An introducing example to clustering with an MLP and the MMD GEMINI

We show in this example how to use the gemclus.mlp.MLPMMD to fit a simple mixture of Gaussian distributions. The architecture here is a two-layer neural network trained with the MMD GEMINI, an objective equivalent to kernel KMeans.

from matplotlib import pyplot as plt
from sklearn import datasets

from gemclus.mlp import MLPMMD

Generate data

X, y = datasets.make_blobs(centers=3, cluster_std=0.5, n_samples=200, random_state=0)

Create the MLP clustering model and fit it

clf = MLPMMD(random_state=0, ovo=True)
clf.fit(X)

/home/circleci/.local/lib/python3.10/site-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.
  warnings.warn(

MLPMMD(ovo=True, random_state=0)

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Plot the final clustering

y_pred = clf.predict(X)
X_0 = X[y_pred == 0]
X_1 = X[y_pred == 1]
X_2 = X[y_pred == 2]

ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=50)
ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=50)
ax2 = plt.scatter(X_2[:, 0], X_2[:, 1], c='darkgreen', s=50)

leg = plt.legend([ax0, ax1, ax2],
                 ['Cluster 0', 'Cluster 1', 'Cluster 2'],
                 loc='upper left', fancybox=True, scatterpoints=1)
leg.get_frame().set_alpha(0.5)

plt.xlabel('Feature 1')
plt.ylabel('Feature 2')

plt.show()