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/** * The [R Squared](http://en.wikipedia.org/wiki/Coefficient_of_determination) * value of data compared with a function `f` * is the sum of the squared differences between the prediction * and the actual value. * * @param {Array<Array<number>>} x input data: this should be doubly-nested * @param {Function} func function called on `[i][0]` values within the dataset * @returns {number} r-squared value * @example * var samples = [[0, 0], [1, 1]]; * var regressionLine = linearRegressionLine(linearRegression(samples)); * rSquared(samples, regressionLine); // = 1 this line is a perfect fit */function rSquared(x, func) {    if (x.length < 2) {        return 1;    }
    // Compute the average y value for the actual    // data set in order to compute the    // _total sum of squares_    let sum = 0;    for (let i = 0; i < x.length; i++) {        sum += x[i][1];    }    const average = sum / x.length;
    // Compute the total sum of squares - the    // squared difference between each point    // and the average of all points.    let sumOfSquares = 0;    for (let j = 0; j < x.length; j++) {        sumOfSquares += Math.pow(average - x[j][1], 2);    }
    // Finally estimate the error: the squared    // difference between the estimate and the actual data    // value at each point.    let err = 0;    for (let k = 0; k < x.length; k++) {        err += Math.pow(x[k][1] - func(x[k][0]), 2);    }
    // As the error grows larger, its ratio to the    // sum of squares increases and the r squared    // value grows lower.    return 1 - err / sumOfSquares;}
export default rSquared;