AffinityPropagation.py 文件源码

def compute_affinity_propagation(preference_, X):
    # DATA FILLING
    #text = io.Input.local_read_text_file(inputFilePath)
    #input_array = text.split('\n')
    centers = [[1, 1], [-1, -1], [1, -1]]
    n_samples = 300
    #Make Blobs used for generating of labels_true array
    if (X == None):
        X, labels_true = make_blobs(n_samples = n_samples, centers=centers, cluster_std=1, random_state=0)
        print("Data is none!!!")
        print("Generating " + str(n_samples) + " samples")
    else :
        data, labels_true = make_blobs(n_samples=len(X), centers=centers, cluster_std=1, random_state=0)
    #slist = list()
    #for line in X:
    #    slist.append(line)
    #io.Output.write_array_to_txt_file("clustering\\Affinity_Propagation\\input_data1.txt", slist)
    #float_array = []
    #for line in input_array:
    #    float_line = [float(i) for i in line.split(' ')]
    #    float_array.append(float_line)
    #X = array(float_array)

    af = AffinityPropagation(preference=preference_).fit(X)
    cluster_centers_indices = af.cluster_centers_indices_
    labels = af.labels_
    n_clusters_ = len(cluster_centers_indices)
    print('Estimated number of clusters: %d' % n_clusters_)
    print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels))
    print("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels))
#    print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels, metric='sqeuclidean'))
    print("Fowlkes Mallows Score: %0.3f" % metrics.fowlkes_mallows_score(labels_true, labels))

    plt.close('all')
    plt.figure(1)
    plt.clf()
    colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
    for k, col in zip(range(n_clusters_), colors):
        class_members = labels == k
        cluster_center = X[cluster_centers_indices[k]]
        plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
        plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)
        for x in X[class_members]:
            plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()