def __init__(self,
k,
threshold=None,
max_memory_size=1000,
*args,
**kwargs):
AdversarialClassifier.__init__(k, threshold, max_memory_size)
MeanShift.__init__(self, *args, **kwargs)
def __init__(self,
distance_merge = 5.,
cluster_all = True,
**args):
self.distance_merge = distance_merge
Clustering.__init__(self, **args)
MeanShift.__init__(self,
bandwidth = self.distance_merge,
bin_seeding = True,
cluster_all = cluster_all,
)
def __init__(self, **kwargs):
self.centroids_distance = None
if 'load' in kwargs:
self._load(kwargs['load'])
else:
try:
method = kwargs['method']
del kwargs['method']
except KeyError as ke:
print(kr)
raise
if method == 'KMeans' or method == 'MiniBatchKMeans':
MiniBatchKMeans.__init__(self, **kwargs)
elif method == 'MeanShift':
MeanShift.__init__(self, **kwargs)
else:
e = "No method '{}' avaiable. Please use KMeans or MeanShift".format(
method)
log = Log(path='.', name='cluster_class')
log.write(error=e, data=self)
raise ValueError(e)
