diff --git a/49张兴宇.xls b/49张兴宇.xls
new file mode 100644
index 0000000..a74a261
Binary files /dev/null and b/49张兴宇.xls differ
diff --git a/config.py b/config.py
index 12a552d..ec6ef16 100644
--- a/config.py
+++ b/config.py
@@ -1,40 +1,40 @@
-# -*- coding: utf-8 -*-
-"""
-配置文件 - 所有超参数集中管理
-
-设计思路：
-将超参数分门别类，学生可以单独修改某一类而不会影响其他
-"""
-
-# ==================== 数据相关 ====================
-DATA_DIR = 'data/ChnSentiCorp'          # 数据集路径
-MAX_FEATURES = 3000                     # 词表最大容量
-MAX_SEQ_LEN = 100                       # 句子最大长度（词数）
-VECTORIZER_TYPE = 'tfidf'               # 'tfidf' 或 'bow'（向量化方式）
-
-# ==================== 模型相关 ====================
-MODEL_TYPE = 'mlp'                       # 'mlp' 或 'lr'（模型类型）
-HIDDEN_SIZE = 64                        # MLP隐藏层大小（LR忽略）
-NUM_CLASSES = 2                         # 类别数（正面/负面二分类）
-KEEP_PROB = 1.0                         # Dropout保留概率（LR忽略，设为1即可）
-
-# ==================== 训练相关 ====================
-LEARNING_RATE = 0.05                    # 学习率
-NUM_EPOCHS = 100                        # 训练轮数
-BATCH_SIZE = 64                         # 批次大小
-
-# ==================== 类别权重（解决数据不平衡问题）====================
-USE_CLASS_WEIGHT = True                 # True=启用类别权重, False=不启用（对比用）
-# 权重计算公式: n_samples / (n_classes * n_class_i)
-# 正面评论多所以权重小，负面评论少所以权重大
-CLASS_WEIGHT_POS = 0.73                # 正面类权重（自动计算）
-CLASS_WEIGHT_NEG = 1.58                # 负面类权重（自动计算）
-
-# ==================== 实验相关 ====================
-RUN_COMPARISON = False                  # True=运行对比实验, False=运行单个模型
-COMPARE_MODELS = ['lr', 'mlp']          # 要对比的模型列表
-COMPARE_VECTORS = ['bow', 'tfidf']      # 要对比的向量化方式
-
-# ==================== 其他 ====================
-RANDOM_SEED = 42                        # 随机种子（保证可复现）
-VERBOSE = True                          # 打印详细日志
+# -*- coding: utf-8 -*-
+"""
+配置文件 - 所有超参数集中管理
+
+设计思路：
+将超参数分门别类，学生可以单独修改某一类而不会影响其他
+"""
+
+# ==================== 数据相关 ====================
+DATA_DIR = 'data/ChnSentiCorp'          # 数据集路径
+MAX_FEATURES = 3000                     # 词表最大容量
+MAX_SEQ_LEN = 100                       # 句子最大长度（词数）
+VECTORIZER_TYPE = 'tfidf'               # 'tfidf' 或 'bow'（向量化方式）
+
+# ==================== 模型相关 ====================
+MODEL_TYPE = 'mlp'                       # 'mlp' 或 'lr'（模型类型）
+HIDDEN_SIZE = 60                        # MLP隐藏层大小（LR忽略）
+NUM_CLASSES = 2                         # 类别数（正面/负面二分类）
+KEEP_PROB = 1.0                         # Dropout保留概率（LR忽略，设为1即可）
+
+# ==================== 训练相关 ====================
+LEARNING_RATE = 0.05                    # 学习率
+NUM_EPOCHS = 100                        # 训练轮数
+BATCH_SIZE = 50                         # 批次大小
+
+# ==================== 类别权重（解决数据不平衡问题）====================
+USE_CLASS_WEIGHT = True                 # True=启用类别权重, False=不启用（对比用）
+# 权重计算公式: n_samples / (n_classes * n_class_i)
+# 正面评论多所以权重小，负面评论少所以权重大
+CLASS_WEIGHT_POS = 1.66                # 正面类权重（自动计算）
+CLASS_WEIGHT_NEG = 0.99               # 负面类权重（自动计算）
+
+# ==================== 实验相关 ====================
+RUN_COMPARISON = False                  # True=运行对比实验, False=运行单个模型
+COMPARE_MODELS = ['lr', 'mlp']          # 要对比的模型列表
+COMPARE_VECTORS = ['bow', 'tfidf']      # 要对比的向量化方式
+
+# ==================== 其他 ====================
+RANDOM_SEED = 42                        # 随机种子（保证可复现）
+VERBOSE = True                          # 打印详细日志
diff --git a/dataset.py b/dataset.py
index e554362..4f4163e 100644
--- a/dataset.py
+++ b/dataset.py
@@ -1,286 +1,286 @@
-# -*- coding: utf-8 -*-
-"""
-数据加载与向量化模块
-
-支持两种向量化方法：
-1. BoW (Bag of Words) - 词频向量
-2. TF-IDF - 词频-逆文档频率向量
-
-TF-IDF 的优势：
-- 降低常见词（如"的"、"是"）的权重
-- 提升罕见词的信息量
-- 通常效果优于简单BoW
-"""
-
-import os
-import re
-import csv
-import math
-import jieba
-import numpy as np
-from collections import Counter
-
-try:
-    import urllib.request
-    import ssl
-    DOWNLOAD_AVAILABLE = True
-except ImportError:
-    DOWNLOAD_AVAILABLE = False
-
-
-DATASET_URL = "https://raw.githubusercontent.com/SophonPlus/ChineseNlpCorpus/master/datasets/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv"
-
-
-def download_dataset(data_dir):
-    """下载数据集（如果不存在）"""
-    csv_path = os.path.join(data_dir, 'ChnSentiCorp_htl_all.csv')
-    
-    if os.path.exists(csv_path):
-        print(f"数据已存在: {csv_path}")
-        return True
-    
-    if not DOWNLOAD_AVAILABLE:
-        return False
-    
-    print("正在下载数据集...")
