#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 31 09:41:54 2025

"""

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import scipy.io


# Step 1: Get data
data = scipy.io.loadmat('HWMar26data.mat')

X=data.get('A')

# Step 2: Apply K-Means clustering
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
kmeans.fit(X)

# Get cluster centers and labels
centers = kmeans.cluster_centers_
labels = kmeans.labels_

# Step 3: Visualize the results
plt.figure(figsize=(8, 6))
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.6, edgecolors='k')
plt.scatter(centers[:, 0], centers[:, 1], c='red', marker='X', s=200, label='Centroids')
plt.title('K-Means Clustering Example')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.legend()
plt.show()