from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
import sklearn.metrics as metrics
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load dataset
dataset = pd.read_csv('iris.csv')
X = dataset.iloc[:, :-1]
y = {'Setosa': 0, 'Versicolor': 1, 'Virginica': 2}[dataset.iloc[:, -1]]

# Visualize real data
plt.scatter(X.Petal_Length, X.Petal_Width, c=np.array(['red', 'lime', 'black'])[y])
plt.title('Real')
plt.show()

# Gaussian Mixture Model (GMM)
gmm = GaussianMixture(n_components=3, random_state=0).fit(X)
y_cluster_gmm = gmm.predict(X)

# Visualize GMM classification
plt.scatter(X.Petal_Length, X.Petal_Width, c=np.array(['red', 'lime', 'black'])[y_cluster_gmm])
plt.title('GMM Classification')
plt.show()

# Print accuracy and confusion matrix
print('The accuracy score of EM:', metrics.accuracy_score(y, y_cluster_gmm))
print('The Confusion matrix of EM:\n', metrics.confusion_matrix(y, y_cluster_gmm))