SYMBOL INDEX (1061 symbols across 133 files)

FILE: src/activation/leaky-relu.ts
  function activate (line 5) | function activate(weight: number): number {
  function measure (line 12) | function measure(weight: number, error: number): number {

FILE: src/activation/relu.ts
  function activate (line 5) | function activate(weight: number): number {
  function measure (line 12) | function measure(weight: number, delta: number): number {

FILE: src/activation/sigmoid.ts
  function activate (line 4) | function activate(value: number): number {
  function measure (line 11) | function measure(weight: number, error: number): number {

FILE: src/activation/tanh.ts
  function activate (line 4) | function activate(weight: number): number {
  function measure (line 11) | function measure(weight: number, error: number): number {

FILE: src/autoencoder.test.ts
  function xor (line 25) | function xor(...args: number[]) {
  function includesAnomalies (line 60) | function includesAnomalies(...args: number[]) {

FILE: src/autoencoder.ts
  type IAEOptions (line 15) | interface IAEOptions {
  class AE (line 24) | class AE<
    method constructor (line 31) | constructor(options?: Partial<IAEOptions>) {
    method denoise (line 57) | denoise(input: DecodedData): DecodedData {
    method decode (line 74) | decode(input: EncodedData): DecodedData {
    method encode (line 88) | encode(input: DecodedData): EncodedData {
    method likelyIncludesAnomalies (line 115) | likelyIncludesAnomalies(input: DecodedData, anomalyThreshold = 0.2): b...
    method train (line 148) | train(
    method createDecoder (line 173) | private createDecoder() {
    method encodedLayer (line 197) | private get encodedLayer(): KernelOutput {
    method encodedLayerIndex (line 204) | private get encodedLayerIndex(): number {

FILE: src/cross-validate.test.ts
  class FakeNN (line 11) | class FakeNN extends NeuralNetwork<number[], number[]> {
    method constructor (line 12) | constructor(
    method train (line 21) | train(data: Array<{ input: number[]; output: number[] }>) {
  class SpyFakeNN (line 37) | class SpyFakeNN extends FakeNN {
    method setActivation (line 38) | setActivation() {
    method setActivation (line 108) | setActivation() {
  class SpyFakeNN (line 107) | class SpyFakeNN extends FakeNN {
    method setActivation (line 38) | setActivation() {
    method setActivation (line 108) | setActivation() {
  type T (line 168) | interface T {
  class FakeNN (line 206) | class FakeNN extends NeuralNetwork<number[], number[]> {}
    method constructor (line 12) | constructor(
    method train (line 21) | train(data: Array<{ input: number[]; output: number[] }>) {
  class FakeNN (line 264) | class FakeNN extends NeuralNetwork<number[], number[]> {}
    method constructor (line 12) | constructor(
    method train (line 21) | train(data: Array<{ input: number[]; output: number[] }>) {

FILE: src/cross-validate.ts
  type InitClassifier (line 7) | type InitClassifier<
  type IClassifier (line 13) | interface IClassifier<TrainOptsType, JsonType, DatumType> {
  type ICrossValidateJSON (line 27) | type ICrossValidateJSON<JsonType> =
  type ICrossValidateStatsAverages (line 31) | interface ICrossValidateStatsAverages {
  type ICrossValidateStats (line 38) | interface ICrossValidateStats<JsonType> {
  type ICrossValidateBinaryStats (line 44) | interface ICrossValidateBinaryStats<NetworkType> {
  type ICrossValidateStatsResultStats (line 50) | interface ICrossValidateStatsResultStats {
  type ICrossValidateStatsResultBinaryStats (line 56) | interface ICrossValidateStatsResultBinaryStats
  type ICrossValidationTestPartitionResults (line 68) | interface ICrossValidationTestPartitionResults<JsonType>
  type ICrossValidationTestPartitionBinaryResults (line 77) | type ICrossValidationTestPartitionBinaryResults<
  class CrossValidate (line 82) | class CrossValidate<
    method constructor (line 107) | constructor(initClassifier: InitClassifierType) {
    method testPartition (line 111) | testPartition(
    method shuffleArray (line 148) | shuffleArray<K>(array: K[]): K[] {
    method train (line 179) | train(
    method toNeuralNetwork (line 286) | toNeuralNetwork(): ReturnType<InitClassifierType> {
    method toJSON (line 290) | toJSON(): ICrossValidateJSON<
    method fromJSON (line 296) | fromJSON(

FILE: src/errors/untrained-neural-network-error.ts
  class UntrainedNeuralNetworkError (line 1) | class UntrainedNeuralNetworkError<
    method constructor (line 4) | constructor(neuralNetwork: T) {

FILE: src/estimator/mean-squared-error.ts
  type mse2dThis (line 4) | interface mse2dThis extends IKernelFunctionThis {
  function mse2d (line 11) | function mse2d(
  class MeanSquaredError (line 24) | class MeanSquaredError {
    method constructor (line 34) | constructor({ width, height }: { width: number; height: number }) {

FILE: src/feed-forward.end-to-end.test.ts
  function setupTwinXORNetworks (line 36) | function setupTwinXORNetworks(useDecimals: boolean) {
  function testOutputsSmaller (line 311) | function testOutputsSmaller() {
  function testCanLearnXOR (line 335) | function testCanLearnXOR() {
  class SuperOutput (line 437) | class SuperOutput extends Target {
    method constructor (line 438) | constructor(settings: ILayerSettings, inputLayer: ILayer) {

FILE: src/feed-forward.ts
  type IFeedForwardTrainingData (line 14) | interface IFeedForwardTrainingData<
  type IFeedForwardNormalizedTrainingData (line 22) | interface IFeedForwardNormalizedTrainingData {
  type IFeedForwardGPUTrainingData (line 27) | interface IFeedForwardGPUTrainingData {
  type ITrainingStatus (line 32) | interface ITrainingStatus {
  type Log (line 37) | type Log = (status: string) => void;
  type FeedForwardCallback (line 38) | type FeedForwardCallback = (status: ITrainingStatus) => void;
  type IFeedForwardTrainingOptions (line 40) | interface IFeedForwardTrainingOptions {
  type IFeedForwardOptions (line 52) | interface IFeedForwardOptions {
  type IFeedForwardPreppedTrainingData (line 72) | interface IFeedForwardPreppedTrainingData {
  type IFeedForwardJSON (line 102) | interface IFeedForwardJSON {
  class FeedForward (line 110) | class FeedForward<
    method _validateTrainingOptions (line 114) | static _validateTrainingOptions(
    method _setLogMethod (line 161) | _setLogMethod(log: Log | undefined | boolean): void {
    method _updateTrainingOptions (line 172) | _updateTrainingOptions(opts: Partial<IFeedForwardTrainingOptions>): vo...
    method constructor (line 200) | constructor(options: IFeedForwardOptions = {}) {
    method _connectOptionsLayers (line 208) | _connectOptionsLayers(): ILayer[] {
    method _connectNewLayers (line 232) | _connectNewLayers(): ILayer[] {
    method _connectHiddenLayers (line 250) | _connectHiddenLayers(previousLayer: ILayer): ILayer[] {
    method initialize (line 267) | initialize(): void {
    method initializeLayers (line 275) | initializeLayers(layers: ILayer[]): void {
    method run (line 300) | run(input: InputType): OutputType {
    method runInput (line 330) | runInput(input: KernelOutput): KernelOutput {
    method train (line 339) | train(
    method trainAsync (line 359) | async trainAsync(
    method _trainingTick (line 391) | _trainingTick(
    method _prepTraining (line 434) | _prepTraining(
    method verifyIsInitialized (line 459) | verifyIsInitialized(): void {
    method _calculateTrainingError (line 465) | _calculateTrainingError(preparedData: IFeedForwardGPUTrainingData[]): ...
    method _trainPatterns (line 493) | _trainPatterns(data: IFeedForwardGPUTrainingData[]): void {
    method _trainPattern (line 499) | _trainPattern(
    method _calculateDeltas (line 522) | _calculateDeltas(target: KernelOutput): void {
    method adjustWeights (line 532) | adjustWeights(): void {
    method formatData (line 544) | formatData(
    method transferData (line 611) | transferData(
    method test (line 654) | test(): void {
    method toJSON (line 661) | toJSON(): IFeedForwardJSON {
    method fromJSON (line 709) | static fromJSON(
    method toFunction (line 790) | toFunction(): void {

FILE: src/feed-forward.unit.test.ts
  class TestLayer (line 242) | class TestLayer extends BaseLayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 272) | class TestLayer extends layerTypes.Model {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 308) | class TestLayer extends BaseLayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 348) | class TestLayer extends BaseLayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 384) | class TestLayer extends BaseLayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 423) | class TestLayer extends layerTypes.Model {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestInputLayer (line 465) | class TestInputLayer extends BaseLayer {
    method constructor (line 466) | constructor(settings: ILayerSettings) {
  class TestLayer1 (line 471) | class TestLayer1 extends BaseLayer {
    method constructor (line 473) | constructor(settings: ILayerSettings, inputLayer: ILayer) {
    method setupKernels (line 479) | setupKernels() {}
  class TestLayer2 (line 482) | class TestLayer2 extends BaseLayer {
    method constructor (line 484) | constructor(settings: ILayerSettings, inputLayer: ILayer) {
    method setupKernels (line 490) | setupKernels() {}
  class TestOperatorLayer (line 493) | class TestOperatorLayer extends BaseLayer {
    method constructor (line 496) | constructor(inputLayer1: ILayer, inputLayer2: ILayer) {
    method setupKernels (line 503) | setupKernels() {}
    method defaults (line 660) | static get defaults() {
    method constructor (line 666) | constructor(
    method setupKernels (line 677) | setupKernels() {}
  class TestOutputLayer (line 506) | class TestOutputLayer extends BaseLayer {
    method constructor (line 508) | constructor(settings: ILayerSettings, inputLayer: ILayer) {
  class TestLayer (line 583) | class TestLayer extends BaseLayer implements ILayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestLayer (line 644) | class TestLayer extends BaseLayer {
    method setupKernels (line 244) | setupKernels() {
    method setupKernels (line 274) | setupKernels() {
    method setupKernels (line 310) | setupKernels() {
    method setupKernels (line 350) | setupKernels() {}
    method predict (line 353) | predict() {
    method setupKernels (line 386) | setupKernels() {}
    method predict (line 389) | predict() {}
    method compare (line 392) | compare() {
    method setupKernels (line 425) | setupKernels() {}
    method predict (line 428) | predict() {}
    method compare (line 431) | compare() {}
    method learn (line 434) | learn() {
    method constructor (line 585) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 591) | setupKernels() {}
    method defaults (line 645) | static get defaults() {
    method constructor (line 650) | constructor(settings: ILayerSettings, inputLayer?: ILayer) {
    method setupKernels (line 656) | setupKernels() {}
  class TestOperatorLayer (line 659) | class TestOperatorLayer extends BaseLayer {
    method constructor (line 496) | constructor(inputLayer1: ILayer, inputLayer2: ILayer) {
    method setupKernels (line 503) | setupKernels() {}
    method defaults (line 660) | static get defaults() {
    method constructor (line 666) | constructor(
    method setupKernels (line 677) | setupKernels() {}

