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brain.js

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Neural networks in JavaScript

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import { INeuralNetworkData, INeuralNetworkTrainOptions } from './neural-network'; import { INeuralNetworkState } from './neural-network-types'; export interface IAEOptions { binaryThresh: number; decodedSize: number; hiddenLayers: number[]; } /** * An autoencoder learns to compress input data down to relevant features and reconstruct input data from its compressed representation. */ export declare class AE<DecodedData extends INeuralNetworkData, EncodedData extends INeuralNetworkData> { private decoder?; private readonly denoiser; constructor(options?: Partial<IAEOptions>); /** * Denoise input data, removing any anomalies from the data. * @param {DecodedData} input * @returns {DecodedData} */ denoise(input: DecodedData): DecodedData; /** * Decode `EncodedData` into an approximation of its original form. * * @param {EncodedData} input * @returns {DecodedData} */ decode(input: EncodedData): DecodedData; /** * Encode data to extract features, reduce dimensionality, etc. * * @param {DecodedData} input * @returns {EncodedData} */ encode(input: DecodedData): EncodedData; /** * Test whether or not a data sample likely contains anomalies. * If anomalies are likely present in the sample, returns `true`. * Otherwise, returns `false`. * * @param {DecodedData} input * @returns {boolean} */ likelyIncludesAnomalies(input: DecodedData, anomalyThreshold?: number): boolean; /** * Train the auto encoder. * * @param {DecodedData[]} data * @param {Partial<INeuralNetworkTrainOptions>} options * @returns {INeuralNetworkState} */ train(data: DecodedData[], options?: Partial<INeuralNetworkTrainOptions>): INeuralNetworkState; /** * Create a new decoder from the trained denoiser. * * @returns {NeuralNetworkGPU<EncodedData, DecodedData>} */ private createDecoder; /** * Get the layer containing the encoded representation. */ private get encodedLayer(); /** * Get the offset of the encoded layer. */ private get encodedLayerIndex(); } export default AE; //# sourceMappingURL=autoencoder.d.ts.map