帮我把完整的代码写出来

好的，以下是完整的代码：

Private Sub CommandButton1_Click()
    Dim Today As Date
    Dim ColNum As Integer
    Dim MergeRange As Range
    
    '获取今日日期所在的列号
    Today = Date
    ColNum = Application.Match(Today, Range("7:7"), 0)
    
    '合并第三行和第371行的单元格并设置为居中对齐
    Range("C3:" & Cells(3, ColNum).Address).Merge
    Range("C371:" & Cells(371, ColNum).Address).Merge
    Range("C3:" & Cells(3, ColNum).Address).HorizontalAlignment = xlCenter
    Range("C371:" & Cells(371, ColNum).Address).HorizontalAlignment = xlCenter
    
    '提示格式已调整
    MsgBox "格式已调整！"
End Sub

这段代码与之前的代码相比，省略了循环搜索和计算需要合并的单元格范围的步骤，直接使用Range函数指定了需要合并的单元格范围。另外，删除了EndCell和EndCell1变量的定义，因为在这个代码中并没有使用到这两个变量。

相关问题

帮我把完整代码写出来

当然可以，这是一个简单的C语言版本，假设链表节点结构如下：

typedef struct ListNode {
    int val;
    struct ListNode *next;
} ListNode;

// 添加节点到结果链表函数
void addNodeToResult(ListNode* list, ListNode** result) {
    ListNode* newNode = malloc(sizeof(ListNode));
    newNode->val = list->val;
    newNode->next = NULL;
    
    if (*result == NULL) {
        *result = newNode;
    } else {
        ListNode* last = *result;
        while (last->next != NULL) {
            last = last->next;
        }
        last->next = newNode;
    }
}

// 寻找交集链表函数
ListNode* findIntersection(ListNode* A, ListNode* B) {
    ListNode* pA = A, *pB = B, *result = NULL;
    
    // 移动指针直到找到交叉点或一个链表结束
    while (pA && pB) {
        if (pA->val == pB->val) {
            addNodeToResult(pA, &result);
            pA = pA->next;
            pB = pB->next;
        } else if (pA->val < pB->val) {
            pA = pA->next;
        } else {
            pB = pB->next;
        }
    }
    
    return result;
}

这段代码首先定义了链表节点结构`ListNode`，然后实现了添加节点到结果链表以及寻找交集链表的功能。`findIntersection`函数最终会返回交集链表的头结点，如果没有交集则返回NULL。

请帮我把完整代码写出来并整合在一起

为了完成这个实验，我们需要编写完整的代码来加载数据、预处理数据、构建和评估不同的分类器及其集成方法。以下是基于 `scikit-learn` 库的完整代码示例：

### 导入必要的库

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

### 加载和预处理 Glass 数据集

# 加载数据
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data"
column_names = ["Id", "RI", "Na", "Mg", "Al", "Si", "K", "Ca", "Ba", "Fe", "Type"]
data = pd.read_csv(url, names=column_names)

# 分离特征和标签
X = data.drop(columns=["Id", "Type"])
y = data["Type"]

# 将多分类问题转换为二分类问题
y_binary = (y != 1).astype(int)  # 假设类别1为一类，其他为另一类

# 划分训练集和测试集
X_train, X_test, y_train_binary, y_test_binary = train_test_split(X, y_binary, test_size=0.3, random_state=42)
X_train, X_test, y_train_multi, y_test_multi = train_test_split(X, y, test_size=0.3, random_state=42)

# 标准化特征
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

### 定义基分类器

classifiers = {
    "Logistic Regression": LogisticRegression(max_iter=1000),
    "Decision Tree": DecisionTreeClassifier(),
    "SVM": SVC(probability=True),
    "KNN": KNeighborsClassifier(),
    "Naive Bayes": GaussianNB()
}

### 训练和评估基分类器

def evaluate_classifiers(classifiers, X_train, y_train, X_test, y_test):
    results = {}
    for name, clf in classifiers.items():
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        results[name] = accuracy
        print(f"{name} Accuracy: {accuracy:.4f}")
    return results

binary_results = evaluate_classifiers(classifiers, X_train_scaled, y_train_binary, X_test_scaled, y_test_binary)
multi_results = evaluate_classifiers(classifiers, X_train_scaled, y_train_multi, X_test_scaled, y_test_multi)

### 构建集成分类器

# Hard Voting Classifier
hard_voting_clf = VotingClassifier(estimators=list(classifiers.items()), voting='hard')
hard_voting_clf.fit(X_train_scaled, y_train_multi)
y_pred_hard = hard_voting_clf.predict(X_test_scaled)
accuracy_hard = accuracy_score(y_test_multi, y_pred_hard)
print(f"Hard Voting Classifier Accuracy: {accuracy_hard:.4f}")

# Soft Voting Classifier
soft_voting_clf = VotingClassifier(estimators=list(classifiers.items()), voting='soft', weights=[1, 1, 1, 1, 1])
soft_voting_clf.fit(X_train_scaled, y_train_multi)
y_pred_soft = soft_voting_clf.predict(X_test_scaled)
accuracy_soft = accuracy_score(y_test_multi, y_pred_soft)
print(f"Soft Voting Classifier Accuracy: {accuracy_soft:.4f}")

