demo_pca.py 文件源码

def plot_simple_demo_1():
    pylab.clf()
    fig = pylab.figure(num=None, figsize=(10, 4))
    pylab.subplot(121)

    title = "Original feature space"
    pylab.title(title)
    pylab.xlabel("$X_1$")
    pylab.ylabel("$X_2$")

    x1 = np.arange(0, 10, .2)
    x2 = x1 + np.random.normal(scale=1, size=len(x1))

    good = (x1 > 5) | (x2 > 5)
    bad = ~good

    x1g = x1[good]
    x2g = x2[good]
    pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")

    x1b = x1[bad]
    x2b = x2[bad]
    pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")

    pylab.grid(True)

    pylab.subplot(122)

    X = np.c_[(x1, x2)]

    pca = decomposition.PCA(n_components=1)
    Xtrans = pca.fit_transform(X)

    Xg = Xtrans[good]
    Xb = Xtrans[bad]

    pylab.scatter(
        Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
    pylab.scatter(
        Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
    title = "Transformed feature space"
    pylab.title(title)
    pylab.xlabel("$X'$")
    fig.axes[1].get_yaxis().set_visible(False)

    print(pca.explained_variance_ratio_)

    pylab.grid(True)

    pylab.autoscale(tight=True)
    filename = "pca_demo_1.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")