K Measns.py

import numpy as np
import sklearn
from sklearn.preprocessing import scale
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
from sklearn import metrics

digits = load_digits()
data = scale(digits)

y = data.targets

# k = len(np.unique(y)) - if dynamic clusters needed
k = 10

samples, features = data.shape


def bench_k_means(estimator, name, data):
    estimator.fit(data)
    print('%-9s\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
          % (name, estimator.inertia_,
             metrics.homogeneity_score(y, estimator.labels_),
             metrics.completeness_score(y, estimator.labels_),
             metrics.v_measure_score(y, estimator.labels_),
             metrics.adjusted_rand_score(y, estimator.labels_),
             metrics.adjusted_mutual_info_score(y, estimator.labels_),
             metrics.silhouette_score(y, estimator.labels_, metric='euclidean'))

          )