def __init__(self, initial_means, priors=None, covariance_matrices=None,
conv_threshold=1e-6, bias=0.1, normalise=False,
svd_dimensions=None):
"""
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
"""
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
def __init__(self, vector_names=None, num_clusters=1, normalise=True, svd_dimensions=None):
VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
self._num_clusters = num_clusters
self._dendogram = None
self._groups_values = None
self._names = vector_names
self._name_dendogram = None
def __init__(self, params, normalise, vector_names=None, svd_dimensions=None, log_it=None):
VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
self._num_clusters = params.min_clusters
self._dendogram = None
self._groups_values = None
self._names = vector_names
self._name_dendogram = None
self._iterative_reassign = params.reassign
self._max_reassign = params.reassign_max
self._reassigned_clusters = {}
self.log_it = log_it
def __init__(self,
vector_names=None,
num_clusters=1,
normalise=True,
svd_dimensions=None):
VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
self._num_clusters = num_clusters
self._dendogram = None
self._groups_values = None
self._names = vector_names
self._name_dendogram = None
def cluster(self, vectors, assign_clusters=False, trace=False):
# stores the merge order
self._dendogram = Dendogram([numpy.array(vector, numpy.float64) for vector in vectors])
if self._names:
self._name_dendogram = Dendogram(self._names)
self._vectors_to_cluster = vectors
return VectorSpaceClusterer.cluster(self, vectors, assign_clusters, trace)
def cluster(self, vectors, assign_clusters=False, trace=False):
# stores the merge order
self._dendogram = Dendogram(
[numpy.array(vector, numpy.float64) for vector in vectors])
if self._names:
self._name_dendogram = Dendogram(self._names)
self._vectors_to_cluster = vectors
return VectorSpaceClusterer.cluster(self, vectors, assign_clusters,
trace)
def cluster(self, vectors, assign_clusters=False, trace=False):
# stores the merge order
if self.msg_handle is not None:
self.msg_handle.dm(str(len(vectors)))
self.msg_handle.tile_yield()
self._dendogram = Dendogram(
[numpy.array(vector, numpy.float64) for vector in vectors])
if self._names:
self._name_dendogram = Dendogram(self._names)
self._vectors_to_cluster = vectors
return VectorSpaceClusterer.cluster(self, vectors, assign_clusters, trace)
def __init__(self,
initial_means,
priors=None,
covariance_matrices=None,
conv_threshold=1e-6,
bias=0.1,
normalise=False,
svd_dimensions=None):
"""
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
"""
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
def cluster(self, vectors, assign_clusters=False, trace=False):
# stores the merge order
self._vectors_to_cluster = vectors
return VectorSpaceClusterer.cluster(self, vectors, assign_clusters, trace)
def __init__(self, params, normalise, vector_names = None, svd_dimensions=None):
VectorSpaceClusterer.__init__(self, normalise, svd_dimensions)
self._num_clusters = params.num_clusters
self._groups_values = None
self._names = vector_names
self._max_iterations = params.max_iterations