-    ssl_context = ssl.create_default_context()
-    ssl_context.check_hostname = False
-    ssl_context.verify_mode = ssl.CERT_NONE
-    
-    try:
-        request = urllib.request.Request(DATASET_URL, headers={'User-Agent': 'Mozilla/5.0'})
-        response = urllib.request.urlopen(request, timeout=120, context=ssl_context)
-        os.makedirs(data_dir, exist_ok=True)
-        with open(csv_path, 'wb') as f:
-            f.write(response.read())
-        print(f"下载完成: {csv_path}")
-        return True
-    except Exception as e:
-        print(f"下载失败: {e}")
-        return False
-
-
-def load_raw_data(data_dir):
-    """加载原始数据"""
-    csv_path = os.path.join(data_dir, 'ChnSentiCorp_htl_all.csv')
-    texts, labels = [], []
-    
-    with open(csv_path, 'r', encoding='utf-8') as f:
-        reader = csv.reader(f)
-        for row in reader:
-            if len(row) < 2:
-                continue
-            try:
-                label = int(row[0])
-                review = row[1].strip()
-                if review:
-                    texts.append(review)
-                    labels.append(label)
-            except (ValueError, IndexError):
-                continue
-    
-    return texts, np.array(labels)
-
-
-def tokenize(text):
-    """中文分词"""
-    text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z]', ' ', text)
-    words = jieba.lcut(text)
-    return [w for w in words if len(w) > 1]
-
-
-# ==================== 向量化器 ====================
-
-class BaseVectorizer:
-    """向量化器基类"""
-    def fit(self, texts): pass
-    def transform(self, texts): pass
-    def fit_transform(self, texts): pass
-
-
-class BoWVectorizer(BaseVectorizer):
-    """
-    词袋模型 (Bag of Words)
-    
-    原理：统计每个词在文本中出现的次数
-    向量维度 = 词表大小
-    每个维度 = 该词在本文本中出现的次数
-    """
-    
-    def __init__(self, max_features, max_seq_len):
-        self.max_features = max_features
-        self.max_seq_len = max_seq_len
-        self.vocab = {}
-        self.doc_freq = {}  # 文档频率
-        self.num_docs = 0
-    
-    def fit(self, texts):
-        """构建词表（基于词频）"""
-        counter = Counter()
-        doc_counter = Counter()  # 统计包含该词的文档数
-        
-        for text in texts:
-            words = tokenize(text)
-            unique_words = set(words)
-            counter.update(words)
-            for w in unique_words:
-                doc_counter[w] += 1
-        
-        self.num_docs = len(texts)
-        
-        # 取最高频的词
-        most_common = counter.most_common(self.max_features)
-        self.vocab = {word: idx for idx, (word, _) in enumerate(most_common)}
-        
-        # 记录文档频率（用于TF-IDF）
-        self.doc_freq = {w: doc_counter[w] for w in self.vocab}
-        
-        print(f"  BoW词表大小: {len(self.vocab)}")
-        return self
-    
-    def transform(self, texts):
-        """将文本转换为词频向量"""
-        vectors = []
-        for text in texts:
-            words = tokenize(text)
-            freq = [0] * self.max_seq_len
-            for i, word in enumerate(words[:self.max_seq_len]):
-                if word in self.vocab:
-                    freq[i] = 1  # 二值（出现=1，不出现=0）
-            vectors.append(freq)
-        return np.array(vectors, dtype=np.float32)
-    
-    def fit_transform(self, texts):
-        self.fit(texts)
-        return self.transform(texts)
-
-
-class TFIDFVectorizer(BaseVectorizer):
-    """
-    TF-IDF 向量器
-    
-    原理：
-    - TF(词频) = 词在本文本中出现的次数
-    - IDF(逆文档频率) = log(总文档数 / 包含该词的文档数)
-    - TF-IDF = TF × IDF
-    
-    优势：
-    - 降低常见无意义词的权重（如"的"、"是"）
-    - 提升罕见但有信息量的词
-    """
-    
-    def __init__(self, max_features, max_seq_len):
-        self.max_features = max_features
-        self.max_seq_len = max_seq_len
-        self.vocab = {}
-        self.idf = {}  # 存储每个词的IDF值
-        self.num_docs = 0
-    
-    def fit(self, texts):
-        """构建词表并计算IDF"""
-        counter = Counter()
-        doc_counter = Counter()
-        
-        for text in texts:
-            words = tokenize(text)
-            unique_words = set(words)
-            counter.update(words)
-            for w in unique_words:
-                doc_counter[w] += 1
-        
-        self.num_docs = len(texts)
-        
-        # 计算每个词的IDF
-        # IDF = log(总文档数 / 包含该词的文档数)
-        idf_values = {}
-        for word, df in doc_counter.items():
-            idf_values[word] = math.log(self.num_docs / (df + 1)) + 1  # 加1防零
-        
-        # 取IDF值最高的词（信息量最大的词）
-        sorted_words = sorted(idf_values.items(), key=lambda x: x[1], reverse=True)
-        self.vocab = {word: idx for idx, (word, _) in enumerate(sorted_words[:self.max_features])}
-        
-        # 保存IDF值
-        self.idf = {word: idf_values[word] for word in self.vocab}
-        
-        print(f"  TF-IDF词表大小: {len(self.vocab)}")
-        print(f"  平均IDF: {np.mean(list(self.idf.values())):.3f}")
-        return self
-    
-    def transform(self, texts):
-        """将文本转换为TF-IDF向量"""
-        vectors = []
-        for text in texts:
-            words = tokenize(text)
-            
-            # 计算TF
-            tf = Counter(words)
-            tf_sum = len(words) if words else 1
-            
-            # 生成向量
-            vec = [0.0] * self.max_seq_len
-            for i, word in enumerate(words[:self.max_seq_len]):
-                if word in self.vocab:
-                    # TF × IDF
-                    vec[i] = (tf[word] / tf_sum) * self.idf.get(word, 0)
-            vectors.append(vec)
-        
-        return np.array(vectors, dtype=np.float32)
-    
-    def fit_transform(self, texts):
-        self.fit(texts)
-        return self.transform(texts)
-
-
-def load_data(data_dir, max_features, max_seq_len, vectorizer_type='tfidf'):
-    """
-    加载并向量化数据
-    
-    参数：
-    - vectorizer_type: 'tfidf' 或 'bow'
-    """
-    if not download_dataset(data_dir):
-        raise RuntimeError("数据加载失败，请检查网络或手动下载数据集")
-    
-    print("正在加载数据...")
