common.py 文件源码

def plot_clusters_pca(responsibilities, color_groups):
    from sklearn.decomposition import RandomizedPCA
    import pylab as pl
    from random import shuffle

    colors = list(colors_dict.values())
    shuffle(colors)

    pca = RandomizedPCA(n_components=2)
    X = pca.fit_transform(responsibilities)
    # print >>stderr, pca.explained_variance_ratio_

    pl.figure()
    pl.scatter(X[:, 0], X[:, 1], c="grey", label="unknown")
    for c, sub, i in zip(colors, color_groups, count(0)):
        pl.scatter(X[sub, 0], X[sub, 1], c=c, label=str(i))
    pl.legend()
    pl.title("PCA responsibility matrix")
    pl.show()