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)