-    texts, labels = load_raw_data(data_dir)
-    print(f"总评论数: {len(texts)}, 正面: {sum(labels)}, 负面: {len(labels) - sum(labels)}")
-    
-    # 选择向量化器
-    if vectorizer_type == 'tfidf':
-        vectorizer = TFIDFVectorizer(max_features, max_seq_len)
-        vec_name = "TF-IDF"
-    else:
-        vectorizer = BoWVectorizer(max_features, max_seq_len)
-        vec_name = "BoW"
-    
-    print(f"正在使用{vec_name}向量化...")
-    X = vectorizer.fit_transform(texts)
-    y = labels
-    
-    # 打乱并划分
-    np.random.seed(42)
-    indices = np.random.permutation(len(X))
-    X = X[indices]
-    y = y[indices]
-    
-    split_idx = int(len(X) * 0.8)
-    X_train, X_test = X[:split_idx], X[split_idx:]
-    y_train, y_test = y[:split_idx], y[split_idx:]
-    
-    print(f"训练集: {len(X_train)}条, 测试集: {len(X_test)}条")
-    
-    return X_train, y_train, X_test, y_test, vectorizer
-
-
-if __name__ == '__main__':
-    # 测试
-    print("=" * 60)
-    print("测试 TF-IDF 向量化")
-    print("=" * 60)
-    X_train, y_train, X_test, y_test, vec = load_data(
-        'data/ChnSentiCorp', max_features=3000, max_seq_len=100, 
-        vectorizer_type='tfidf'
-    )
-    print(f"\nX_train shape: {X_train.shape}")
-    print(f"X_train sample (前5个特征): {X_train[0][:5]}")
+# -*- coding: utf-8 -*-
+"""
+数据加载与向量化模块
+
+支持两种向量化方法：
+1. BoW (Bag of Words) - 词频向量
+2. TF-IDF - 词频-逆文档频率向量
+
+TF-IDF 的优势：
+- 降低常见词（如"的"、"是"）的权重
+- 提升罕见词的信息量
+- 通常效果优于简单BoW
+"""
+
+import os
+import re
+import csv
+import math
+import jieba
+import numpy as np
+from collections import Counter
+
+try:
+    import urllib.request
+    import ssl
+    DOWNLOAD_AVAILABLE = True
+except ImportError:
+    DOWNLOAD_AVAILABLE = False
+
+
+DATASET_URL = "https://raw.githubusercontent.com/SophonPlus/ChineseNlpCorpus/master/datasets/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv"
+
+
+def download_dataset(data_dir):
+    """下载数据集（如果不存在）"""
+    csv_path = os.path.join(data_dir, 'ChnSentiCorp_htl_all.csv')
+    
+    if os.path.exists(csv_path):
+        print(f"数据已存在: {csv_path}")
+        return True
+    
+    if not DOWNLOAD_AVAILABLE:
+        return False
+    
+    print("正在下载数据集...")
+    ssl_context = ssl.create_default_context()
+    ssl_context.check_hostname = False
+    ssl_context.verify_mode = ssl.CERT_NONE
+    
+    try:
+        request = urllib.request.Request(DATASET_URL, headers={'User-Agent': 'Mozilla/5.0'})
+        response = urllib.request.urlopen(request, timeout=120, context=ssl_context)
+        os.makedirs(data_dir, exist_ok=True)
+        with open(csv_path, 'wb') as f:
+            f.write(response.read())
+        print(f"下载完成: {csv_path}")
+        return True
+    except Exception as e:
+        print(f"下载失败: {e}")
+        return False
+
+
+def load_raw_data(data_dir):
+    """加载原始数据"""
+    csv_path = os.path.join(data_dir, 'ChnSentiCorp_htl_all.csv')
+    texts, labels = [], []
+    
+    with open(csv_path, 'r', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        for row in reader:
+            if len(row) < 2:
+                continue
+            try:
+                label = int(row[0])
+                review = row[1].strip()
+                if review:
+                    texts.append(review)
+                    labels.append(label)
+            except (ValueError, IndexError):
+                continue
+    
+    return texts, np.array(labels)
+
+
+def tokenize(text):
+    """中文分词"""
+    text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z]', ' ', text)
+    words = jieba.lcut(text)
+    return [w for w in words if len(w) > 1]
+
+
+# ==================== 向量化器 ====================
+
+class BaseVectorizer:
+    """向量化器基类"""
+    def fit(self, texts): pass
+    def transform(self, texts): pass
+    def fit_transform(self, texts): pass
+
+
+class BoWVectorizer(BaseVectorizer):
+    """
+    词袋模型 (Bag of Words)
+    
+    原理：统计每个词在文本中出现的次数
+    向量维度 = 词表大小
+    每个维度 = 该词在本文本中出现的次数
+    """
+    
+    def __init__(self, max_features, max_seq_len):
+        self.max_features = max_features
+        self.max_seq_len = max_seq_len
+        self.vocab = {}
+        self.doc_freq = {}  # 文档频率
+        self.num_docs = 0
+    
+    def fit(self, texts):
+        """构建词表（基于词频）"""
+        counter = Counter()
+        doc_counter = Counter()  # 统计包含该词的文档数
+        
+        for text in texts:
+            words = tokenize(text)
+            unique_words = set(words)
+            counter.update(words)
+            for w in unique_words:
+                doc_counter[w] += 1
+        
+        self.num_docs = len(texts)
+        
+        # 取最高频的词
+        most_common = counter.most_common(self.max_features)
+        self.vocab = {word: idx for idx, (word, _) in enumerate(most_common)}
+        
+        # 记录文档频率（用于TF-IDF）
+        self.doc_freq = {w: doc_counter[w] for w in self.vocab}
+        
+        print(f"  BoW词表大小: {len(self.vocab)}")
+        return self
+    
+    def transform(self, texts):
+        """将文本转换为词频向量"""
+        vectors = []
+        for text in texts:
+            words = tokenize(text)
+            freq = [0] * self.max_seq_len
+            for i, word in enumerate(words[:self.max_seq_len]):
+                if word in self.vocab:
+                    freq[i] = 1  # 二值（出现=1，不出现=0）
+            vectors.append(freq)
+        return np.array(vectors, dtype=np.float32)
