model.split(0, kmeans.labels_)
print(len(model.X_by_label[3]))
print(len(model.X_by_label[4]))
print(len(model.X_by_label[5]))
print(model.X_by_label.keys())
model.relabel()
print(model.X_by_label.keys())
print(len(model.X_by_label[0]))
print(len(model.X_by_label[1]))
print(len(model.X_by_label[2]))
print(len(model.X_by_label[3]))
print(len(model.X_by_label[4]))
result = model.predict(dataset.X)
print(result)
for i, r in enumerate(result):
if r == 3:
print(i)
ball_tree = BallTree(dataset.X)
dist, _ = ball_tree.query([dataset.X[50], dataset.X[100]], 2)
print(dist)
dist, _ = ball_tree.query(dataset.X, 2)
avg = sum(d[1] for d in dist) / len(dist)
print('avg:', avg)
from dividable_clustering import DividableClustering
from agglomerative_clustering import AgglomerativeClustering
from sklearn.cluster import KMeans
from dataset import *
from sklearn.neighbors import BallTree
from l_method import agglomerative_l_method
dataset = get_iris()
l_method = agglomerative_l_method(dataset.X)
model = DividableClustering()
model.fit(dataset.X, l_method.labels_)
print('labels:', l_method.labels_)
print('predicts:', model.predict(dataset.X))
