9da6e2d594
Co-authored-by: bastien ollier <bastien.ollier@etu.uca.fr> Reviewed-on: https://codefirst.iut.uca.fr/git/clement.freville2/miner/pulls/13 Reviewed-by: Hugo PRADIER <hugo.pradier2@etu.uca.fr> Reviewed-by: Clément FRÉVILLE <clement.freville2@etu.uca.fr> Co-authored-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr> Co-committed-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
from sklearn.cluster import DBSCAN, KMeans
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import numpy as np
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class DBSCAN_cluster():
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def __init__(self, eps, min_samples,data):
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self.eps = eps
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self.min_samples = min_samples
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self.data = data
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self.labels = np.array([])
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def run(self):
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dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
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self.labels = dbscan.fit_predict(self.data)
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return self.labels
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def get_stats(self):
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unique_labels = np.unique(self.labels)
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stats = []
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for label in unique_labels:
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if label == -1:
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continue
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cluster_points = self.data[self.labels == label]
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num_points = len(cluster_points)
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density = num_points / (np.max(cluster_points, axis=0) - np.min(cluster_points, axis=0)).prod()
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stats.append({
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"cluster": label,
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"num_points": num_points,
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"density": density
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})
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return stats
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class KMeans_cluster():
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def __init__(self, n_clusters, n_init, max_iter, data):
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self.n_clusters = n_clusters
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self.n_init = n_init
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self.max_iter = max_iter
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self.data = data
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self.labels = np.array([])
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self.centers = []
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def run(self):
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kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
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self.labels = kmeans.fit_predict(self.data)
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self.centers = kmeans.cluster_centers_
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return self.labels
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def get_stats(self):
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unique_labels = np.unique(self.labels)
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stats = []
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for label in unique_labels:
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cluster_points = self.data[self.labels == label]
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num_points = len(cluster_points)
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center = self.centers[label]
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stats.append({
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'cluster': label,
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'num_points': num_points,
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'center': center
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})
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return stats
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