sklearn_pca_dimensionality_reduction_quickstart.py

1import numpy as np
2from sklearn.decomposition import PCA
3
4# Create sample data
5X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
6
7# Initialize PCA to find 2 components
8pca = PCA(n_components=2)
9
10# Fit the model with X
11pca.fit(X)
12
13# Print results
14print(pca.explained_variance_ratio_)
15print(pca.singular_values_)
16
17# Example with 1 component
18pca = PCA(n_components=1)
19pca.fit(X)
20X_pca = pca.transform(X)
21print("original shape:   ", X.shape)
22print("transformed shape:", X_pca.shape)