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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def approximate_entropy(x, m, r):
 """ Implements a vectorized Approximate entropy algorithm. https://en.wikipedia.org/wiki/Approximate_entropy For short time-series this method is highly dependent on the parameters, but should be stable for N > 2000, see: Yentes et al. (2012) - *The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets* Other shortcomings and alternatives discussed in: Richman & Moorman (2000) - *Physiological time-series analysis using approximate entropy and sample entropy* :param x: the time series to calculate the feature of :type x: numpy.ndarray :param m: Length of compared run of data :type m: int :param r: Filtering level, must be positive :type r: float :return: Approximate entropy :return type: float """

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)

    N = x.size
    r *= np.std(x)
    if r < 0:
        raise ValueError("Parameter r must be positive.")
    if N <= m+1:
        return 0

    def _phi(m):
        x_re = np.array([x[i:i+m] for i in range(N - m + 1)])
        C = np.sum(np.max(np.abs(x_re[:, np.newaxis] - x_re[np.newaxis, :]),
                          axis=2) <= r, axis=0) / (N-m+1)
        return np.sum(np.log(C)) / (N - m + 1.0)

    return np.abs(_phi(m) - _phi(m + 1))