ml-spectra-processing
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    Interface XWhittakerSmootherOptions

    interface XWhittakerSmootherOptions {
        controlPoints?: NumberArray;
        factorStd?: number;
        lambda?: number;
        learningRate?: number;
        maxIterations?: number;
        minWeight?: number;
        tolerance?: number;
        weights?: NumberArray;
    }

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    controlPoints? factorStd? lambda? learningRate? maxIterations? minWeight? tolerance? weights?

    Properties

    controlPoints?: NumberArray

    Control points to determine which weights to update.

    factorStd?: number

    Factor used to calculate the threshold for determining outliers in the residuals. Higher values mean more tolerance for outliers. The default value is based on noise follow the normal distribution values over 3 times the standard-deviation could be marked as signals or outliers.

    3
    lambda?: number

    Factor of weights matrix in -> [I + lambda D'D]z = x

    100
    learningRate?: number

    Learning rate for weight updates.

    0.5
    maxIterations?: number

    Maximum number of iterations for the baseline refinement process.

    100
    minWeight?: number

    Minimum weight value to avoid division by zero or extremely small weights.

    0.01
    tolerance?: number

    Tolerance for convergence. The process stops if the change in baseline is less than this value.

    1e-6
    weights?: NumberArray

    Array of weights

    [1,1,...,1]

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