FILE: src/layer/activation.ts
  type ActivationType (line 5) | type ActivationType = new (
  class Activation (line 10) | class Activation extends BaseLayer {
    method width (line 13) | get width(): number {
    method height (line 17) | get height(): number {
    method depth (line 21) | get depth(): number {
    method constructor (line 25) | constructor(inputLayer: ILayer, settings?: Partial<ILayerSettings>) {

FILE: src/layer/add.ts
  function predict (line 7) | function predict(
  class Add (line 18) | class Add extends Operator {
    method width (line 19) | get width(): number {
    method height (line 23) | get height(): number {
    method depth (line 27) | get depth(): number {
    method validate (line 31) | validate(): void {
    method setupKernels (line 36) | setupKernels(): void {
    method predict (line 43) | predict(): void {
    method compare (line 51) | compare(): void {
  function add (line 60) | function add(

FILE: src/layer/arthur-feed-forward.ts
  type IArthurFeedForwardPraxisSettings (line 17) | interface IArthurFeedForwardPraxisSettings
  type IArthurFeedForwardSettings (line 21) | interface IArthurFeedForwardSettings extends IRandomSettings {
  function arthurFeedForward (line 28) | function arthurFeedForward(

FILE: src/layer/base-layer.test.ts
  type IPraxisExtendedSettings (line 29) | interface IPraxisExtendedSettings extends IPraxisSettings {

FILE: src/layer/base-layer.ts
  type ILayerJSON (line 12) | interface ILayerJSON {
  type ILayer (line 24) | interface ILayer {
  type ILayerSettings (line 48) | interface ILayerSettings {
  type BaseLayerType (line 74) | type BaseLayerType = new (settings?: Partial<ILayerSettings>) => ILayer;
  class BaseLayer (line 76) | class BaseLayer implements ILayer {
    method width (line 82) | get width(): number {
    method height (line 86) | get height(): number {
    method depth (line 90) | get depth(): number {
    method weights (line 94) | get weights(): KernelOutput | Input {
    method weights (line 98) | set weights(weights: KernelOutput | Input) {
    method deltas (line 105) | get deltas(): KernelOutput {
    method deltas (line 109) | set deltas(deltas: KernelOutput) {
    method id (line 113) | get id(): string {
    method id (line 117) | set id(title: string) {
    method constructor (line 121) | constructor(settings?: Partial<ILayerSettings>) {
    method setupPraxis (line 130) | setupPraxis(): void {
    method validate (line 196) | validate(): void {
    method setupKernels (line 211) | setupKernels(isTraining?: boolean): void {}
    method reuseKernels (line 213) | reuseKernels(layer: ILayer): void {
    method predict (line 243) | predict(inputs?: KernelOutput): void {}
    method compare (line 245) | compare(targetValues?: KernelOutput): void {}
    method learn (line 247) | learn(learningRate?: number): void {}
    method toArray (line 249) | toArray(): TextureArrayOutput {
    method toJSON (line 255) | toJSON(): Partial<ILayerJSON> {
    method toJSON (line 259) | static toJSON(layer: ILayer): Partial<ILayerJSON> {
  function toUntypedArray (line 283) | function toUntypedArray(

FILE: src/layer/convolution.ts
  type IConvolutionConstantsBase (line 16) | interface IConvolutionConstantsBase extends IConstantsThis {
  type IPredictConstants (line 25) | interface IPredictConstants extends IConvolutionConstantsBase {
  function predict (line 31) | function predict(
  type ICompareFilterDeltasConstants (line 76) | interface ICompareFilterDeltasConstants
  function compareFilterDeltas (line 85) | function compareFilterDeltas(
  type ICompareInputDeltasConstants (line 152) | interface ICompareInputDeltasConstants
  function compareInputDeltas (line 159) | function compareInputDeltas(
  type ICompareBiasesConstants (line 214) | interface ICompareBiasesConstants extends IConstantsThis {
  function compareBiases (line 219) | function compareBiases(
  type IConvolutionSettingsBase (line 233) | interface IConvolutionSettingsBase {
  type IConvolutionSettings (line 245) | interface IConvolutionSettings
  class Convolution (line 264) | class Convolution extends Filter {
    method strideX (line 267) | get strideX(): number {
    method strideY (line 271) | get strideY(): number {
    method paddingX (line 275) | get paddingX(): number {
    method paddingY (line 279) | get paddingY(): number {
    method width (line 283) | get width(): number {
    method height (line 291) | get height(): number {
    method bias (line 299) | get bias(): number {
    method depth (line 303) | get depth(): number {
    method biases (line 307) | get biases(): KernelOutput {
    method biases (line 311) | set biases(biases: KernelOutput) {
    method biasDeltas (line 315) | get biasDeltas(): KernelOutput {
    method biasDeltas (line 319) | set biasDeltas(weights: KernelOutput) {
    method filters (line 323) | get filters(): KernelOutput {
    method filters (line 327) | set filters(filters: KernelOutput) {
    method filterDeltas (line 331) | get filterDeltas(): KernelOutput {
    method filterDeltas (line 335) | set filterDeltas(filterDeltas: KernelOutput) {
    method constructor (line 339) | constructor(settings: IConvolutionSettings, inputLayer: ILayer) {
    method setupKernels (line 369) | setupKernels(): void {
    method predict (line 439) | predict(): void {
    method compare (line 447) | compare(): void {
    method learn (line 471) | learn(learningRate: number): void {
  function convolution (line 480) | function convolution(

FILE: src/layer/dropout.ts
  function setDropout (line 13) | function setDropout(dropout: number): number {
  type IDropoutConstants (line 17) | interface IDropoutConstants extends IConstantsThis {
  function trainingPredict (line 21) | function trainingPredict(
  function predict (line 31) | function predict(
  function compare (line 38) | function compare(
  type IDropoutSettings (line 49) | interface IDropoutSettings extends ILayerSettings {
  class Dropout (line 58) | class Dropout extends Filter {
    method constructor (line 62) | constructor(
    method setupKernels (line 72) | setupKernels(isTraining?: boolean): void {
    method predict (line 92) | predict(): void {
    method compare (line 105) | compare(): void {
  function dropout (line 114) | function dropout(

FILE: src/layer/feed-forward.ts
  function feedForward (line 7) | function feedForward(settings: ILayerSettings, input: ILayer): ILayer {

FILE: src/layer/filter.ts
  type IFilterSettings (line 4) | interface IFilterSettings extends ILayerSettings {
  type FilterType (line 12) | type FilterType = new (
  class Filter (line 17) | class Filter extends BaseLayer {
    method width (line 18) | get width(): number {
    method height (line 22) | get height(): number {
    method depth (line 26) | get depth(): number {
    method filterCount (line 30) | get filterCount(): number {
    method filterWidth (line 34) | get filterWidth(): number {
    method filterHeight (line 38) | get filterHeight(): number {
    method filters (line 42) | get filters(): KernelOutput {
    method filters (line 46) | set filters(filters: KernelOutput) {
    method filterDeltas (line 50) | get filterDeltas(): KernelOutput {
    method filterDeltas (line 54) | set filterDeltas(filterDeltas: KernelOutput) {
    method constructor (line 60) | constructor(settings: Partial<IFilterSettings>, inputLayer: ILayer) {

FILE: src/layer/fully-connected.ts
  type IPredictConstants (line 16) | interface IPredictConstants extends IConstantsThis {
  function predict (line 21) | function predict(
  function predict3D (line 38) | function predict3D(
  type ICompareInputDeltasConstants (line 57) | interface ICompareInputDeltasConstants extends IConstantsThis {
  function compareInputDeltas (line 61) | function compareInputDeltas(
  function compareInputDeltas3D (line 75) | function compareInputDeltas3D(
  function compareBiases (line 89) | function compareBiases(
  type ICompareFiltersDeltas (line 97) | interface ICompareFiltersDeltas extends IConstantsThis {
  function compareFilterDeltas (line 104) | function compareFilterDeltas(
  function compareFilterDeltas3D (line 117) | function compareFilterDeltas3D(
  type IFullyConnectedDefaultSettings (line 140) | interface IFullyConnectedDefaultSettings
  class FullyConnected (line 151) | class FullyConnected extends Filter {
    method bias (line 152) | get bias(): number {
    method biases (line 156) | get biases(): KernelOutput {
    method biases (line 160) | set biases(biases: KernelOutput) {
    method biasDeltas (line 164) | get biasDeltas(): KernelOutput {
    method biasDeltas (line 168) | set biasDeltas(biasDeltas: KernelOutput) {
    method constructor (line 176) | constructor(
    method validate (line 202) | validate(): void {
    method setupKernels (line 207) | setupKernels(): void {
    method predict (line 271) | predict(): void {
    method compare (line 279) | compare(): void {
  function fullyConnected (line 307) | function fullyConnected(

FILE: src/layer/gru.ts
  function gru (line 13) | function gru(

FILE: src/layer/input.ts
  class Input (line 11) | class Input extends EntryPoint {
    method constructor (line 13) | constructor(settings: ILayerSettings) {
    method setupKernels (line 20) | setupKernels(): void {
    method reuseKernels (line 35) | reuseKernels(layer: ILayer): void {
    method predict (line 40) | predict(inputs: KernelOutput): void {
    method predict1D (line 60) | predict1D(inputs: KernelOutput): void {
    method compare (line 69) | compare(): void {
    method learn (line 73) | learn(): void {}
  function input (line 76) | function input(settings: ILayerSettings): Input {