+    
+    def fit_transform(self, texts):
+        self.fit(texts)
+        return self.transform(texts)
+
+
+class TFIDFVectorizer(BaseVectorizer):
+    """
+    TF-IDF 向量器
+    
+    原理：
+    - TF(词频) = 词在本文本中出现的次数
+    - IDF(逆文档频率) = log(总文档数 / 包含该词的文档数)
+    - TF-IDF = TF × IDF
+    
+    优势：
+    - 降低常见无意义词的权重（如"的"、"是"）
+    - 提升罕见但有信息量的词
+    """
+    
+    def __init__(self, max_features, max_seq_len):
+        self.max_features = max_features
+        self.max_seq_len = max_seq_len
+        self.vocab = {}
+        self.idf = {}  # 存储每个词的IDF值
+        self.num_docs = 0
+    
+    def fit(self, texts):
+        """构建词表并计算IDF"""
+        counter = Counter()
+        doc_counter = Counter()
+        
+        for text in texts:
+            words = tokenize(text)
+            unique_words = set(words)
+            counter.update(words)
+            for w in unique_words:
+                doc_counter[w] += 1
+        
+        self.num_docs = len(texts)
+        
+        # 计算每个词的IDF
+        # IDF = log(总文档数 / 包含该词的文档数)
+        idf_values = {}
+        for word, df in doc_counter.items():
+            idf_values[word] = math.log(self.num_docs / (df + 1)) + 1  # 加1防零
+        
+        # 取IDF值最高的词（信息量最大的词）
+        sorted_words = sorted(idf_values.items(), key=lambda x: x[1], reverse=True)
+        self.vocab = {word: idx for idx, (word, _) in enumerate(sorted_words[:self.max_features])}
+        
+        # 保存IDF值
+        self.idf = {word: idf_values[word] for word in self.vocab}
+        
+        print(f"  TF-IDF词表大小: {len(self.vocab)}")
+        print(f"  平均IDF: {np.mean(list(self.idf.values())):.3f}")
+        return self
+    
+    def transform(self, texts):
+        """将文本转换为TF-IDF向量"""
+        vectors = []
+        for text in texts:
+            words = tokenize(text)
+            
+            # 计算TF
+            tf = Counter(words)
+            tf_sum = len(words) if words else 1
+            
+            # 生成向量
+            vec = [0.0] * self.max_seq_len
+            for i, word in enumerate(words[:self.max_seq_len]):
+                if word in self.vocab:
+                    # TF × IDF
+                    vec[i] = (tf[word] / tf_sum) * self.idf.get(word, 0)
+            vectors.append(vec)
+        
+        return np.array(vectors, dtype=np.float32)
+    
+    def fit_transform(self, texts):
+        self.fit(texts)
+        return self.transform(texts)
+
+
+def load_data(data_dir, max_features, max_seq_len, vectorizer_type='tfidf'):
+    """
+    加载并向量化数据
+    
+    参数：
+    - vectorizer_type: 'tfidf' 或 'bow'
+    """
+    if not download_dataset(data_dir):
+        raise RuntimeError("数据加载失败，请检查网络或手动下载数据集")
+    
+    print("正在加载数据...")
+    texts, labels = load_raw_data(data_dir)
+    print(f"总评论数: {len(texts)}, 正面: {sum(labels)}, 负面: {len(labels) - sum(labels)}")
+    
+    # 选择向量化器
+    if vectorizer_type == 'tfidf':
+        vectorizer = TFIDFVectorizer(max_features, max_seq_len)
+        vec_name = "TF-IDF"
+    else:
+        vectorizer = BoWVectorizer(max_features, max_seq_len)
+        vec_name = "BoW"
+    
+    print(f"正在使用{vec_name}向量化...")
+    X = vectorizer.fit_transform(texts)
+    y = labels
+    
+    # 打乱并划分
+    np.random.seed(42)
+    indices = np.random.permutation(len(X))
+    X = X[indices]
+    y = y[indices]
+    
+    split_idx = int(len(X) * 0.8)
+    X_train, X_test = X[:split_idx], X[split_idx:]
+    y_train, y_test = y[:split_idx], y[split_idx:]
+    
+    print(f"训练集: {len(X_train)}条, 测试集: {len(X_test)}条")
+    
+    return X_train, y_train, X_test, y_test, vectorizer
+
+
+if __name__ == '__main__':
+    # 测试
+    print("=" * 60)
+    print("测试 TF-IDF 向量化")
+    print("=" * 60)
+    X_train, y_train, X_test, y_test, vec = load_data(
+        'data/ChnSentiCorp', max_features=3000, max_seq_len=100, 
+        vectorizer_type='tfidf'
+    )
+    print(f"\nX_train shape: {X_train.shape}")
+    print(f"X_train sample (前5个特征): {X_train[0][:5]}")
diff --git a/main.py b/main.py
index eaeaadc..2dbbe1c 100644
--- a/main.py
+++ b/main.py
@@ -1,34 +1,34 @@
-# -*- coding: utf-8 -*-
-"""
-主程序入口
-
-使用方式：
-
-1. 运行单个模型（默认）：
-   python main.py
-   
-   修改 config.py 中的 MODEL_TYPE 和 VECTORIZER_TYPE 来切换配置
-
-2. 运行对比实验：
-   修改 config.py 中 RUN_COMPARISON = True
-   
-   这会依次运行：
-   - 实验1: BoW vs TF-IDF (固定LR模型)
-   - 实验2: LR vs MLP (固定TF-IDF)
-   - 实验3: 不同学习率对比
-   - 实验4: 不同隐藏层大小对比
-   
-   最后输出汇总报告
-"""
-
-from train import main
-
-if __name__ == '__main__':
-    print("\n" + "=" * 70)
-    print("文本分类实验 - 纯NumPy实现")
-    print("数据集: ChnSentiCorp (中文酒店评论)")
-    print("模型: Logistic Regression / MLP")
-    print("向量化: BoW / TF-IDF")
-    print("=" * 70 + "\n")
-    
-    main()
+# -*- coding: utf-8 -*-
+"""
+主程序入口
+
+使用方式：
+
+1. 运行单个模型（默认）：
+   python main.py
+   
+   修改 config.py 中的 MODEL_TYPE 和 VECTORIZER_TYPE 来切换配置
+
+2. 运行对比实验：
+   修改 config.py 中 RUN_COMPARISON = True
+   
+   这会依次运行：
+   - 实验1: BoW vs TF-IDF (固定LR模型)
+   - 实验2: LR vs MLP (固定TF-IDF)
+   - 实验3: 不同学习率对比
+   - 实验4: 不同隐藏层大小对比
+   
+   最后输出汇总报告
+"""
+
+from train import main
+
+if __name__ == '__main__':
+    print("\n" + "=" * 70)
+    print("文本分类实验 - 纯NumPy实现")
+    print("数据集: ChnSentiCorp (中文酒店评论)")
+    print("模型: Logistic Regression / MLP")
+    print("向量化: BoW / TF-IDF")
+    print("=" * 70 + "\n")
+    
+    main()
