def __init__(self, num_means, distance, repeats=1,
                       conv_test=1e-6, initial_means=None,
                       normalise=False, svd_dimensions=None,
                       rng=None, avoid_empty_clusters=False):

        """
        :param  num_means:  the number of means to use (may use fewer)
        :type   num_means:  int
        :param  distance:   measure of distance between two vectors
        :type   distance:   function taking two vectors and returing a float
        :param  repeats:    number of randomised clustering trials to use
        :type   repeats:    int
        :param  conv_test:  maximum variation in mean differences before
                            deemed convergent
        :type   conv_test:  number
        :param  initial_means: set of k initial means
        :type   initial_means: sequence of vectors
        :param  normalise:  should vectors be normalised to length 1
        :type   normalise:  boolean
        :param svd_dimensions: number of dimensions to use in reducing vector
                               dimensionsionality with SVD
        :type svd_dimensions: int
        :param  rng:        random number generator (or None)
        :type   rng:        Random
        :param avoid_empty_clusters: include current centroid in computation
                                     of next one; avoids undefined behavior
                                     when clusters become empty
        :type avoid_empty_clusters: boolean
        """
        VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
        self._num_means = num_means
        self._distance = distance
        self._max_difference = conv_test
        assert not initial_means or len(initial_means) == num_means
        self._means = initial_means
        assert repeats >= 1
        assert not (initial_means and 1 > repeats)
        self._repeats = repeats
        self._rng = (rng if rng else random.Random())
        self._avoid_empty_clusters = avoid_empty_clusters
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