brain.js
Version:
Neural networks in JavaScript
58 lines • 2.17 kB
TypeScript
import { KernelOutput } from 'gpu.js';
export interface INumberHash {
[character: string]: number;
}
export interface INumberArray {
length: number;
buffer?: ArrayBuffer;
[index: number]: number;
}
export declare type InputOutputValue = INumberArray | Partial<INumberHash>;
export interface ITrainingDatum {
input: InputOutputValue | InputOutputValue[] | KernelOutput;
output: InputOutputValue | InputOutputValue[] | KernelOutput;
}
export declare type FormattableData = number | ITrainingDatum | InputOutputValue | InputOutputValue[];
export declare const lookup: {
/**
* Performs `[{a: 1}, {b: 6, c: 7}] -> {a: 0, b: 1, c: 2}`
* @param {Object} hashes
* @returns {Object}
*/
toTable(hashes: INumberHash[]): INumberHash;
/**
* Performs `[{a: 1}, {b: 6, c: 7}] -> {a: 0, b: 1, c: 2}`
*/
toTable2D(objects2D: INumberHash[][]): INumberHash;
toInputTable2D(data: {
input: {
[key: string]: number;
}[];
}[]): INumberHash;
toOutputTable2D(data: {
output: {
[key: string]: number;
}[];
}[]): INumberHash;
/**
* performs `{a: 6, b: 7} -> {a: 0, b: 1}`
*/
toHash(hash: INumberHash): INumberHash;
/**
* performs `{a: 0, b: 1}, {a: 6} -> [6, 0]`
*/
toArray(lookup: INumberHash, object: INumberHash, arrayLength: number): Float32Array;
toArrayShort(lookup: INumberHash, object: INumberHash): Float32Array;
toArrays(lookup: INumberHash, objects: INumberHash[], arrayLength: number): Float32Array[];
/**
* performs `{a: 0, b: 1}, [6, 7] -> {a: 6, b: 7}`
* @param {Object} lookup
* @param {Array} array
* @returns {Object}
*/
toObject(lookup: INumberHash, array: number[] | Float32Array): INumberHash;
toObjectPartial(lookup: INumberHash, array: number[] | Float32Array, offset?: number, limit?: number): INumberHash;
dataShape(data: FormattableData[] | FormattableData): string[];
addKeys(value: number[] | INumberHash, table: INumberHash): INumberHash;
};
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