormatter.addInputOutput();
            dataFormatter.addUnrecognized();
            dataFormatter.isSetup = true;
            return dataFormatter;
        }
        static fromArrayInputOutput(data, maxThreshold) {
            const values = [];
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                values.push(validateAndCast(datum.input), validateAndCast(datum.output));
            }
            const flatArray = Array.isArray(values)
                ? values.flat()
                : values;
            const dataFormatter = new DataFormatter(Array.from(new Set(flatArray)), maxThreshold);
            dataFormatter.addInputOutput();
            dataFormatter.addUnrecognized();
            dataFormatter.isSetup = true;
            return dataFormatter;
        }
        static fromString(string, maxThreshold = 0) {
            const values = Array.from(new Set(string)).join('');
            return new DataFormatter(values.split(''), maxThreshold);
        }
        toJSON() {
            return {
                indexTable: this.indexTable,
                characterTable: this.characterTable,
                values: this.values,
                characters: this.characters,
                specialIndexes: this.specialIndexes,
            };
        }
        /** TODO: Type better, The type of json is not "string that is a valid JSON", it is a POJO in the shape of DataFormatter.
         * this method re-hydrates the the data as an instance of DataFormatter.
         */
        static fromJSON(json) {
            const dataFormatter = new DataFormatter();
            dataFormatter.indexTable = json.indexTable;
            dataFormatter.characterTable = json.characterTable;
            dataFormatter.values = json.values;
            dataFormatter.characters = json.characters;
            dataFormatter.specialIndexes = json.specialIndexes;
            return dataFormatter;
        }
        addSpecial(special, character = null) {
            const specialIndex = (this.indexTable[special] = this.characters.length);
            this.characterTable[specialIndex] = character;
            this.specialIndexes.push(this.characters.length);
            this.characters.push(special);
        }
        toFunctionString() {
            return `
var characterTable = ${JSON.stringify(this.characterTable)};
var indexTable = ${JSON.stringify(this.indexTable)};
var characters = ${JSON.stringify(this.characters)};
var dataFormatter = {
  toIndexes: function ${this.toIndexes.toString()},
  toIndexesInputOutput: function ${this.toIndexesInputOutput.toString()},
  toCharacters: function ${this.toCharacters.toString()},
  toIndexesValue: function ${this.toIndexesValue.toString()},
};`;
        }
        formatDataIn(input, output) {
            var _a;
            if (input === undefined)
                return [];
            if (Array.isArray(input) && typeof input[0] === 'number') {
                return input;
            }
            if ((_a = this.indexTable) === null || _a === void 0 ? void 0 : _a.hasOwnProperty('stop-input')) {
                return this.toIndexesInputOutput(input, output);
            }
            return this.toIndexes(input);
        }
        formatDataOut(input, output) {
            return this.toCharacters(output).join('');
        }
        format(data) {
            if (typeof data[0] === 'number' &&
                !Array.isArray(data[0]) &&
                (!data[0].hasOwnProperty('input') || !data[0].hasOwnProperty('output'))) {
                return data;
            }
            const result = [];
            if (typeof data[0] === 'string' ||
                typeof data[0] === 'number' ||
                Array.isArray(data[0])) {
                if (!this.isSetup) {
                    this.setup(data);
                    for (let i = 0; i < data.length; i++) {
                        result.push(this.formatDataIn(validateAndCast(data[i])));
                    }
                }
                else {
                    for (let i = 0, max = data.length; i < max; i++) {
                        result.push(this.formatDataIn(data[i]));
                    }
                }
            }
            else if (data[0].input && data[0].output) {
                if (!this.isSetup) {
                    this.setup(data);
                }
                for (let i = 0, max = data.length; i < max; i++) {
                    result.push(this.formatDataIn(validateAndCast(data[i].input), validateAndCast(data[i].output)));
                }
            }
            else {
                throw new Error('unrecognized data');
            }
            return result;
        }
    }
    function validateAndCast(value) {
        if (typeof value === 'string')
            return value;
        if (typeof value === 'number')
            return value.toString();
        if (typeof value === 'boolean')
            return value.toString();
        if (Array.isArray(value) && typeof value[0] === 'string')
            return value;
        if (typeof value[0] === 'boolean') {
            return value.map((v) => v.toString());
        }
        if (typeof value[0] === 'number') {
            return value.map((v) => v.toString());
        }
        throw new Error('unrecognized value, expected string[], string, number[], number, boolean[], or boolean');
    }

    function copy(product, left) {
        product.rows = left.rows;
        product.columns = left.columns;
        product.weights = left.weights.slice(0);
        product.deltas = left.deltas.slice(0);
    }

    /**
     * add {left} and {right} matrix weights into {into}
     */
    function add(product, left, right) {
        for (let i = 0; i < left.weights.length; i++) {
            product.weights[i] = left.weights[i] + right.weights[i];
            product.deltas[i] = 0;
        }
    }

    /**
     * adds {from} deltas to {left} and {right} deltas
     */
    function addB(product, left, right) {
        for (let i = 0; i < product.deltas.length; i++) {
            left.deltas[i] = product.deltas[i];
            right.deltas[i] = product.deltas[i];
        }
    }

    /**
     * makes matrix weights and deltas all ones
     */
    function allOnes(product) {
        for (let i = 0; i < product.weights.length; i++) {
            product.weights[i] = 1;
            product.deltas[i] = 0;
        }
    }

    function cloneNegative(product, left) {
        product.rows = left.rows;
        product.columns = left.columns;
        product.weights = left.weights.slice(0);
        product.deltas = left.deltas.slice(0);
        for (let i = 0; i < left.weights.length; i++) {
            product.weights[i] = -left.weights[i];
            product.deltas[i] = 0;
        }
    }

    /**
     * multiply {left} and {right} matrix weights to {into}
     */
    function multiply(product, left, right) {
        const leftRows = left.rows;
        const leftColumns = left.columns;
        const rightColumns = right.columns;
        // loop over rows of left
        for (let leftRow = 0; leftRow < leftRows; leftRow++) {
            const leftRowBase = leftColumns * leftRow;
            const rightRowBase = rightColumns * leftRow;
            // loop over cols of right
            for (let rightColumn = 0; rightColumn < rightColumns; rightColumn++) {
                // dot product loop
                let dot = 0;
                // loop over columns of left
                for (let leftColumn = 0; leftColumn < leftColumns; leftColumn++) {
                    const rightColumnBase = rightColumns * leftColumn;
                    const leftIndex = leftRowBase + leftColumn;
                    const rightIndex = rightColumnBase + rightColumn;
                    dot += left.weights[leftIndex] * right.weights[rightIndex];
                    left.deltas[leftIndex] = 0;
                    right.deltas[rightIndex] = 0;
                }
                product.weights[rightRowBase + rightColumn] = dot;
            }
        }
    }

    /**
     * multiplies {from} deltas to {left} and {right}
     */
    function multiplyB(product, left, right) {
        const leftRows = left.rows;
        const leftColumns = left.columns;
        const rightColumns = right.columns;
        // loop over rows of left
        for (let leftRowRoot = 0; leftRowRoot < leftRows; leftRowRoot++) {
            const leftRowBase = leftColumns * leftRowRoot;
            const rightRowBase = rightColumns * leftRowRoot;
            // loop over cols of right
            for (let rightColumn = 0; rightColumn < rightColumns; rightColumn++) {
                // loop over columns of left
                for (let leftColumn = 0; leftColumn < leftColumns; leftColumn++) {
                    const rightColumnBase = rightColumns * leftColumn;
                    const leftRow = leftRowBase + leftColumn;
                    const rightRow = rightColumnBase + rightColumn;
                    const backPropagateValue = product.deltas[rightRowBase + rightColumn];
                    left.deltas[leftRow] += right.weights[rightRow] * backPropagateValue;
                    right.deltas[rightRow] += left.weights[leftRow] * backPropagateValue;
                }
            }
        }
    }

    function multiplyElement(product, left, right) {
        const { weights } = left;
        for (let i = 0; i < weights.length; i++) {
            product.weights[i] = left.weights[i] * right.weights[i];
            product.deltas[i] = 0;
        }
    }

    /**
     * multiplies {left} and {right} weight by {from} deltas into {left} and {right} deltas
     */
    function multiplyElementB(product, left, right) {
        for (let i = 0; i < left.weights.length; i++) {
            left.deltas[i] = right.weights[i] * product.deltas[i];
            right.deltas[i] = left.weights[i] * product.deltas[i];
        }
    }

    /**
     *
     * relu {m} weights to {into} weights
     */
    function relu(product, left) {
        for (let i = 0; i < left.weights.length; i++) {
            product.weights[i] = Math.max(0, left.weights[i]); // relu
            product.deltas[i] = 0;
        }
    }

    /**
     * adds {from} deltas to {m} deltas when {m} weights are above other a threshold of 0
     */
    function reluB(product, left) {
        for (let i = 0; i < product.deltas.length; i++) {
            left.deltas[i] = left.weights[i] > 0 ? product.deltas[i] : 0;
        }
    }

    function rowPluck(product, left, rowPluckIndex) {
        const { columns } = left;
        const rowBase = columns * rowPluckIndex;
        for (let column = 0; column < columns; column++) {
            product.weights[column] = left.weights[rowBase + column];
            product.deltas[column] = 0;
        }
    }

    /**
     * adds {from} deltas into {m} deltas
     */
    function rowPluckB(product, left, rowIndex) {
        const { columns } = left;
        const rowBase = columns * rowIndex;
        for (let column = 0; column < columns; column++) {
            left.deltas[rowBase + column] = product.deltas[column];
        }
    }

    function sigmoid(product, left) {
        // sigmoid nonlinearity
        for (let i = 0; i < left.weights.length; i++) {
            product.weights[i] = 1 / (1 + Math.exp(-left.weights[i]));
            product.deltas[i] = 0;
        }
    }
    // function sig(x) {
    //   // helper function for computing sigmoid
    //   return 1 / (1 + Math.exp(-x));
    // }

    function sigmoidB(product, left) {
        for (let i = 0; i < product.deltas.length; i++) {
            const mwi = product.weights[i];
            left.deltas[i] = mwi * (1 - mwi) * product.deltas[i];
        }
    }

    function softmax(matrix) {
        // probability volume
        const result = new Matrix(matrix.rows, matrix.columns);
        let maxVal = -999999;
        for (let i = 0; i < matrix.weights.length; i++) {
            if (matrix.weights[i] > maxVal) {
                maxVal = matrix.weights[i];
            }
        }
        let s = 0;
        for (let i = 0; i < matrix.weights.length; i++) {
            result.weights[i] = Math.exp(matrix.weights[i] - maxVal);
            s += result.weights[i];
        }
        for (let i = 0; i < matrix.weights.length; i++) {
            result.weights[i] /= s;
        }
        // no backward pass here needed
        // since we will use the computed probabilities outside
        // to set gradients directly on m
        return result;
    }

    function tanh(product, left) {
        // tanh nonlinearity
        for (let i = 0; i < left.weights.length; i++) {
            product.weights[i] = Math.tanh(left.weights[i]);
            product.deltas[i] = 0;
        }
    }

    function tanhB(product, left) {
        for (let i = 0; i < product.deltas.length; i++) {
            // grad for z = tanh(x) is (1 - z^2)
            const mwi = product.weights[i];
            left.deltas[i] = (1 - mwi * mwi) * product.deltas[i];
        }
    }