FILE: src/layer/internal.ts
  type InternalType (line 5) | type InternalType = new (settings: Partial<ILayerSettings>) => ILayer;
  method width (line 17) | get width(): number {
  method height (line 21) | get height(): number {
  method depth (line 25) | get depth(): number {
  method weights (line 29) | get weights(): KernelOutput | Input {
  method weights (line 33) | set weights(weights: KernelOutput | Input) {
  method deltas (line 37) | get deltas(): KernelOutput {
  method deltas (line 41) | set deltas(deltas: KernelOutput) {
  method toJSON (line 45) | toJSON(): Partial<ILayerJSON> {

FILE: src/layer/leaky-relu.ts
  function predict2D (line 7) | function predict2D(
  function predict3D (line 14) | function predict3D(
  function compare2D (line 21) | function compare2D(
  function compare3D (line 32) | function compare3D(
  class LeakyRelu (line 43) | class LeakyRelu extends Activation {
    method setupKernels (line 44) | setupKernels(): void {
    method predict (line 73) | predict(): void {
    method compare (line 80) | compare(): void {
  function leakyRelu (line 90) | function leakyRelu(

FILE: src/layer/lstm-cell.ts
  function lstmCell (line 11) | function lstmCell(

FILE: src/layer/modifier.ts
  type ModifierType (line 3) | type ModifierType = new (
  class Modifier (line 8) | class Modifier extends BaseLayer {
    method constructor (line 10) | constructor(inputLayer: ILayer, settings?: Partial<ILayerSettings>) {
    method validate (line 20) | validate(): void {

FILE: src/layer/multiply-element.ts
  function predict (line 7) | function predict(
  function compare (line 18) | function compare(
  class MultiplyElement (line 28) | class MultiplyElement extends Operator {
    method width (line 29) | get width(): number {
    method height (line 33) | get height(): number {
    method depth (line 37) | get depth(): number {
    method validate (line 41) | validate(): void {
    method setupKernels (line 46) | setupKernels(): void {
    method predict (line 58) | predict(): void {
    method compare (line 66) | compare(): void {
  function multiplyElement (line 80) | function multiplyElement(

FILE: src/layer/multiply.ts
  type IMultiplyConstants (line 11) | interface IMultiplyConstants extends IConstantsThis {
  function predict (line 15) | function predict(
  function compareFromX (line 27) | function compareFromX(
  function compareFromY (line 40) | function compareFromY(
  class Multiply (line 53) | class Multiply extends Operator {
    method width (line 57) | get width(): number {
    method width (line 61) | set width(width: number) {
    method height (line 65) | get height(): number {
    method height (line 69) | set height(height: number) {
    method depth (line 73) | get depth(): number {
    method depth (line 77) | set depth(depth: number) {
    method validate (line 81) | validate(): void {
    method setupKernels (line 90) | setupKernels(): void {
    method reuseKernels (line 114) | reuseKernels(layer: ILayer): void {
    method predict (line 120) | predict(): void {
    method compare (line 129) | compare(): void {
    method setupPraxis (line 154) | setupPraxis(): void {}
    method toJSON (line 156) | toJSON(): Partial<ILayerJSON> {
  function multiply (line 165) | function multiply(

FILE: src/layer/negative.ts
  function predict (line 6) | function predict(
  class Negative (line 13) | class Negative extends Modifier {
    method constructor (line 14) | constructor(inputLayer: ILayer, settings?: ILayerSettings) {
    method setupKernels (line 19) | setupKernels(): void {
    method predict (line 25) | predict(): void {
  function negative (line 32) | function negative(

FILE: src/layer/ones.ts
  class Ones (line 7) | class Ones extends Model {
    method constructor (line 8) | constructor(settings: ILayerSettings) {
  function ones (line 16) | function ones(settings: ILayerSettings): Ones {

FILE: src/layer/operator.ts
  type OperatorType (line 4) | type OperatorType = new (
  method constructor (line 13) | constructor(

FILE: src/layer/output.ts
  function output (line 7) | function output(settings: ILayerSettings, inputLayer: ILayer): ILayer {

FILE: src/layer/pool.ts
  function setSwitchY (line 19) | function setSwitchY(value: number): number {
  function setSwitchX (line 23) | function setSwitchX(value: number): number {
  type IPredictConstants (line 27) | interface IPredictConstants extends IConvolutionConstantsBase {
  function predict (line 32) | function predict(
  type ICompareConstants (line 67) | interface ICompareConstants extends IConvolutionConstantsBase {
  function compare (line 75) | function compare(
  function compare3D (line 126) | function compare3D(
  type IPoolSettings (line 179) | interface IPoolSettings
  class Pool (line 194) | class Pool extends Filter {
    method strideX (line 197) | get strideX(): number {
    method strideY (line 201) | get strideY(): number {
    method paddingX (line 205) | get paddingX(): number {
    method paddingY (line 209) | get paddingY(): number {
    method width (line 213) | get width(): number {
    method height (line 223) | get height(): number {
    method depth (line 233) | get depth(): number {
    method filterCount (line 237) | get filterCount(): number {
    method switchX (line 242) | get switchX(): KernelOutput {
    method switchX (line 246) | set switchX(switchX: KernelOutput) {
    method switchY (line 250) | get switchY(): KernelOutput {
    method switchY (line 254) | set switchY(switchY: KernelOutput) {
    method constructor (line 259) | constructor(settings: IPoolSettings, inputLayer: ILayer) {
    method setupKernels (line 272) | setupKernels(): void {
    method predict (line 315) | predict(): void {
    method compare (line 325) | compare(): void {
  function pool (line 346) | function pool(settings: IPoolSettings, inputLayer: ILayer): Pool {

FILE: src/layer/random.ts
  type IRandomSettings (line 6) | interface IRandomSettings extends ILayerSettings {
  class Random (line 15) | class Random extends Model implements ILayer {
    method constructor (line 17) | constructor(settings: Partial<IRandomSettings>) {
    method predict (line 31) | predict(): void {}
    method compare (line 33) | compare(): void {}
  function random (line 36) | function random(settings: IRandomSettings): Random {

FILE: src/layer/recurrent-connection.ts
  class RecurrentConnection (line 7) | class RecurrentConnection extends Internal {
    method setLayer (line 10) | setLayer(layer: ILayer): void {
    method width (line 14) | get width(): number {
    method width (line 19) | set width(value: number) {
    method height (line 23) | get height(): number {
    method height (line 28) | set height(value: number) {
    method deltas (line 32) | get deltas(): KernelOutput {
    method deltas (line 37) | set deltas(deltas: KernelOutput) {
    method weights (line 43) | get weights(): KernelOutput {
    method weights (line 48) | set weights(weights: KernelOutput) {
    method predict (line 54) | predict(): void {
    method compare (line 58) | compare(): void {
    method learn (line 62) | learn(): void {
    method setupKernels (line 66) | setupKernels(): void {
    method reuseKernels (line 72) | reuseKernels(): void {

FILE: src/layer/recurrent-input.ts
  type IRecurrentInput (line 7) | interface IRecurrentInput extends ILayer {
  class RecurrentInput (line 11) | class RecurrentInput extends Internal implements IRecurrentInput {
    method constructor (line 17) | constructor(recurrentInput: ILayer) {
    method width (line 23) | get width(): number {
    method height (line 27) | get height(): number {
    method depth (line 31) | get depth(): number {
    method deltas (line 35) | get deltas(): KernelOutput {
    method deltas (line 39) | set deltas(deltas: KernelOutput) {
    method weights (line 45) | get weights(): KernelOutput {
    method weights (line 49) | set weights(weights: KernelOutput) {
    method validate (line 55) | validate(): void {
    method setDimensions (line 70) | setDimensions(width: number, height: number): void {
    method predict (line 75) | predict(): void {
    method compare (line 79) | compare(): void {
    method learn (line 83) | learn(): void {
    method setupKernels (line 87) | setupKernels(): void {
    method reuseKernels (line 93) | reuseKernels(): void {

FILE: src/layer/recurrent-zeros.ts
  class RecurrentZeros (line 8) | class RecurrentZeros extends Internal implements IRecurrentInput {
    method constructor (line 14) | constructor(settings?: Partial<ILayerSettings>) {
    method setDimensions (line 21) | setDimensions(width: number, height: number): void {
    method setupKernels (line 32) | setupKernels(): void {
    method reuseKernels (line 38) | reuseKernels(): void {
    method predict (line 44) | predict(): void {
    method compare (line 48) | compare(): void {
    method learn (line 52) | learn(learningRate: number): void {
  function recurrentZeros (line 68) | function recurrentZeros(): RecurrentZeros {

FILE: src/layer/regression.ts
  class Regression (line 6) | class Regression extends BaseLayer {
    method constructor (line 8) | constructor(settings: ILayerSettings, inputLayer: ILayer) {
    method predict (line 14) | predict(): void {
    method learn (line 19) | learn(): void {
  function learn (line 25) | function learn(
  function regression (line 34) | function regression(

FILE: src/layer/relu.ts
  function predict2D (line 8) | function predict2D(
  function compare2D (line 15) | function compare2D(
  function predict3D (line 26) | function predict3D(
  function compare3D (line 33) | function compare3D(
  class Relu (line 44) | class Relu extends Activation {
    method setupKernels (line 45) | setupKernels(): void {
    method predict (line 74) | predict(): void {
    method compare (line 81) | compare(): void {
  function relu (line 90) | function relu(inputLayer: ILayer, settings?: ILayerSettings): Relu {

FILE: src/layer/rnn-cell.ts
  function rnnCell (line 9) | function rnnCell(

FILE: src/layer/sigmoid.ts
  function predict2D (line 8) | function predict2D(
  function predict3D (line 15) | function predict3D(
  function compare2D (line 24) | function compare2D(
  function compare3D (line 34) | function compare3D(
  class Sigmoid (line 44) | class Sigmoid extends Activation {
    method setupKernels (line 45) | setupKernels(): void {
    method predict (line 73) | predict(): void {
    method compare (line 80) | compare(): void {
    method learn (line 88) | learn(learningRate?: number): void {}
  function sigmoid (line 91) | function sigmoid(

