def cluster(self):
l_method = agglomerative_l_method(self.X)
suggest_n = len(l_method.cluster_centers_)
agg = AgglomerativeClustering(suggest_n)
agg.fit(np.array(self.X, copy=True))
# agg.fit(self.X)
# agg_labels = agg.labels_
# l_method_labels = l_method.labels_
#
# print('agg_labels:', agg_labels)
# print('l_method_labels:', l_method_labels)
# first tier clustering, using agglomerative clustering
self.clustering_model = DividableClustering()
self.clustering_model.fit(self.X, l_method.labels_)
from agglomerative_clustering import AgglomerativeClusteringMaxMergeDist, AgglomerativeClustering
from dataset import *
dataset = get_iris()
print("dataset size:", len(dataset.X))
#
# agg = AgglomerativeClusteringMaxMergeDist()
# centroids, cluster_member_cnt = agg.fit(dataset.X, 0.2)
#
# print('grouped size:', len(centroids))
agg = AgglomerativeClustering(3)
agg.fit(dataset.X)
predict_X = agg.predict(dataset.X)
print("predict_X:", predict_X)