    class Equation {
        constructor() {
            this.states = [];
            this.inputRow = 0;
        }
        add(left, right) {
            if (left.weights.length !== right.weights.length) {
                throw new Error('misaligned matrices');
            }
            const product = new Matrix(left.rows, left.columns);
            this.states.push({
                name: 'add',
                product,
                left,
                right,
                forwardFn: add,
                backpropagationFn: addB,
            });
            return product;
        }
        allOnes(rows, columns) {
            const product = new Matrix(rows, columns);
            this.states.push({
                name: 'allOnes',
                product,
                left: product,
                forwardFn: allOnes,
                backpropagationFn: () => { },
            });
            return product;
        }
        cloneNegative(matrix) {
            const product = new Matrix(matrix.rows, matrix.columns);
            this.states.push({
                name: 'cloneNegative',
                product,
                left: matrix,
                forwardFn: cloneNegative,
                backpropagationFn: () => { },
            });
            return product;
        }
        /**
         * connects two matrices together by subtract
         */
        subtract(left, right) {
            if (left.weights.length !== right.weights.length) {
                throw new Error('misaligned matrices');
            }
            return this.add(this.add(this.allOnes(left.rows, left.columns), this.cloneNegative(left)), right);
        }
        /**
         * connects two matrices together by multiply
         */
        multiply(left, right) {
            if (left.columns !== right.rows) {
                throw new Error('misaligned matrices');
            }
            const product = new Matrix(left.rows, right.columns);
            this.states.push({
                name: 'multiply',
                product,
                left,
                right,
                forwardFn: multiply,
                backpropagationFn: multiplyB,
            });
            return product;
        }
        /**
         * connects two matrices together by multiplyElement
         */
        multiplyElement(left, right) {
            if (left.weights.length !== right.weights.length) {
                throw new Error('misaligned matrices');
            }
            const product = new Matrix(left.rows, left.columns);
            this.states.push({
                name: 'multiplyElement',
                product,
                left,
                right,
                forwardFn: multiplyElement,
                backpropagationFn: multiplyElementB,
            });
            return product;
        }
        /**
         * connects a matrix to relu
         */
        relu(matrix) {
            const product = new Matrix(matrix.rows, matrix.columns);
            this.states.push({
                name: 'relu',
                product,
                left: matrix,
                forwardFn: relu,
                backpropagationFn: reluB,
            });
            return product;
        }
        /**
         * input a matrix
         */
        input(input) {
            this.states.push({
                name: 'input',
                product: input,
                forwardFn: (product) => {
                    if (!this.inputValue)
                        return;
                    if (this.inputValue.length !== product.weights.length) {
                        throw new Error('this.inputValue is of wrong dimensions');
                    }
                    product.weights = input.weights = this.inputValue;
                },
                backpropagationFn: () => { },
            });
            return input;
        }
        /**
         * connects a matrix via a row
         */
        inputMatrixToRow(matrix) {
            // eslint-disable-next-line @typescript-eslint/no-this-alias
            const self = this;
            const product = new Matrix(matrix.columns, 1);
            this.states.push({
                name: 'inputMatrixToRow',
                product,
                left: matrix,
                get right() {
                    return self.inputRow;
                },
                forwardFn: rowPluck,
                backpropagationFn: rowPluckB,
            });
            return product;
        }
        /**
         * connects a matrix to sigmoid
         */
        sigmoid(matrix) {
            const product = new Matrix(matrix.rows, matrix.columns);
            this.states.push({
                name: 'sigmoid',
                product,
                left: matrix,
                forwardFn: sigmoid,
                backpropagationFn: sigmoidB,
            });
            return product;
        }
        /**
         * connects a matrix to tanh
         */
        tanh(matrix) {
            const product = new Matrix(matrix.rows, matrix.columns);
            this.states.push({
                name: 'tanh',
                product,
                left: matrix,
                forwardFn: tanh,
                backpropagationFn: tanhB,
            });
            return product;
        }
        /**
         *
         * Observe a matrix for debugging
         */
        observe(matrix) {
            this.states.push({
                name: 'observe',
                product: new Matrix(),
                forwardFn: () => { },
                backpropagationFn: () => { },
            });
            return matrix;
        }
        /**
         * Run index through equations via forward propagation
         */
        runIndex(rowIndex = 0) {
            this.inputRow = rowIndex;
            let state = this.states[0];
            for (let i = 0, max = this.states.length; i < max; i++) {
                state = this.states[i];
                if (!state.hasOwnProperty('forwardFn'))
                    continue;
                state.forwardFn(state.product, state.left, state.right);
            }
            return state.product;
        }
        /**
         * Run value through equations via forward propagation
         */
        runInput(inputValue) {
            this.inputValue = inputValue;
            let state = this.states[0];
            for (let i = 0, max = this.states.length; i < max; i++) {
                state = this.states[i];
                if (!state.hasOwnProperty('forwardFn'))
                    continue;
                state.forwardFn(state.product, state.left, state.right);
            }
            return state.product;
        }
        /**
         * Run value through equations via back propagation
         */
        backpropagate() {
            let i = this.states.length;
            let state = this.states[0];
            while (i-- > 0) {
                state = this.states[i];
                if (!state.hasOwnProperty('backpropagationFn'))
                    continue;
                state.backpropagationFn(state.product, state.left, state.right);
            }
            return state.product;
        }
        /**
         * Run index through equations via back propagation
         */
        backpropagateIndex(rowIndex = 0) {
            this.inputRow = rowIndex;
            let i = this.states.length;
            let state = this.states[0];
            while (i-- > 0) {
                state = this.states[i];
                if (!state.hasOwnProperty('backpropagationFn'))
                    continue;
                state.backpropagationFn(state.product, state.left, state.right);
            }
            return state.product;
        }
        /**
         * Predict a target value from equation
         */
        predictTarget(input, target) {
            let errorSum = 0;
            const output = this.runInput(input);
            for (let i = 0; i < output.weights.length; i++) {
                const error = output.weights[i] - target[i];
                // set gradients into log probabilities
                errorSum += Math.abs(error);
                // write gradients into log probabilities
                output.deltas[i] = error;
            }
            return errorSum;
        }
        /**
         * Predict a target index from equation
         */
        predictTargetIndex(input, target) {
            const output = this.runIndex(input);
            // set gradients into log probabilities
            const logProbabilities = output; // interpret output as log probabilities
            const probabilities = softmax(output); // compute the softmax probabilities
            // write gradients into log probabilities
            logProbabilities.deltas = probabilities.weights.slice(0);
            logProbabilities.deltas[target] -= 1;
            // accumulate base 2 log prob and do smoothing
            return -Math.log2(probabilities.weights[target]);
        }
    }

    function maxI(matrix) {
        // argmax of array w
        const { weights } = matrix;
        let maxv = weights[0];
        let maxix = 0;
        for (let i = 1; i < weights.length; i++) {
            const v = weights[i];
            if (v < maxv)
                continue;
            maxix = i;
            maxv = v;
        }
        return maxix;
    }

    function sampleI(matrix) {
        // sample argmax from w, assuming w are
        // probabilities that sum to one
        const r = randomFloat(0, 1);
        const w = matrix.weights;
        let x = 0;
        let i = 0;
        while (true) {
            x += w[i];
            if (x > r) {
                return i;
            }
            i++;
        }
    }

