python代码实现kd树分类mnist数据集

抱歉，我是语言模型AI，并不能实际运行代码。但是，我可以给你提供一些关于Python实现KD树分类MNIST数据集的思路和参考代码。

1.导入MNIST数据集：

from keras.datasets import mnist

(train_X, train_y), (test_X, test_y) = mnist.load_data()

2.将图像数据展开成一维向量：

train_X = train_X.reshape(-1, 784)
test_X = test_X.reshape(-1, 784)

3.定义KD树节点：

class KdNode:
    def __init__(self, point, split, left, right):
        self.point = point
        self.split = split
        self.left = left
        self.right = right

4.定义KD树构建函数：

def build_kdtree(points, depth=0):
    if not points:
        return None
    k = len(points[0])
    axis = depth % k
    sorted_points = sorted(points, key=lambda point: point[axis])
    mid = len(points) // 2
    return KdNode(
        sorted_points[mid],
        axis,
        build_kdtree(sorted_points[:mid], depth + 1),
        build_kdtree(sorted_points[mid + 1:], depth + 1)
    )

5.定义KD树搜索函数：

import math

def search_kdtree(tree, point, k=1):
    def search_node(node, point, k, depth):
        if node is None:
            return []
        axis = node.split
        if point[axis] < node.point[axis]:
            near_node, far_node = node.left, node.right
        else:
            near_node, far_node = node.right, node.left
        result = search_node(near_node, point, k, depth + 1)
        if len(result) < k or abs(point[axis] - node.point[axis]) < result[-1][0]:
            result += search_node(far_node, point, k, depth + 1)
        if len(result) < k or abs(point[axis] - node.point[axis]) < result[-1][0]:
            result.append((math.sqrt(sum((point[i] - node.point[i]) ** 2 for i in range(len(point)))), node.point))
            result.sort(key=lambda res: res[0])
            result = result[:k]
        return result
    return [res[1] for res in search_node(tree, point, k, 0)]

6.用KD树进行分类：

kdtree = build_kdtree(train_X)

correct = 0
for i in range(len(test_X)):
    neighbors = search_kdtree(kdtree, test_X[i])
    labels = [train_y[train_X.tolist().index(neighbor.tolist())] for neighbor in neighbors]
    label = max(set(labels), key=labels.count)
    if label == test_y[i]:
        correct += 1

accuracy = correct / len(test_X)
print('Accuracy:', accuracy)

上述代码仅是一个简单示例，你可以根据实际情况进行调整和优化。希望能对你有所帮助。