Function main

unsupervised_class/kmeans_mnist.py:133–154

()

def main():
    # mnist data
    X, Y = get_data(10000)

    # simple data
    # X = get_simple_data()
    # Y = np.array([0]*300 + [1]*300 + [2]*300)

    print("Number of data points:", len(Y))
    M, R = plot_k_means(X, len(set(Y)))
    # Exercise: Try different values of K and compare the evaluation metrics
    print("Purity:", purity(Y, R))
    print("Purity 2 (hard clusters):", purity2(Y, R))
    print("DBI:", DBI(X, M, R))
    print("DBI 2 (hard clusters):", DBI2(X, R))

    # plot the mean images
    # they should look like digits
    for k in range(len(M)):
        im = M[k].reshape(28, 28)
        plt.imshow(im, cmap='gray')
        plt.show()


if __name__ == "__main__":