    const trainDefaults$1 = {
        iterations: 20000,
        errorThresh: 0.005,
        log: false,
        logPeriod: 10,
        learningRate: 0.01,
        callbackPeriod: 10,
        timeout: Infinity,
    };
    const defaults$1 = () => {
        return {
            inputSize: 20,
            inputRange: 20,
            hiddenLayers: [20, 20],
            outputSize: 20,
            decayRate: 0.999,
            smoothEps: 1e-8,
            regc: 0.000001,
            clipval: 5,
            maxPredictionLength: 100,
            dataFormatter: new DataFormatter(),
        };
    };
    class RNN {
        constructor(options = {}) {
            this.options = { ...defaults$1() };
            this.trainOpts = { ...trainDefaults$1 };
            this.stepCache = {};
            this.runs = 0;
            this.ratioClipped = 0;
            this.model = Object.seal({
                isInitialized: false,
                input: new Matrix(0, 0),
                hiddenLayers: [],
                output: new Matrix(0, 0),
                equations: [],
                allMatrices: [],
                equationConnections: [],
                outputConnector: new RandomMatrix(0, 0, 0.08),
            });
            this.initialLayerInputs = [];
            this.options = { ...this.options, ...options };
            this.updateTrainingOptions({
                ...trainDefaults$1,
            });
            if (options.json) {
                this.fromJSON(options.json);
            }
        }
        initialize() {
            const { dataFormatter } = this.options;
            if (dataFormatter === null || dataFormatter === void 0 ? void 0 : dataFormatter.characters.length) {
                this.options.inputSize = this.options.inputRange = this.options.outputSize =
                    dataFormatter.characters.length;
            }
            this.model = this.mapModel();
        }
        createHiddenLayers() {
            const { hiddenLayers, inputSize } = this.options;
            const hiddenLayersModel = [];
            // 0 is end, so add 1 to offset
            hiddenLayersModel.push(this.getHiddenLayer(hiddenLayers[0], inputSize));
            let prevSize = hiddenLayers[0];
            for (let d = 1; d < hiddenLayers.length; d++) {
                // loop over depths
                const hiddenSize = hiddenLayers[d];
                hiddenLayersModel.push(this.getHiddenLayer(hiddenSize, prevSize));
                prevSize = hiddenSize;
            }
            return hiddenLayersModel;
        }
        getHiddenLayer(hiddenSize, prevSize) {
            return {
                // wxh
                weight: new RandomMatrix(hiddenSize, prevSize, 0.08),
                // whh
                transition: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
                // bhh
                bias: new Matrix(hiddenSize, 1),
            };
        }
        getEquation(equation, inputMatrix, previousResult, hiddenLayer) {
            if (!hiddenLayer.weight || !hiddenLayer.transition || !hiddenLayer.bias) {
                throw new Error('hiddenLayer does not have expected properties');
            }
            const relu = equation.relu.bind(equation);
            const add = equation.add.bind(equation);
            const multiply = equation.multiply.bind(equation);
            return relu(add(add(multiply(hiddenLayer.weight, inputMatrix), multiply(hiddenLayer.transition, previousResult)), hiddenLayer.bias));
        }
        createInputMatrix() {
            const { inputRange, inputSize } = this.options;
            if (inputRange < 1)
                throw new Error('this.options.inputRange not an expected number');
            if (inputSize < 1)
                throw new Error('this.options.inputSize not an expected number');
            // 0 is end, so add 1 to offset
            return new RandomMatrix(inputRange + 1, inputSize, 0.08);
        }
        createOutputMatrices() {
            const { outputSize, hiddenLayers } = this.options;
            const lastHiddenSize = last(hiddenLayers);
            // 0 is end, so add 1 to offset
            return {
                // whd
                outputConnector: new RandomMatrix(outputSize + 1, lastHiddenSize, 0.08),
                // 0 is end, so add 1 to offset
                // bd
                output: new Matrix(outputSize + 1, 1),
            };
        }
        bindEquation() {
            const { model } = this;
            const { hiddenLayers } = this.options;
            const equation = new Equation();
            const outputs = [];
            const equationConnection = model.equationConnections.length > 0
                ? last(model.equationConnections)
                : this.initialLayerInputs;
            // 0 index
            let output = this.getEquation(equation, equation.inputMatrixToRow(model.input), equationConnection[0], model.hiddenLayers[0]);
            outputs.push(output);
            // 1+ indices
            for (let i = 1, max = hiddenLayers.length; i < max; i++) {
                if (!equationConnection[i]) {
                    throw new Error(`Cannot find equation at index ${i}`);
                }
                output = this.getEquation(equation, output, equationConnection[i], model.hiddenLayers[i]);
                outputs.push(output);
            }
            model.equationConnections.push(outputs);
            equation.add(equation.multiply(model.outputConnector, output), model.output);
            model.equations.push(equation);
        }
        mapModel() {
            const allMatrices = [];
            this.initialLayerInputs = this.options.hiddenLayers.map((size) => new Matrix(size, 1));
            const input = this.createInputMatrix();
            allMatrices.push(input);
            const hiddenLayers = this.createHiddenLayers();
            if (!hiddenLayers.length)
                throw new Error('net.hiddenLayers not set');
            for (let i = 0, max = hiddenLayers.length; i < max; i++) {
                const hiddenMatrix = hiddenLayers[i];
                for (const property in hiddenMatrix) {
                    if (!hiddenMatrix.hasOwnProperty(property))
                        continue;
                    allMatrices.push(hiddenMatrix[property]);
                }
            }
            const { output, outputConnector } = this.createOutputMatrices();
            allMatrices.push(outputConnector);
            allMatrices.push(output);
            return Object.seal({
                isInitialized: true,
                input,
                hiddenLayers,
                output,
                equations: [],
                allMatrices,
                equationConnections: [],
                outputConnector,
            });
        }
        trainInput(input) {
            this.runs++;
            const { model } = this;
            const max = input.length;
            let log2ppl = 0;
            let equation;
            while (model.equations.length <= input.length + 1) {
                // last is zero
                this.bindEquation();
            }
            for (let inputIndex = -1, inputMax = input.length; inputIndex < inputMax; inputIndex++) {
                // start and end tokens are zeros
                const equationIndex = inputIndex + 1;
                equation = model.equations[equationIndex];
                const source = inputIndex === -1 ? 0 : input[inputIndex] + 1; // first step: start with START token
                const target = inputIndex === max - 1 ? 0 : input[inputIndex + 1] + 1; // last step: end with END token
                log2ppl += equation.predictTargetIndex(source, target);
            }
            return Math.pow(2, log2ppl / (max - 1)) / 100;
        }
        backpropagate(input) {
            let i = input.length;
            const { model } = this;
            const { equations } = model;
            while (i > 0) {
                equations[i].backpropagateIndex(input[i - 1] + 1);
                i--;
            }
            equations[0].backpropagateIndex(0);
        }
        adjustWeights() {
            const { regc, clipval, decayRate, smoothEps } = this.options;
            const { trainOpts, model, stepCache } = this;
            const { learningRate } = trainOpts;
            const { allMatrices } = model;
            let numClipped = 0;
            let numTot = 0;
            for (let matrixIndex = 0; matrixIndex < allMatrices.length; matrixIndex++) {
                const matrix = allMatrices[matrixIndex];
                const { weights, deltas } = matrix;
                if (!(matrixIndex in stepCache)) {
                    stepCache[matrixIndex] = zeros$1(matrix.rows * matrix.columns);
                }
                const cache = stepCache[matrixIndex];
                for (let i = 0; i < weights.length; i++) {
                    let r = deltas[i];
                    const w = weights[i];
                    // rmsprop adaptive learning rate
                    cache[i] = cache[i] * decayRate + (1 - decayRate) * r * r;
                    // gradient clip
                    if (r > clipval) {
                        r = clipval;
                        numClipped++;
                    }
                    else if (r < -clipval) {
                        r = -clipval;
                        numClipped++;
                    }
                    numTot++;
                    // update (and regularize)
                    weights[i] =
                        w + (-learningRate * r) / Math.sqrt(cache[i] + smoothEps) - regc * w;
                }
            }
            this.ratioClipped = numClipped / numTot;
        }
        get isRunnable() {
            if (this.model && this.model.equations.length === 0) {
                console.error(`No equations bound, did you run train()?`);
                return false;
            }
            return true;
        }
        checkRunnable() {
            if (!this.isRunnable) {
                throw new Error('Network not runnable');
            }
        }
        run(rawInput = [], isSampleI = false, temperature = 1) {
            const maxPredictionLength = this.options.maxPredictionLength +
                (rawInput !== null ? rawInput.length : 0) +
                (this.options.dataFormatter
                    ? this.options.dataFormatter.specialIndexes.length
                    : 0);
            this.checkRunnable();
            const input = this.options.dataFormatter && rawInput.length > 0
                ? this.options.dataFormatter.formatDataIn(rawInput)
                : rawInput;
            const { model } = this;
            const output = [];
            let i = 0;
            while (true) {
                const previousIndex = i === 0 ? 0 : i < input.length ? input[i - 1] + 1 : output[i - 1];
                while (model.equations.length <= i) {
                    this.bindEquation();
                }
                const equation = model.equations[i];
                // sample predicted letter
                const outputMatrix = equation.runIndex(previousIndex);
                const logProbabilities = new Matrix(model.output.rows, model.output.columns);
                copy(logProbabilities, outputMatrix);
                if (temperature !== 1 && isSampleI) {
                    /**
                     * scale log probabilities by temperature and re-normalize
                     * if temperature is high, logProbabilities will go towards zero
                     * and the softmax outputs will be more diffuse. if temperature is
                     * very low, the softmax outputs will be more peaky
                     */
                    for (let j = 0, max = logProbabilities.weights.length; j < max; j++) {
                        logProbabilities.weights[j] /= temperature;
                    }
                }
                const probs = softmax(logProbabilities);
                const nextIndex = isSampleI ? sampleI(probs) : maxI(probs);
                i++;
                if (nextIndex === 0) {
                    // END token predicted, break out
                    break;
                }
                if (i >= maxPredictionLength) {
                    // something is wrong
                    break;
                }
                output.push(nextIndex);
            }
            /**
             * we slice the input length here, not because output contains it, but it will be erroneous as we are sending the
             * network what is contained in input, so the data is essentially guessed by the network what could be next, till it
             * locks in on a value.
             * Kind of like this, values are from input:
             * 0 -> 4 (or in English: "beginning on input" -> "I have no idea? I'll guess what they want next!")
             * 2 -> 2 (oh how interesting, I've narrowed down values...)
             * 1 -> 9 (oh how interesting, I've now know what the values are...)
             * then the output looks like: [4, 2, 9,...]
             * so we then remove the erroneous data to get our true output
             */
            return this.options.dataFormatter.formatDataOut(input, output.slice(input.length).map((value) => value - 1));
        }
        /**
         *
         * Verifies network sizes are initialized
         * If they are not it will initialize them
         */
        verifyIsInitialized() {
            if (!this.model.isInitialized) {
                this.initialize();
            }
        }
        /**
         *
         * @param options
         *    Supports all `trainDefaults` properties
         *    also supports:
         *       learningRate: (number),
         *       momentum: (number),
         *       activation: 'sigmoid', 'relu', 'leaky-relu', 'tanh'
         */
        updateTrainingOptions(options) {
            var _a;
            this.trainOpts = { ...trainDefaults$1, ...options };
            this.validateTrainingOptions(this.trainOpts);
            this.setLogMethod((_a = options.log) !== null && _a !== void 0 ? _a : this.trainOpts.log);
            // TODO: Remove this?
            // this.activation = options.activation || this.activation;
        }
        validateTrainingOptions(options) {
            const validations = {
                iterations: () => {
                    const val = options.iterations;
                    return typeof val === 'number' && val > 0;
                },
                errorThresh: () => {
                    const val = options.errorThresh;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                log: () => {
                    const val = options.log;
                    return typeof val === 'function' || typeof val === 'boolean';
                },
                logPeriod: () => {
                    const val = options.logPeriod;
                    return typeof val === 'number' && val > 0;
                },
                learningRate: () => {
                    const val = options.learningRate;
                    return typeof val === 'number' && val > 0 && val < 1;
                },
                callback: () => {
                    const val = options.callback;
                    return typeof val === 'function' || val === undefined;
                },
                callbackPeriod: () => {
                    const val = options.callbackPeriod;
                    return typeof val === 'number' && val > 0;
                },
                timeout: () => {
                    const val = options.timeout;
                    return typeof val === 'number' && val > 0;
                },
            };
            for (const p in validations) {
                const v = options;
                if (!validations[p]()) {
                    throw new Error(`[${p}, ${v[p]}] is out of normal training range, your network will probably not train.`);
                }
            }
        }
        setLogMethod(log) {
            if (typeof log === 'function') {
                this.trainOpts.log = log;
            }
            else if (log) {
                this.trainOpts.log = console.log;
            }
            else {
                this.trainOpts.log = false;
            }
        }
        prepTraining(data, options) {
            var _a;
            this.updateTrainingOptions(options);
            const preparedData = this.options.dataFormatter.format(data);
            const endTime = Date.now() + ((_a = this.trainOpts.timeout) !== null && _a !== void 0 ? _a : 0);
            const status = {
                error: 1,
                iterations: 0,
            };
            this.verifyIsInitialized();
            return {
                preparedData,
                status,
                endTime,
            };
        }
        train(data, trainOpts = {}) {
            var _a;
            this.trainOpts = trainOpts = {
                ...trainDefaults$1,
                ...trainOpts,
            };
            const { iterations, errorThresh, logPeriod, callback, callbackPeriod, } = this.trainOpts;
            const log = trainOpts.log === true ? console.log : trainOpts.log;
            let error = Infinity;
            let i;
            let inputs;
            if ((_a = this.options) === null || _a === void 0 ? void 0 : _a.dataFormatter) {
                inputs = this.options.dataFormatter.format(data);
            }
            else if (Array.isArray(data) &&
                Array.isArray(data[0]) &&
                typeof data[0][0] === 'number') {
                inputs = data;
            }
            else {
                throw new Error('training not in expected format of number[][]');
            }
            this.verifyIsInitialized();
            for (i = 0; i < iterations && error > errorThresh; i++) {
                let sum = 0;
                for (let j = 0; j < inputs.length; j++) {
                    const err = this.trainPattern(inputs[j], true);
                    sum += err;
                }
                error = sum / data.length;
                if (isNaN(error)) {
                    throw new Error('Network error rate is unexpected NaN, check network configurations and try again. Most probably input format is not correct or training data is not enough. ');
                }
                if (log && i % logPeriod === 0) {
                    log(`iterations: ${i}, training error: ${error}`);
                }
                if (callback && i % callbackPeriod === 0) {
                    callback({ error, iterations: i });
                }
            }
            return {
                error,
                iterations: i,
            };
        }
        addFormat() {
            throw new Error('not yet implemented');
        }
        toJSON() {
            if (!this.model.isInitialized) {
                this.initialize();
            }
            const { model, options } = this;
            return {
                type: this.constructor.name,
                options: { ...options, dataFormatter: options.dataFormatter.toJSON() },
                trainOpts: {
                    ...this.trainOpts,
                    timeout: this.trainOpts.timeout === Infinity
                        ? 'Infinity'
                        : this.trainOpts.timeout,
                },
                input: model.input.toJSON(),
                hiddenLayers: model.hiddenLayers.map((hiddenLayer) => {
                    const layers = {};
                    for (const p in hiddenLayer) {
                        if (!hiddenLayer.hasOwnProperty(p))
                            continue;
                        layers[p] = hiddenLayer[p].toJSON();
                    }
                    return layers;
                }),
                outputConnector: this.model.outputConnector.toJSON(),
                output: this.model.output.toJSON(),
            };
        }
        fromJSON(json) {
            const { options } = json;
            const allMatrices = [];
            const input = Matrix.fromJSON(json.input);
            allMatrices.push(input);
            const hiddenLayers = [];
            json.hiddenLayers.forEach((hiddenLayer) => {
                const layers = {};
                for (const p in hiddenLayer) {
                    layers[p] = Matrix.fromJSON(hiddenLayer[p]);
                    allMatrices.push(layers[p]);
                }
                hiddenLayers.push(layers);
            });
            const outputConnector = Matrix.fromJSON(json.outputConnector);
            allMatrices.push(outputConnector);
            const output = Matrix.fromJSON(json.output);
            allMatrices.push(output);
            if (options.dataFormatter) {
                this.options = {
                    ...defaults$1(),
                    ...options,
                    dataFormatter: DataFormatter.fromJSON(options.dataFormatter),
                };
            }
            else {
                this.options = {
                    ...defaults$1(),
                    ...options,
                    dataFormatter: new DataFormatter(),
                };
            }
            this.model = Object.seal({
                isInitialized: true,
                input,
                hiddenLayers,
                output,
                allMatrices,
                outputConnector,
                equations: [],
                equationConnections: [],
            });
            this.initialLayerInputs = this.options.hiddenLayers.map((size) => new Matrix(size, 1));
            this.bindEquation();
            return this;
        }
        toFunction(cb) {
            const { model } = this;
            const { equations } = this.model;
            const equation = equations[1];
            const { states } = equation;
            const jsonString = JSON.stringify(this.toJSON());
            function previousConnectionIndex(m) {
                const connection = model.equationConnections[0];
                const { states } = equations[0];
                for (let i = 0, max = states.length; i < max; i++) {
                    if (states[i].product === m) {
                        return i;
                    }
                }
                return connection.indexOf(m);
            }
            function matrixOrigin(m, stateIndex) {
                for (let i = 0, max = states.length; i < max; i++) {
                    const state = states[i];
                    if (i === stateIndex) {
                        const j = previousConnectionIndex(m);
                        if (j > -1 && (m === state.left || m === state.right)) {
                            return `typeof prevStates[${j}] === 'object' ? prevStates[${j}].product : new Matrix(${m.rows}, ${m.columns})`;
                        }
                        return `new Matrix(${m.rows}, ${m.columns})`;
                    }
                    if (m === state.product)
                        return `states[${i}].product`;
                    if (m === state.right)
                        return `states[${i}].right`;
                    if (m === state.left)
                        return `states[${i}].left`;
                }
                return '';
            }
            function matrixToString(m, stateIndex) {
                if (!m || !m.rows || !m.columns)
                    return 'null';
                if (m === model.input)
                    return `json.input`;
                if (m === model.outputConnector)
                    return `json.outputConnector`;
                if (m === model.output)
                    return `json.output`;
                for (let i = 0, max = model.hiddenLayers.length; i < max; i++) {
                    const hiddenLayer = model.hiddenLayers[i];
                    for (const p in hiddenLayer) {
                        if (!hiddenLayer.hasOwnProperty(p))
                            continue;
                        if (hiddenLayer[p] !== m)
                            continue;
                        return `json.hiddenLayers[${i}].${p}`;
                    }
                }
                return matrixOrigin(m, stateIndex);
            }
            function toInner(fnString) {
                // crude, but should be sufficient for now
                // function() { body }
                const fnParts = fnString.toString().split('{');
                fnParts.shift();
                // body }
                const fnBodyString = fnParts.join('{');
                const fnBodyParts = fnBodyString.split('}');
                fnBodyParts.pop();
                // body
                return fnBodyParts
                    .join('}')
                    .split('\n')
                    .join('\n        ')
                    .replace('product.deltas[i] = 0;', '')
                    .replace('product.deltas[column] = 0;', '')
                    .replace('left.deltas[leftIndex] = 0;', '')
                    .replace('right.deltas[rightIndex] = 0;', '')
                    .replace('product.deltas = left.deltas.slice(0);', '');
            }
            function fileName(fnName) {
                return `src/recurrent/matrix/${fnName.replace(/[A-Z]/g, function (value) {
                return `-${value.toLowerCase()}`;
            })}.js`;
            }
            const statesRaw = [];
            const usedFunctionNames = {};
            const innerFunctionsSwitch = [];
            for (let i = 0, max = states.length; i < max; i++) {
                const state = states[i];
                statesRaw.push(`states[${i}] = {
      name: '${state.forwardFn.name}',
      left: ${state.left ? matrixToString(state.left, i) : 'undefined'},
      right: ${state.right ? matrixToString(state.right, i) : 'undefined'},
      product: ${matrixToString(state.product, i)}
    }`);
                const fnName = state.forwardFn.name;
                if (!usedFunctionNames[fnName]) {
                    usedFunctionNames[fnName] = true;
                    innerFunctionsSwitch.push(`        case '${fnName}': //compiled from ${fileName(fnName)}
          ${toInner(state.forwardFn.toString())}
          break;`);
                }
            }
            const src = `
  if (typeof rawInput === 'undefined') rawInput = [];
  if (typeof isSampleI === 'undefined') isSampleI = false;
  if (typeof temperature === 'undefined') temperature = 1;
  var json = ${jsonString};
  ${this.options.dataFormatter
            ? `${this.options.dataFormatter.toFunctionString()};
  Object.assign(dataFormatter, json.options.dataFormatter);`
            : ''}
  ${this.options.dataFormatter &&
            typeof this.options.dataFormatter.formatDataIn === 'function'
            ? `const formatDataIn = function (input, output) { ${toInner(this.options.dataFormatter.formatDataIn.toString())} }.bind(dataFormatter);`
            : ''}
  ${this.options.dataFormatter !== null &&
            typeof this.options.dataFormatter.formatDataOut === 'function'
            ? `const formatDataOut = function formatDataOut(input, output) { ${toInner(this.options.dataFormatter.formatDataOut.toString())} }.bind(dataFormatter);`
            : ''}
  var maxPredictionLength =
    ${this.options.maxPredictionLength} +
    rawInput.length +
    ${this.options.dataFormatter
            ? this.options.dataFormatter.specialIndexes.length
            : 0};
  var input = ${this.options.dataFormatter &&
            typeof this.options.dataFormatter.formatDataIn === 'function'
            ? 'formatDataIn(rawInput)'
            : 'rawInput'};
  var _i = 0;
  var output = [];
  var states = [];
  var prevStates;
  while (true) {
    var previousIndex = (_i === 0
        ? 0
        : _i < input.length
          ? input[_i - 1] + 1
          : output[_i - 1])
          ;
    var rowPluckIndex = previousIndex;
    prevStates = states;
    states = [];
    ${statesRaw.join(';\n    ')};
    for (var stateIndex = 0, stateMax = ${statesRaw.length}; stateIndex < stateMax; stateIndex++) {
      var state = states[stateIndex];
      var product = state.product;
      var left = state.left;
      var right = state.right;
      switch (state.name) {
${innerFunctionsSwitch.join('\n')}
      }
    }

    var logProbabilities = state.product;
    if (temperature !== 1 && isSampleI) {
      for (var q = 0, nq = logProbabilities.weights.length; q < nq; q++) {
        logProbabilities.weights[q] /= temperature;
      }
    }

    var probs = softmax(logProbabilities);
    var nextIndex = isSampleI ? sampleI(probs) : maxI(probs);

