数据集10行8列csv文件，第一列是序号，2到7列是特征，最后一列是标签，分别选择欧氏距离和曼哈顿距离，对前8个数据，分别进行K=3和K=5的KNN 分类，输出每一个分类结果的序号，再进行当K=3时，第9,10 预测结果是？ 当K=5时，第9,10 预测结果是？python代码实现

好的，根据你的要求，我来给你实现一个既可以选择欧氏距离又可以选择曼哈顿距离的KNN分类的代码，代码如下：

import math

# 计算两个样本之间的欧氏距离
def euclidean_distance(instance1, instance2):
    distance = 0
    for i in range(1, 7):
        distance += pow((float(instance1[i]) - float(instance2[i])), 2)
    return math.sqrt(distance)

# 计算两个样本之间的曼哈顿距离
def manhattan_distance(instance1, instance2):
    distance = 0
    for i in range(1, 7):
        distance += abs(float(instance1[i]) - float(instance2[i]))
    return distance

# 找到距离样本最近的k个邻居
def get_neighbors(training_set, test_instance, k, distance_func):
    distances = []
    for i in range(len(training_set)):
        dist = distance_func(test_instance, training_set[i])
        distances.append((training_set[i], dist))
    distances.sort(key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

# 对k个邻居进行投票，返回预测结果
def get_response(neighbors):
    class_votes = {}
    for i in range(len(neighbors)):
        response = neighbors[i][-1]
        if response in class_votes:
            class_votes[response] += 1
        else:
            class_votes[response] = 1
    sorted_votes = sorted(class_votes.items(), key=lambda x: x[1], reverse=True)
    return sorted_votes[0][0]

# 读取数据集
def load_dataset(filename):
    dataset = []
    with open(filename, 'r') as f:
        for line in f:
            instance = line.strip().split(',')
            dataset.append(instance)
    return dataset

# 对数据集进行KNN分类
def knn(training_set, test_set, k, distance_func):
    predictions = []
    for i in range(len(test_set)):
        neighbors = get_neighbors(training_set, test_set[i], k, distance_func)
        result = get_response(neighbors)
        predictions.append(result)
    return predictions

# 测试代码
filename = 'data.csv'
dataset = load_dataset(filename)

# 对前8个数据进行KNN分类
training_set = dataset[:8]
test_set = dataset[:8]
k = 3
predictions_euclidean_k3 = knn(training_set, test_set, k, euclidean_distance)
predictions_manhattan_k3 = knn(training_set, test_set, k, manhattan_distance)
print('K=3时，前8个数据的欧氏距离分类结果的序号为：', predictions_euclidean_k3)
print('K=3时，前8个数据的曼哈顿距离分类结果的序号为：', predictions_manhattan_k3)

k = 5
predictions_euclidean_k5 = knn(training_set, test_set, k, euclidean_distance)
predictions_manhattan_k5 = knn(training_set, test_set, k, manhattan_distance)
print('K=5时，前8个数据的欧氏距离分类结果的序号为：', predictions_euclidean_k5)
print('K=5时，前8个数据的曼哈顿距离分类结果的序号为：', predictions_manhattan_k5)

# 预测第9、10个数据的分类结果
test_set = dataset[8:]
k = 3
predictions_euclidean_k3 = knn(training_set, test_set, k, euclidean_distance)
predictions_manhattan_k3 = knn(training_set, test_set, k, manhattan_distance)
print('K=3时，第9、10个数据的欧氏距离分类结果的序号为：', predictions_euclidean_k3)
print('K=3时，第9、10个数据的曼哈顿距离分类结果的序号为：', predictions_manhattan_k3)

k = 5
predictions_euclidean_k5 = knn(training_set, test_set, k, euclidean_distance)
predictions_manhattan_k5 = knn(training_set, test_set, k, manhattan_distance)
print('K=5时，第9、10个数据的欧氏距离分类结果的序号为：', predictions_euclidean_k5)
print('K=5时，第9、10个数据的曼哈顿距离分类结果的序号为：', predictions_manhattan_k5)

这里我们定义了两个距离函数，分别是欧氏距离和曼哈顿距离，然后在进行KNN分类时，通过`distance_func`参数来选择使用哪个距离函数。其他部分的代码和之前的代码类似，只是在输出分类结果时，分别输出了欧氏距离和曼哈顿距离的分类结果序号。