@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
59 lines (58 loc) • 1.53 kB
TypeScript
/**
* Confidence weighted
*/
export class ConfidenceWeighted {
/**
* @param {number} eta Confidence value
*/
constructor(eta: number);
_eta: number;
_phi: any;
_psi: number;
_xi: number;
/**
* Initialize this model.
* @param {Array<Array<number>>} train_x Training data
* @param {Array<1 | -1>} train_y Target values
*/
init(train_x: Array<Array<number>>, train_y: Array<1 | -1>): void;
_x: Matrix<number[]>;
_c: Matrix<number>;
_y: (1 | -1)[];
_d: number;
_m: Matrix<number>;
_s: Matrix<number>;
_cdf(x: any): number;
_ppf(x: any): any;
_alpha(v: any, m: any): number;
/**
* Update model parameters with one data.
* @param {Matrix} x Training data
* @param {1 | -1} y Target value
*/
update(x: Matrix, y: 1 | -1): void;
/**
* Fit model parameters.
*/
fit(): void;
/**
* Returns predicted datas.
* @param {Array<Array<number>>} data Sample data
* @returns {(1 | -1)[]} Predicted values
*/
predict(data: Array<Array<number>>): (1 | -1)[];
}
/**
* Soft confidence weighted
*/
export class SoftConfidenceWeighted extends ConfidenceWeighted {
/**
* @param {number} eta Confidence value
* @param {number} cost Tradeoff value between passiveness and aggressiveness
* @param {1 | 2} v Version number
*/
constructor(eta: number, cost: number, v: 1 | 2);
_cost: number;
_v: 2 | 1;
}
import Matrix from '../util/matrix.js';