24 lines
710 B
Python
24 lines
710 B
Python
# Importieren der notwendigen Bibliotheken
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import numpy as np
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from sklearn import datasets
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from sklearn.manifold import TSNE
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import matplotlib.pyplot as plt
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# Laden des Digits-Datensatzes
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digits = datasets.load_digits()
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X = digits.data
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y = digits.target
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# Anwendung von t-SNE
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tsne = TSNE(n_components=2, random_state=42)
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X_tsne = tsne.fit_transform(X)
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# Visualisierung der Ergebnisse
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plt.figure(figsize=(10, 8))
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scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y, cmap='viridis', s=50)
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legend1 = plt.legend(*scatter.legend_elements(), title="Classes")
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plt.gca().add_artist(legend1)
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plt.xlabel('t-SNE feature 1')
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plt.ylabel('t-SNE feature 2')
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plt.title('t-SNE-Abbildung des Digits-Datensatzes')
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plt.show() |