@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
73 lines (72 loc) • 1.62 kB
TypeScript
/**
* Balanced iterative reducing and clustering using hierarchies
*/
export default class BIRCH {
/**
* @param {number} k
* @param {number} [b] Maximum number of entries for each non-leaf nodes
* @param {number} [t] Threshold
* @param {number} [l] Maximum number of entries for each leaf nodes
*/
constructor(k: number, b?: number, t?: number, l?: number);
_k: number;
_tree: CFTree;
/**
* Fit model.
* @param {Array<Array<number>>} datas Training data
*/
fit(datas: Array<Array<number>>): void;
/**
* Returns predicted categories.
* @param {Array<Array<number>>} datas Sample data
* @returns {number[]} Predicted values
*/
predict(datas: Array<Array<number>>): number[];
}
declare class CFTree {
constructor(b?: number, t?: number, l?: number);
_b: number;
_l: number;
_t: number;
_datas: any[];
_children: any[];
_parent: any;
get size(): number;
get length(): number;
get depth(): any;
get cf(): {
n: number;
ls: any;
ss: number;
} | {
n: number;
ls: any[];
ss: number;
} | {
n: any;
ls: any;
ss: any;
};
_cf: {
n: number;
ls: any;
ss: number;
} | {
n: number;
ls: any[];
ss: number;
} | {
n: any;
ls: any;
ss: any;
};
get r(): any;
_r: any;
get c(): any;
at(index: any): any;
isRoot(): boolean;
isLeaf(): boolean;
push(data: any): void;
_separate(): void;
}
export {};