May 2020
Intermediate to advanced
456 pages
9h 13m
Japanese
Content preview from ゼロからはじめるデータサイエンス 第2版 ―Pythonで学ぶ基本と実践
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20.6
凝集型階層的クラスタリング
■
297
clusterer = KMeans(5)
clusterer.train(pixels) #
計算には時間がかかる
結果が得られたら、同じフォーマットで新しいイメージを作ります。
def recolor(pixel: Vector) -> Vector:
cluster = clusterer.classify(pixel) #
ピクセルが分類されたクラスタのイン
デックス
return clusterer.means[cluster] #
クラスタの平均値
new_img = [[recolor(pixel) for pixel in row] #
ピクセル行の色を再設定する
for row in img] #
イメージの行を順に処理する
最後に
plt.imshow
で新しいイメージを表示します。
plt.imshow(new_img)
plt.axis('off')
plt.show()
白黒印刷された書籍ではカラーを表示できないため、元のフルカラー画像と
5
色に
減色した画像のグレースケール版を図20-5としました。
図20-5 オリジナル画像と 5色に減色した画像
20.6
凝集型階層的クラスタリング
クラスタリング手法には、ボトムアップでクラスタを「成長」させるものもあります。
具体的な手順は次の通りです。
1.
各入力から、その入力だけを含むクラスタを作る
2.
クラスタが複数存在していれば、近傍にある
2
つのクラスタを結合する
298
■
20
章 ク
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