lda.py

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

def setup_lda():
    iris = datasets.load_iris()

    X = iris.data
    y = iris.target
    target_names = iris.target_names

    lda = LinearDiscriminantAnalysis(n_components=2)
    X_r2 = lda.fit(X, y).transform(X)
    print("explained variance ratio (first two components): %s" % str(lda.explained_variance_ratio_))

    colors = ["navy", "turquoise", "darkorange"]

    plt.figure()
    for color, i, target_name in zip(colors, [0, 1, 2], target_names):
        plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], alpha=0.8, color=color, label=target_name)
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.title("LDA of IRIS dataset")