diff --git a/model_numpy.py b/model_numpy.py
index e8d7adf..cdec28a 100644
--- a/model_numpy.py
+++ b/model_numpy.py
@@ -1,342 +1,342 @@
-# -*- coding: utf-8 -*-
-"""
-模型模块 - 纯NumPy实现
-
-支持两种模型:
-1. Logistic Regression(逻辑回归)- 线性模型
-2. MLP(多层感知机)- 两层全连接网络
-
-设计思路:
-- 两种模型都共享相同的接口,方便对比
-- 代码简洁,每行都有详细注释
-- 手动实现反向传播,原理透明
-"""
-
-import numpy as np
-
-
-class BaseModel:
-    """模型基类"""
-    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True): pass
-    def predict(self, X): pass
-    def predict_proba(self, X): pass
-    def accuracy(self, X, y): pass
-
-
-class LogisticRegression(BaseModel):
-    """
-    逻辑回归(线性分类器)
-
-    结构:输入 → 线性变换 → Softmax → 输出
-
-    原理:
-    - 线性变换: z = X @ W + b
-    - Softmax: 将线性输出转为概率分布
-
-    参数量:input_size × num_classes + num_classes
-    """
-
-    def __init__(self, input_size, num_classes=2, learning_rate=0.1,
-                 class_weight=None, seed=42):
-        np.random.seed(seed)
-
-        # 权重初始化(Xavier)
-        self.W = np.random.randn(input_size, num_classes) * np.sqrt(2.0 / input_size)
-        self.b = np.zeros(num_classes)
-
-        self.lr = learning_rate
-        self.input_size = input_size
-        self.num_classes = num_classes
-        self.class_weight = class_weight  # 类别权重
-
-        total_params = input_size * num_classes + num_classes
-        print(f"LogisticRegression: {input_size} -> {num_classes}, 参数量: {total_params}")
-
-    def softmax(self, x):
-        """Softmax函数"""
-        x_shifted = x - np.max(x, axis=1, keepdims=True)
-        exp_x = np.exp(x_shifted)
-        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
-
-    def forward(self, X):
-        """前向传播"""
-        # 线性变换
-        z = X @ self.W + self.b
-        # Softmax输出概率
-        return self.softmax(z)
-
-    def backward(self, X, y):
-        """反向传播(梯度下降)"""
-        batch_size = X.shape[0]
-        probs = self.forward(X)
-
-        # Softmax + 交叉熵梯度
-        d_z = probs.copy()
-
-        # 应用类别权重:减去权重值而不是1
-        # 公式: dL/dz_y = w_y * (p_y - 1) = w_y*p_y - w_y
-        if self.class_weight is not None:
-            for i in range(batch_size):
-                d_z[i, y[i]] -= self.class_weight[y[i]]
-        else:
-            d_z[np.arange(batch_size), y] -= 1
-
-        # 梯度
-        d_W = X.T @ d_z
-        d_b = np.sum(d_z, axis=0)
-
-        # 更新
-        self.W -= self.lr * d_W / batch_size
-        self.b -= self.lr * d_b / batch_size
-
-    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True):
-        """训练"""
-        num_samples = len(X)
-        num_batches = (num_samples + batch_size - 1) // batch_size
-
-        for epoch in range(epochs):
-            # 打乱
-            indices = np.random.permutation(num_samples)
-            X_shuffled = X[indices]
-            y_shuffled = y[indices]
-
-            epoch_loss = 0
-            for batch_idx in range(num_batches):
-                start = batch_idx * batch_size
-                end = min(start + batch_size, num_samples)
-                X_batch = X_shuffled[start:end]
-                y_batch = y_shuffled[start:end]
-
-                # 前向 + 反向
-                probs = self.forward(X_batch)
-                self.backward(X_batch, y_batch)
-
-                # 损失
-                loss = -np.mean(np.log(np.clip(probs[np.arange(len(y_batch)), y_batch], 1e-10, 1)))
-                epoch_loss += loss
-
-            # 评估
-            if verbose and (epoch + 1) % 20 == 0:
-                train_acc = self.accuracy(X, y)
-                msg = f"Epoch {epoch+1:3d}/{epochs} | Loss: {epoch_loss/num_batches:.4f} | 训练准确率: {train_acc:.4f}"
-                if X_val is not None:
-                    val_acc = self.accuracy(X_val, y_val)
-                    msg += f" | 测试准确率: {val_acc:.4f}"
-                print(msg)
-
-        return self
-
-    def predict(self, X):
-        return np.argmax(self.forward(X), axis=1)
-
-    def predict_proba(self, X):
-        return self.forward(X)
-
-    def accuracy(self, X, y):
-        return np.mean(self.predict(X) == y)
-
-    def save(self, filepath):
-        """保存模型权重"""
-        np.save(filepath + '_W.npy', self.W)
-        np.save(filepath + '_b.npy', self.b)
-        print(f"模型已保存: {filepath}")
-
-    @staticmethod
-    def load(filepath, input_size, num_classes=2, learning_rate=0.1):
-        """加载模型权重"""
-        model = LogisticRegression(input_size, num_classes, learning_rate)
-        model.W = np.load(filepath + '_W.npy')
-        model.b = np.load(filepath + '_b.npy')
-        print(f"模型已加载: {filepath}")
-        return model
-
-
-class MLP(BaseModel):
-    """
-    多层感知机(神经网络)
-
-    结构:输入 → 线性变换 → ReLU → 线性变换 → Softmax → 输出
-
-    和LogisticRegression的区别:
-    - 多了一层隐藏层 + 非线性激活
-    - 可以学习非线性关系
-    - 参数量更大
-
-    参数量:
-    - W1: input_size × hidden_size
-    - b1: hidden_size
-    - W2: hidden_size × num_classes
-    - b2: num_classes
-    """
-
-    def __init__(self, input_size, hidden_size=64, num_classes=2,
-                 learning_rate=0.1, keep_prob=1.0, class_weight=None, seed=42):
-        np.random.seed(seed)
-
-        # 第一层权重
-        self.W1 = np.random.randn(input_size, hidden_size) * np.sqrt(2.0 / input_size)
-        self.b1 = np.zeros(hidden_size)
-
-        # 第二层权重
-        self.W2 = np.random.randn(hidden_size, num_classes) * np.sqrt(2.0 / hidden_size)
-        self.b2 = np.zeros(num_classes)
