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

    Both input ndarrays should have the same number of elements.

    If provided empty one-dimensional ndarrays, the function returns `NaN`.

    Parameters
    ----------
    arrays: ArrayLikeObject<ndarray>
        Array-like object containing the following ndarrays:

        - a one-dimensional input ndarray.
        - a one-dimensional input ndarray.
        - a zero-dimensional ndarray specifying the 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 and `N` corresponds to the number of
        elements in each input ndarray. 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).
        - a zero-dimensional ndarray specifying the mean of the first one-
        dimensional ndarray.
        - a zero-dimensional ndarray specifying the mean of the second one-
        dimensional ndarray.

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

    Examples
    --------
    // Create the input ndarrays:
    > var x = new {{alias:@stdlib/ndarray/vector/float32}}( [ 1.0, -2.0, 2.0 ] );
    > var y = new {{alias:@stdlib/ndarray/vector/float32}}( [ 2.0, -2.0, 1.0 ] );

    // Specify the degrees of freedom adjustment:
    > var opts = { 'dtype': 'float32' };
    > var correction = {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );

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

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

    See Also
    --------

