plt.show()
# In[107]:
from mlxtend.cluster import Kmeans
km = Kmeans(k=3, max_iter=50, random_seed=1, print_progress=3)
km.fit(X)
print('Iterations until convergence:', km.iterations_)
print('Final centroids:\n', km.centroids_)
# In[108]:
y_clust = km.predict(X)
plt.scatter(X[y_clust == 0, 0],
X[y_clust == 0, 1],
s=50,
c='lightgreen',
marker='s',
label='cluster 1')
plt.scatter(X[y_clust == 1, 0],
X[y_clust == 1, 1],
s=50,
c='orange',
marker='o',
label='cluster 2')
X, y = iris_data()
X = X[:, [
2, 3
]] # choose combination 2 of 4 features, then will results in 6 kinds of results. see the png.
#plt.scatter(X[:,0],X[:,1], c='red')
#plt.show()
km = Kmeans(k=3, max_iter=50, random_seed=1, print_progress=3)
km.fit(X)
print(':\nIterations until convergence:', km.iterations_)
print('Final centroids:\n', km.centroids_)
# Visualize the cluster memberships
y_cluster = km.predict(X)
plt.scatter(X[y_cluster == 0, 0],
X[y_cluster == 0, 1],
s=50,
c='lightgreen',
marker='s',
label='cluster 1')
plt.scatter(X[y_cluster == 1, 0],
X[y_cluster == 1, 1],
s=50,
c='orange',
marker='o',
label='cluster 2')
