python - 你如何访问 scipy 中的 ward/centroid/median 聚类？

Question

当使用 scipy.spatial.distance.pdist 创建压缩距离矩阵并将其传递给 ward 时，出现此错误:

Valid methods when the raw observations are omitted are 'single', 'complete', 'weighted', and 'average' error. 

虽然文档说 linkage() 函数需要一个压缩距离矩阵。我该如何解决这个问题？

foo = np.random.randint(3, size=(10,10))
scipy.spatial.distance.pdist(foo)
scipy.cluster.hierarchy.linkage(foo)
bar = scipy.spatial.distance.pdist(foo)
scipy.cluster.hierarchy.linkage(bar, method='ward')

给出:

 Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/usr/lib/python2.7/dist-packages/scipy /cluster/hierarchy.py", line 627, in linkage
raise ValueError("Valid methods when the raw observations are "
 ValueError: Valid methods when the raw observations are omitted are 'single', 'complete', 'weighted', and 'average'. 

我搜索了一下，找到了 this link ，表明其他一些人也有问题，但我无法找到一种解决方法来以 scipy 可以接受的形式提供数据。

Answer 1

来自文档字符串:

y : ndarray

A condensed or redundant distance matrix. A condensed distance matrix is a flat array containing the upper triangular of the distance matrix. This is the form that pdist returns. Alternatively, a collection of m observation vectors in n dimensions may be passed as an m by n array.

传入您的原始观察 x 维度数组 foo 似乎有效:

scipy.cluster.hierarchy.linkage(foo, method='ward')

给出:

array([[  1.        ,   2.        ,   2.23606798,   2.        ],
       [  5.        ,   8.        ,   2.23606798,   2.        ],
       [  3.        ,   7.        ,   2.64575131,   2.        ],
       [  9.        ,  11.        ,   2.64575131,   3.        ],
       [  0.        ,  10.        ,   3.31662479,   3.        ],
       [ 12.        ,  13.        ,   3.71483512,   5.        ],
       [  6.        ,  14.        ,   4.12310563,   4.        ],
       [  4.        ,  16.        ,   4.17133072,   5.        ],
       [ 15.        ,  17.        ,   5.5136195 ,  10.        ]])

我同意 linkage() 的文档至少可以改进。