gaussian mixture

scripts/gaussian_mixture.py

@@ -0,0 +1,23 @@
+import numpy as np
+from sklearn.mixture import GaussianMixture
+import matplotlib.pyplot as plt
+
+np.random.seed(42)
+data1 = np.random.normal(loc=0, scale=1, size=300)
+data2 = np.random.normal(loc=5, scale=2, size=200)
+data = np.concatenate([data1, data2]).reshape(-1, 1)
+
+num_components = 2  
+gmm = GaussianMixture(n_components=num_components, random_state=42)
+gmm.fit(data)
+
+# Predict the component assignment and get the probabilities
+predictions = gmm.predict(data)
+probabilities = gmm.predict_proba(data)
+
+# Plot the data and color points by their predicted components
+plt.scatter(data, np.zeros_like(data), c=predictions, cmap='viridis', s=50)
+plt.title('Gaussian Mixture Model')
+plt.xlabel('Data Points')
+plt.show()
+