@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
39 lines (38 loc) • 1.24 kB
TypeScript
/**
* RankNet
*/
export default class RankNet {
/**
* @param {number[]} layer_sizes Sizes of all layers
* @param {string | string[]} [activations] Activation names
* @param {number} [rate] Learning rate
*/
constructor(layer_sizes: number[], activations?: string | string[], rate?: number);
_rate: number;
_layer_sizes: number[];
_activations: string | string[];
_a: any[];
_w: any[];
_b: any[];
_optimizer: {
readonly lr: number;
params: {};
delta(key: any, value: any): any;
};
_init(sizes: any): void;
_calc(x: any): any[][];
/**
* Fit model.
* @param {Array<Array<number>>} x1 Training data 1
* @param {Array<Array<number>>} x2 Training data 2
* @param {Array<-1 | 0 | 1>} comp Sign of (data 1 rank - data 2 rank). If data 1 rank is bigger than data 2, corresponding value is 1.
* @returns {number} loss
*/
fit(x1: Array<Array<number>>, x2: Array<Array<number>>, comp: Array<-1 | 0 | 1>): number;
/**
* Returns predicted values.
* @param {Array<Array<number>>} x Sample data
* @returns {Array<number>} Predicted values
*/
predict(x: Array<Array<number>>): Array<number>;
}