3.4.8.1. Demo PCA in 2D

Load the iris data

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target

Fit a PCA

from sklearn.decomposition import PCA
pca = PCA(n_components=2, whiten=True)
pca.fit(X)
PCA(n_components=2, whiten=True)
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Project the data in 2D

Visualize the data

target_ids = range(len(iris.target_names))
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 5))
for i, c, label in zip(target_ids, "rgbcmykw", iris.target_names):
plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1], c=c, label=label)
plt.legend()
plt.show()
plot pca

Total running time of the script: (0 minutes 0.116 seconds)

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