estimator.py 文件源码

def entropy(X, bins=None):
    """
    Use the Shannon Entropy H to describe the distribution of the given sample.
    For calculating the Shannon Entropy, the bin edges are needed and can be passed as pk.
    If pk is None, these edges will be calculated using the numpy.histogram function with bins='fq'.
    This uses Freedman Diacons Estimator and is fairly resilient to outliers.
    If the input data X is 2D (Entropy for more than one bin needed), it will derive the histogram once and
    use the same edges in all bins.
    CAUTION: this is actually an changed behaviour to scikit-gstat<=0.1.4

    :param X:  np.ndarray with the given sample to calculate the Shannon entropy from
    :param bins: The bin edges for entropy calculation, or an amount of even spaced bins
    :return:
    """
    _X = np.array(X)

    if any([isinstance(_, (list, np.ndarray)) for _ in _X]):
        # if bins is not set, use the histogram over the full value range
        if bins is None:
            # could not fiugre out a better way here. I need the values before calculating the entropy
            # in order to use the full value range in all bins
            vals = [[np.abs(_[i] - _[i + 1]) for i in np.arange(0, len(_), 2)] for _ in _X]
            bins = np.histogram(vals, bins=15)[1][1:]
        return np.array([entropy(_, bins=bins) for _ in _X])

    # check even
    if len(_X) % 2 > 0:
        raise ValueError('The sample does not have an even length: {}'.format(_X))

    # calculate the values
    vals = [np.abs(_X[i] - _X[i + 1]) for i in np.arange(0, len(_X), 2)]

    # claculate the bins
    if bins is None:
        bins = 15
    pk = np.histogram(vals, bins)[0]

    return scipy_entropy(pk=pk)