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


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

X=data.get('A')

# Run DBSCAN Clustering Algorithm

epsilon=0.06
MinPts=4
dbscan=DBSCAN(eps=epsilon,min_samples=MinPts)
IDX=dbscan.fit_predict(X)


# Plot results
plt.figure(figsize=(8, 6))
unique_labels = set(IDX)
colors = [plt.cm.jet(each) for each in np.linspace(0, 1, len(unique_labels))]

for label, col in zip(unique_labels, colors):
    if label == -1:
        col = (0, 0, 0, 1)  # Black for noise
    mask = (IDX == label)
    plt.scatter(X[mask, 0], X[mask, 1], c=[col], label=f"Cluster {label}")

plt.legend()
plt.title("DBSCAN Clustering")
plt.show()