repl.txt

{{alias}}( arrays )
    Computes the covariance of two one-dimensional single-precision
    floating-point ndarrays provided known means and using a one-pass textbook
    algorithm.

    If provided an empty ndarray, the function returns `NaN`.

    Parameters
    ----------
    arrays: ArrayLikeObject<ndarray>
        The function expects the following ndarrays in order:

        -   first one-dimensional input ndarray.
        -   second one-dimensional input ndarray.
        -   a zero-dimensional ndarray specifying mean of the first
        one-dimensional ndarray.
        -   a zero-dimensional ndarray specifying mean of the second
        one-dimensional ndarray.
        -   a zero-dimensional ndarray specifying degrees of freedom
        adjustment. Setting this parameter to a value other than `0` has the
        effect of adjusting the divisor during the calculation of the
        covariance according to `N-c` where `c` corresponds to the provided
        degrees of freedom adjustment. When computing the population
        covariance, setting this parameter to `0` is the standard choice (i.e.,
        the provided arrays contain data constituting entire populations). When
        computing the unbiased sample covariance, setting this parameter to `1`
        is the standard choice (i.e., the provided arrays contain data sampled
        from larger populations; this is commonly referred to as Bessel's
        correction).

    Returns
    -------
    out: number
        The covariance.

    Examples
    --------
    // Create the input ndarrays:
    > var xbuf = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, 2.0 ] );
    > var ybuf = new {{alias:@stdlib/array/float32}}( [ 2.0, -2.0, 1.0 ] );
    > var dt = 'float32';
    > var sh = [ xbuf.length ];
    > var st = [ 1 ];
    > var oo = 0;
    > var ord = 'row-major';
    > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, st, oo, ord );
    > var y = new {{alias:@stdlib/ndarray/ctor}}( dt, ybuf, sh, st, oo, ord );

    // Specify the known means:
    > var opts = { 'dtype': dt };
    > var meanx = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0/3.0, opts );
    > var meany = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0/3.0, opts );

    // Specify the degrees of freedom adjustment:
    > var correction = new {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );

    // Calculate the sample covariance:
    > {{alias}}( [ x, y, meanx, meany, correction ] )
    ~3.8333

    See Also
    --------