'Creates an EM clusterer with the given starting parameters,
convergence threshold and vector mangling parameters.
:param  initial_means: the means of the gaussian cluster centers
:type   initial_means: [seq of] numpy array or seq of SparseArray
:param  priors: the prior probability for each cluster
:type   priors: numpy array or seq of float
:param  covariance_matrices: the covariance matrix for each cluster
:type   covariance_matrices: [seq of] numpy array
:param  conv_threshold: maximum change in likelihood before deemed
convergent
:type   conv_threshold: int or float
:param  bias: variance bias used to ensure non-singular covariance
matrices
:type   bias: float
: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'

def __init__(self, initial_means, priors=None, covariance_matrices=None, conv_threshold=1e-06, bias=0.1, normalise=False, svd_dimensions=None):

    VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
    self._means = numpy.array(initial_means, numpy.float64)
    self._num_clusters = len(initial_means)
    self._conv_threshold = conv_threshold
    self._covariance_matrices = covariance_matrices
    self._priors = priors
    self._bias = bias
