@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
67 lines (66 loc) • 2 kB
TypeScript
/**
* Projectron
*/
export class Projectron {
/**
* @param {number} [eta] Threshold
* @param {'gaussian' | 'polynomial' | { name: 'gaussian', s?: number } | { name: 'polynomial', d?: number } | function (number[], number[]): number} [kernel] Kernel name
*/
constructor(eta?: number, kernel?: "gaussian" | "polynomial" | {
name: "gaussian";
s?: number;
} | {
name: "polynomial";
d?: number;
} | ((arg0: number[], arg1: number[]) => number));
_eta: number;
_kernel: any;
_S: any[];
_a: any[];
_kinv: any[];
/**
* Fit model parameters.
* @param {Array<Array<number>>} x Training data
* @param {Array<1 | -1>} y Target values
*/
fit(x: Array<Array<number>>, y: Array<1 | -1>): void;
/**
* Returns predicted values.
* @param {Array<Array<number>>} data Sample data
* @returns {(1 | -1)[]} Predicted values
*/
predict(data: Array<Array<number>>): (1 | -1)[];
}
/**
* Projectron++
*/
export class Projectronpp {
/**
* @param {number} [eta] Threshold
* @param {'gaussian' | 'polynomial' | { name: 'gaussian', s?: number } | { name: 'polynomial', d?: number } | function (number[], number[]): number} [kernel] Kernel name
*/
constructor(eta?: number, kernel?: "gaussian" | "polynomial" | {
name: "gaussian";
s?: number;
} | {
name: "polynomial";
d?: number;
} | ((arg0: number[], arg1: number[]) => number));
_eta: number;
_kernel: any;
_S: any[];
_a: any[];
_kinv: any[];
/**
* Fit model parameters.
* @param {Array<Array<number>>} x Training data
* @param {Array<1 | -1>} y Target values
*/
fit(x: Array<Array<number>>, y: Array<1 | -1>): void;
/**
* Returns predicted values.
* @param {Array<Array<number>>} data Sample data
* @returns {(1 | -1)[]} Predicted values
*/
predict(data: Array<Array<number>>): (1 | -1)[];
}