# Weighted Soft Voting Classifier
weights = [cross_val_score(clf, X_train_scaled, y_train_multi, cv=5).mean() for clf in classifiers.values()]
weighted_soft_voting_clf = VotingClassifier(estimators=list(classifiers.items()), voting='soft', weights=weights)
weighted_soft_voting_clf.fit(X_train_scaled, y_train_multi)
y_pred_weighted_soft = weighted_soft_voting_clf.predict(X_test_scaled)
accuracy_weighted_soft = accuracy_score(y_test_multi, y_pred_weighted_soft)
print(f"Weighted Soft Voting Classifier Accuracy: {accuracy_weighted_soft:.4f}")

### 加载和预处理 Leukemia 数据集

# 加载数据
url_leukemia = "path_to_leukemia_data.csv"  # 替换为实际路径
leukemia_data = pd.read_csv(url_leukemia)

# 分离特征和标签
X_leukemia = leukemia_data.iloc[:, 1:].values
y_leukemia = leukemia_data.iloc[:, 0].values

# 划分训练集和测试集
X_train_leukemia, X_test_leukemia, y_train_leukemia, y_test_leukemia = train_test_split(X_leukemia, y_leukemia, test_size=0.3, random_state=42)

# 标准化特征
scaler_leukemia = StandardScaler()
X_train_leukemia_scaled = scaler_leukemia.fit_transform(X_train_leukemia)
X_test_leukemia_scaled = scaler_leukemia.transform(X_test_leukemia)

### 训练和评估 Leukemia 数据集上的分类器

leukemia_results = evaluate_classifiers(classifiers, X_train_leukemia_scaled, y_train_leukemia, X_test_leukemia_scaled, y_test_leukemia)

# Hard Voting Classifier
hard_voting_clf_leukemia = VotingClassifier(estimators=list(classifiers.items()), voting='hard')
hard_voting_clf_leukemia.fit(X_train_leukemia_scaled, y_train_leukemia)
y_pred_hard_leukemia = hard_voting_clf_leukemia.predict(X_test_leukemia_scaled)
accuracy_hard_leukemia = accuracy_score(y_test_leukemia, y_pred_hard_leukemia)
print(f"Hard Voting Classifier on Leukemia Data Accuracy: {accuracy_hard_leukemia:.4f}")

# Soft Voting Classifier
soft_voting_clf_leukemia = VotingClassifier(estimators=list(classifiers.items()), voting='soft', weights=[1, 1, 1, 1, 1])
soft_voting_clf_leukemia.fit(X_train_leukemia_scaled, y_train_leukemia)
y_pred_soft_leukemia = soft_voting_clf_leukemia.predict(X_test_leukemia_scaled)
accuracy_soft_leukemia = accuracy_score(y_test_leukemia, y_pred_soft_leukemia)
print(f"Soft Voting Classifier on Leukemia Data Accuracy: {accuracy_soft_leukemia:.4f}")

# Weighted Soft Voting Classifier
weights_leukemia = [cross_val_score(clf, X_train_leukemia_scaled, y_train_leukemia, cv=5).mean() for clf in classifiers.values()]
weighted_soft_voting_clf_leukemia = VotingClassifier(estimators=list(classifiers.items()), voting='soft', weights=weights_leukemia)
weighted_soft_voting_clf_leukemia.fit(X_train_leukemia_scaled, y_train_leukemia)
y_pred_weighted_soft_leukemia = weighted_soft_voting_clf_leukemia.predict(X_test_leukemia_scaled)
accuracy_weighted_soft_leukemia = accuracy_score(y_test_leukemia, y_pred_weighted_soft_leukemia)
print(f"Weighted Soft Voting Classifier on Leukemia Data Accuracy: {accuracy_weighted_soft_leukemia:.4f}")

### 总结和分析

print("Summary of Results:")
print("Glass Data - Binary Classification")
for name, accuracy in binary_results.items():
    print(f"{name}: {accuracy:.4f}")

print("\nGlass Data - Multi-Class Classification")
for name, accuracy in multi_results.items():
    print(f"{name}: {accuracy:.4f}")

print("\nGlass Data - Ensemble Methods")
print(f"Hard Voting Classifier: {accuracy_hard:.4f}")
print(f"Soft Voting Classifier: {accuracy_soft:.4f}")
print(f"Weighted Soft Voting Classifier: {accuracy_weighted_soft:.4f}")

print("\nLeukemia Data - Multi-Class Classification")
for name, accuracy in leukemia_results.items():
    print(f"{name}: {accuracy:.4f}")

print("\nLeukemia Data - Ensemble Methods")
print(f"Hard Voting Classifier: {accuracy_hard_leukemia:.4f}")
print(f"Soft Voting Classifier: {accuracy_soft_leukemia:.4f}")
print(f"Weighted Soft Voting Classifier: {accuracy_weighted_soft_leukemia:.4f}")

以上代码涵盖了从数据加载、预处理到模型训练和评估的完整流程。你可以根据具体需求调整参数和数据路径。希望这对你有帮助！