-
-        self.lr = learning_rate
-        self.keep_prob = keep_prob
-        self.hidden_size = hidden_size
-        self.input_size = input_size
-        self.num_classes = num_classes
-        self.class_weight = class_weight  # 类别权重
-
-        total_params = (input_size * hidden_size + hidden_size +
-                       hidden_size * num_classes + num_classes)
-        print(f"MLP: {input_size} -> {hidden_size} -> {num_classes}, 参数量: {total_params}")
-
-    def relu(self, x):
-        """ReLU激活"""
-        return np.maximum(0, x)
-
-    def relu_derivative(self, x):
-        """ReLU导数"""
-        return (x > 0).astype(float)
-
-    def softmax(self, x):
-        """Softmax函数"""
-        x_shifted = x - np.max(x, axis=1, keepdims=True)
-        exp_x = np.exp(x_shifted)
-        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
-
-    def forward(self, X):
-        """前向传播"""
-        # 第一层
-        self.z1 = X @ self.W1 + self.b1
-        self.a1 = self.relu(self.z1)
-
-        # Dropout(训练时)
-        if self.keep_prob < 1.0 and hasattr(self, 'training'):
-            self.d1 = (np.random.rand(*self.a1.shape) < self.keep_prob).astype(float)
-            self.a1 *= self.d1
-            self.a1 /= self.keep_prob
-
-        # 第二层
-        self.z2 = self.a1 @ self.W2 + self.b2
-        self.probs = self.softmax(self.z2)
-
-        return self.probs
-
-    def backward(self, X, y):
-        """反向传播"""
-        batch_size = X.shape[0]
-        
-        # 输出层梯度
-        d_z2 = self.probs.copy()
-        
-        # 应用类别权重
-        if self.class_weight is not None:
-            for i in range(batch_size):
-                d_z2[i, y[i]] -= self.class_weight[y[i]]
-        else:
-            d_z2[np.arange(batch_size), y] -= 1
-        
-        # 第二层梯度
-        d_W2 = self.a1.T @ d_z2
-        d_b2 = np.sum(d_z2, axis=0)
-        
-        # 隐藏层梯度
-        d_a1 = d_z2 @ self.W2.T
-        d_z1 = d_a1 * self.relu_derivative(self.z1)
-        
-        # Dropout梯度
-        if self.keep_prob < 1.0 and hasattr(self, 'd1'):
-            d_z1 *= self.d1
-            d_z1 /= self.keep_prob
-        
-        # 第一层梯度
-        d_W1 = X.T @ d_z1
-        d_b1 = np.sum(d_z1, axis=0)
-        
-        # 更新
-        self.W1 -= self.lr * d_W1 / batch_size
-        self.b1 -= self.lr * d_b1 / batch_size
-        self.W2 -= self.lr * d_W2 / batch_size
-        self.b2 -= self.lr * d_b2 / batch_size
-
-    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True):
-        """训练"""
-        num_samples = len(X)
-        num_batches = (num_samples + batch_size - 1) // batch_size
-
-        for epoch in range(epochs):
-            # 打乱
-            indices = np.random.permutation(num_samples)
-            X_shuffled = X[indices]
-            y_shuffled = y[indices]
-
-            epoch_loss = 0
-            self.training = True  # 开启Dropout
-
-            for batch_idx in range(num_batches):
-                start = batch_idx * batch_size
-                end = min(start + batch_size, num_samples)
-                X_batch = X_shuffled[start:end]
-                y_batch = y_shuffled[start:end]
-
-                # 前向 + 反向
-                probs = self.forward(X_batch)
-                self.backward(X_batch, y_batch)
-
-                # 损失
-                loss = -np.mean(np.log(np.clip(probs[np.arange(len(y_batch)), y_batch], 1e-10, 1)))
-                epoch_loss += loss
-
-            self.training = False  # 关闭Dropout
-
-            # 评估
-            if verbose and (epoch + 1) % 20 == 0:
-                train_acc = self.accuracy(X, y)
-                msg = f"Epoch {epoch+1:3d}/{epochs} | Loss: {epoch_loss/num_batches:.4f} | 训练准确率: {train_acc:.4f}"
-                if X_val is not None:
-                    val_acc = self.accuracy(X_val, y_val)
-                    msg += f" | 测试准确率: {val_acc:.4f}"
-                print(msg)
-
-        return self
-
-    def predict(self, X):
-        return np.argmax(self.forward(X), axis=1)
-
-    def predict_proba(self, X):
-        return self.forward(X)
-
-    def accuracy(self, X, y):
-        return np.mean(self.predict(X) == y)
-
-    def save(self, filepath):
-        """保存模型权重"""
-        np.save(filepath + '_W1.npy', self.W1)
-        np.save(filepath + '_b1.npy', self.b1)
-        np.save(filepath + '_W2.npy', self.W2)
-        np.save(filepath + '_b2.npy', self.b2)
-        print(f"模型已保存: {filepath}")
-
-    @staticmethod
-    def load(filepath, input_size, hidden_size=64, num_classes=2, learning_rate=0.1, keep_prob=1.0):
-        """加载模型权重"""
-        model = MLP(input_size, hidden_size, num_classes, learning_rate, keep_prob)
-        model.W1 = np.load(filepath + '_W1.npy')
-        model.b1 = np.load(filepath + '_b1.npy')
-        model.W2 = np.load(filepath + '_W2.npy')
-        model.b2 = np.load(filepath + '_b2.npy')
-        print(f"模型已加载: {filepath}")
-        return model
-
-
-def create_model(model_type, input_size, hidden_size=64, num_classes=2,
-                 learning_rate=0.1, keep_prob=1.0, class_weight=None):
-    """工厂函数:创建模型"""
-    if model_type == 'lr':
-        return LogisticRegression(input_size, num_classes, learning_rate, class_weight)
-    elif model_type == 'mlp':
-        return MLP(input_size, hidden_size, num_classes, learning_rate, keep_prob, class_weight)
-    else:
