如何解决无法散列的类型：numpy ndarray

Question

当我将常规knn方法应用于算法时，遇到了此问题：

  

TypeError：不可哈希类型：'numpy.ndarray'

def classify(inx, dataset, labels, k):

    dist = tf.sqrt(tf.reduce_sum(tf.square(inx - dataset), axis=1))

    k_labels = [labels[index] for index in np.argsort(dist)[0 : k]]

    lab = collections.Counter(k_labels).most_common(1)[0][0]
    return lab

错误：

 File "C:/Users/xjy/untitled0.py", line 166, in classify
    lab = collections.Counter(k_labels).most_common(1)[0][0]

  File "C:\Users\xjy\Anaconda3\envs\xjy\lib\collections\__init__.py", line 535, in __init__
    self.update(*args, **kwds)

  File "C:\Users\xjy\Anaconda3\envs\xjy\lib\collections\__init__.py", line 622, in update
    _count_elements(self, iterable)

TypeError: unhashable type: 'numpy.ndarray'

Answer 1

如果您的k_labels为[array([1., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]，则

使用

lab = collections.Counter(k_labels[0]).most_common(1)[0][0]