from sklearn.cluster import DBSCAN, KMeans
import numpy as np

class DBSCAN_cluster():
    def __init__(self, eps, min_samples,data):
        self.eps = eps
        self.min_samples = min_samples
        self.data = data
        self.labels = np.array([])

    def run(self):
        dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
        self.labels = dbscan.fit_predict(self.data)
        return self.labels

    def get_stats(self):
        unique_labels = np.unique(self.labels)
        stats = []
        for label in unique_labels:
            if label == -1:
                continue
            cluster_points = self.data[self.labels == label]
            num_points = len(cluster_points)
            density = num_points / (np.max(cluster_points, axis=0) - np.min(cluster_points, axis=0)).prod()
            stats.append({
                "cluster": label,
                "num_points": num_points,
                "density": density
            })

        return stats
    

class KMeans_cluster():
    def __init__(self, n_clusters, n_init, max_iter, data):
        self.n_clusters = n_clusters
        self.n_init = n_init
        self.max_iter = max_iter
        self.data = data
        self.labels = np.array([])
        self.centers = []

    def run(self):
        kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
        self.labels = kmeans.fit_predict(self.data)
        self.centers = kmeans.cluster_centers_
        return self.labels


    def get_stats(self):
        unique_labels = np.unique(self.labels)
        stats = []

        for label in unique_labels:
            cluster_points = self.data[self.labels == label]
            num_points = len(cluster_points)
            center = self.centers[label]
            stats.append({
                'cluster': label,
                'num_points': num_points,
                'center': center
            })
        return stats