PCA example with Iris Data-set

Python source code: plot_pca_iris.py

print __doc__


# Code source: Gael Varoqueux
# License: BSD

import numpy as np
import pylab as pl
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 = pl.figure(1, figsize=(4, 3))
pl.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
pl.set_cmap(pl.cm.spectral)

pl.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)

x_surf = [X[:, 0].min(), X[:, 0].max(),
          X[:, 0].min(), X[:, 0].max(),]
y_surf = [X[:, 0].max(), X[:, 0].max(),
          X[:, 0].min(), X[:, 0].min()]
x_surf = np.array(x_surf)
y_surf = np.array(y_surf)
v0 = pca.transform(pca.components_[0])
v0 /= v0[-1]
v1 = pca.transform(pca.components_[1])
v1 /= v1[-1]

#ax.plot_surface(x_surf, y_surf,




ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])