特徵聚合#

這些圖片顯示如何使用特徵聚合將相似的特徵合併在一起。

Original data, Agglomerated data, Labels

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import cluster, datasets
from sklearn.feature_extraction.image import grid_to_graph

digits = datasets.load_digits()
images = digits.images
X = np.reshape(images, (len(images), -1))
connectivity = grid_to_graph(*images[0].shape)

agglo = cluster.FeatureAgglomeration(connectivity=connectivity, n_clusters=32)

agglo.fit(X)
X_reduced = agglo.transform(X)

X_restored = agglo.inverse_transform(X_reduced)
images_restored = np.reshape(X_restored, images.shape)
plt.figure(1, figsize=(4, 3.5))
plt.clf()
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.91)
for i in range(4):
    plt.subplot(3, 4, i + 1)
    plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
    plt.xticks(())
    plt.yticks(())
    if i == 1:
        plt.title("Original data")
    plt.subplot(3, 4, 4 + i + 1)
    plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
    if i == 1:
        plt.title("Agglomerated data")
    plt.xticks(())
    plt.yticks(())

plt.subplot(3, 4, 10)
plt.imshow(
    np.reshape(agglo.labels_, images[0].shape),
    interpolation="nearest",
    cmap=plt.cm.nipy_spectral,
)
plt.xticks(())
plt.yticks(())
plt.title("Labels")
plt.show()