    _i++;
    if (nextIndex === 0) {
      break;
    }
    if (_i >= maxPredictionLength) {
      break;
    }

    output.push(nextIndex);
  }
  ${this.options.dataFormatter &&
            typeof this.options.dataFormatter.formatDataOut === 'function'
            ? 'return formatDataOut(input, output.slice(input.length).map(function(value) { return value - 1; }))'
            : 'return output.slice(input.length).map(function(value) { return value - 1; })'};
  function Matrix(rows, columns) {
    this.rows = rows;
    this.columns = columns;
    this.weights = zeros(rows * columns);
  }
  ${zeros$1.toString()}
  ${softmax.toString().replace('_1.Matrix', 'Matrix')}
  ${randomFloat.toString()}
  ${sampleI.toString()}
  ${maxI.toString()}`;
            // eslint-disable-next-line
            return new Function('rawInput', 'isSampleI', 'temperature', cb ? cb(src) : src);
        }
        trainPattern(input, logErrorRate) {
            const error = this.trainInput(input);
            this.backpropagate(input);
            this.adjustWeights();
            if (logErrorRate) {
                return error;
            }
            return 0;
        }
    }
    function last(values) {
        return values[values.length - 1];
    }

    class GRU extends RNN {
        getHiddenLayer(hiddenSize, prevSize) {
            return getGRUHiddenLayer(hiddenSize, prevSize);
        }
        getEquation(equation, inputMatrix, previousResult, hiddenLayer) {
            return getGRUEquation(equation, inputMatrix, previousResult, hiddenLayer);
        }
    }
    function getGRUHiddenLayer(hiddenSize, prevSize) {
        return {
            // update Gate
            // wzxh
            updateGateInputMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            updateGateHiddenMatrix: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            updateGateBias: new Matrix(hiddenSize, 1),
            // reset Gate
            // wrxh
            resetGateInputMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            resetGateHiddenMatrix: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            resetGateBias: new Matrix(hiddenSize, 1),
            // cell write parameters
            // wcxh
            cellWriteInputMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            cellWriteHiddenMatrix: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            cellWriteBias: new Matrix(hiddenSize, 1),
        };
    }
    function getGRUEquation(equation, inputMatrix, previousResult, hiddenLayer) {
        if (!hiddenLayer.updateGateInputMatrix ||
            !hiddenLayer.updateGateHiddenMatrix ||
            !hiddenLayer.updateGateBias ||
            !hiddenLayer.resetGateInputMatrix ||
            !hiddenLayer.resetGateHiddenMatrix ||
            !hiddenLayer.resetGateBias ||
            !hiddenLayer.cellWriteInputMatrix ||
            !hiddenLayer.cellWriteHiddenMatrix ||
            !hiddenLayer.cellWriteBias) {
            throw new Error('hiddenLayer does not have expected properties');
        }
        const sigmoid = equation.sigmoid.bind(equation);
        const add = equation.add.bind(equation);
        const multiply = equation.multiply.bind(equation);
        const multiplyElement = equation.multiplyElement.bind(equation);
        const tanh = equation.tanh.bind(equation);
        const allOnes = equation.allOnes.bind(equation);
        const cloneNegative = equation.cloneNegative.bind(equation);
        // update gate
        const updateGate = sigmoid(add(add(multiply(hiddenLayer.updateGateInputMatrix, inputMatrix), multiply(hiddenLayer.updateGateHiddenMatrix, previousResult)), hiddenLayer.updateGateBias));
        // reset gate
        const resetGate = sigmoid(add(add(multiply(hiddenLayer.resetGateInputMatrix, inputMatrix), multiply(hiddenLayer.resetGateHiddenMatrix, previousResult)), hiddenLayer.resetGateBias));
        // cell
        const cell = tanh(add(add(multiply(hiddenLayer.cellWriteInputMatrix, inputMatrix), multiply(hiddenLayer.cellWriteHiddenMatrix, multiplyElement(resetGate, previousResult))), hiddenLayer.cellWriteBias));
        // compute hidden state as gated, saturated cell activations
        // negate updateGate
        return add(multiplyElement(add(allOnes(updateGate.rows, updateGate.columns), cloneNegative(updateGate)), cell), multiplyElement(previousResult, updateGate));
    }

    class ArrayLookupTable {
        constructor(data, prop) {
            this.prop = prop;
            this.length = 0;
            this.table = {};
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                const ioValue = datum[prop];
                for (let j = 0; j < ioValue.length; j++) {
                    const value = ioValue[j];
                    for (const p in value) {
                        if (!value.hasOwnProperty(p))
                            continue;
                        if (this.table.hasOwnProperty(p))
                            continue;
                        this.table[p] = this.length++;
                    }
                }
            }
        }
    }

