basicVersionOfKMeans

# set up the parameters
n_init = 12
max_iter = 225
tol = 0.0001
random_state = 42
n_jobs = -1
n_clusters = 3

t0 = dt.now()
print("=========  Start training ... ")

# Initialize the clusterer with n_clusters value and a random generator
# seed of 42 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, random_state=random_state)
cluster_labels = clusterer.fit_predict(X_train)

# inertia
inertia = clusterer.inertia_

# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X_train, cluster_labels)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)

print("For n_clusters =", n_clusters,
      ", the inertia is :", inertia,
      ", the average silhouette_score is :", silhouette_avg)

t1 = dt.now()-t0
print("=========  Finished in ",t1)