python sklearn库中的AffinityPropagation( )聚类算法主要参数的使用,

た入场券 2022-05-29 04:37 217阅读 0赞

*class* ``sklearn.cluster.AffinityPropagation``(*damping=0.5*, *max\_iter=200*, *convergence\_iter=15*, *copy=True*, *preference=None*, *affinity=’euclidean’*, *verbose=False*)

函数参数

**damping** : float, optional, default: 0.5,阻尼系数,默认值0.5

**max\_iter** : int, optional, default: 200,最大迭代次数,默认值是200

**convergence\_iter** : int, optional, default: 15,在停止收敛的估计集群数量上没有变化的迭代次数。默认15

**copy** : boolean, optional, default: True,布尔值,可选,默认为true,即允许对输入数据的复制

**preference** : array-like, shape (n\_samples,) or float, optional,近似数组,每个点的偏好 - 具有较大偏好值的点更可能被选为聚类的中心点。 簇的数量，即集群的数量受输入偏好值的影响。 如果该项未作为参数，则选择输入相似度的中位数作为偏好

**affinity** : string, optional, default=\`\`euclidean\`\`目前支持计算预欧几里得距离。 即点之间的负平方欧氏距离。

**verbose** : boolean, optional, default: False

成员属性:

**cluster\_centers\_indices\_** : array, shape (n\_clusters,)聚类的中心索引,Indices of cluster centers

**cluster\_centers\_** : array, shape (n\_clusters, n\_features)聚类中心（如果亲和力=预先计算）。

**labels\_** : array, shape (n\_samples,)每个点的标签

**affinity\_matrix\_** : array, shape (n\_samples, n\_samples)存储拟合中使用的亲和度矩阵。

**n\_iter\_** : int,收敛的迭代次数。

近邻传播算法的复杂度是点数的二次

成员方法:

[`fit`][fit](X\[, y\]) 从负欧氏距离创建相似度矩阵，然后应用于近邻传播聚类。

fit\_predict 在X上执行聚类并返回聚类标签

get\_params 获取此估算器的参数

predict(X) 预测X中每个样本所属的最近聚类

set\_params 设置此估算器的参数。

`__init__`(*damping=0.5*, *max\_iter=200*, *convergence\_iter=15*, *copy=True*, *preference=None*, *affinity=’euclidean’*, *verbose=False*) 初始化函数

# Demo of affinity propagation clustering algorithm #

输出:

Estimated number of clusters: 3
    Homogeneity: 0.872
    Completeness: 0.872
    V-measure: 0.872
    Adjusted Rand Index: 0.912
    Adjusted Mutual Information: 0.871
    Silhouette Coefficient: 0.753

实例代码:

print(__doc__)
    
    from sklearn.cluster import AffinityPropagation
    from sklearn import metrics
    from sklearn.datasets.samples_generator import make_blobs
    
    # #############################################################################
    # Generate sample data
    centers = [[1, 1], [-1, -1], [1, -1]]
    X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,
                                random_state=0)
    
    # #############################################################################
    # Compute Affinity Propagation
    af = AffinityPropagation(preference=-50).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'))
    
    # #############################################################################
    # Plot result
    import matplotlib.pyplot as plt
    from itertools import cycle
    
    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()

![sphx_glr_plot_affinity_propagation_001.png][]

[fit]: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation.fit
[sphx_glr_plot_affinity_propagation_001.png]: /images/20220529/ccc81cc1873a41f6bd55f685be924c5e.png