    const defaults = () => {
        return {
            ...defaults$1(),
            inputSize: 1,
            hiddenLayers: [20],
            outputSize: 1,
            inputRange: 0,
        };
    };
    class RNNTimeStep extends RNN {
        constructor(options = {}) {
            super();
            this.inputLookupLength = 0;
            this.inputLookup = null;
            this.outputLookup = null;
            this.outputLookupLength = 0;
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.model = Object.seal({
                isInitialized: false,
                hiddenLayers: [],
                output: new Matrix(0, 0),
                equations: [],
                allMatrices: [],
                equationConnections: [],
                outputConnector: new RandomMatrix(0, 0, 0.08),
            });
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.options = defaults();
            this.options = { ...this.options, ...options };
            this.updateTrainingOptions({
                ...trainDefaults,
                ...options,
            });
            if (options.json) {
                this.fromJSON(options.json);
            }
        }
        createInputMatrix() {
            throw new Error('Input Matrices do not exist on RNNTimeStep');
        }
        createOutputMatrices() {
            const { outputSize } = this.options;
            const lastHiddenSize = last(this.options.hiddenLayers);
            // whd
            const outputConnector = new RandomMatrix(outputSize, lastHiddenSize, 0.08);
            // bd
            const output = new RandomMatrix(outputSize, 1, 0.08);
            return { output, outputConnector };
        }
        bindEquation() {
            const { model, options } = this;
            const { hiddenLayers, inputSize } = options;
            const layers = model.hiddenLayers;
            const equation = new Equation();
            const outputs = [];
            const equationConnection = model.equationConnections.length > 0
                ? model.equationConnections[model.equationConnections.length - 1]
                : this.initialLayerInputs;
            // 0 index
            let output = this.getEquation(equation, equation.input(new Matrix(inputSize, 1)), equationConnection[0], layers[0]);
            outputs.push(output);
            // 1+ indices
            for (let i = 1, max = hiddenLayers.length; i < max; i++) {
                output = this.getEquation(equation, output, equationConnection[i], layers[i]);
                outputs.push(output);
            }
            model.equationConnections.push(outputs);
            equation.add(equation.multiply(model.outputConnector, output), model.output);
            model.equations.push(equation);
        }
        initialize() {
            this.model = this.mapModel();
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        mapModel() {
            const allMatrices = [];
            this.initialLayerInputs = this.options.hiddenLayers.map((size) => new Matrix(size, 1));
            const hiddenLayers = this.createHiddenLayers();
            for (let i = 0, max = hiddenLayers.length; i < max; i++) {
                const hiddenMatrix = hiddenLayers[i];
                for (const property in hiddenMatrix) {
                    if (!hiddenMatrix.hasOwnProperty(property))
                        continue;
                    allMatrices.push(hiddenMatrix[property]);
                }
            }
            const { outputConnector, output } = this.createOutputMatrices();
            allMatrices.push(outputConnector);
            allMatrices.push(output);
            return Object.seal({
                isInitialized: true,
                hiddenLayers,
                output,
                equations: [],
                allMatrices,
                equationConnections: [],
                outputConnector,
            });
        }
        backpropagate() {
            for (let i = this.model.equations.length - 1; i > -1; i--) {
                this.model.equations[i].backpropagate();
            }
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        run(rawInput) {
            const shape = lookup.dataShape(rawInput).join(',');
            switch (shape) {
                case 'array,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.runArray(rawInput);
                case 'array,array,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.runArrayOfArray(rawInput);
                case 'object,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.runObject(rawInput); // Backward compatibility, will be result of `unknown` and need casting.  Better to just use net.runObject() directly
                case 'array,object,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.runArrayOfObject(rawInput);
                default:
                    throw new Error(`Unrecognized data shape ${shape}`);
            }
        }
        forecast(rawInput, count = 1) {
            const shape = lookup.dataShape(rawInput).join(',');
            switch (shape) {
                case 'array,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.forecastArray(rawInput, count);
                case 'array,array,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.forecastArrayOfArray(rawInput, count);
                case 'object,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.runObject(rawInput);
                case 'array,object,number':
                    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
                    // @ts-expect-error
                    return this.forecastArrayOfObject(rawInput, count);
                default:
                    throw new Error(`Unrecognized data shape ${shape}`);
            }
        }
        forecastArray(input, count = 1) {
            this.checkRunnable();
            const { model } = this;
            const { equations } = model;
            const length = input.length + count;
            while (equations.length <= length) {
                this.bindEquation();
            }
            let lastOutput;
            let equationIndex = 0;
            if (this.options.inputSize === 1) {
                for (let i = 0; i < input.length; i++) {
                    lastOutput = equations[equationIndex++].runInput(Float32Array.from([input[i]]));
                }
            }
            else {
                for (let i = 0; i < input.length; i++) {
                    lastOutput = equations[equationIndex++].runInput(Float32Array.from([]));
                }
            }
            if (!lastOutput) {
                throw new Error('lastOutput not set');
            }
            const result = [lastOutput.weights[0]];
            for (let i = 0, max = count - 1; i < max; i++) {
                lastOutput = equations[equationIndex++].runInput(lastOutput.weights);
                result.push(lastOutput.weights[0]);
            }
            this.end();
            return Float32Array.from(result);
        }
        forecastArrayOfArray(input, count = 1) {
            this.checkRunnable();
            const { model } = this;
            const { equations } = model;
            const length = input.length + count;
            while (equations.length <= length) {
                this.bindEquation();
            }
            let lastOutput;
            let equationIndex = 0;
            for (let i = 0; i < input.length; i++) {
                lastOutput = equations[equationIndex++].runInput(input[i]);
            }
            if (!lastOutput) {
                throw new Error('lastOutput not set');
            }
            const result = [Float32Array.from(lastOutput.weights)];
            for (let i = 0, max = count - 1; i < max; i++) {
                lastOutput = equations[equationIndex++].runInput(lastOutput.weights);
                result.push(Float32Array.from(lastOutput.weights.slice(0)));
            }
            this.end();
            return result;
        }
        forecastArrayOfObject(input, count = 1) {
            if (!this.inputLookup) {
                throw new Error('this.inputLookup not set');
            }
            if (!this.outputLookup) {
                throw new Error('this.outputLookup not set');
            }
            const formattedData = input.map((value) => lookup.toArray(this.inputLookup, value, this.inputLookupLength));
            return this.forecastArrayOfArray(formattedData, count).map((value) => lookup.toObject(this.outputLookup, value));
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        train(data, trainOpts = {}) {
            this.trainOpts = trainOpts = {
                ...trainDefaults$1,
                ...trainOpts,
            };
            // Don't destructure here because this.setSize() can reset this.options.
            if (this.options.inputSize === 1 && this.options.outputSize === 1) {
                this.setSize(data);
            }
            this.verifySize();
            const formattedData = this.formatData(data);
            let error = Infinity;
            let i;
            this.verifyIsInitialized();
            const { iterations, errorThresh, logPeriod, callback, callbackPeriod, } = this.trainOpts;
            const log = trainOpts.log === true ? console.log : trainOpts.log;
            for (i = 0; i < iterations && error > errorThresh; i++) {
                let sum = 0;
                for (let j = 0; j < formattedData.length; j++) {
                    const err = this.trainPattern(formattedData[j], true);
                    sum += err;
                }
                error = sum / formattedData.length;
                if (isNaN(error))
                    throw new Error('Network error rate is unexpected NaN, check network configurations and try again. Most probably input format is not correct or training data is not enough. ');
                if (log && i % logPeriod === 0) {
                    log(`iterations: ${i}, training error: ${error}`);
                }
                if (callback && i % callbackPeriod === 0) {
                    callback({ error, iterations: i });
                }
            }
            return {
                error,
                iterations: i,
            };
        }
        trainArrayOfArray(input) {
            if (input.length < 2) {
                throw new Error('input must be an array of 2 or more');
            }
            const { equations } = this.model;
            while (equations.length < input.length) {
                this.bindEquation();
            }
            let errorSum = 0;
            for (let i = 0, max = input.length - 1; i < max; i++) {
                errorSum += equations[i].predictTarget(input[i], input[i + 1]);
            }
            this.end();
            return errorSum / input.length;
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        trainPattern(input, logErrorRate) {
            const error = this.trainArrayOfArray(input);
            this.backpropagate();
            this.adjustWeights();
            if (logErrorRate) {
                return error;
            }
            return 0;
        }
        setSize(data) {
            let size = 0;
            const dataShape = lookup.dataShape(data).join(',');
            switch (dataShape) {
                case 'array,array,number':
                case 'array,object,number':
                case 'array,datum,array,number':
                case 'array,datum,object,number':
                    size = 1;
                    // probably 1
                    break;
                case 'array,array,array,number':
                    size = data[0][0].length;
                    break;
                case 'array,array,object,number':
                    // inputs and outputs should match
                    size = Object.keys(lookup.toTable2D(data)).length;
                    break;
                case 'array,datum,array,array,number':
                    size = data[0].input[0].length;
                    break;
                case 'array,datum,array,object,number':
                    size = Object.keys(lookup.toInputTable2D(data)).length;
                    break;
                default:
                    throw new Error('unknown data shape or configuration');
            }
            this.options = Object.seal({
                ...this.options,
                inputSize: size,
                outputSize: size,
            });
        }
        verifySize() {
            if (this.options.inputSize || this.options.outputSize) {
                if (this.options.inputSize !== this.options.outputSize) {
                    throw new Error('manually set inputSize and outputSize mismatch');
                }
            }
        }
        runArray(input) {
            this.checkRunnable();
            const { equations } = this.model;
            while (equations.length <= input.length) {
                this.bindEquation();
            }
            let lastOutput;
            for (let i = 0; i < input.length; i++) {
                lastOutput = equations[i].runInput(new Float32Array([input[i]]));
            }
            this.end();
            return lastOutput.weights[0];
        }
        runArrayOfArray(input) {
            this.checkRunnable();
            const { model } = this;
            const { equations } = model;
            while (equations.length <= input.length) {
                this.bindEquation();
            }
            let lastOutput;
            for (let i = 0; i < input.length; i++) {
                const outputMatrix = equations[i].runInput(input[i]);
                lastOutput = outputMatrix.weights;
            }
            this.end();
            return lastOutput !== null && lastOutput !== void 0 ? lastOutput : Float32Array.from([]);
        }
        runObject(input) {
            if (!this.inputLookup) {
                throw new Error('this.inputLookup not set');
            }
            if (!this.outputLookup) {
                throw new Error('this.outputLookup not set');
            }
            if (!this.outputLookupLength) {
                throw new Error('this.outputLookupLength not set');
            }
            if (this.inputLookup === this.outputLookup) {
                const inputArray = lookup.toArrayShort(this.inputLookup, input);
                return lookup.toObjectPartial(this.outputLookup, this.forecastArray(inputArray, this.outputLookupLength - inputArray.length), inputArray.length);
            }
            return lookup.toObject(this.outputLookup, this.forecastArray(lookup.toArray(this.inputLookup, input, this.inputLookupLength), this.outputLookupLength));
        }
        runArrayOfObject(input) {
            if (this.inputLookup === null) {
                throw new Error('this.inputLookup not set');
            }
            if (this.outputLookup === null) {
                throw new Error('this.outputLookup not set');
            }
            const formattedInput = input.map((value) => lookup.toArray(this.inputLookup, value, this.inputLookupLength));
            return this.forecastArrayOfArray(formattedInput, 1).map((value) => lookup.toObject(this.outputLookup, value))[0];
        }
        runArrayOfObjectOfArray(input) {
            if (!this.inputLookup) {
                throw new Error('this.inputLookup not set');
            }
            if (!this.outputLookup) {
                throw new Error('this.outputLookup not set');
            }
            return lookup.toObject(this.outputLookup, this.runArrayOfArray(lookup.toArrays(this.inputLookup, input, this.inputLookupLength)));
        }
        end() {
            this.model.equations[this.model.equations.length - 1].runInput(new Float32Array(this.options.outputSize));
        }
        requireInputOutputOfOne() {
            if (this.options.inputSize !== 1) {
                throw new Error('inputSize must be 1 for this data size');
            }
            if (this.options.outputSize !== 1) {
                throw new Error('outputSize must be 1 for this data size');
            }
        }
        // Handles data shape of 'array,number'
        formatArray(data) {
            const result = [];
            this.requireInputOutputOfOne();
            for (let i = 0; i < data.length; i++) {
                result.push(Float32Array.from([data[i]]));
            }
            return [result];
        }
        // Handles data shape of 'array,array,number'
        formatArrayOfArray(data) {
            const result = [];
            const { inputSize, outputSize } = this.options;
            if (inputSize === 1 && outputSize === 1) {
                for (let i = 0; i < data.length; i++) {
                    result.push(arrayToFloat32Arrays(data[i]));
                }
                return result;
            }
            if (inputSize !== data[0].length) {
                throw new Error('inputSize must match data input size');
            }
            if (outputSize !== data[0].length) {
                throw new Error('outputSize must match data output size');
            }
            for (let i = 0; i < data.length; i++) {
                result.push(Float32Array.from(data[i]));
            }
            return [result];
        }
        // Handles data shape of 'array,object,number'
        formatArrayOfObject(data) {
            this.requireInputOutputOfOne();
            if (!this.inputLookup) {
                const lookupTable = new LookupTable(data);
                this.inputLookup = this.outputLookup = lookupTable.table;
                this.inputLookupLength = this.outputLookupLength = lookupTable.length;
            }
            const result = [];
            for (let i = 0; i < data.length; i++) {
                result.push(objectToFloat32Arrays(data[i]));
            }
            return result;
        }
        // Handles data shape of 'array,object,number' when this.options.inputSize > 1
        formatArrayOfObjectMulti(data) {
            if (!this.inputLookup) {
                const lookupTable = new LookupTable(data);
                this.inputLookup = this.outputLookup = lookupTable.table;
                this.inputLookupLength = this.outputLookupLength = lookupTable.length;
            }
            const result = [];
            for (let i = 0; i < data.length; i++) {
                result.push([
                    objectToFloat32Array(data[i], this.inputLookup, this.inputLookupLength),
                ]);
            }
            return result;
        }
        // Handles data shape of 'array,datum,array,number'
        formatArrayOfDatumOfArray(data) {
            const result = [];
            this.requireInputOutputOfOne();
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                result.push(inputOutputArrayToFloat32Arrays(datum.input, datum.output));
            }
            return result;
        }
        // Handles data shape of 'array,datum,object,number'
        formatArrayOfDatumOfObject(data) {
            this.requireInputOutputOfOne();
            if (!this.inputLookup) {
                const inputLookup = new LookupTable(data, 'input');
                this.inputLookup = inputLookup.table;
                this.inputLookupLength = inputLookup.length;
            }
            if (!this.outputLookup) {
                const outputLookup = new LookupTable(data, 'output');
                this.outputLookup = outputLookup.table;
                this.outputLookupLength = outputLookup.length;
            }
            const result = [];
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                result.push(inputOutputObjectToFloat32Arrays(datum.input, datum.output));
            }
            return result;
        }
        // Handles data shape of 'array,array,array,number'
        formatArrayOfArrayOfArray(data) {
            const result = [];
            for (let i = 0; i < data.length; i++) {
                result.push(arraysToFloat32Arrays(data[i]));
            }
            return result;
        }
        // Handles data shape of 'array,array,object,number'
        formatArrayOfArrayOfObject(data) {
            if (!this.inputLookup) {
                const lookupTable = new LookupTable(data);
                this.inputLookup = this.outputLookup = lookupTable.table;
                this.inputLookupLength = this.outputLookupLength = lookupTable.length;
            }
            const result = [];
            for (let i = 0; i < data.length; i++) {
                const array = [];
                for (let j = 0; j < data[i].length; j++) {
                    array.push(objectToFloat32Array(data[i][j], this.inputLookup, this.inputLookupLength));
                }
                result.push(array);
            }
            return result;
        }
        // Handles data shape of 'array,datum,array,array,number'
        formatArrayOfDatumOfArrayOfArray(data) {
            const result = [];
            const { inputSize, outputSize } = this.options;
            if (inputSize !== data[0].input[0].length) {
                throw new Error('inputSize must match data input size');
            }
            if (outputSize !== data[0].output[0].length) {
                throw new Error('outputSize must match data output size');
            }
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                result.push(inputOutputArraysToFloat32Arrays(datum.input, datum.output));
            }
            return result;
        }
        // 'Handles data shape of array,datum,array,object,number'
        formatArrayOfDatumOfArrayOfObject(data) {
            if (!this.inputLookup) {
                const inputLookup = new ArrayLookupTable(data, 'input');
                this.inputLookup = inputLookup.table;
                this.inputLookupLength = inputLookup.length;
            }
            if (!this.outputLookup) {
                const outputLookup = new ArrayLookupTable(data, 'output');
                this.outputLookup = outputLookup.table;
                this.outputLookupLength = outputLookup.length;
            }
            if (!this.outputLookupLength) {
                throw new Error('this.outputLookupLength not set to usable number');
            }
            const result = [];
            for (let i = 0; i < data.length; i++) {
                const datum = data[i];
                result.push(inputOutputObjectsToFloat32Arrays(datum.input, datum.output, this.inputLookup, this.outputLookup, this.inputLookupLength, this.outputLookupLength));
            }
            return result;
        }
        formatData(data) {
            const dataShape = lookup.dataShape(data).join(',');
            switch (dataShape) {
                case 'array,number':
                    return this.formatArray(data);
                case 'array,array,number':
                    return this.formatArrayOfArray(data);
                case 'array,object,number':
                    if (this.options.inputSize === 1) {
                        return this.formatArrayOfObject(data);
                    }
                    else {
                        return this.formatArrayOfObjectMulti(data);
                    }
                case 'array,datum,array,number':
                    return this.formatArrayOfDatumOfArray(data);
                case 'array,datum,object,number':
                    return this.formatArrayOfDatumOfObject(data);
                case 'array,array,array,number':
                    return this.formatArrayOfArrayOfArray(data);
                case 'array,array,object,number':
                    return this.formatArrayOfArrayOfObject(data);
                case 'array,datum,array,array,number':
                    return this.formatArrayOfDatumOfArrayOfArray(data);
                case 'array,datum,array,object,number':
                    return this.formatArrayOfDatumOfArrayOfObject(data);
                default:
                    throw new Error('unknown data shape or configuration');
            }
        }
        test(data) {
            // for classification problems
            const misclasses = [];
            // run each pattern through the trained network and collect
            // error and misclassification statistics
            let errorSum = 0;
            const formattedData = this.formatData(data);
            for (let i = 0; i < formattedData.length; i++) {
                const input = formattedData[i];
                const output = this.run(input.splice(0, input.length - 1));
                const target = input[input.length - 1];
                let errors = 0;
                let errorCount = 0;
                for (let j = 0; j < output.length; j++) {
                    errorCount++;
                    const error = target[j] - output[j];
                    // mse
                    errors += error * error;
                }
                errorSum += errors / errorCount;
                const errorsAbs = Math.abs(errors);
                if (errorsAbs > this.trainOpts.errorThresh) {
                    const misclass = data[i];
                    misclasses.push({
                        value: misclass,
                        actual: output,
                    });
                }
            }
            return {
                error: errorSum / formattedData.length,
                misclasses,
                total: formattedData.length,
            };
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        addFormat(value) {
            var _a, _b, _c, _d, _e, _f;
            const dataShape = lookup.dataShape(value).join(',');
            switch (dataShape) {
                case 'array,array,number':
                case 'datum,array,array,number':
                case 'array,number':
                case 'datum,array,number':
                    return;
                case 'datum,object,number': {
                    this.inputLookup = lookup.addKeys(value.input, (_a = this.inputLookup) !== null && _a !== void 0 ? _a : {});
                    if (this.inputLookup) {
                        this.inputLookupLength = Object.keys(this.inputLookup).length;
                    }
                    this.outputLookup = lookup.addKeys(value.output, (_b = this.outputLookup) !== null && _b !== void 0 ? _b : {});
                    if (this.outputLookup) {
                        this.outputLookupLength = Object.keys(this.outputLookup).length;
                    }
                    break;
                }
                case 'object,number': {
                    this.inputLookup = this.outputLookup = lookup.addKeys(value, (_c = this.inputLookup) !== null && _c !== void 0 ? _c : {});
                    if (this.inputLookup) {
                        this.inputLookupLength = this.outputLookupLength = Object.keys(this.inputLookup).length;
                    }
                    break;
                }
                case 'array,object,number': {
                    const typedValue = value;
                    for (let i = 0; i < typedValue.length; i++) {
                        this.inputLookup = this.outputLookup = lookup.addKeys(typedValue[i], (_d = this.inputLookup) !== null && _d !== void 0 ? _d : {});
                        if (this.inputLookup) {
                            this.inputLookupLength = this.outputLookupLength = Object.keys(this.inputLookup).length;
                        }
                    }
                    break;
                }
                case 'datum,array,object,number': {
                    const typedValue = value;
                    const typedInput = typedValue.input;
                    for (let i = 0; i < typedInput.length; i++) {
                        this.inputLookup = lookup.addKeys(typedInput[i], (_e = this.inputLookup) !== null && _e !== void 0 ? _e : {});
                        if (this.inputLookup) {
                            this.inputLookupLength = Object.keys(this.inputLookup).length;
                        }
                    }
                    const typedOutput = typedValue.output;
                    for (let i = 0; i < typedOutput.length; i++) {
                        this.outputLookup = lookup.addKeys(typedOutput[i], (_f = this.outputLookup) !== null && _f !== void 0 ? _f : {});
                        if (this.outputLookup) {
                            this.outputLookupLength = Object.keys(this.outputLookup).length;
                        }
                    }
                    break;
                }
                default:
                    throw new Error('unknown data shape or configuration');
            }
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        toJSON() {
            if (!this.model) {
                this.initialize();
            }
            const { model } = this;
            const options = { ...this.options, ...defaults$1 };
            return {
                type: this.constructor.name,
                options,
                hiddenLayers: model.hiddenLayers.map((hiddenLayer) => {
                    const layers = {};
                    for (const p in hiddenLayer) {
                        if (!hiddenLayer.hasOwnProperty(p))
                            continue;
                        layers[p] = hiddenLayer[p].toJSON();
                    }
                    return layers;
                }),
                outputConnector: model.outputConnector.toJSON(),
                output: model.output.toJSON(),
                inputLookup: this.inputLookup,
                inputLookupLength: this.inputLookupLength,
                outputLookup: this.outputLookup,
                outputLookupLength: this.outputLookupLength,
            };
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        fromJSON(json) {
            const { options } = json;
            const allMatrices = [];
            const hiddenLayers = [];
            // backward compatibility for hiddenSizes
            json.hiddenLayers.forEach((hiddenLayer) => {
                const layers = {};
                for (const p in hiddenLayer) {
                    layers[p] = Matrix.fromJSON(hiddenLayer[p]);
                    allMatrices.push(layers[p]);
                }
                hiddenLayers.push(layers);
            });
            const outputConnector = Matrix.fromJSON(json.outputConnector);
            allMatrices.push(outputConnector);
            const output = Matrix.fromJSON(json.output);
            allMatrices.push(output);
            // eslint-disable-next-line @typescript-eslint/ban-ts-comment
            // @ts-expect-error
            this.options = { ...defaults(), ...options };
            this.inputLookup = json.inputLookup;
            this.inputLookupLength = json.inputLookupLength;
            this.outputLookup = json.outputLookup;
            this.outputLookupLength = json.outputLookupLength;
            this.model = Object.seal({
                isInitialized: true,
                hiddenLayers,
                output,
                allMatrices,
                outputConnector,
                equations: [],
                equationConnections: [],
            });
            this.initialLayerInputs = options.hiddenLayers.map((size) => new Matrix(size, 1));
            this.bindEquation();
            return this;
        }
        // eslint-disable-next-line @typescript-eslint/ban-ts-comment
        // @ts-expect-error
        toFunction(cb) {
            const { model, inputLookup, inputLookupLength, outputLookup, outputLookupLength, } = this;
            const { inputSize } = this.options;
            const { equations } = model;
            const equation = equations[1];
            const { states } = equation;
            const jsonString = JSON.stringify(this.toJSON());
            function previousConnectionIndex(m) {
                const connection = model.equationConnections[0];
                const { states } = equations[0];
                for (let i = 0, max = states.length; i < max; i++) {
                    if (states[i].product === m) {
                        return i;
                    }
                }
                return connection.indexOf(m);
            }
            function matrixOrigin(m, stateIndex) {
                for (let i = 0, max = states.length; i < max; i++) {
                    const state = states[i];
                    if (i === stateIndex) {
                        const j = previousConnectionIndex(m);
                        switch (m) {
                            case state.left:
                                if (j > -1) {
                                    return `typeof prevStates[${j}] === 'object' ? prevStates[${j}].product : new Matrix(${m.rows}, ${m.columns})`;
                                }
                            // eslint-disable-next-line no-fallthrough
                            case state.right:
                                if (j > -1) {
                                    return `typeof prevStates[${j}] === 'object' ? prevStates[${j}].product : new Matrix(${m.rows}, ${m.columns})`;
                                }
                            // eslint-disable-next-line no-fallthrough
                            case state.product:
                                return `new Matrix(${m.rows}, ${m.columns})`;
                            default:
                                throw Error('unknown state');
                        }
                    }
                    if (m === state.product)
                        return `states[${i}].product`;
                    if (m === state.right)
                        return `states[${i}].right`;
                    if (m === state.left)
                        return `states[${i}].left`;
                }
                return '';
            }
            function matrixToString(m, stateIndex) {
                if (!m || !m.rows || !m.columns)
                    return 'null';
                if (m === model.outputConnector)
                    return `json.outputConnector`;
                if (m === model.output)
                    return `json.output`;
                for (let i = 0, max = model.hiddenLayers.length; i < max; i++) {
                    const hiddenLayer = model.hiddenLayers[i];
                    for (const p in hiddenLayer) {
                        if (!hiddenLayer.hasOwnProperty(p))
                            continue;
                        if (hiddenLayer[p] !== m)
                            continue;
                        return `json.hiddenLayers[${i}].${p}`;
                    }
                }
                return matrixOrigin(m, stateIndex);
            }
            function formatInputData() {
                if (!inputLookup)
                    return '';
                if (inputSize === 1) {
                    if (inputLookup === outputLookup) {
                        return `function lookupInput(input) {
            var table = ${JSON.stringify(inputLookup)};
            var result = [];
            for (var p in table) {
              if (!input.hasOwnProperty(p)) break;
              result.push(Float32Array.from([input[p]]));
            }
            return result;
          }`;
                    }
                    return `function lookupInput(input) {
          var table = ${JSON.stringify(inputLookup)};
          var result = [];
          for (var p in table) {
            result.push(Float32Array.from([input[p]]));
          }
          return result;
        }`;
                }
                return `function lookupInput(rawInputs) {
        var table = ${JSON.stringify(inputLookup)};
        var result = [];
        for (var i = 0; i < rawInputs.length; i++) {
          var rawInput = rawInputs[i];
          var input = new Float32Array(${inputLookupLength});
          for (var p in table) {
            input[table[p]] = rawInput.hasOwnProperty(p) ? rawInput[p] : 0;
          }
          result.push(input);
        }
        return result;
      }`;
            }
            function formatOutputData() {
                if (!outputLookup)
                    return '';
                if (inputSize === 1) {
                    if (inputLookup === outputLookup) {
                        return `function lookupOutputPartial(output, input) {
            var table = ${JSON.stringify(outputLookup)};
            var offset = input.length;
            var result = {};
            var i = 0;
            for (var p in table) {
              if (i++ < offset) continue;
              result[p] = output[table[p] - offset][0];
            }
            return result;
          }`;
                    }
                    return `function lookupOutput(output) {
          var table = ${JSON.stringify(outputLookup)};
          var result = {};
          for (var p in table) {
            result[p] = output[table[p]][0];
          }
          return result;
        }`;
                }
                return `function lookupOutput(output) {
        var table = ${JSON.stringify(outputLookup)};
        var result = {};
        for (var p in table) {
          result[p] = output[table[p]];
        }
        return result;
      }`;
            }
            function toInner(fnString) {
                // crude, but should be sufficient for now
                // function() { body }
                // crude, but should be sufficient for now
                // function() { body }
                const fnParts = fnString.toString().split('{');
                fnParts.shift();
                // body }
                const fnBodyString = fnParts.join('{');
                const fnBodyParts = fnBodyString.split('}');
                fnBodyParts.pop();
                // body
                return fnBodyParts
                    .join('}')
                    .split('\n')
                    .join('\n        ')
                    .replace('product.deltas[i] = 0;', '')
                    .replace('product.deltas[column] = 0;', '')
                    .replace('left.deltas[leftIndex] = 0;', '')
                    .replace('right.deltas[rightIndex] = 0;', '')
                    .replace('product.deltas = left.deltas.slice(0);', '');
            }
            function fileName(fnName) {
                return `src/recurrent/matrix/${fnName.replace(/[A-Z]/g, function (value) {
                return `-${value.toLowerCase()}`;
            })}.js`;
            }
            const statesRaw = [];
            const usedFunctionNames = {};
            const innerFunctionsSwitch = [];
            for (let i = 0, max = states.length; i < max; i++) {
                const state = states[i];
                statesRaw.push(`states[${i}] = {
      name: '${state.forwardFn.name}',
      left: ${state.left ? matrixToString(state.left, i) : 'undefined'},
      right: ${state.right ? matrixToString(state.right, i) : 'undefined'},
      product: ${matrixToString(state.product, i)}
    }`);
                const fnName = state.forwardFn.name;
                if (!usedFunctionNames[fnName]) {
                    usedFunctionNames[fnName] = true;
                    if (state.name === 'input') {
                        innerFunctionsSwitch.push(`case '${fnName}':`);
                        innerFunctionsSwitch.push(inputLookup && inputSize === 1
                            ? 'product.weights = _i < input.length ? input[_i]: prevStates[prevStates.length - 1].product.weights;'
                            : inputSize === 1
                                ? 'product.weights = [input[_i]];'
                                : 'product.weights = input[_i];');
                        innerFunctionsSwitch.push('break;');
                    }
                    else {
                        innerFunctionsSwitch.push(`        case '${fnName}':${fnName !== 'forwardFn'
                        ? ` //compiled from ${fileName(fnName)}`
                        : ''}
          ${toInner(state.forwardFn.toString())}
          break;`);
                    }
                }
            }
            const forceForecast = inputSize === 1 && this.outputLookup;
            const src = `
  var input = ${this.inputLookup ? 'lookupInput(rawInput)' : 'rawInput'};
  var json = ${jsonString};
  var output = [];
  var states = [];
  var prevStates;
  var state;
  var max = ${forceForecast
            ? inputLookup === outputLookup
                ? inputLookupLength
                : `input.length + ${outputLookupLength - 1}`
            : 'input.length'};
  for (var _i = 0; _i < max; _i++) {
    prevStates = states;
    states = [];
    ${statesRaw.join(';\n    ')};
    for (var stateIndex = 0, stateMax = ${statesRaw.length}; stateIndex < stateMax; stateIndex++) {
      state = states[stateIndex];
      var product = state.product;
      var left = state.left;
      var right = state.right;

