import DBCV
from sklearn import datasets
from sklearn.cluster import KMeans
from scipy.spatial.distance import euclidean
def generate_data(n_samples=300, noise=0.05):
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=noise)
X = noisy_moons[0]
return X
def generate_labels(X):
kmeans = KMeans(n_clusters=2)
kmeans_labels = kmeans.fit_predict(X)
return kmeans_labels
if __name__ == '__main__':
X = generate_data()
labels = generate_labels(X)
score = DBCV.DBCV(X, labels, dist_function=euclidean)
