def __init__(self,
obj,
X=None,
labels=None,
labels_true=None,
store_X=False,
digits=3):
self.model = obj
self.components = obj.n_components
self.variables = obj.n_features_in_
self.covariance_type = obj.covariance_type
self.centers = _clust_centers(obj.means_)
self.covariance = obj.covariances_
self.labels, self.proba, self.cluster_size = _clust_proba(
obj, X, labels)
self.labels_names = np.unique(self.labels)
self.cluster_weights = pd.DataFrame(obj.weights_)
self.ARI, self.FM = _clustering_evaluation(self.labels, labels_true,
digits)
self.SIL, self.DB, self.CH = _clustering_metrics(
self.labels, X, digits)
self.BIC, self.AIC = _criterion_gmm(obj, X, digits)
self.iter = obj.n_iter_
self.init = obj.n_init
self.init_type = obj.init_params
self.X = _store_X(X, store_X)
self.labels_true = labels_true
def plot(self, X=None, palette='Set2'):
if X is None:
X = self.X
labels = self.labels
_scatter_clusters(_store_X(X, True), labels, palette)
def __init__(self, obj, X = 'None', labels_true=None, store_X=False, digits = 3):
self.model = obj
self.n_clusters = obj.n_clusters
self.variables = np.shape(obj.centers)[1]
self.centers = _clust_centers(obj.centers)
self.labels = MAP(obj.u)
self.cluster_size, self.cluster_weights = _clust_weight(self.labels)
self.ARI, self.FM = _clustering_evaluation(self.labels, labels_true, digits)
self.fuzzy = obj.m
self.membership = obj.u
self.X = _store_X(X, store_X)
self.labels_true = labels_true
def __init__(self, obj, X=None, labels_true=None, store_X=False, digits = 3):
self.model = obj
self.n_clusters = obj.n_clusters
self.variables = np.shape(obj.cluster_centers_)[1]
self.SIL, self.DB, self.CH = _clustering_metrics(obj.labels_, X, digits)
self.centers = _clust_centers(obj.cluster_centers_)
self.labels = obj.labels_
self.labels_names = np.unique(obj.labels_)
self.cluster_size, self.cluster_weights = _clust_weight(obj.labels_)
self.ARI, self.FM = _clustering_evaluation(obj.labels_, labels_true, digits)
self.iter = obj.n_iter_
self.init = obj.n_init
self.init_type = obj.init
self.algorithm_type = obj._algorithm
self.X = _store_X(X, store_X)
self.labels_true = labels_true
def __init__(self, obj, X=None, labels_true=None, store_X=False, digits=3):
self.model = obj
self.n_clusters = np.unique(obj.labels_).shape[0]
self.variables = obj.n_features_in_
self.SIL, self.DB, self.CH = _clustering_metrics(
obj.labels_, X, digits)
self.centers = _clust_centers_X(X, obj.labels_)
self.labels = obj.labels_
self.labels_names = obj.n_features_in_
self.cluster_size, self.cluster_weights = _clust_weight(obj.labels_)
self.ARI, self.FM = _clustering_evaluation(obj.labels_, labels_true,
digits)
self.eps = obj.eps
self.min_samples = obj.min_samples
self.metric = obj.metric
self.power_mink = obj.p
self.leaf = obj.leaf_size
self.algorithm_type = obj.algorithm
self.X = _store_X(X, store_X)
self.labels_true = labels_true