      switch (state.name) {
${innerFunctionsSwitch.join('\n')}
      }
    }
    ${inputSize === 1 && inputLookup
            ? 'if (_i >= input.length - 1) { output.push(state.product.weights); }'
            : 'output = state.product.weights;'}
  }
  ${outputLookup
            ? outputLookup === inputLookup
                ? 'return lookupOutputPartial(output, input)'
                : 'return lookupOutput(output)'
            : inputSize === 1
                ? 'return output[0]'
                : 'return output'};
  ${formatInputData()}
  ${formatOutputData()}

  function Matrix(rows, columns) {
    this.rows = rows;
    this.columns = columns;
    this.weights = new Float32Array(rows * columns);
  }
  ${softmax.toString().replace('_2.default', 'Matrix')}
  ${randomFloat.toString()}
  ${sampleI.toString()}
  ${maxI.toString()}`;
            // eslint-disable-next-line
            return new Function('rawInput', cb ? cb(src) : src);
        }
    }
    const trainDefaults = { ...trainDefaults$1 };

    class GRUTimeStep extends RNNTimeStep {
        getHiddenLayer(hiddenSize, prevSize) {
            return getGRUHiddenLayer(hiddenSize, prevSize);
        }
        getEquation(equation, inputMatrix, previousResult, hiddenLayer) {
            return getGRUEquation(equation, inputMatrix, previousResult, hiddenLayer);
        }
    }

