8-easierEM_Knn.py

from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import sklearn.metrics as sm
l1 = [0, 1, 2]


def rename(s):
	l2 = []
	for i in s:
		if i not in l2:
			l2.append(i)

	for i in range(len(s)):
		pos = l2.index(s[i])
		s[i] = l1[pos]
	return s


iris_dataset = load_iris()
X = iris_dataset["data"]
y = iris_dataset["target"]
# k-means
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=40, cmap='viridis')
km = rename(y_kmeans)
print("Accuracy KM : ", sm.accuracy_score(y, km))
plt.show()
# EM part
gmm = GaussianMixture(n_components=3)
gmm.fit(X)
y_kmeans = gmm.predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=40, cmap='viridis')
em = rename(y_kmeans)
print("Accuracy EM : ", sm.accuracy_score(y, em))
plt.show()