-        raise ValueError(f"未知模型类型: {model_type}")
+# -*- coding: utf-8 -*-
+"""
+模型模块 - 纯NumPy实现
+
+支持两种模型:
+1. Logistic Regression(逻辑回归)- 线性模型
+2. MLP(多层感知机)- 两层全连接网络
+
+设计思路:
+- 两种模型都共享相同的接口,方便对比
+- 代码简洁,每行都有详细注释
+- 手动实现反向传播,原理透明
+"""
+
+import numpy as np
+
+
+class BaseModel:
+    """模型基类"""
+    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True): pass
+    def predict(self, X): pass
+    def predict_proba(self, X): pass
+    def accuracy(self, X, y): pass
+
+
+class LogisticRegression(BaseModel):
+    """
+    逻辑回归(线性分类器)
+
+    结构:输入 → 线性变换 → Softmax → 输出
+
+    原理:
+    - 线性变换: z = X @ W + b
+    - Softmax: 将线性输出转为概率分布
+
+    参数量:input_size × num_classes + num_classes
+    """
+
+    def __init__(self, input_size, num_classes=2, learning_rate=0.1,
+                 class_weight=None, seed=42):
+        np.random.seed(seed)
+
+        # 权重初始化(Xavier)
+        self.W = np.random.randn(input_size, num_classes) * np.sqrt(2.0 / input_size)
+        self.b = np.zeros(num_classes)
+
+        self.lr = learning_rate
+        self.input_size = input_size
+        self.num_classes = num_classes
+        self.class_weight = class_weight  # 类别权重
+
+        total_params = input_size * num_classes + num_classes
+        print(f"LogisticRegression: {input_size} -> {num_classes}, 参数量: {total_params}")
+
+    def softmax(self, x):
+        """Softmax函数"""
+        x_shifted = x - np.max(x, axis=1, keepdims=True)
+        exp_x = np.exp(x_shifted)
+        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
+
+    def forward(self, X):
+        """前向传播"""
+        # 线性变换
+        z = X @ self.W + self.b
+        # Softmax输出概率
+        return self.softmax(z)
+
+    def backward(self, X, y):
+        """反向传播(梯度下降)"""
+        batch_size = X.shape[0]
+        probs = self.forward(X)
+
+        # Softmax + 交叉熵梯度
+        d_z = probs.copy()
+
+        # 应用类别权重:减去权重值而不是1
+        # 公式: dL/dz_y = w_y * (p_y - 1) = w_y*p_y - w_y
+        if self.class_weight is not None:
+            for i in range(batch_size):
+                d_z[i, y[i]] -= self.class_weight[y[i]]
+        else:
+            d_z[np.arange(batch_size), y] -= 1
+
+        # 梯度
+        d_W = X.T @ d_z
+        d_b = np.sum(d_z, axis=0)
+
+        # 更新
+        self.W -= self.lr * d_W / batch_size
+        self.b -= self.lr * d_b / batch_size
+
+    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True):
+        """训练"""
+        num_samples = len(X)
+        num_batches = (num_samples + batch_size - 1) // batch_size
+
+        for epoch in range(epochs):
+            # 打乱
+            indices = np.random.permutation(num_samples)
+            X_shuffled = X[indices]
+            y_shuffled = y[indices]
+
+            epoch_loss = 0
+            for batch_idx in range(num_batches):
+                start = batch_idx * batch_size
+                end = min(start + batch_size, num_samples)
+                X_batch = X_shuffled[start:end]
+                y_batch = y_shuffled[start:end]
+
+                # 前向 + 反向
+                probs = self.forward(X_batch)
+                self.backward(X_batch, y_batch)
+
+                # 损失
+                loss = -np.mean(np.log(np.clip(probs[np.arange(len(y_batch)), y_batch], 1e-10, 1)))
+                epoch_loss += loss
+
+            # 评估
+            if verbose and (epoch + 1) % 20 == 0:
+                train_acc = self.accuracy(X, y)
+                msg = f"Epoch {epoch+1:3d}/{epochs} | Loss: {epoch_loss/num_batches:.4f} | 训练准确率: {train_acc:.4f}"
+                if X_val is not None:
+                    val_acc = self.accuracy(X_val, y_val)
+                    msg += f" | 测试准确率: {val_acc:.4f}"
+                print(msg)
+
+        return self
+
+    def predict(self, X):
+        return np.argmax(self.forward(X), axis=1)
+
+    def predict_proba(self, X):
+        return self.forward(X)
+
+    def accuracy(self, X, y):
+        return np.mean(self.predict(X) == y)
+
+    def save(self, filepath):
+        """保存模型权重"""
+        np.save(filepath + '_W.npy', self.W)
+        np.save(filepath + '_b.npy', self.b)
+        print(f"模型已保存: {filepath}")
+
+    @staticmethod
+    def load(filepath, input_size, num_classes=2, learning_rate=0.1):
+        """加载模型权重"""
+        model = LogisticRegression(input_size, num_classes, learning_rate)
+        model.W = np.load(filepath + '_W.npy')
+        model.b = np.load(filepath + '_b.npy')
+        print(f"模型已加载: {filepath}")
+        return model
+
+
+class MLP(BaseModel):
+    """
+    多层感知机(神经网络)
+
+    结构:输入 → 线性变换 → ReLU → 线性变换 → Softmax → 输出
+
+    和LogisticRegression的区别:
+    - 多了一层隐藏层 + 非线性激活
+    - 可以学习非线性关系
+    - 参数量更大
+
+    参数量:
+    - W1: input_size × hidden_size
+    - b1: hidden_size
+    - W2: hidden_size × num_classes
+    - b2: num_classes
+    """
+
+    def __init__(self, input_size, hidden_size=64, num_classes=2,
+                 learning_rate=0.1, keep_prob=1.0, class_weight=None, seed=42):
+        np.random.seed(seed)
+
+        # 第一层权重
+        self.W1 = np.random.randn(input_size, hidden_size) * np.sqrt(2.0 / input_size)
+        self.b1 = np.zeros(hidden_size)
+
+        # 第二层权重
+        self.W2 = np.random.randn(hidden_size, num_classes) * np.sqrt(2.0 / hidden_size)
+        self.b2 = np.zeros(num_classes)
+
+        self.lr = learning_rate
+        self.keep_prob = keep_prob