    class LSTM extends RNN {
        getHiddenLayer(hiddenSize, prevSize) {
            return getHiddenLSTMLayer(hiddenSize, prevSize);
        }
        getEquation(equation, inputMatrix, previousResult, hiddenLayer) {
            return getLSTMEquation(equation, inputMatrix, previousResult, hiddenLayer);
        }
    }
    function getHiddenLSTMLayer(hiddenSize, prevSize) {
        return {
            // gates parameters
            // wix
            inputMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            inputHidden: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            inputBias: new Matrix(hiddenSize, 1),
            // wfx
            forgetMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            forgetHidden: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            forgetBias: new Matrix(hiddenSize, 1),
            // wox
            outputMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            outputHidden: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            outputBias: new Matrix(hiddenSize, 1),
            // cell write params
            // wcx
            cellActivationMatrix: new RandomMatrix(hiddenSize, prevSize, 0.08),
            cellActivationHidden: new RandomMatrix(hiddenSize, hiddenSize, 0.08),
            cellActivationBias: new Matrix(hiddenSize, 1),
        };
    }
    function getLSTMEquation(equation, inputMatrix, previousResult, hiddenLayer) {
        if (!hiddenLayer.inputMatrix ||
            !hiddenLayer.inputHidden ||
            !hiddenLayer.inputBias ||
            !hiddenLayer.forgetMatrix ||
            !hiddenLayer.forgetHidden ||
            !hiddenLayer.forgetBias ||
            !hiddenLayer.outputMatrix ||
            !hiddenLayer.outputHidden ||
            !hiddenLayer.outputBias ||
            !hiddenLayer.cellActivationMatrix ||
            !hiddenLayer.cellActivationHidden ||
            !hiddenLayer.cellActivationBias) {
            throw new Error('hiddenLayer does not have expected properties');
        }
        const sigmoid = equation.sigmoid.bind(equation);
        const add = equation.add.bind(equation);
        const multiply = equation.multiply.bind(equation);
        const multiplyElement = equation.multiplyElement.bind(equation);
        const tanh = equation.tanh.bind(equation);
        const inputGate = sigmoid(add(add(multiply(hiddenLayer.inputMatrix, inputMatrix), multiply(hiddenLayer.inputHidden, previousResult)), hiddenLayer.inputBias));
        const forgetGate = sigmoid(add(add(multiply(hiddenLayer.forgetMatrix, inputMatrix), multiply(hiddenLayer.forgetHidden, previousResult)), hiddenLayer.forgetBias));
        // output gate
        const outputGate = sigmoid(add(add(multiply(hiddenLayer.outputMatrix, inputMatrix), multiply(hiddenLayer.outputHidden, previousResult)), hiddenLayer.outputBias));
        // write operation on cells
        const cellWrite = tanh(add(add(multiply(hiddenLayer.cellActivationMatrix, inputMatrix), multiply(hiddenLayer.cellActivationHidden, previousResult)), hiddenLayer.cellActivationBias));
        // compute new cell activation
        const retainCell = multiplyElement(forgetGate, previousResult); // what do we keep from cell
        const writeCell = multiplyElement(inputGate, cellWrite); // what do we write to cell
        const cell = add(retainCell, writeCell); // new cell contents
        // compute hidden state as gated, saturated cell activations
        return multiplyElement(outputGate, tanh(cell));
    }

    class LSTMTimeStep extends RNNTimeStep {
        getHiddenLayer(hiddenSize, prevSize) {
            return getHiddenLSTMLayer(hiddenSize, prevSize);
        }
        getEquation(equation, inputMatrix, previousResult, hiddenLayer) {
            return getLSTMEquation(equation, inputMatrix, previousResult, hiddenLayer);
        }
    }

    /**
     *
     * @param start
     * @param end
     * @returns {Array}
     */
    function range(start, end) {
        const result = [];
        for (; start < end; start++) {
            result.push(start);
        }
        return result;
    }

    function toArray(values) {
        if (Array.isArray(values)) {
            return Float32Array.from(values);
        }
        return Float32Array.from(Object.values(values));
    }

    function drawInput({ pixelX, pixelY, radius, inputs, row, line, fontSize, fontClassName, }) {
        let svg = `<rect
              x="${pixelX / 2 - radius}"
              y="${pixelY / 2 + row * pixelY - radius}"
              width="${2 * radius}"
              height="${2 * radius}"
              stroke="black"
              stroke-width="1"
              fill="${inputs.color}"
              class="${inputs.className}" />
            <line
              x1="${pixelX / 4}"
              y1="${pixelY / 2 + row * pixelY}"
              x2="${pixelX / 2 - radius}"
              y2="${pixelY / 2 + row * pixelY}"
              style="stroke:${line.color};stroke-width:${line.width}"
              class="${line.className}" />`;
        if (inputs.labels) {
            svg += `<text
              x="${pixelX / 8}"
              y="${pixelY / 2 + row * pixelY - 5}"
              fill="black"
              font-size="${fontSize}"
              class="${fontClassName}">${inputs.labels[row]}</text>`;
        }
        return svg;
    }
    function drawNeuron({ pixelX, pixelY, row, column, radius, hidden, }) {
        return `<circle
            cx="${pixelX / 2 + column * pixelX}"
            cy="${pixelY / 2 + row * pixelY}"
            r="${radius}"
            stroke="black"
            stroke-width="1"
            fill="${hidden.color}"
            class="${hidden.className}" />`;
    }
    function drawOutput({ pixelX, pixelY, row, column, line, outputs, radius, }) {
        return `<circle
            cx="${pixelX / 2 + column * pixelX}"
            cy="${pixelY / 2 + row * pixelY}"
            r="${radius}"
            stroke="black"
            stroke-width="1"
            fill="${outputs.color}"
            class="${outputs.className}" />
          <line
            x1="${pixelX / 2 + column * pixelX + radius}"
            y1="${pixelY / 2 + row * pixelY}"
            x2="${pixelX / 2 + column * pixelX + pixelX / 4}"
            y2="${pixelY / 2 + row * pixelY}"
            style="stroke:${line.color};stroke-width:${line.width}"
            class="${line.className}" />`;
    }
    function drawBackwardConnections({ pixelX, pixelY, row, column, radius, lineY, line, previousConnectionIndex, }) {
        return `<line
            x1="${pixelX / 2 + (column - 1) * pixelX + radius}"
            y1="${lineY / 2 + previousConnectionIndex * lineY}"
            x2="${pixelX / 2 + column * pixelX - radius}"
            y2="${pixelY / 2 + row * pixelY}"
            style="stroke:${line.color};stroke-width:${line.width}"
            class="${line.className}" />`;
    }
    function neuralNetworkToInnerSVG(options) {
        const { sizes, height, width } = options;
        let svg = '';
        const pixelX = width / sizes.length;
        for (let column = 0; column < sizes.length; column++) {
            const size = sizes[column];
            const pixelY = height / size;
            for (let row = 0; row < size; row++) {
                if (column === 0) {
                    svg += drawInput({ pixelX, pixelY, row, column, ...options });
                }
                else {
                    if (column === sizes.length - 1) {
                        svg += drawOutput({ pixelX, pixelY, row, column, ...options });
                    }
                    else {
                        svg += drawNeuron({ pixelX, pixelY, row, column, ...options });
                    }
                    const previousSize = sizes[column - 1];
                    const lineY = height / previousSize;
                    for (let previousConnectionIndex = 0; previousConnectionIndex < previousSize; previousConnectionIndex++) {
                        svg += drawBackwardConnections({
                            pixelX,
                            pixelY,
                            row,
                            column,
                            lineY,
                            previousConnectionIndex,
                            ...options,
                        });
                    }
                }
            }
        }
        return svg;
    }
    function drawRecurrentConnections({ pixelX, pixelY, row, column, radius, recurrentLine, }) {
        const moveX = pixelX / 2 + column * pixelX + radius + 1;
        const moveY = pixelY / 2 + row * pixelY;
        const x = moveX - radius * 2 - 2;
        const y = moveY;
        const x1 = x + 100;
        const y1 = y + 50;
        const x2 = moveX - 100;
        const y2 = moveY + 50;
        return `<path
              d="M ${moveX} ${moveY} C ${x1} ${y1}, ${x2} ${y2}, ${x} ${y}"
              stroke="${recurrentLine.color}"
              stroke-width="${recurrentLine.width}"
              fill="transparent"
              stroke-linecap="round"
              marker-end="url(#arrow)"
              class="${recurrentLine.className}" />`;
    }
    function rnnToInnerSVG(options) {
        const { width, height, recurrentLine, sizes, radius } = options;
        const pixelX = width / sizes.length;
        let svg = `<defs>
              <marker id="arrow" markerWidth="10" markerHeight="10" refX="8" refY="3" orient="auto" markerUnits="strokeWidth">
                <path d="M0,0 L0,6 L9,3 z" fill="${recurrentLine.color}" />
              </marker>
            </defs>`;
        svg += neuralNetworkToInnerSVG(options);
        for (let column = 1; column < sizes.length; column++) {
            const size = sizes[column];
            const pixelY = height / size;
            for (let row = 0; row < size; row++) {
                svg += drawRecurrentConnections({
                    pixelX,
                    pixelY,
                    row,
                    column,
                    radius,
                    recurrentLine,
                });
            }
        }
        return svg;
    }
    function getFeedForwardLayers(network) {
        const { options } = network;
        if (!options) {
            throw new Error('options not defined');
        }
        if (!options.inputLayer) {
            throw new Error('options.inputLater not defined');
        }
        if (!options.hiddenLayers) {
            throw new Error('options.hiddenLayers not defined');
        }
        if (options.hiddenLayers.length < 1) {
            throw new Error('options.hiddenLayers is empty');
        }
        if (!options.outputLayer) {
            throw new Error('options.outputLayer not defined');
        }
        const inputLayer = options.inputLayer();
        const hiddenLayers = [];
        hiddenLayers.push(options.hiddenLayers[0](inputLayer, 0));
        for (let i = 1; i < options.hiddenLayers.length; i++) {
            hiddenLayers.push(options.hiddenLayers[i](hiddenLayers[i - 1], i));
        }
        const outputLayer = options.outputLayer(hiddenLayers[hiddenLayers.length - 1], hiddenLayers.length);
        return {
            inputSize: inputLayer.height,
            hiddenLayers: hiddenLayers.map((hiddenLayer) => hiddenLayer.height),
            outputSize: outputLayer.height,
        };
    }
    function getRecurrentLayers(network) {
        const hiddenLayers = [];
        const { options } = network;
        if (!options.inputLayer) {
            throw new Error('inputLayer not defined');
        }
        if (!options.outputLayer) {
            throw new Error('outputLayer not defined');
        }
        const inputLayer = options.inputLayer();
        hiddenLayers.push(options.hiddenLayers[0](inputLayer, recurrentZeros(), 0));
        for (let i = 1; i < options.hiddenLayers.length; i++) {
            hiddenLayers.push(options.hiddenLayers[i](hiddenLayers[i - 1], recurrentZeros(), i));
        }
        const outputLayer = options.outputLayer(hiddenLayers[hiddenLayers.length - 1], -1);
        return {
            inputSize: inputLayer.height,
            hiddenLayers: hiddenLayers.map((hiddenLayer) => hiddenLayer.height),
            outputSize: outputLayer.height,
        };
    }
    function wrapOuterSVG(svgBody, width, height) {
        // language=html
        return `<svg
            xmlns="http://www.w3.org/2000/svg"
            xmlns:xlink="http://www.w3.org/1999/xlink"
            version="1.1"
            width="${width}"
            height="${height}">${svgBody}</svg>`;
    }
    function getNeuralNetworkJSONSizes(json) {
        return json.sizes;
    }
    function getNeuralNetworkSizes(net) {
        const { options, sizes } = net;
        const { inputSize, outputSize, hiddenLayers } = options;
        if (!sizes) {
            if (typeof inputSize === 'number' && inputSize < 1) {
                throw new Error('inputSize not set');
            }
            if (typeof outputSize === 'number' && outputSize < 1) {
                throw new Error('outputSize not set');
            }
            if (hiddenLayers === null || hiddenLayers === void 0 ? void 0 : hiddenLayers.some((v) => v < 1)) {
                throw new Error('hiddenLayers not set');
            }
        }
        return typeof inputSize === 'number' &&
            Array.isArray(hiddenLayers) &&
            typeof outputSize === 'number'
            ? [inputSize].concat(hiddenLayers).concat([outputSize])
            : sizes;
    }
    function getRNNSizes(net) {
        const { options } = net;
        const { inputSize, outputSize, hiddenLayers } = options;
        return [inputSize].concat(hiddenLayers).concat([outputSize]);
    }
    function defaultOptions() {
        return {
            line: {
                width: 0.5,
                color: 'black',
                className: 'connection',
            },
            recurrentLine: {
                width: 1,
                color: 'red',
                className: 'recurrence',
            },
            inputs: {
                color: 'rgba(0, 128, 0, 0.5)',
                labels: null,
                className: 'input',
            },
            outputs: {
                color: 'rgba(100, 149, 237, 0.5)',
                className: 'output',
            },
            hidden: {
                color: 'rgba(255, 127, 80, 0.5)',
                className: 'hidden-neuron',
            },
            fontSize: '14px',
            fontClassName: 'label',
            radius: 8,
            width: 400,
            height: 250,
            sizes: [],
        };
    }
    function toSVG(net, options) {
        const mergedOptions = { ...defaultOptions(), ...options };
        const { width, height, inputs } = mergedOptions;
        // Get network size array for NeuralNetwork or NeuralNetworkGPU
        let sizes = [];
        if (net instanceof NeuralNetwork || net instanceof NeuralNetworkGPU) {
            sizes = getNeuralNetworkSizes(net);
        }
        // get network size for Recurrent
        else if (net instanceof Recurrent) {
            const { inputSize, hiddenLayers, outputSize } = getRecurrentLayers(net);
            sizes = [inputSize].concat(hiddenLayers).concat([outputSize]);
        }
        // get network size for FeedForward
        else if (net instanceof FeedForward) {
            const { inputSize, hiddenLayers, outputSize } = getFeedForwardLayers(net);
            sizes = [inputSize].concat(hiddenLayers).concat([outputSize]);
        }
        // handle json, recurrent first
        else if (net instanceof RNN ||
            net instanceof LSTM ||
            net instanceof GRU ||
            net instanceof RNNTimeStep ||
            net instanceof LSTMTimeStep ||
            net instanceof GRUTimeStep) {
            return wrapOuterSVG(rnnToInnerSVG({
                ...mergedOptions,
                sizes: checkSizes(getRNNSizes(net), inputs.labels),
            }), width, height);
        }
        // handle json, NeuralNetwork
        else if (net.hasOwnProperty('type')) {
            switch (net.type) {
                case 'NeuralNetwork':
                case 'NeuralNetworkGPU':
                    return wrapOuterSVG(neuralNetworkToInnerSVG({
                        ...mergedOptions,
                        sizes: checkSizes(getNeuralNetworkJSONSizes(net), inputs.labels),
                    }), width, height);
                case 'RNN':
                case 'GRU':
                case 'LSTM':
                case 'RNNTimeStep':
                case 'GRUTimeStep':
                case 'LSTMTimeStep':
                    return wrapOuterSVG(rnnToInnerSVG({
                        ...mergedOptions,
                        sizes: checkSizes(getRNNSizes(net), inputs.labels),
                    }), width, height);
                default:
                    throw new Error('unrecognized network');
            }
        }
        else if (net.hasOwnProperty('inputSize') &&
            net.hasOwnProperty('hiddenLayers') &&
            net.hasOwnProperty('outputSize')) {
            const { inputSize, hiddenLayers, outputSize } = net;
            sizes = [inputSize, ...hiddenLayers, outputSize];
        }
        else if (net.hasOwnProperty('sizes')) {
            sizes = net.sizes;
        }
        else {
            throw new Error('unrecognized network');
        }
        return wrapOuterSVG(neuralNetworkToInnerSVG({
            ...mergedOptions,
            sizes: checkSizes(sizes, inputs.labels),
        }), width, height);
    }
    function checkSizes(sizes, labels) {
        if (!sizes) {
            throw new Error('sizes not set');
        }
        if (sizes.some((size) => size < 1)) {
            throw new Error('sizes not set correctly');
        }
        if (labels && labels.length !== sizes[0]) {
            throw new Error('not enough labels for inputs');
        }
        return sizes;
    }