FILE: src/layer/soft-max.ts
  type ISoftMaxConstants (line 17) | interface ISoftMaxConstants extends IConstantsThis {
  function getMaxValue (line 21) | function getMaxValue(
  function getMaxValue2D (line 35) | function getMaxValue2D(
  function getMaxValue3D (line 51) | function getMaxValue3D(
  function getSum (line 69) | function getSum(
  function getSum2D (line 80) | function getSum2D(
  function getSum3D (line 93) | function getSum3D(
  function getExponentials (line 108) | function getExponentials(
  function getExponentials2D (line 116) | function getExponentials2D(
  function getExponentials3D (line 124) | function getExponentials3D(
  function predict (line 134) | function predict(
  function predict2D (line 142) | function predict2D(
  function predict3D (line 150) | function predict3D(
  function compare (line 161) | function compare(
  function compare2D (line 173) | function compare2D(
  function compare3D (line 186) | function compare3D(
  function loss (line 204) | function loss(): number {
  class SoftMax (line 210) | class SoftMax extends Modifier {
    method constructor (line 215) | constructor(inputLayer: ILayer, settings?: ILayerSettings) {
    method setupKernels (line 234) | setupKernels(): void {
    method predict (line 291) | predict(): void {
    method compare (line 308) | compare(targetValues: KernelOutput): void {
  function softMax (line 324) | function softMax(

FILE: src/layer/svm.ts
  class SVM (line 6) | class SVM extends BaseLayer {
    method constructor (line 8) | constructor(inputLayer: ILayer, settings: ILayerSettings) {
    method predict (line 13) | predict(): void {
    method learn (line 19) | learn(): void {
  function svm (line 37) | function svm(inputLayer: ILayer, settings: ILayerSettings): SVM {

FILE: src/layer/tanh.ts
  function predict2D (line 8) | function predict2D(
  function predict3D (line 15) | function predict3D(
  function compare2D (line 22) | function compare2D(
  function compare3D (line 33) | function compare3D(
  class Tanh (line 44) | class Tanh extends Activation {
    method setupKernels (line 45) | setupKernels(): void {
    method predict (line 73) | predict(): void {
    method compare (line 80) | compare(): void {
  function tanh (line 89) | function tanh(inputLayer: ILayer, settings?: ILayerSettings): Tanh {

FILE: src/layer/target.ts
  function compare1D (line 8) | function compare1D(
  function compare2D (line 16) | function compare2D(
  type TargetType (line 27) | type TargetType = new (
  class Target (line 32) | class Target extends BaseLayer {
    method constructor (line 35) | constructor(settings: Partial<ILayerSettings>, inputLayer: ILayer) {
    method setupKernels (line 52) | setupKernels(): void {
    method predict (line 66) | predict(): void {
    method compare (line 73) | compare(targetValues: KernelOutput): void {
    method setupPraxis (line 87) | setupPraxis(): void {}
  function target (line 90) | function target(settings: ILayerSettings, inputLayer: ILayer): Target {

FILE: src/layer/transpose.ts
  function predict (line 6) | function predict(this: IKernelFunctionThis, value: number[][]): number {
  class Transpose (line 12) | class Transpose extends Modifier {
    method width (line 13) | get width(): number {
    method height (line 17) | get height(): number {
    method constructor (line 21) | constructor(inputLayer: ILayer) {
    method setupKernels (line 26) | setupKernels(): void {
    method predict (line 35) | predict(): void {
    method compare (line 41) | compare(): void {
  function transpose (line 48) | function transpose(inputLayer: ILayer): Transpose {

FILE: src/layer/types.ts
  class InternalModel (line 11) | class InternalModel {}
  type EntryPointType (line 13) | type EntryPointType = new (settings: Partial<ILayerSettings>) => ILayer;
  class EntryPoint (line 15) | class EntryPoint extends BaseLayer {}
  class Model (line 18) | class Model extends BaseLayer {
    method learn (line 19) | learn(learningRate?: number): void {

FILE: src/layer/zeros.ts
  class Zeros (line 5) | class Zeros extends Model {
    method constructor (line 6) | constructor(settings: ILayerSettings) {
    method predict (line 13) | predict(): void {
    method compare (line 17) | compare(): void {
  function zeros (line 24) | function zeros(settings: ILayerSettings): Zeros {

FILE: src/likely.test.ts
  function integer (line 7) | function integer(character: string): number {
  function character (line 15) | function character(string: string): number[] {

FILE: src/likely.ts
  type ILikelyNet (line 1) | interface ILikelyNet<InputType, OutputType> {
  function likely (line 5) | function likely<

FILE: src/lookup.ts
  type INumberHash (line 3) | interface INumberHash {
  type INumberArray (line 7) | interface INumberArray {
  type InputOutputValue (line 13) | type InputOutputValue = INumberArray | Partial<INumberHash>;
  type ITrainingDatum (line 15) | interface ITrainingDatum {
  type FormattableData (line 20) | type FormattableData =
  method toTable (line 33) | toTable(hashes: INumberHash[]): INumberHash {
  method toTable2D (line 44) | toTable2D(objects2D: INumberHash[][]): INumberHash {
  method toInputTable2D (line 61) | toInputTable2D(
  method toOutputTable2D (line 81) | toOutputTable2D(
  method toHash (line 104) | toHash(hash: INumberHash): INumberHash {
  method toArray (line 117) | toArray(
  method toArrayShort (line 130) | toArrayShort(lookup: INumberHash, object: INumberHash): Float32Array {
  method toArrays (line 140) | toArrays(
  method toObject (line 158) | toObject(lookup: INumberHash, array: number[] | Float32Array): INumberHa...
  method toObjectPartial (line 167) | toObjectPartial(
  method dataShape (line 188) | dataShape(data: FormattableData[] | FormattableData): string[] {
  method addKeys (line 255) | addKeys(value: number[] | INumberHash, table: INumberHash): INumberHash {

FILE: src/neural-network-gpu.ts
  type INeuralNetworkGPUDatumFormatted (line 26) | interface INeuralNetworkGPUDatumFormatted {
  type INeuralNetworkGPUPreppedTrainingData (line 31) | interface INeuralNetworkGPUPreppedTrainingData
  type ISizedKernelThis (line 37) | interface ISizedKernelThis extends IKernelFunctionThis {
  function weightedSumSigmoid (line 43) | function weightedSumSigmoid(
  function weightedSumRelu (line 57) | function weightedSumRelu(
  function weightedSumLeakyRelu (line 71) | function weightedSumLeakyRelu(
  function weightedSumTanh (line 85) | function weightedSumTanh(
  function calcErrorOutput (line 99) | function calcErrorOutput(output: number, target: number): number {
  function calcDeltasSigmoid (line 103) | function calcDeltasSigmoid(error: number, output: number): number {
  function calcDeltasRelu (line 108) | function calcDeltasRelu(error: number, output: number): number {
  function calcDeltasLeakyRelu (line 113) | function calcDeltasLeakyRelu(error: number, output: number): number {
  function calcDeltasTanh (line 118) | function calcDeltasTanh(error: number, output: number): number {
  function calcError (line 123) | function calcError(
  type ILearningKernelThis (line 136) | interface ILearningKernelThis extends IKernelFunctionThis {
  function calcChanges (line 143) | function calcChanges(
  function addWeights (line 153) | function addWeights(change: number, weight: number): number {
  function addBiases (line 157) | function addBiases(
  function mse (line 168) | function mse(this: ISizedKernelThis, errors: number[]): number {
  type INeuralNetworkGPUOptions (line 176) | interface INeuralNetworkGPUOptions extends INeuralNetworkOptions {
  type BackPropagateOutput (line 180) | type BackPropagateOutput = (
  type BackPropagateLayer (line 186) | type BackPropagateLayer = (
  class NeuralNetworkGPU (line 193) | class NeuralNetworkGPU<
    method constructor (line 263) | constructor(options: Partial<INeuralNetworkGPUOptions> = {}) {
    method initialize (line 269) | initialize(): void {
    method setActivation (line 278) | setActivation(): void {}
    method trainPattern (line 282) | trainPattern(
    method calculateTrainingError (line 299) | calculateTrainingError(data: INeuralNetworkGPUDatumFormatted[]): number {
    method adjustWeights (line 315) | adjustWeights(): void {
    method buildRunInput (line 320) | buildRunInput(): void {
    method buildCalculateDeltas (line 382) | buildCalculateDeltas(): void {
    method buildGetChanges (line 496) | buildGetChanges(): void {
    method getChanges (line 539) | getChanges(): void {
    method buildChangeBiases (line 557) | buildChangeBiases(): void {
    method changeBiases (line 570) | changeBiases(): void {
    method buildGetMSE (line 581) | buildGetMSE(): void {
    method run (line 614) | run(input: InputType): OutputType {
    method prepTraining (line 646) | prepTraining(
    method toFunction (line 683) | toFunction(): (input: InputType) => OutputType {
    method toJSON (line 689) | toJSON(): INeuralNetworkJSON {

FILE: src/neural-network-types.ts
  type INeuralNetworkOptions (line 2) | interface INeuralNetworkOptions {
  type INeuralNetworkTrainingOptions (line 16) | interface INeuralNetworkTrainingOptions {
  type INeuralNetworkTrainingCallback (line 72) | type INeuralNetworkTrainingCallback = (
  type INeuralNetworkState (line 76) | interface INeuralNetworkState {
  type INeuralNetworkTestResult (line 81) | interface INeuralNetworkTestResult {
  type INeuralNetworkBinaryTestResult (line 87) | interface INeuralNetworkBinaryTestResult

FILE: src/neural-network.bitwise.test.ts
  function testBitwise (line 5) | function testBitwise(
  function testBitwiseAdam (line 60) | function testBitwiseAdam(
  function testBitwiseAsync (line 119) | async function testBitwiseAsync(

FILE: src/neural-network.to-function.test.ts
  type IInput (line 28) | interface IInput extends INumberHash {
  type IOutput (line 34) | interface IOutput extends INumberHash {

