def __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=1e-4, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm='full', norm='L2'): KMeans.__init__(self, n_clusters=n_clusters, init=init, n_init=n_init, max_iter=max_iter, tol=tol, precompute_distances=precompute_distances, verbose=verbose, random_state=random_state, copy_x=copy_x, n_jobs=n_jobs, algorithm=algorithm) self.norm = norm.lower() if self.norm == 'l1' and self.algorithm != 'full': raise NotImplementedError( # pragma no cover "Only algorithm 'full' is implemented with norm 'l1'.")
def __init__(self, rank=10, clusters=1, iterations=3, metric='euclidean'): """ Iterations is the max iterations """ sk_kmeans.__init__(self, n_clusters=clusters, max_iter=iterations) # Cluster ranks is a list of lists of knn sorted elements for each cluster w.r.t. the cluster mean self.rank = rank self.metric = metric
def __init__(self, n_clusters=8, **kwargs): """ Initialize and inherits from Scikit-Learn's KMeans class. Currently only takes in Parameters ---------- n_clusters: int, number of clusters to use. Equivalent to n_clusters for KMeans. **kwargs: other acceptable arguments to KMeans. """ KMeans.__init__(self, n_clusters=n_clusters, **kwargs) # A list of input data files from which data was read and stored self.source_files = [] # Initialize indices and coordinates (lon, lat) in 3D and 2D formats self.ind = None self.ind2d = None self.coords = None self.coords2d = None # Initialize the mask that indicates null values # Note that True in the mask indicates null values/values out of domain self.mask = None # Initialize data in 3D and 2D formats self.raw_data = None self.data2d = None
def __init__(self, n_clusters=8, m=1, init='k-means++', n_init=10, max_iter=300, tol=1e-4, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm='auto'): FuzzyKMeans.__init__(self, k=n_clusters, m=m, max_iter=max_iter, random_state=random_state, tol=tol) KMeans.__init__(self, n_clusters=n_clusters, init=init, n_init=n_init, max_iter=max_iter, tol=tol, precompute_distances=precompute_distances, verbose=verbose, random_state=random_state, copy_x=copy_x, n_jobs=n_jobs, algorithm=algorithm)
def __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=500, tol=0.0001, precompute_distances='deprecated', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto', balanced_predictions=False, strategy='gain', kmeans0=True, learning_rate=1., history=False): """ @param n_clusters number of clusters @param init used by :epkg:`k-means` @param n_init used by :epkg:`k-means` @param max_iter used by :epkg:`k-means` @param tol used by :epkg:`k-means` @param precompute_distances used by :epkg:`k-means` @param verbose used by :epkg:`k-means` @param random_state used by :epkg:`k-means` @param copy_x used by :epkg:`k-means` @param n_jobs used by :epkg:`k-means` @param algorithm used by :epkg:`k-means` @param balanced_predictions produced balanced prediction or the regular ones @param strategy strategy or algorithm used to abide by the constraint @param kmeans0 if True, applies *k-means* algorithm first @param history keeps centers accress iterations @param learning_rate learning rate, used by strategy `'weights'` """ KMeans.__init__(self, n_clusters=n_clusters, init=init, n_init=n_init, max_iter=max_iter, tol=tol, precompute_distances=precompute_distances, verbose=verbose, random_state=random_state, copy_x=copy_x, n_jobs=n_jobs, algorithm=algorithm) self.balanced_predictions = balanced_predictions self.strategy = strategy self.kmeans0 = kmeans0 self.history = history self._n_threads = None self.learning_rate = learning_rate if strategy not in ConstraintKMeans._strategy_value: raise ValueError('strategy must be in {0}'.format( ConstraintKMeans._strategy_value))
def __init__(self, dataset, labels=None, inertia=0.): KMeans.__init__(self) self.cluster_centers_ = dataset self.n_clusters = dataset.shape[0] if labels is None: self.labels_ = numpy.arange(dataset.shape[0]).astype(numpy.int32) else: self.labels_ = labels self.inertia_ = inertia
def __init__(self): KMeans.__init__(self) # Setting of k-means model self.n_clusters = 15 self.init = 'k-means++' self.max_iter = 300 self.n_init = 10 self.random_state = 0 # Other attributes self.users_cluster = None self.clusters_movies_df = None
def __init__( self, data, max_k=8, min_k=2, init="k-means++", n_clusters=8, n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto", ): KMeans.__init__( self, n_clusters=8, n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto", ) self.data = data self.k_range = range(min_k, max_k + 1) self.km_results = {k: {} for k in self.k_range} for k in tqdm(self.k_range): kms = KMeans(n_clusters=k) self.km_results[k]["cluster_labels"] = kms.fit_predict(self.data) self.km_results[k]["centers"] = kms.cluster_centers_ self.km_results[k]["inertia"] = kms.inertia_
def __init__(self, *args, **kwargs): KMeans.__init__(self, *args, **kwargs)