@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
60 lines (59 loc) • 1.54 kB
TypeScript
/**
* Density-based Optimal projective Clustering
*/
export class DOC {
/**
* @param {number} alpha Dense scale
* @param {number} beta Balanced value
* @param {number} w Width of cluster
*/
constructor(alpha: number, beta: number, w: number);
_alpha: number;
_beta: number;
_w: number;
_p: any[];
_d: any[];
_mu: (a: any, b: any) => number;
_select(n: any, k: any): number[];
/**
* Fit model.
* @param {Array<Array<number>>} datas Sample data
*/
fit(datas: Array<Array<number>>): void;
/**
* Returns predicted categories.
* @returns {number[]} Predicted values
*/
predict(): number[];
}
/**
* Fast Density-based Optimal projective Clustering
*/
export class FastDOC {
/**
* @param {number} alpha Dense scale
* @param {number} beta Balanced value
* @param {number} w Width of cluster
* @param {number} maxiter Maximum inner iteration
* @param {number} d0 Threshold of selected dimension count
*/
constructor(alpha: number, beta: number, w: number, maxiter: number, d0: number);
_alpha: number;
_beta: number;
_w: number;
_maxiter: number;
_d0: number;
_p: any[];
_d: any[];
_select(n: any, k: any): number[];
/**
* Fit model.
* @param {Array<Array<number>>} datas Sample data
*/
fit(datas: Array<Array<number>>): void;
/**
* Returns predicted categories.
* @returns {number[]} Predicted values
*/
predict(): number[];
}