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brain.js

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Neural networks in JavaScript

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/** TODO: The following should be moved to neural-network.ts once that is converted to typescript. Added here until neural-network.js is converted */ export interface INeuralNetworkOptions { /** * @default 0.5 */ binaryThresh?: number; /** * array of int for the sizes of the hidden layers in the network * * @default [3] */ hiddenLayers?: number[]; } export interface INeuralNetworkTrainingOptions { /** * the maximum times to iterate the training data --> number greater than 0 * @default 20000 */ iterations?: number; /** * the acceptable error percentage from training data --> number between 0 and 1 * @default 0.005 */ errorThresh?: number; /** * true to use console.log, when a function is supplied it is used --> Either true or a function * @default false */ log?: boolean | INeuralNetworkTrainingCallback; /** * iterations between logging out --> number greater than 0 * @default 10 */ logPeriod?: number; /** * scales with delta to effect training rate --> number between 0 and 1 * @default 0.3 */ learningRate?: number; /** * scales with next layer's change value --> number between 0 and 1 * @default 0.1 */ momentum?: number; /** * a periodic call back that can be triggered while training --> null or function * @default null */ callback?: INeuralNetworkTrainingCallback | number; /** * the number of iterations through the training data between callback calls --> number greater than 0 * @default 10 */ callbackPeriod?: number; /** * the max number of milliseconds to train for --> number greater than 0 * @default Infinity */ timeout?: number; praxis?: null | 'adam'; } export declare type INeuralNetworkTrainingCallback = (state: INeuralNetworkState) => void; export interface INeuralNetworkState { iterations: number; error: number; } export interface INeuralNetworkTestResult { misclasses: unknown[]; error: number; total: number; } export interface INeuralNetworkBinaryTestResult extends INeuralNetworkTestResult { trueNeg: number; truePos: number; falseNeg: number; falsePos: number; precision: number; recall: number; accuracy: number; } //# sourceMappingURL=neural-network-types.d.ts.map