fork download
copy 
  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3.  
  4. def k_means_clustering(data, k, max_iterations=100):
  5.     """
  6.     Perform K-means clustering on the given data.
  7.  
  8.     Parameters:
  9.         data (numpy.ndarray): The data to be clustered.
  10.         k (int): The number of clusters.
  11.         max_iterations (int): Maximum number of iterations for convergence.
  12.  
  13.     Returns:
  14.         tuple: A tuple containing centroids and labels.
  15.             centroids (numpy.ndarray): The final centroids of the clusters.
  16.             labels (numpy.ndarray): The labels for each data point.
  17.     """
  18.     centroids = data[np.random.choice(range(len(data)), k, replace=False)]
  19.     for _ in range(max_iterations):
  20.         labels = np.argmin(np.linalg.norm(data - centroids[:, np.newaxis], axis=2), axis=0)
  21.         new_centroids = np.array([data[labels == i].mean(axis=0) for i in range(k)])
  22.         if np.all(centroids == new_centroids):
  23.             break
  24.         centroids = new_centroids
  25.     return centroids, labels
  26.  
  27. np.random.seed(42)
  28. data = np.concatenate([np.random.normal(loc=i, scale=1, size=(100, 2)) for i in range(3)])
  29. k = 3
  30. centroids, labels = k_means_clustering(data, k)
  31.  
  32. plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='viridis', alpha=0.7)
  33. plt.scatter(centroids[:, 0], centroids[:, 1], c='red', marker='X', s=200, label='Centroids')
  34. plt.title('K-means Clustering')
  35. plt.legend()
  36. plt.show()
  37.  
Success #stdin #stdout 0.68s 55980KB
comments (?)
stdin
copy 
mushroom
stdout
copy 
Standard output is empty
https://ideone.com/ZkWcMi
language:
Python (cpython 2.7.16)
created:
34 days ago
visibility:
public

Share or Embed source code

Discover > Sphere Engine API

The brand new service which powers Ideone!

Discover > IDE Widget

Widget for compiling and running the source code in a web browser!