115 lines
3.3 KiB
TypeScript
115 lines
3.3 KiB
TypeScript
export interface Stat {
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Bhattacharyya: (xs: number[], ys: number[]) => number;
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BivariateMoment: (
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q: number,
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p: number,
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xs: number[],
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ys: number[],
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weights: number[],
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) => number;
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CDF: (q: number, xs: number[], weights: number[]) => number;
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ChiSquare: (xs: number[], ys: number[]) => number;
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CircularMean: (xs: number[], weights: number[]) => number;
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Correlation: (xs: number[], ys: number[], weights: number[]) => number;
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Covariance: (xs: number[], ys: number[], weights: number[]) => number;
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CrossEntropy: (xs: number[], ys: number[]) => number;
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Entropy: (xs: number[]) => number;
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ExKurtosis: (xs: number[], weights: number[]) => number;
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GeometricMean: (xs: number[], weights: number[]) => number;
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HarmonicMean: (xs: number[], weights: number[]) => number;
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Hellinger: (xs: number[], ys: number[]) => number;
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Histogram: (
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counts: number[],
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divs: number[],
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xs: number[],
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bins: number,
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) => number[];
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JensenShannon: (xs: number[], ys: number[]) => number;
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Kendall: (xs: number[], ys: number[], weights: number[]) => number;
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KolmogorovSmirnov: (
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xs: number[],
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xw: number[],
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ys: number[],
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yw: number[],
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) => number;
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KullbackLeibler: (xs: number[], ys: number[]) => number;
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LinearRegression: (
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xs: number[],
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ys: number[],
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weights: number[],
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origin: boolean,
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) => { alpha: number; beta: number };
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Mean: (xs: number[], weights: number[]) => number;
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MeanStdDev: (
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xs: number[],
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weights: number[],
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) => { mean: number; stdDev: number };
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MeanVariance: (
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xs: number[],
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weights: number[],
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) => { mean: number; variance: number };
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Mode: (xs: number[], weights: number[]) => { value: number; count: number };
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Moment: (q: number, xs: number[], weights: number[]) => number;
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MomentAbout: (
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q: number,
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xs: number[],
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mean: number,
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weights: number[],
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) => number;
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PopMeanStdDev: (
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xs: number[],
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weights: number[],
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) => { mean: number; stdDev: number };
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PopMeanVariance: (
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xs: number[],
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weights: number[],
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) => { mean: number; variance: number };
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PopStdDev: (xs: number[], weights: number[]) => number;
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PopVariance: (xs: number[], weights: number[]) => number;
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Quantile: (
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q: number,
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type: "linear" | "empirical",
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xs: number[],
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weights: number[],
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) => number;
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RNoughtSquared: (
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xs: number[],
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ys: number[],
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weights: number[],
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beta: number,
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) => number;
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ROC: (
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cutoffs: number[],
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ys: number[],
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classes: boolean[],
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weights: number[],
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) => { tpr: number[]; fpr: number[]; tresh: number[] };
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RSquared: (
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xs: number[],
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ys: number[],
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weights: number[],
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alpha: number,
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beta: number,
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) => number;
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RSquaredFrom: (
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estimates: number[],
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ys: number[],
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weights: number[],
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) => number;
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Skew: (xs: number[], weights: number[]) => number;
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SortWeighted: (xs: number[], weights: number[]) => number[];
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SortWeightedLabeled: (
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xs: number[],
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labels: boolean[],
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weights: number[],
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) => { xs: number[]; labels: boolean[] };
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StdDev: (xs: number[], weights: number[]) => number;
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StdErr: (std: number, size: number) => number;
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StdScore: (x: number, mean: number, stdDev: number) => number;
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TOC: (
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classes: boolean[],
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ys: number[],
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) => { min: number[]; ntp: number[]; max: number[] };
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Variance: (xs: number[], weights: number[]) => number;
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}
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