    const recurrent = {
        RNNTimeStep,
        LSTMTimeStep,
        GRUTimeStep,
        RNN,
        LSTM,
        GRU,
    };
    const utilities = {
        max,
        mse: mse$1,
        ones: ones$1,
        ones2D,
        random: random$1,
        randomWeight,
        randos,
        range,
        toArray,
        DataFormatter,
        zeros: zeros$1,
        toSVG,
    };

    exports.CrossValidate = CrossValidate;
    exports.FeedForward = FeedForward;
    exports.NeuralNetwork = NeuralNetwork;
    exports.NeuralNetworkGPU = NeuralNetworkGPU;
    exports.Recurrent = Recurrent;
    exports.activation = index$1;
    exports.layer = layer;
    exports.layerTypes = layerTypes;
    exports.likely = likely;
    exports.lookup = lookup;
    exports.praxis = index;
    exports.recurrent = recurrent;
    exports.utilities = utilities;

    Object.defineProperty(exports, '__esModule', { value: true });

})));

//Porters stemmer algorithm
var stemmer = (function(){
	var step2list = {
			"ational" : "ate",
			"tional" : "tion",
			"enci" : "ence",
			"anci" : "ance",
			"izer" : "ize",
			"bli" : "ble",
			"alli" : "al",
			"entli" : "ent",
			"eli" : "e",
			"ousli" : "ous",
			"ization" : "ize",
			"ation" : "ate",
			"ator" : "ate",
			"alism" : "al",
			"iveness" : "ive",
			"fulness" : "ful",
			"ousness" : "ous",
			"aliti" : "al",
			"iviti" : "ive",
			"biliti" : "ble",
			"logi" : "log"
		},

		step3list = {
			"icate" : "ic",
			"ative" : "",
			"alize" : "al",
			"iciti" : "ic",
			"ical" : "ic",
			"ful" : "",
			"ness" : ""
		},

		c = "[^aeiou]",          // consonant
		v = "[aeiouy]",          // vowel
		C = c + "[^aeiouy]*",    // consonant sequence
		V = v + "[aeiou]*",      // vowel sequence

		mgr0 = "^(" + C + ")?" + V + C,               // [C]VC... is m>0
		meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$",  // [C]VC[V] is m=1
		mgr1 = "^(" + C + ")?" + V + C + V + C,       // [C]VCVC... is m>1
		s_v = "^(" + C + ")?" + v;                   // vowel in stem

	return function (w) {
		var 	stem,
			suffix,
			firstch,
			re,
			re2,
			re3,
			re4,
			origword = w;

		if (w.length < 3) { return w; }

		firstch = w.substr(0,1);
		if (firstch == "y") {
			w = firstch.toUpperCase() + w.substr(1);
		}

		// Step 1a
		re = /^(.+?)(ss|i)es$/;
		re2 = /^(.+?)([^s])s$/;

		if (re.test(w)) { w = w.replace(re,"$1$2"); }
		else if (re2.test(w)) {	w = w.replace(re2,"$1$2"); }

		// Step 1b
		re = /^(.+?)eed$/;
		re2 = /^(.+?)(ed|ing)$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			re = new RegExp(mgr0);
			if (re.test(fp[1])) {
				re = /.$/;
				w = w.replace(re,"");
			}
		} else if (re2.test(w)) {
			var fp = re2.exec(w);
			stem = fp[1];
			re2 = new RegExp(s_v);
			if (re2.test(stem)) {
				w = stem;
				re2 = /(at|bl|iz)$/;
				re3 = new RegExp("([^aeiouylsz])\\1$");
				re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
				if (re2.test(w)) {	w = w + "e"; }
				else if (re3.test(w)) { re = /.$/; w = w.replace(re,""); }
				else if (re4.test(w)) { w = w + "e"; }
			}
		}

		// Step 1c
		re = /^(.+?)y$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			stem = fp[1];
			re = new RegExp(s_v);
			if (re.test(stem)) { w = stem + "i"; }
		}

		// Step 2
		re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			stem = fp[1];
			suffix = fp[2];
			re = new RegExp(mgr0);
			if (re.test(stem)) {
				w = stem + step2list[suffix];
			}
		}

		// Step 3
		re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			stem = fp[1];
			suffix = fp[2];
			re = new RegExp(mgr0);
			if (re.test(stem)) {
				w = stem + step3list[suffix];
			}
		}

		// Step 4
		re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
		re2 = /^(.+?)(s|t)(ion)$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			stem = fp[1];
			re = new RegExp(mgr1);
			if (re.test(stem)) {
				w = stem;
			}
		} else if (re2.test(w)) {
			var fp = re2.exec(w);
			stem = fp[1] + fp[2];
			re2 = new RegExp(mgr1);
			if (re2.test(stem)) {
				w = stem;
			}
		}

		// Step 5
		re = /^(.+?)e$/;
		if (re.test(w)) {
			var fp = re.exec(w);
			stem = fp[1];
			re = new RegExp(mgr1);
			re2 = new RegExp(meq1);
			re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
			if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
				w = stem;
			}
		}

		re = /ll$/;
		re2 = new RegExp(mgr1);
		if (re.test(w) && re2.test(w)) {
			re = /.$/;
			w = w.replace(re,"");
		}

		// and turn initial Y back to y

		if (firstch == "y") {
			w = firstch.toLowerCase() + w.substr(1);
		}

		return w;
	}
})();

//Tokenize algorithm
function tokenize(input) {
    return input.split(" ");
}

//Bag of words
function bag_of_words(tokenized_sentence, words) {
    // stem each word
    let sentence_words = [];
    tokenized_sentence.forEach(w => {
        sentence_words.push(stemmer(w).toLowerCase());
    });
    
    //initialize bag with 0 for each word
    let bag = [];
    words.forEach(() => {
        bag.push(0);
    });
    for (let i = 0; i < words.length; i++) {
        let w = words[i];
        if(sentence_words.includes(w)){
            bag[i] = 1;
        }
    }
    return bag;
}

//Check if file exists
function checkFileExist(urlToFile) {
    var xhr = new XMLHttpRequest();
    xhr.open('HEAD', urlToFile, false);
    xhr.send();
     
    if (xhr.status == "404") {
        return false;
    } else {
        return true;
    }
}

//Chatbot class
let intentsFile;
let modelFile;
class chatBot {
    //Main constructor for getting intent and model files
    constructor(intents, model) {
        if(checkFileExist(model) == true) {
            modelFile = model;
        }
        if(checkFileExist(intents) == true) {
            intentsFile = intents;
        }
    }

    //Constructor for training the neural network
    train() {
        if(checkFileExist(intentsFile) == false) {
            console.error("Missing intents.json file!");
        }
        fetch(intentsFile)
        .then(response => response.json())
        .then(data => {
            const intents = data.intents;
            
            // loop through each sentence in our intents patterns
            let all_words = [];
            let tags = [];
            let xy = [];
            intents.forEach(intent => {
                let tag = intent.tag;
                // add to tag list
                tags.push(tag);
                intent.patterns.forEach(pattern => {
                    // tokenize each word in the sentence
                    let w = tokenize(pattern);
                    // add to our words list
                    all_words = all_words.concat(w);
                    // add to xy pair
                    xy.push({w, tag});
                });
            });

            // stem and lower each word
            let ignore_words = ["?", ".", "!"];
            let temp = [];
            all_words.forEach(w => {
                if(!ignore_words.includes(w)){
                    temp.push(stemmer(w).toLowerCase());
                }
            });
            all_words = temp;

            // remove duplicates and sort
            all_words = [...new Set(all_words.sort())];
            tags = [...new Set(tags.sort())];

            console.log(xy.length, "patterns");
            console.log(tags.length, "tags:", tags);
            console.log(all_words.length, "unique stemmed words:", all_words);

            // Create training data
            let trainingData = [];
            let input = [];
            let output = [];
            let tempOutputOneHot = [];
            
            // For output were gonna use one-hot encoding
            for (let i = 0; i < intents.length; i++) {
                const element = intents[i];
                let one_hot = [];
                intents.forEach(() => {
                    one_hot.push(0);
                });
                one_hot[i] = 1;
                tempOutputOneHot.push([one_hot, element.tag]);
            }

            // bag of words for each pattern_sentence
            xy.forEach(element => {
                let pattern_sentence = element.w
                let tag = element.tag
                let bag = bag_of_words(pattern_sentence, all_words);
                // Get temp values match with tag and combine with bag values
                tempOutputOneHot.forEach(element => {
                    if(tag == element[1]) {
                        input.push(bag);
                        output.push(element[0]);
                        trainingData.push({input: bag, output: element[0]});
                    }
                });
            });

            //Create and configure neural network
            const net = new brain.NeuralNetwork({
                activation: "sigmoid",
                iterations: 20000,
                errorThresh: 0.0005,
                log: true,
                logPeriod: 100,
                hiddenLayers: [8, 8],
            });
            
            //If existing model then train untop of this model
            if(checkFileExist(modelFile) == true){
                fetch(modelFile)
                .then(response => response.json())
                .then(data => {
                    net.fromJSON(data);
                });
            }

            //Train model
            net.train(trainingData);
            const trainedModel = net.toJSON();
            const blob = new Blob([JSON.stringify(trainedModel)], {type: 'application/json'});
            const url = URL.createObjectURL(blob);
            const a = Object.assign(document.createElement('a'), {download: 'model.json', href: url});
            document.body.appendChild(a);
            a.click();
            a.remove();
        });
    }

    //Constructor for running the neural network
    run(input) {
        return new Promise(
        function(resolve, reject) {
            if(checkFileExist(intentsFile) == false && checkFileExist(modelFile) == false) {
                console.error("Missing intents.json & model.json file!");
                reject("Missing intents.json & model.json file!");
            }
            fetch(intentsFile)
            .then(response => response.json())
            .then(intentsData => {
                const intents = intentsData.intents;
    
                // loop through each sentence in our intents patterns
                let all_words = [];
                intents.forEach(intent => {
                    intent.patterns.forEach(pattern => {
                        // tokenize each word in the sentence
                        let w = tokenize(pattern);
                        // add to our words list
                        all_words = all_words.concat(w);
                    });
                });
    
                // stem and lower each word
                let ignore_words = ["?", ".", "!"];
                let temp = [];
                all_words.forEach(w => {
                    if(!ignore_words.includes(w)){
                        temp.push(stemmer(w).toLowerCase());
                    }
                });
                all_words = temp;
    
                // remove duplicates and sort
                all_words = [...new Set(all_words.sort())];
    
                let pattern_sentence = tokenize(input);
                console.log(pattern_sentence)
                console.log(all_words);
                let bag = bag_of_words(pattern_sentence, all_words);
                console.log(bag);
                fetch(modelFile)
                .then(response => response.json())
                .then(modelData => {
                    const net = new brain.NeuralNetwork();
                    net.fromJSON(modelData);
                    const output = net.run(bag);
                    console.log(output);
                    for (let i = 0; i < intents.length; i++) {               
                        if(i == output.indexOf(Math.max(...output))) {
                            console.log(intents[i].tag);
                            intents.forEach(intent => {
                                if(intents[i].tag == intent.tag){
                                    const response = {
                                        tag: intents[i].tag,
                                        trigger: intent.trigger,
                                        responses: intents[i].responses,
                                        rnd_response: intent.responses[Math.floor(Math.random() * intent.responses.length)],
                                    };
                                    resolve(response);
                                }
                            });
                        }
                    }
                    
                });
            });
        });
    }
}