python - 如何优化这段代码以进行 nn 预测？

Question

如何优化这段代码？目前，它的运行速度因经过此循环的数据量而变慢。此代码运行 1-最近邻。它将根据p_data_set预测training_element的标签

#               [x] ,           [[x1],[x2],[x3]],    [l1, l2, l3]
def prediction(training_element, p_data_set, p_label_set):
    temp = np.array([], dtype=float)
    for p in p_data_set:
        temp = np.append(temp, distance.euclidean(training_element, p))

    minIndex = np.argmin(temp)
    return p_label_set[minIndex]

Answer 1

使用 k-D tree用于快速最近邻查找，例如scipy.spatial.cKDTree :

from scipy.spatial import cKDTree

# I assume that p_data_set is (nsamples, ndims)
tree = cKDTree(p_data_set)

# training_elements is also assumed to be (nsamples, ndims)
dist, idx = tree.query(training_elements, k=1)

predicted_labels = p_label_set[idx]