利用近邻法和决策树算法完成对Iris数据集的分类任务，其中近邻法需要实现剪辑近邻和压缩近邻两种优化。决策树算法需要至少实现ID3和C4.5两种；写出代码和设计思路

设计思路：

1. 数据预处理：读取数据集，将数据集分为训练集和测试集，并对数据进行归一化处理。
2. 实现剪辑近邻和压缩近邻两种优化方法，以提高KNN算法的分类准确率。
3. 实现ID3算法和C4.5算法，对数据集进行分类。
4. 对分类结果进行评估，计算分类的准确率。

代码实现：

1.数据预处理

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 读取数据集
data = pd.read_csv('iris.data', header=None)

# 划分特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 归一化处理
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

2.实现剪辑近邻和压缩近邻两种优化方法

# 剪辑近邻
def clip_neighbors(X_train, X_test, k):
    y_pred = []
    for i in range(len(X_test)):
        distances = []
        for j in range(len(X_train)):
            distance = np.linalg.norm(X_test[i] - X_train[j])
            distances.append((distance, y_train[j]))
        distances.sort()
        neighbors = distances[:k]
        # 剪辑近邻
        for neighbor in neighbors:
            if neighbor[0] == 0:
                y_pred.append(neighbor[1])
                break
        else:
            y_pred.append(max(set([neighbor[1] for neighbor in neighbors]), key=[neighbor[1] for neighbor in neighbors].count))
    return np.array(y_pred)

# 压缩近邻
def compress_neighbors(X_train, X_test, k):
    y_pred = []
    for i in range(len(X_test)):
        distances = []
        for j in range(len(X_train)):
            distance = np.linalg.norm(X_test[i] - X_train[j])
            distances.append((distance, y_train[j]))
        distances.sort()
        # 压缩近邻
        neighbors = []
        for neighbor in distances:
            if neighbor[0] <= distances[k-1][0]:
                neighbors.append(neighbor)
        y_pred.append(max(set([neighbor[1] for neighbor in neighbors]), key=[neighbor[1] for neighbor in neighbors].count))
    return np.array(y_pred)

3.实现ID3算法和C4.5算法

# 计算信息熵
def calc_entropy(y):
    labels = set(y)
    entropy = 0
    for label in labels:
        p_label = y[y == label].size / y.size
        entropy -= p_label * np.log2(p_label)
    return entropy

# 计算条件熵
def calc_conditional_entropy(X, y, feature):
    values = set(X[:, feature])
    conditional_entropy = 0
    for value in values:
        sub_y = y[X[:, feature] == value]
        p_value = sub_y.size / y.size
        conditional_entropy += p_value * calc_entropy(sub_y)
    return conditional_entropy

# 计算信息增益
def calc_information_gain(X, y, feature):
    entropy = calc_entropy(y)
    conditional_entropy = calc_conditional_entropy(X, y, feature)
    information_gain = entropy - conditional_entropy
    return information_gain

# ID3算法
def id3(X, y, features):
    if len(set(y)) == 1:
        return y[0]
    if len(features) == 0:
        return max(set(y), key=y.count)
    information_gains = []
    for feature in features:
        information_gain = calc_information_gain(X, y, feature)
        information_gains.append(information_gain)
    best_feature_index = np.argmax(information_gains)
    best_feature = features[best_feature_index]
    tree = {best_feature: {}}
    values = set(X[:, best_feature])
    for value in values:
        sub_X = X[X[:, best_feature] == value]
        sub_y = y[X[:, best_feature] == value]
        sub_tree = id3(sub_X, sub_y, np.delete(features, best_feature_index))
        tree[best_feature][value] = sub_tree
    return tree

# C4.5算法
def c45(X, y, features):
    if len(set(y)) == 1:
        return y[0]
    if len(features) == 0:
        return max(set(y), key=y.count)
    information_gains_ratio = []
    for feature in features:
        information_gain = calc_information_gain(X, y, feature)
        split_info = calc_entropy(X[:, feature])
        if split_info == 0:
            information_gains_ratio.append(information_gain)
        else:
            information_gain_ratio = information_gain / split_info
            information_gains_ratio.append(information_gain_ratio)
    best_feature_index = np.argmax(information_gains_ratio)
    best_feature = features[best_feature_index]
    tree = {best_feature: {}}
    values = set(X[:, best_feature])
    for value in values:
        sub_X = X[X[:, best_feature] == value]
        sub_y = y[X[:, best_feature] == value]
        sub_tree = c45(sub_X, sub_y, np.delete(features, best_feature_index))
        tree[best_feature][value] = sub_tree
    return tree

4.对分类结果进行评估

# KNN算法分类结果评估
def evaluate_knn(y_pred, y_test):
    accuracy = sum(y_pred == y_test) / len(y_test)
    return accuracy

# 决策树算法分类结果评估
def evaluate_tree(tree, X_test, y_test):
    y_pred = []
    for i in range(len(X_test)):
        sub_tree = tree.copy()
        while isinstance(sub_tree, dict):
            feature = list(sub_tree.keys())[0]
            value = X_test[i, feature]
            sub_tree = sub_tree[feature][value]
        y_pred.append(sub_tree)
    accuracy = sum(y_pred == y_test) / len(y_test)
    return accuracy

# 剪辑近邻KNN算法分类结果
y_pred = clip_neighbors(X_train, X_test, 3)
accuracy = evaluate_knn(y_pred, y_test)
print('剪辑近邻KNN算法分类准确率：', accuracy)

# 压缩近邻KNN算法分类结果
y_pred = compress_neighbors(X_train, X_test, 3)
accuracy = evaluate_knn(y_pred, y_test)
print('压缩近邻KNN算法分类准确率：', accuracy)

# ID3算法分类结果
tree = id3(X_train, y_train, np.arange(X_train.shape[1]))
accuracy = evaluate_tree(tree, X_test, y_test)
print('ID3算法分类准确率：', accuracy)

# C4.5算法分类结果
tree = c45(X_train, y_train, np.arange(X_train.shape[1]))
accuracy = evaluate_tree(tree, X_test, y_test)
print('C4.5算法分类准确率：', accuracy)