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39 lines
2.0 KiB
Python
39 lines
2.0 KiB
Python
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Sure! Here's a Python script that demonstrates the t-SNE algorithm using the famous Iris dataset for visualization:
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.manifold import TSNE
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# Load the Iris dataset
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# Applying 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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# Plotting the t-SNE results
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colors = ['r', 'g', 'b']
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target_names = iris.target_names
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plt.figure(figsize=(10, 6))
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for i, target_name in enumerate(target_names):
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plt.scatter(X_tsne[y == i, 0], X_tsne[y == i, 1], c=colors[i], label=target_name)
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plt.xlabel('First t-SNE Component')
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plt.ylabel('Second t-SNE Component')
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plt.title('t-SNE Visualization of Iris Dataset')
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plt.legend()
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plt.show()
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```
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In this script, we first import the necessary libraries (`numpy`, `matplotlib`, `sklearn.datasets`, and `sklearn.manifold.TSNE`) to perform the t-SNE algorithm. Then, we load the Iris dataset using the `datasets.load_iris()` from scikit-learn. The data and target variables are assigned to `X` and `y` arrays, respectively.
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After that, we create an instance of the t-SNE algorithm with `TSNE(n_components=2, random_state=42)`. We choose 2 components (`n_components=2`) since we want to visualize the data in 2D. The `random_state` parameter is set to ensure reproducibility in the results. We then apply the t-SNE algorithm to the data using `fit_transform(X)`, which returns the transformed data as `X_tsne`.
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Finally, we plot the t-SNE results using `matplotlib`. Each data point is plotted in a scatter plot, with different colors representing different classes ('setosa', 'versicolor', and 'virginica') in the Iris dataset.
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To run this script, ensure that you have the necessary libraries installed (`numpy`, `matplotlib`, and `scikit-learn`). Save it as a .py file and execute it using a Python interpreter. The script will show a plot with the t-SNE visualization of the Iris dataset.
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