print(__doc__)

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.decomposition import PCA

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

iris = datasets.load_iris()

X = iris.data

y = iris.target

target_names = iris.target_names

pca = PCA(n_components=2)

X_r = pca.fit(X).transform(X)

lda = LinearDiscriminantAnalysis(n_components=2)

X_r2 = lda.fit(X, y).transform(X)

# Percentage of variance explained for each components

print('explained variance ratio (first two components): %s'

      % str(pca.explained_variance_ratio_))

plt.figure()

for c, i, target_name in zip("rgb", [0, 1, 2], target_names):

    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)

plt.legend()

plt.title('PCA of IRIS dataset')

plt.figure()

for c, i, target_name in zip("rgb", [0, 1, 2], target_names):

    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], c=c, label=target_name)

plt.legend()

plt.title('LDA of IRIS dataset')

plt.show()