FILE: src/neural-network.ts
  type NeuralNetworkFormatter (line 16) | type NeuralNetworkFormatter =
  function getTypedArrayFn (line 20) | function getTypedArrayFn(
  type NeuralNetworkActivation (line 43) | type NeuralNetworkActivation =
  type IJSONLayer (line 49) | interface IJSONLayer {
  type INeuralNetworkJSON (line 54) | interface INeuralNetworkJSON {
  type INeuralNetworkOptions (line 66) | interface INeuralNetworkOptions {
  function defaults (line 73) | function defaults(): INeuralNetworkOptions {
  type INeuralNetworkTrainOptionsJSON (line 81) | interface INeuralNetworkTrainOptionsJSON {
  type INeuralNetworkPreppedTrainingData (line 98) | interface INeuralNetworkPreppedTrainingData<T> {
  type INeuralNetworkTrainOptions (line 104) | interface INeuralNetworkTrainOptions {
  function trainDefaults (line 122) | function trainDefaults(): INeuralNetworkTrainOptions {
  type INeuralNetworkData (line 140) | type INeuralNetworkData = number[] | Float32Array | Partial<INumberHash>;
  type INeuralNetworkDatum (line 143) | interface INeuralNetworkDatum<InputType, OutputType> {
  type INeuralNetworkDatumFormatted (line 148) | interface INeuralNetworkDatumFormatted<T> {
  class NeuralNetwork (line 153) | class NeuralNetwork<
    method constructor (line 198) | constructor(
    method initialize (line 214) | initialize(): void {
    method setActivation (line 254) | setActivation(activation?: NeuralNetworkActivation): void {
    method isRunnable (line 280) | get isRunnable(): boolean {
    method run (line 284) | run(input: Partial<InputType>): OutputType {
    method _runInputSigmoid (line 309) | _runInputSigmoid(input: Float32Array): Float32Array {
    method _runInputRelu (line 336) | _runInputRelu(input: Float32Array): Float32Array {
    method _runInputLeakyRelu (line 363) | _runInputLeakyRelu(input: Float32Array): Float32Array {
    method _runInputTanh (line 390) | _runInputTanh(input: Float32Array): Float32Array {
    method verifyIsInitialized (line 422) | verifyIsInitialized(
    method updateTrainingOptions (line 443) | updateTrainingOptions(trainOpts: Partial<INeuralNetworkTrainOptions>):...
    method validateTrainingOptions (line 450) | validateTrainingOptions(options: INeuralNetworkTrainOptions): void {
    method getTrainOptsJSON (line 529) | getTrainOptsJSON(): INeuralNetworkTrainOptionsJSON {
    method setLogMethod (line 569) | setLogMethod(log: boolean | ((state: INeuralNetworkState) => void)): v...
    method logTrainingStatus (line 579) | logTrainingStatus(status: INeuralNetworkState): void {
    method calculateTrainingError (line 585) | calculateTrainingError(
    method trainPatterns (line 595) | trainPatterns(data: Array<INeuralNetworkDatumFormatted<Float32Array>>)...
    method trainingTick (line 601) | trainingTick(
    method prepTraining (line 643) | prepTraining(
    method train (line 665) | train(
    method trainAsync (line 682) | async trainAsync(
    method trainPattern (line 707) | trainPattern(
    method _calculateDeltasSigmoid (line 724) | _calculateDeltasSigmoid(target: Float32Array): void {
    method _calculateDeltasRelu (line 750) | _calculateDeltasRelu(target: Float32Array): void {
    method _calculateDeltasLeakyRelu (line 776) | _calculateDeltasLeakyRelu(target: Float32Array): void {
    method _calculateDeltasTanh (line 803) | _calculateDeltasTanh(target: Float32Array): void {
    method adjustWeights (line 833) | adjustWeights(): void {
    method _setupAdam (line 859) | _setupAdam(): void {
    method _adjustWeightsAdam (line 885) | _adjustWeightsAdam(): void {
    method validateData (line 946) | validateData(data: Array<INeuralNetworkDatumFormatted<Float32Array>>):...
    method validateInput (line 965) | validateInput(formattedInput: Float32Array): void {
    method formatData (line 974) | formatData(
    method addFormat (line 1049) | addFormat(data: INeuralNetworkDatum<InputType, OutputType>): void {
    method test (line 1070) | test(
    method toJSON (line 1165) | toJSON(): INeuralNetworkJSON {
    method fromJSON (line 1197) | fromJSON(json: INeuralNetworkJSON): this {
    method toFunction (line 1233) | toFunction(

FILE: src/praxis/arthur-deviation-biases.ts
  type IUpdateThis (line 6) | interface IUpdateThis extends IKernelFunctionThis {
  function update (line 12) | function update(
  type IArthurDeviationBiasesSettings (line 23) | interface IArthurDeviationBiasesSettings extends IPraxisSettings {
  class ArthurDeviationBiases (line 31) | class ArthurDeviationBiases extends BasePraxis {
    method constructor (line 34) | constructor(layer: ILayer, settings?: IArthurDeviationBiasesSettings) {
    method run (line 40) | run(layer: ILayer): KernelOutput {
    method setupKernels (line 44) | setupKernels(): void {
  function arthurDeviationBiases (line 54) | function arthurDeviationBiases(

FILE: src/praxis/arthur-deviation-biases.unit.test.ts
  type I (line 63) | interface I {

FILE: src/praxis/arthur-deviation-weights.ts
  function updateChange (line 14) | function updateChange(value: number): number {
  type IUpdateConstants (line 18) | interface IUpdateConstants extends IConstantsThis {
  function update (line 23) | function update(
  type IArthurDeviationWeightsSettings (line 42) | interface IArthurDeviationWeightsSettings extends IPraxisSettings {
  type IKernelMapResults (line 50) | interface IKernelMapResults extends ISubKernelsResults {
  class ArthurDeviationWeights (line 62) | class ArthurDeviationWeights extends BasePraxis {
    method learningRate (line 66) | get learningRate(): number {
    method momentum (line 70) | get momentum(): number {
    method weightsLayer (line 74) | get weightsLayer(): ILayer {
    method weightsLayer (line 78) | set weightsLayer(layer: ILayer) {
    method deltaLayer (line 82) | get deltaLayer(): ILayer {
    method deltaLayer (line 86) | set deltaLayer(layer: ILayer) {
    method incomingLayer (line 90) | get incomingLayer(): ILayer {
    method incomingLayer (line 94) | set incomingLayer(layer: ILayer) {
    method constructor (line 98) | constructor(layer: ILayer, settings?: IArthurDeviationWeightsSettings) {
    method run (line 104) | run(): KernelOutput {
    method setupKernels (line 115) | setupKernels(): void {
  function arthurDeviationWeights (line 132) | function arthurDeviationWeights(

FILE: src/praxis/base-praxis.ts
  type ILayerTemplate (line 4) | interface ILayerTemplate {
  type IPraxisJSON (line 10) | interface IPraxisJSON {
  type IPraxisSettings (line 16) | interface IPraxisSettings {
  type IPraxis (line 23) | interface IPraxis {
  method width (line 42) | get width(): number {
  method height (line 46) | get height(): number {
  method depth (line 50) | get depth(): number {
  method constructor (line 54) | constructor(
  method setupKernels (line 63) | setupKernels(): void {}
  method reuseKernels (line 65) | reuseKernels(praxis: IPraxis): void {
  method toJSON (line 83) | toJSON(): Partial<IPraxisSettings> {

FILE: src/praxis/momentum-root-mean-squared-propagation.ts
  function getMomentum (line 14) | function getMomentum(
  function clipByValue (line 22) | function clipByValue(value: number, max: number, min: number): number {
  type IUpdate (line 32) | interface IUpdate extends IConstantsThis {
  function update (line 41) | function update(
  function isClippedByValue (line 68) | function isClippedByValue(
  type IMomentumRootMeanSquaredPropagationSettings (line 82) | interface IMomentumRootMeanSquaredPropagationSettings
  class MomentumRootMeanSquaredPropagation (line 99) | class MomentumRootMeanSquaredPropagation extends BasePraxis {
    method clipValue (line 104) | get clipValue(): number {
    method decayRate (line 108) | get decayRate(): number {
    method learningRate (line 112) | get learningRate(): number {
    method regularizationStrength (line 116) | get regularizationStrength(): number {
    method smoothEps (line 120) | get smoothEps(): number {
    method constructor (line 124) | constructor(
    method run (line 133) | run(layer: ILayer): KernelOutput {
    method setupKernels (line 142) | setupKernels(): void {
  function momentumRootMeanSquaredPropagation (line 164) | function momentumRootMeanSquaredPropagation(

FILE: src/recurrent.baseline.test.ts
  class MockRandomMatrix (line 25) | class MockRandomMatrix extends Matrix {
    method constructor (line 26) | constructor(rows: number, columns: number, std: number) {
  function asArrayOfArrayOfNumber (line 43) | function asArrayOfArrayOfNumber(v: KernelOutput | Input): number[][] {
  function asMatrix (line 49) | function asMatrix(v?: Matrix): Matrix {
  function setupNets (line 66) | function setupNets(): {
  function testRecurrentLayerSet (line 96) | function testRecurrentLayerSet(
  function testRecurrentLayerSet (line 256) | function testRecurrentLayerSet(index: number) {
  function testRecurrentModel (line 567) | function testRecurrentModel() {
  function testRecurrentLayerSetWeights (line 659) | function testRecurrentLayerSetWeights(

FILE: src/recurrent.end-to-end.test.ts
  function deltasAreZero (line 64) | function deltasAreZero() {
  function deltasAreSet (line 74) | function deltasAreSet() {
  function modelWeightsAreUpdated (line 84) | function modelWeightsAreUpdated() {
  function modelDeltasAreZero (line 104) | function modelDeltasAreZero() {

