PCA example with Iris Data-set

Principal Component Analysis applied to the Iris dataset.

See here for more information on this dataset.

../../_images/sphx_glr_plot_pca_iris_001.png

print(__doc__)
 
 
# Code source: Ga Varoquaux
# License: BSD 3 clause
 
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
 
from sklearn import decomposition
from sklearn import datasets
 
np.random.seed(5)
 
centers = [[1, 1], [-1, -1], [1, -1]]
iris = datasets.load_iris()
X = iris.data
y = iris.target
 
fig = plt.figure(1, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
 
plt.cla()
pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)
 
for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:
    ax.text3D(X[y == label, 0].mean(),
              X[y == label, 1].mean() + 1.5,
              X[y == label, 2].mean(), name,
              horizontalalignment='center',
              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(np.float)
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral)
 
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
 
plt.show()

Total running time of the script: (0 minutes 0.115 seconds)

Download Python source code: plot_pca_iris.py

Download IPython notebook: plot_pca_iris.ipynb

Links:

http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html

doc_scikit_learn

2025-01-10 15:47:30

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