+        self.hidden_size = hidden_size
+        self.input_size = input_size
+        self.num_classes = num_classes
+        self.class_weight = class_weight  # 类别权重
+
+        total_params = (input_size * hidden_size + hidden_size +
+                       hidden_size * num_classes + num_classes)
+        print(f"MLP: {input_size} -> {hidden_size} -> {num_classes}, 参数量: {total_params}")
+
+    def relu(self, x):
+        """ReLU激活"""
+        return np.maximum(0, x)
+
+    def relu_derivative(self, x):
+        """ReLU导数"""
+        return (x > 0).astype(float)
+
+    def softmax(self, x):
+        """Softmax函数"""
+        x_shifted = x - np.max(x, axis=1, keepdims=True)
+        exp_x = np.exp(x_shifted)
+        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
+
+    def forward(self, X):
+        """前向传播"""
+        # 第一层
+        self.z1 = X @ self.W1 + self.b1
+        self.a1 = self.relu(self.z1)
+
+        # Dropout(训练时)
+        if self.keep_prob < 1.0 and hasattr(self, 'training'):
+            self.d1 = (np.random.rand(*self.a1.shape) < self.keep_prob).astype(float)
+            self.a1 *= self.d1
+            self.a1 /= self.keep_prob
+
+        # 第二层
+        self.z2 = self.a1 @ self.W2 + self.b2
+        self.probs = self.softmax(self.z2)
+
+        return self.probs
+
+    def backward(self, X, y):
+        """反向传播"""
+        batch_size = X.shape[0]
+        
+        # 输出层梯度
+        d_z2 = self.probs.copy()
+        
+        # 应用类别权重
+        if self.class_weight is not None:
+            for i in range(batch_size):
+                d_z2[i, y[i]] -= self.class_weight[y[i]]
+        else:
+            d_z2[np.arange(batch_size), y] -= 1
+        
+        # 第二层梯度
+        d_W2 = self.a1.T @ d_z2
+        d_b2 = np.sum(d_z2, axis=0)
+        
+        # 隐藏层梯度
+        d_a1 = d_z2 @ self.W2.T
+        d_z1 = d_a1 * self.relu_derivative(self.z1)
+        
+        # Dropout梯度
+        if self.keep_prob < 1.0 and hasattr(self, 'd1'):
+            d_z1 *= self.d1
+            d_z1 /= self.keep_prob
+        
+        # 第一层梯度
+        d_W1 = X.T @ d_z1
+        d_b1 = np.sum(d_z1, axis=0)
+        
+        # 更新
+        self.W1 -= self.lr * d_W1 / batch_size
+        self.b1 -= self.lr * d_b1 / batch_size
+        self.W2 -= self.lr * d_W2 / batch_size
+        self.b2 -= self.lr * d_b2 / batch_size
+
+    def fit(self, X, y, X_val=None, y_val=None, epochs=100, batch_size=32, verbose=True):
+        """训练"""
+        num_samples = len(X)
+        num_batches = (num_samples + batch_size - 1) // batch_size
+
+        for epoch in range(epochs):
+            # 打乱
+            indices = np.random.permutation(num_samples)
+            X_shuffled = X[indices]
+            y_shuffled = y[indices]
+
+            epoch_loss = 0
+            self.training = True  # 开启Dropout
+
+            for batch_idx in range(num_batches):
+                start = batch_idx * batch_size
+                end = min(start + batch_size, num_samples)
+                X_batch = X_shuffled[start:end]
+                y_batch = y_shuffled[start:end]
+
+                # 前向 + 反向
+                probs = self.forward(X_batch)
+                self.backward(X_batch, y_batch)
+
+                # 损失
+                loss = -np.mean(np.log(np.clip(probs[np.arange(len(y_batch)), y_batch], 1e-10, 1)))
+                epoch_loss += loss
+
+            self.training = False  # 关闭Dropout
+
+            # 评估
+            if verbose and (epoch + 1) % 20 == 0:
+                train_acc = self.accuracy(X, y)
+                msg = f"Epoch {epoch+1:3d}/{epochs} | Loss: {epoch_loss/num_batches:.4f} | 训练准确率: {train_acc:.4f}"
+                if X_val is not None:
+                    val_acc = self.accuracy(X_val, y_val)
+                    msg += f" | 测试准确率: {val_acc:.4f}"
+                print(msg)
+
+        return self
+
+    def predict(self, X):
+        return np.argmax(self.forward(X), axis=1)
+
+    def predict_proba(self, X):
+        return self.forward(X)
+
+    def accuracy(self, X, y):
+        return np.mean(self.predict(X) == y)
+
+    def save(self, filepath):
+        """保存模型权重"""
+        np.save(filepath + '_W1.npy', self.W1)
+        np.save(filepath + '_b1.npy', self.b1)
+        np.save(filepath + '_W2.npy', self.W2)
+        np.save(filepath + '_b2.npy', self.b2)
+        print(f"模型已保存: {filepath}")
+
+    @staticmethod
+    def load(filepath, input_size, hidden_size=64, num_classes=2, learning_rate=0.1, keep_prob=1.0):
+        """加载模型权重"""
+        model = MLP(input_size, hidden_size, num_classes, learning_rate, keep_prob)
+        model.W1 = np.load(filepath + '_W1.npy')
+        model.b1 = np.load(filepath + '_b1.npy')
+        model.W2 = np.load(filepath + '_W2.npy')
+        model.b2 = np.load(filepath + '_b2.npy')
+        print(f"模型已加载: {filepath}")
+        return model
+
+
+def create_model(model_type, input_size, hidden_size=64, num_classes=2,
+                 learning_rate=0.1, keep_prob=1.0, class_weight=None):
+    """工厂函数:创建模型"""
+    if model_type == 'lr':
+        return LogisticRegression(input_size, num_classes, learning_rate, class_weight)
+    elif model_type == 'mlp':
+        return MLP(input_size, hidden_size, num_classes, learning_rate, keep_prob, class_weight)
+    else:
+        raise ValueError(f"未知模型类型: {model_type}")