FILE: src/recurrent.ts
  type IRecurrentTrainingOptions (line 36) | interface IRecurrentTrainingOptions
  type IRecurrentOptions (line 41) | interface IRecurrentOptions extends IFeedForwardOptions {
  type IRecurrentPreppedTrainingData (line 51) | interface IRecurrentPreppedTrainingData<T> {
  class Recurrent (line 57) | class Recurrent<
    method constructor (line 70) | constructor(
    method _connectLayers (line 78) | _connectLayers(): {
    method _connectLayersDeep (line 102) | _connectLayersDeep(): ILayer[] {
    method _connectHiddenLayers (line 188) | _connectHiddenLayers(previousLayer: ILayer): ILayer[] {
    method initialize (line 207) | initialize(): void {
    method initializeDeep (line 230) | initializeDeep(): void {
    method run (line 242) | run(inputs: T[]): T[] {
    method runInput (line 253) | runInput(input: KernelOutput): KernelOutput {
    method runInputs (line 257) | runInputs(inputs: T[]): KernelOutput {
    method train (line 277) | train(
    method end (line 297) | end(): void {
    method transferData (line 308) | transferData(formattedData: T[][]): T[][] {
    method _prepTraining (line 314) | _prepTraining(
    method _calculateTrainingError (line 339) | _calculateTrainingError(data: T[][]): number {
    method formatData (line 363) | formatData(data: Float32Array): Float32Array {
    method _calculateDeltas (line 369) | _calculateDeltas(target: T[]): void {
    method adjustWeights (line 385) | adjustWeights(): void {
    method _trainPatterns (line 394) | _trainPatterns(data: T[][]): void {
    method _trainPattern (line 402) | _trainPattern(inputs: T[], logErrorRate: boolean): KernelOutput | null {

FILE: src/recurrent.unit.test.ts
  function copy2D (line 17) | function copy2D(matrix: Partial<Matrix> & number[][]) {
  class SuperLayer (line 235) | class SuperLayer extends Filter {
    method constructor (line 238) | constructor(inputLayer: ILayer) {
    method setupKernels (line 250) | setupKernels() {}
    method reuseKernels (line 252) | reuseKernels() {}
    method predict (line 254) | predict(inputs: KernelOutput) {
    method compare (line 258) | compare() {
    method learn (line 262) | learn() {}

FILE: src/recurrent/gru-time-step.ts
  class GRUTimeStep (line 7) | class GRUTimeStep extends RNNTimeStep {
    method getHiddenLayer (line 8) | getHiddenLayer(hiddenSize: number, prevSize: number): IRNNHiddenLayer {
    method getEquation (line 12) | getEquation(

FILE: src/recurrent/gru.test.ts
  function compare (line 111) | function compare(left: IMatrixJSON, right: IMatrixJSON) {

FILE: src/recurrent/gru.ts
  type IGRUHiddenLayer (line 6) | interface IGRUHiddenLayer extends IRNNHiddenLayer {
  class GRU (line 18) | class GRU extends RNN {
    method getHiddenLayer (line 19) | getHiddenLayer(hiddenSize: number, prevSize: number): IRNNHiddenLayer {
    method getEquation (line 23) | getEquation(
  function getGRUHiddenLayer (line 38) | function getGRUHiddenLayer(
  function getGRUEquation (line 61) | function getGRUEquation(

FILE: src/recurrent/lstm-time-step.test.ts
  class MockRandomMatrix (line 8) | class MockRandomMatrix {
    method rows (line 9) | get rows(): number {
    method columns (line 13) | get columns(): number {
    method weights (line 17) | get weights(): Float32Array {
    method weights (line 21) | set weights(weights: Float32Array) {
    method deltas (line 25) | get deltas(): Float32Array {
    method deltas (line 29) | set deltas(deltas: Float32Array) {
    method setWeight (line 33) | get setWeight(): (row: number, column: number, value: number) => void {
    method getWeight (line 37) | get getWeight(): (row: number, column: number) => number {
    method setDelta (line 41) | get setDelta(): (row: number, column: number, value: number) => void {
    method getDelta (line 45) | get getDelta(): (row: number, column: number) => number {
    method constructor (line 50) | constructor(rows: number, columns: number, std: number) {

FILE: src/recurrent/lstm-time-step.ts
  class LSTMTimeStep (line 7) | class LSTMTimeStep extends RNNTimeStep {
    method getHiddenLayer (line 8) | getHiddenLayer(hiddenSize: number, prevSize: number): IRNNHiddenLayer {
    method getEquation (line 12) | getEquation(

FILE: src/recurrent/lstm.test.ts
  function compare (line 50) | function compare(left: IMatrixJSON, right: Matrix) {

FILE: src/recurrent/lstm.ts
  type ILSTMHiddenLayer (line 6) | interface ILSTMHiddenLayer extends IRNNHiddenLayer {
  class LSTM (line 21) | class LSTM extends RNN {
    method getHiddenLayer (line 22) | getHiddenLayer(hiddenSize: number, prevSize: number): IRNNHiddenLayer {
    method getEquation (line 26) | getEquation(
  function getHiddenLSTMLayer (line 41) | function getHiddenLSTMLayer(
  function getLSTMEquation (line 67) | function getLSTMEquation(

FILE: src/recurrent/matrix/add-b.ts
  function addB (line 6) | function addB(product: Matrix, left: Matrix, right: Matrix): void {

FILE: src/recurrent/matrix/add.ts
  function add (line 6) | function add(product: Matrix, left: Matrix, right: Matrix): void {

FILE: src/recurrent/matrix/all-ones.ts
  function allOnes (line 6) | function allOnes(product: Matrix): void {

FILE: src/recurrent/matrix/clone-negative.ts
  function cloneNegative (line 3) | function cloneNegative(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/clone.ts
  function clone (line 3) | function clone(product: Matrix): Matrix {

FILE: src/recurrent/matrix/copy.ts
  function copy (line 3) | function copy(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/equation.test.ts
  function fourSquareMatrix (line 5) | function fourSquareMatrix(value: number): Matrix {
  function getState (line 13) | function getState(props: Partial<IState>): IState {

FILE: src/recurrent/matrix/equation.ts
  type PropagateIndex (line 20) | type PropagateIndex = (product: Matrix, left: Matrix, index: number) => ...
  type PropagateProduct (line 21) | type PropagateProduct = (product: Matrix) => void;
  type PropagateProductFromLeft (line 22) | type PropagateProductFromLeft = (product: Matrix, left: Matrix) => void;
  type PropagateProductFromLeftRight (line 23) | type PropagateProductFromLeftRight = (
  type PropagateFunction (line 28) | type PropagateFunction =
  type IState (line 34) | interface IState {
  class Equation (line 43) | class Equation {
    method add (line 48) | add(left: Matrix, right: Matrix): Matrix {
    method allOnes (line 67) | allOnes(rows: number, columns: number): Matrix {
    method cloneNegative (line 81) | cloneNegative(matrix: Matrix): Matrix {
    method subtract (line 98) | subtract(left: Matrix, right: Matrix): Matrix {
    method multiply (line 112) | multiply(left: Matrix, right: Matrix): Matrix {
    method multiplyElement (line 134) | multiplyElement(left: Matrix, right: Matrix): Matrix {
    method relu (line 156) | relu(matrix: Matrix): Matrix {
    method input (line 173) | input(input: Matrix): Matrix {
    method inputMatrixToRow (line 193) | inputMatrixToRow(matrix: Matrix): Matrix {
    method sigmoid (line 215) | sigmoid(matrix: Matrix): Matrix {
    method tanh (line 232) | tanh(matrix: Matrix): Matrix {
    method observe (line 250) | observe(matrix: Matrix): Matrix {
    method runIndex (line 264) | runIndex(rowIndex = 0): Matrix {
    method runInput (line 285) | runInput(inputValue: Float32Array): Matrix {
    method backpropagate (line 306) | backpropagate(): Matrix {
    method backpropagateIndex (line 327) | backpropagateIndex(rowIndex = 0): Matrix {
    method predictTarget (line 350) | predictTarget(input: Float32Array, target: Float32Array): number {
    method predictTargetIndex (line 368) | predictTargetIndex(input: number, target: number): number {

FILE: src/recurrent/matrix/index.ts
  type IMatrixJSON (line 3) | interface IMatrixJSON {
  class Matrix (line 11) | class Matrix {
    method constructor (line 17) | constructor(rows?: number, columns?: number) {
    method getWeight (line 25) | getWeight(row: number, col: number): number {
    method setWeight (line 37) | setWeight(row: number, col: number, v: number): Matrix {
    method getDelta (line 50) | getDelta(row: number, col: number): number {
    method setDelta (line 62) | setDelta(row: number, col: number, v: number): Matrix {
    method toJSON (line 75) | toJSON(): IMatrixJSON {
    method fromJSON (line 83) | static fromJSON(json: IMatrixJSON): Matrix {
    method fromArray (line 93) | static fromArray(weights: Float32Array[] | number[][]): Matrix {
    method deltasToArray (line 99) | deltasToArray(): number[][] {
    method weightsToArray (line 103) | weightsToArray(): number[][] {
    method toArray (line 107) | toArray(prop: 'weights' | 'deltas' = 'weights'): number[][] {
    method fromArray (line 124) | fromArray(
    method iterate (line 150) | iterate(callbacks: {

FILE: src/recurrent/matrix/max-i.ts
  function maxI (line 3) | function maxI(matrix: Matrix): number {

FILE: src/recurrent/matrix/multiply-b.ts
  function multiplyB (line 6) | function multiplyB(product: Matrix, left: Matrix, right: Matrix): void {

FILE: src/recurrent/matrix/multiply-element-b.ts
  function multiplyElementB (line 6) | function multiplyElementB(

FILE: src/recurrent/matrix/multiply-element.ts
  function multiplyElement (line 3) | function multiplyElement(

FILE: src/recurrent/matrix/multiply.ts
  function multiply (line 6) | function multiply(product: Matrix, left: Matrix, right: Matrix): void {

FILE: src/recurrent/matrix/ones-matrix.ts
  class OnesMatrix (line 6) | class OnesMatrix extends Matrix {
    method constructor (line 7) | constructor(rows: number, columns: number) {

FILE: src/recurrent/matrix/random-matrix.ts
  class RandomMatrix (line 6) | class RandomMatrix extends Matrix {
    method constructor (line 9) | constructor(rows: number, columns: number, std: number) {

FILE: src/recurrent/matrix/random-n-matrix.ts
  class RandomNMatrix (line 4) | class RandomNMatrix extends Matrix {
    method constructor (line 8) | constructor(rows: number, columns: number, mu: number, std: number) {
    method fillRandN (line 18) | fillRandN(): void {

FILE: src/recurrent/matrix/relu-b.ts
  function reluB (line 6) | function reluB(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/relu.ts
  function relu (line 7) | function relu(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/row-pluck-b.ts
  function rowPluckB (line 6) | function rowPluckB(

FILE: src/recurrent/matrix/row-pluck.ts
  function rowPluck (line 3) | function rowPluck(

