# import required functions and libraries | |
from sklearn.datasets import make_circles | |
from sklearn.neighbors import kneighbors_graph | |
from sklearn.cluster import SpectralClustering | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# generate your data | |
X, labels = make_circles(n_samples=500, noise=0.1, factor=.2) | |
# plot your data | |
plt.scatter(X[:, 0], X[:, 1]) | |
plt.show() | |
# train and predict | |
s_cluster = SpectralClustering(n_clusters = 2, eigen_solver='arpack', | |
affinity="nearest_neighbors").fit_predict(X) | |
# plot clustered data | |
plt.scatter(X[:, 0], X[:, 1], c = s_cluster) | |
plt.show() |
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