import { MLModelAPI, ModelConfig, PredictionInput, PredictionResult } from './types';

export declare class SimpleNeuralNetwork implements MLModelAPI {
    private network;
    private config;
    private ready;
    constructor(config: ModelConfig);
    get id(): string;
    get name(): string;
    get type(): string;
    predict(input: PredictionInput): Promise<PredictionResult>;
    private forwardPass;
    private denseLayer;
    private applyActivation;
    getInfo(): ModelConfig;
    isReady(): boolean;
    warmup(): Promise<void>;
    dispose(): void;
}
/** Result returned when training a small on-chart model (task 3.17). */
export interface TrainingResult {
    coefficients: number[];
    intercept: number;
    /** Fitted values for the training inputs. */
    fitted: number[];
    /** Residuals (observed - fitted), suitable for a residual plot. */
    residuals: number[];
    /** Coefficient of determination on the training set. */
    r2: number;
    /** Root mean squared error on the training set. */
    rmse: number;
}
export declare class NativeLinearRegression implements MLModelAPI {
    private config;
    private coefficients;
    private intercept;
    private ready;
    private lastTraining;
    constructor(config: ModelConfig);
    get id(): string;
    get name(): string;
    get type(): string;
    predict(input: PredictionInput): Promise<PredictionResult>;
    /** Predict from full feature rows (multivariate). */
    predictRows(rows: number[][]): number[];
    train(data: {
        x: number[][];
        y: number[];
    }): TrainingResult;
    getTrainingResult(): TrainingResult | null;
    getInfo(): ModelConfig;
    isReady(): boolean;
    warmup(): Promise<void>;
    dispose(): void;
}
/**
 * General square-matrix inverse via Gauss-Jordan elimination with partial
 * pivoting. Falls back to the identity for singular matrices so callers never
 * receive NaNs. Replaces the previous 2x2-only stub (task 3.17 fix).
 */
export declare function matrixInverse(matrix: number[][]): number[][];
export declare class NativeSignalProcessor implements MLModelAPI {
    private config;
    private filterType;
    private cutoffFrequency;
    private sampleRate;
    private ready;
    constructor(config: ModelConfig);
    get id(): string;
    get name(): string;
    get type(): string;
    predict(input: PredictionInput): Promise<PredictionResult>;
    private applyFilter;
    private lowpassFilter;
    private highpassFilter;
    private bandpassFilter;
    getInfo(): ModelConfig;
    isReady(): boolean;
    warmup(): Promise<void>;
    dispose(): void;
}
export declare function createNativeModel(config: ModelConfig): MLModelAPI;
export declare function nativeFFT(data: number[]): {
    real: number[];
    imag: number[];
};
export declare function nativeMean(data: number[]): number;
export declare function nativeStandardDeviation(data: number[]): number;
export declare function nativeCorrelation(x: number[], y: number[]): number;