FILE: src/recurrent/matrix/sample-i.ts
  function sampleI (line 4) | function sampleI(matrix: Matrix): number {

FILE: src/recurrent/matrix/sigmoid-b.ts
  function sigmoidB (line 3) | function sigmoidB(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/sigmoid.ts
  function sigmoid (line 3) | function sigmoid(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/softmax.ts
  function softmax (line 3) | function softmax(matrix: Matrix): Matrix {

FILE: src/recurrent/matrix/tanh-b.ts
  function tanhB (line 3) | function tanhB(product: Matrix, left: Matrix): void {

FILE: src/recurrent/matrix/tanh.ts
  function tanh (line 3) | function tanh(product: Matrix, left: Matrix): void {

FILE: src/recurrent/rnn-data-types.ts
  type Value (line 3) | type Value = string | number | boolean | string[] | number[] | boolean[];
  type IRNNDatum (line 5) | interface IRNNDatum {
  type ITimeStepObject (line 10) | interface ITimeStepObject {
  type TimeStepArray (line 14) | type TimeStepArray = number[];
  type TimeStepValue (line 16) | type TimeStepValue = Array<
  type ITimeStepRNNDatum (line 20) | interface ITimeStepRNNDatum {

FILE: src/recurrent/rnn-time-step.ts
  type ValuesOf (line 40) | type ValuesOf<
  type IRNNTimeStepOptions (line 44) | interface IRNNTimeStepOptions extends IRNNTimeStepJSONOptions {
  type IRNNTimeStepJSONOptions (line 57) | interface IRNNTimeStepJSONOptions {
  type IRNNTimeStepJSON (line 69) | interface IRNNTimeStepJSON {
  type IMisclass (line 81) | interface IMisclass {
  type ITestResults (line 86) | interface ITestResults {
  type IRNNTimeStepModel (line 92) | interface IRNNTimeStepModel {
  class RNNTimeStep (line 112) | class RNNTimeStep extends RNN {
    method constructor (line 133) | constructor(
    method createInputMatrix (line 148) | createInputMatrix(): RandomMatrix {
    method createOutputMatrices (line 152) | createOutputMatrices(): { outputConnector: RandomMatrix; output: Matri...
    method bindEquation (line 163) | bindEquation(): void {
    method initialize (line 200) | initialize(): void {
    method mapModel (line 206) | mapModel(): IRNNTimeStepModel {
    method backpropagate (line 236) | backpropagate(): void {
    method run (line 244) | run<InputType extends InputOutputValue | InputOutputValue[]>(
    method forecast (line 270) | forecast<InputType extends InputOutputValue | InputOutputValue[]>(
    method forecastArray (line 297) | forecastArray(input: Float32Array, count = 1): Float32Array {
    method forecastArrayOfArray (line 330) | forecastArrayOfArray(input: Float32Array[], count = 1): Float32Array[] {
    method forecastArrayOfObject (line 355) | forecastArrayOfObject(input: INumberHash[], count = 1): INumberHash[] {
    method train (line 376) | train(
    method trainArrayOfArray (line 429) | trainArrayOfArray(input: Float32Array[]): number {
    method trainPattern (line 447) | trainPattern(input: Float32Array[], logErrorRate?: boolean): number {
    method setSize (line 458) | setSize(data: FormattableData[]): void {
    method verifySize (line 498) | verifySize(): void {
    method runArray (line 506) | runArray(input: Float32Array): number {
    method runArrayOfArray (line 520) | runArrayOfArray(input: Float32Array[]): Float32Array {
    method runObject (line 536) | runObject(input: INumberHash): INumberHash {
    method runArrayOfObject (line 566) | runArrayOfObject(input: INumberHash[]): INumberHash {
    method runArrayOfObjectOfArray (line 585) | runArrayOfObjectOfArray(input: INumberHash[]): INumberHash {
    method end (line 600) | end(): void {
    method requireInputOutputOfOne (line 606) | requireInputOutputOfOne(): void {
    method formatArray (line 616) | formatArray(data: number[]): Float32Array[][] {
    method formatArrayOfArray (line 626) | formatArrayOfArray(data: number[][]): Float32Array[][] {
    method formatArrayOfObject (line 648) | formatArrayOfObject(data: INumberHash[]): Float32Array[][] {
    method formatArrayOfObjectMulti (line 663) | formatArrayOfObjectMulti(data: INumberHash[]): Float32Array[][] {
    method formatArrayOfDatumOfArray (line 679) | formatArrayOfDatumOfArray(data: ITrainingDatum[]): Float32Array[][] {
    method formatArrayOfDatumOfObject (line 695) | formatArrayOfDatumOfObject(data: ITrainingDatum[]): Float32Array[][] {
    method formatArrayOfArrayOfArray (line 721) | formatArrayOfArrayOfArray(data: number[][][]): Float32Array[][] {
    method formatArrayOfArrayOfObject (line 730) | formatArrayOfArrayOfObject(data: INumberHash[][]): Float32Array[][] {
    method formatArrayOfDatumOfArrayOfArray (line 754) | formatArrayOfDatumOfArrayOfArray(data: ITrainingDatum[]): Float32Array...
    method formatArrayOfDatumOfArrayOfObject (line 776) | formatArrayOfDatumOfArrayOfObject(
    method formatData (line 814) | formatData(data: FormattableData[]): Float32Array[][] {
    method test (line 849) | test(data: FormattableData[]): ITestResults {
    method addFormat (line 887) | addFormat(value: FormattableData): void {
    method toJSON (line 971) | toJSON(): IRNNTimeStepJSON {
    method fromJSON (line 1000) | fromJSON(json: IRNNTimeStepJSON): this {
    method toFunction (line 1046) | toFunction(cb?: (src: string) => string): RNNTimeStepFunction {
  type RNNTimeStepFunction (line 1323) | type RNNTimeStepFunction = <

FILE: src/recurrent/rnn.test.ts
  function notZero (line 7) | function notZero(v: number) {
  function xorNet (line 193) | function xorNet() {
  function checkExploded (line 315) | function checkExploded() {
  function compare (line 374) | function compare(left: IMatrixJSON, right: Matrix) {

FILE: src/recurrent/rnn.ts
  type IRNNModel (line 19) | interface IRNNModel {
  type IRNNOptions (line 30) | interface IRNNOptions {
  type IRNNJSONOptions (line 44) | interface IRNNJSONOptions {
  type IRNNTrainingOptions (line 57) | interface IRNNTrainingOptions {
  type IRNNJSONTrainOptions (line 68) | interface IRNNJSONTrainOptions {
  type IRNNHiddenLayer (line 89) | interface IRNNHiddenLayer {
  type IRNNHiddenLayerModel (line 93) | interface IRNNHiddenLayerModel extends IRNNHiddenLayer {
  type IRNNStatus (line 117) | interface IRNNStatus {
  type IRNNPreppedTrainingData (line 122) | interface IRNNPreppedTrainingData {
  class RNN (line 128) | class RNN {
    method constructor (line 147) | constructor(options: Partial<IRNNOptions & IRNNTrainingOptions> = {}) {
    method initialize (line 159) | initialize(): void {
    method createHiddenLayers (line 168) | createHiddenLayers(): IRNNHiddenLayer[] {
    method getHiddenLayer (line 184) | getHiddenLayer(hiddenSize: number, prevSize: number): IRNNHiddenLayer {
    method getEquation (line 195) | getEquation(
    method createInputMatrix (line 219) | createInputMatrix(): RandomMatrix {
    method createOutputMatrices (line 230) | createOutputMatrices(): { outputConnector: RandomMatrix; output: Matri...
    method bindEquation (line 244) | bindEquation(): void {
    method mapModel (line 283) | mapModel(): IRNNModel {
    method trainInput (line 318) | trainInput(input: number[]): number {
    method backpropagate (line 344) | backpropagate(input: number[]): void {
    method adjustWeights (line 355) | adjustWeights(): void {
    method isRunnable (line 391) | get isRunnable(): boolean {
    method checkRunnable (line 400) | checkRunnable(): void {
    method run (line 406) | run(rawInput: Value = [], isSampleI = false, temperature = 1): string {
    method verifyIsInitialized (line 487) | verifyIsInitialized(): void {
    method updateTrainingOptions (line 502) | updateTrainingOptions(options: Partial<IRNNTrainingOptions>): void {
    method validateTrainingOptions (line 510) | validateTrainingOptions(options: INeuralNetworkTrainOptions): void {
    method setLogMethod (line 555) | setLogMethod(log: Log | undefined | boolean): void {
    method prepTraining (line 565) | protected prepTraining(
    method train (line 587) | train(
    method addFormat (line 648) | addFormat(data: Value): void {}
    method formatData (line 650) | formatData(data: Value[]): number[][] {
    method toJSON (line 658) | toJSON(): IRNNJSON {
    method fromJSON (line 688) | fromJSON(json: IRNNJSON): this {
    method toFunction (line 740) | toFunction(cb?: (src: string) => string): RNNFunction {
    method trainPattern (line 959) | trainPattern(input: number[], logErrorRate?: boolean): number {
  type IRNNJSON (line 971) | interface IRNNJSON {
  function last (line 981) | function last<T>(values: T[]): T {
  type RNNFunction (line 985) | type RNNFunction = (

FILE: src/test-utils.ts
  function onePlusPlus3D (line 13) | function onePlusPlus3D(width: number, height: number, depth: number): nu...
  function onePlusPlus2D (line 30) | function onePlusPlus2D(width: number, height: number): number[][] {
  function zero3D (line 43) | function zero3D(width: number, height: number, depth: number): number[][...
  function zero2D (line 59) | function zero2D(width: number, height: number): number[][] {
  function allDeltas (line 90) | function allDeltas(model: any, fn: any): void {
  function allMatrices (line 109) | function allMatrices(model: any, fn: any): void {
  function shave (line 145) | function shave(value: Float32Array): Float32Array {
  function shave2D (line 153) | function shave2D(value: Float32Array[]): Float32Array[] {
  function shave3D (line 161) | function shave3D(value: Float32Array[][]): Float32Array[][] {
  function expectFunction (line 171) | function expectFunction(source: string, fn: Function): void {
  class TestLayer (line 175) | class TestLayer extends BaseLayer {
    method width (line 176) | get width(): number {
    method height (line 179) | get height(): number {
    method depth (line 182) | get depth(): number {
    method constructor (line 185) | constructor(settings: ILayerSettings) {
  function mockLayer (line 190) | function mockLayer(settings: ILayerSettings = {}): ILayer {
  function mockTexture (line 194) | function mockTexture(settings?: Partial<IGPUTextureSettings>): Texture {
  function mockPraxis (line 206) | function mockPraxis(layerTemplate: ILayerTemplate, praxisSettings: Parti...
  type IWithCompareKernel (line 228) | interface IWithCompareKernel {
  type IWithPredictKernel (line 232) | interface IWithPredictKernel {
  type IWithPredictKernelMap (line 236) | interface IWithPredictKernelMap {

