mirror of
https://github.com/The-Art-of-Hacking/h4cker.git
synced 2024-10-01 01:25:43 -04:00
39 lines
2.0 KiB
Python
39 lines
2.0 KiB
Python
Sure! Here's a Python script that demonstrates the t-SNE algorithm using the famous Iris dataset for visualization:
|
|
|
|
```python
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn import datasets
|
|
from sklearn.manifold import TSNE
|
|
|
|
# Load the Iris dataset
|
|
iris = datasets.load_iris()
|
|
X = iris.data
|
|
y = iris.target
|
|
|
|
# Applying t-SNE
|
|
tsne = TSNE(n_components=2, random_state=42)
|
|
X_tsne = tsne.fit_transform(X)
|
|
|
|
# Plotting the t-SNE results
|
|
colors = ['r', 'g', 'b']
|
|
target_names = iris.target_names
|
|
|
|
plt.figure(figsize=(10, 6))
|
|
for i, target_name in enumerate(target_names):
|
|
plt.scatter(X_tsne[y == i, 0], X_tsne[y == i, 1], c=colors[i], label=target_name)
|
|
|
|
plt.xlabel('First t-SNE Component')
|
|
plt.ylabel('Second t-SNE Component')
|
|
plt.title('t-SNE Visualization of Iris Dataset')
|
|
plt.legend()
|
|
plt.show()
|
|
```
|
|
|
|
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.
|
|
|
|
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`.
|
|
|
|
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.
|
|
|
|
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. |