FILE: src/utilities/array-lookup-table.ts
  class ArrayLookupTable (line 1) | class ArrayLookupTable {
    method constructor (line 5) | constructor(

FILE: src/utilities/cast.ts
  function arraysToFloat32Arrays (line 1) | function arraysToFloat32Arrays(arrays: number[][]): Float32Array[] {
  function inputOutputArraysToFloat32Arrays (line 9) | function inputOutputArraysToFloat32Arrays(
  function arrayToFloat32Arrays (line 23) | function arrayToFloat32Arrays(array: number[]): Float32Array[] {
  function inputOutputArrayToFloat32Arrays (line 31) | function inputOutputArrayToFloat32Arrays(
  function arrayToFloat32Array (line 45) | function arrayToFloat32Array(array: number[]): Float32Array {
  function objectsToFloat32Arrays (line 49) | function objectsToFloat32Arrays(
  function inputOutputObjectsToFloat32Arrays (line 68) | function inputOutputObjectsToFloat32Arrays(
  function objectToFloat32Arrays (line 100) | function objectToFloat32Arrays(
  function inputOutputObjectToFloat32Arrays (line 111) | function inputOutputObjectToFloat32Arrays(
  function objectToFloat32Array (line 127) | function objectToFloat32Array(

FILE: src/utilities/data-formatter.ts
  type IDataFormatter (line 3) | interface IDataFormatter {
  class DataFormatter (line 18) | class DataFormatter implements IDataFormatter {
    method constructor (line 25) | constructor(private values?: Array<IRNNDatum | Value>, maxThreshold = ...
    method setup (line 31) | setup(values: Array<IRNNDatum | Value>, maxThreshold = 0): void {
    method buildCharactersFromIterable (line 46) | buildCharactersFromIterable(values: Array<IRNNDatum | Value>): void {
    method addCharacters (line 124) | addCharacters(
    method buildTables (line 136) | buildTables(maxThreshold: number): void {
    method toIndexes (line 153) | toIndexes(value: Value, maxThreshold = 0): number[] {
    method toIndexesInputOutput (line 179) | toIndexesInputOutput(
    method toIndexesValue (line 190) | toIndexesValue(
    method toCharacters (line 215) | toCharacters(indices: number[], maxThreshold = 0): string[] {
    method toString (line 237) | toString(indices: number[], maxThreshold: number): string {
    method addInputOutput (line 241) | addInputOutput(): void {
    method addUnrecognized (line 246) | addUnrecognized(): void {
    method fromAllPrintable (line 250) | static fromAllPrintable(
    method fromAllPrintableInputOutput (line 260) | static fromAllPrintableInputOutput(
    method fromStringInputOutput (line 270) | static fromStringInputOutput(
    method fromArrayInputOutput (line 282) | static fromArrayInputOutput(
    method fromString (line 305) | static fromString(string: string, maxThreshold = 0): DataFormatter {
    method toJSON (line 310) | toJSON(): IDataFormatterJSON {
    method fromJSON (line 323) | static fromJSON(json: IDataFormatterJSON): DataFormatter {
    method addSpecial (line 334) | addSpecial(special: string | number, character = null): void {
    method toFunctionString (line 341) | toFunctionString(): string {
    method formatDataIn (line 354) | formatDataIn(input?: Value, output?: Value): number[] {
    method formatDataOut (line 365) | formatDataOut(input: number[], output: number[]): string {
    method format (line 369) | format(data: Array<IRNNDatum | Value>): number[][] {
  function validateAndCast (line 412) | function validateAndCast(value: Value): string | string[] {
  type IDataFormatterJSON (line 429) | interface IDataFormatterJSON {

FILE: src/utilities/flatten-layers.ts
  function flattenLayers (line 4) | function flattenLayers(layers: ILayer[]): ILayer[] {

FILE: src/utilities/kernel.ts
  function setup (line 22) | function setup(value: GPU): void {
  function teardown (line 29) | function teardown(): void {
  function makeKernel (line 36) | function makeKernel<
  function makeKernelMap (line 54) | function makeKernelMap<
  function kernelInput (line 87) | function kernelInput(value: number[], size: OutputDimensions): Input {
  function release (line 94) | function release(possibleTexture: KernelOutput | Input): void {
  function clear (line 103) | function clear(value: KernelOutput): void {
  function clone (line 141) | function clone(value: KernelOutput): KernelOutput {

FILE: src/utilities/layer-from-json.ts
  function layerFromJSON (line 13) | function layerFromJSON(

FILE: src/utilities/layer-setup.ts
  type IStride (line 3) | interface IStride {
  function getStride (line 8) | function getStride(
  type IPadding (line 27) | interface IPadding {
  function getPadding (line 32) | function getPadding(

FILE: src/utilities/layer-size.ts
  function checkSameSize (line 3) | function checkSameSize(layer1: ILayer, layer2: ILayer): void {

FILE: src/utilities/lookup-table.ts
  type LookupTableProp (line 3) | type LookupTableProp = 'input' | 'output';
  class LookupTable (line 5) | class LookupTable {
    method constructor (line 9) | constructor(

FILE: src/utilities/max.ts
  function max (line 1) | function max(

FILE: src/utilities/mse.ts
  function mse (line 1) | function mse(errors: Float32Array): number {

FILE: src/utilities/ones.ts
  function ones (line 1) | function ones(size: number): Float32Array {
  function ones2D (line 5) | function ones2D(width: number, height: number): Float32Array[] {

FILE: src/utilities/random-weight.ts
  function randomWeight (line 1) | function randomWeight(): number {

FILE: src/utilities/random.ts
  function randomFloat (line 6) | function randomFloat(min: number, max: number): number {
  function gaussRandom (line 14) | function gaussRandom(): number {
  function randomInteger (line 36) | function randomInteger(min: number, max: number): number {
  function randomN (line 45) | function randomN(mu: number, std: number): number {

FILE: src/utilities/randos.ts
  function randos (line 7) | function randos(size: number, std: number | null = null): Float32Array {
  function randos2D (line 24) | function randos2D(
  function randos3D (line 39) | function randos3D(

FILE: src/utilities/range.ts
  function range (line 7) | function range(start: number, end: number): number[] {

FILE: src/utilities/to-array.ts
  function toArray (line 1) | function toArray(

FILE: src/utilities/to-svg.ts
  type LineDrawInfo (line 18) | interface LineDrawInfo {
  type NodeDrawInfo (line 24) | interface NodeDrawInfo {
  type BaseDrawArgs (line 29) | interface BaseDrawArgs {
  type InputDrawArgs (line 37) | interface InputDrawArgs extends BaseDrawArgs {
  function drawInput (line 44) | function drawInput({
  type NeuronDrawArgs (line 81) | interface NeuronDrawArgs extends BaseDrawArgs {
  function drawNeuron (line 86) | function drawNeuron({
  type OutputDrawArgs (line 104) | interface OutputDrawArgs extends BaseDrawArgs {
  function drawOutput (line 110) | function drawOutput({
  type BackwardConnectionsDrawArgs (line 136) | interface BackwardConnectionsDrawArgs extends BaseDrawArgs {
  function drawBackwardConnections (line 143) | function drawBackwardConnections({
  type NeuralNetworkDrawOptions (line 162) | interface NeuralNetworkDrawOptions {
  function neuralNetworkToInnerSVG (line 175) | function neuralNetworkToInnerSVG(
  type RecurrentConnectionsDrawArgs (line 216) | interface RecurrentConnectionsDrawArgs extends BaseDrawArgs {
  function drawRecurrentConnections (line 221) | function drawRecurrentConnections({
  type RecurrentNeuralNetworkDrawOptions (line 247) | interface RecurrentNeuralNetworkDrawOptions
  function rnnToInnerSVG (line 252) | function rnnToInnerSVG(
  function getFeedForwardLayers (line 280) | function getFeedForwardLayers(network: FeedForward): ISimpleNet {
  function getRecurrentLayers (line 314) | function getRecurrentLayers(network: Recurrent): ISimpleNet {
  function wrapOuterSVG (line 341) | function wrapOuterSVG(
  function getNeuralNetworkJSONSizes (line 355) | function getNeuralNetworkJSONSizes(json: INeuralNetworkJSON): number[] {
  function getNeuralNetworkSizes (line 359) | function getNeuralNetworkSizes<
  function getRNNSizes (line 387) | function getRNNSizes(
  function defaultOptions (line 395) | function defaultOptions(): RecurrentNeuralNetworkDrawOptions {
  type ISimpleNet (line 429) | interface ISimpleNet {
  type ISizes (line 434) | interface ISizes {
  function toSVG (line 438) | function toSVG<
  function checkSizes (line 566) | function checkSizes(

FILE: src/utilities/traverse-layers-excluding-from.ts
  function traverseLayersExcludingFrom (line 3) | function traverseLayersExcludingFrom(

FILE: src/utilities/traverse-layers-from.ts
  function traverseLayersFrom (line 3) | function traverseLayersFrom(

FILE: src/utilities/values-2d.ts
  function values2D (line 6) | function values2D(

FILE: src/utilities/values-3d.ts
  function values3D (line 6) | function values3D(

FILE: src/utilities/values.ts
  function values (line 4) | function values(size: number, value: number): Float32Array {

FILE: src/utilities/zeros-2d.ts
  function zeros2D (line 6) | function zeros2D(width: number, height: number): Float32Array[] {

FILE: src/utilities/zeros-3d.ts
  function zeros3D (line 6) | function zeros3D(

FILE: src/utilities/zeros.ts
  function zeros (line 4) | function zeros(size: number): Float32Array {