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Date: Thu, 30 Apr 2026 15:59:08 +0800
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---
 hh.py | 132 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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diff --git a/hh.py b/hh.py
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+++ b/hh.py
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+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+# -------------------------- 多层感知机（MLP）实现 --------------------------
+class MLP:
+    def __init__(self, input_size, hidden_size, num_classes, learning_rate=0.1, keep_prob=1.0):
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.num_classes = num_classes
+        self.learning_rate = learning_rate
+        self.keep_prob = keep_prob
+
+        self.W1 = np.random.randn(input_size, hidden_size) / np.sqrt(input_size)
+        self.b1 = np.zeros((1, hidden_size))
+        self.W2 = np.random.randn(hidden_size, num_classes) / np.sqrt(hidden_size)
+        self.b2 = np.zeros((1, num_classes))
+
+    def relu(self, x):
+        return np.maximum(0, x)
+
+    def softmax(self, x):
+        exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))
+        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
+
+    def forward(self, X, training=True):
+        self.z1 = np.dot(X, self.W1) + self.b1
+        self.a1 = self.relu(self.z1)
+        if training and self.keep_prob < 1.0:
+            self.dropout_mask = np.random.rand(*self.a1.shape) < self.keep_prob
+            self.a1 = self.a1 * self.dropout_mask / self.keep_prob
+        self.z2 = np.dot(self.a1, self.W2) + self.b2
+        self.a2 = self.softmax(self.z2)
+        return self.a2
+
+    def backward(self, X, y, output):
+        m = X.shape[0]
+        delta2 = output.copy()
+        delta2[range(m), y] -= 1
+        delta2 /= m
+        dW2 = np.dot(self.a1.T, delta2)
+        db2 = np.sum(delta2, axis=0, keepdims=True)
+        delta1 = np.dot(delta2, self.W2.T)
+        delta1[self.z1 <= 0] = 0
+        if self.keep_prob < 1.0:
+            delta1 = delta1 * self.dropout_mask / self.keep_prob
+        dW1 = np.dot(X.T, delta1)
+        db1 = np.sum(delta1, axis=0, keepdims=True)
+        self.W1 -= self.learning_rate * dW1
+        self.b1 -= self.learning_rate * db1
+        self.W2 -= self.learning_rate * dW2
+        self.b2 -= self.learning_rate * db2
+
+    def train(self, X, y, epochs=30, batch_size=2, verbose=True):
+        m = X.shape[0]
+        for epoch in range(epochs):
+            permutation = np.random.permutation(m)
+            X_shuffled = X[permutation]
+            y_shuffled = y[permutation]
+            num_batches = max(1, m // batch_size)
+            epoch_loss = 0.0
+            for i in range(num_batches):
+                start = i * batch_size
+                end = start + batch_size
+                batch_X = X_shuffled[start:end]
+                batch_y = y_shuffled[start:end]
+                output = self.forward(batch_X, training=True)
+                batch_loss = -np.mean(np.log(output[range(len(batch_y)), batch_y] + 1e-8))
+                epoch_loss += batch_loss
+                self.backward(batch_X, batch_y, output)
+            if verbose and (epoch + 1) % 10 == 0:
+                train_acc = self.accuracy(X, y)
+                print(f"Epoch {epoch+1:3d}/{epochs} | Loss: {epoch_loss/num_batches:.4f} | 训练准确率: {train_acc:.4f}")
+        return self
+
+    def predict(self, X):
+        return np.argmax(self.forward(X, training=False), axis=1)
+
+    def predict_proba(self, X):
+        return self.forward(X, training=False)
+
+    def accuracy(self, X, y):
+        return np.mean(self.predict(X) == y)
+
+    def save(self, filepath):
+        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)
+
+    @staticmethod
+    def load(filepath, input_size, hidden_size=8, num_classes=2, learning_rate=0.1, keep_prob=1.0):
+        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')
+        return model
+
+# -------------------------- 主程序（训练+预测一体化） --------------------------
+if __name__ == "__main__":
+    # 1. 内置训练数据
+    texts = [
+        "房间干净整洁，前台服务态度特别好，住着很舒服",
+        "隔音特别差，卫生不干净，设施老旧，住得非常不满意",
+        "环境很好，服务周到，下次还会再来",
+        "空调噪音大，洗澡水忽冷忽热，体验很差",
+        "床很软，枕头舒服，睡得很香",
+        "灯光昏暗，床品不干净，体验极差"
+    ]
+    labels = [1, 0, 1, 0, 1, 0]
+
+    # 2. 训练并保存向量器和模型
+    vectorizer = TfidfVectorizer(max_features=100)
+    X = vectorizer.fit_transform(texts).toarray()
+    y = np.array(labels)
+    np.save("tfidf_vocab.npy", vectorizer.vocabulary_)
+
+    mlp = MLP(input_size=X.shape[1], hidden_size=8, num_classes=2)
+    mlp.train(X, y, epochs=30, batch_size=2, verbose=True)
+    mlp.save("model_mlp")
+    print("\n✅ 模型训练完成！")
+
+    # 3. 直接用训练好的向量器预测，不重新加载
+    print("\n=== 酒店评论情感分类预测 ===")
+    text = input("请输入酒店评论文本：")
+    X_new = vectorizer.transform([text]).toarray()
+    pred = mlp.predict(X_new)[0]
+    prob = mlp.predict_proba(X_new)[0]
+
+    label_map = {0: "负面", 1: "正面"}
+    print(f"\n预测结果：{label_map[pred]}")
+    print(f"置信度：正面概率={prob[1]*100:.1f}%，负面概率={prob[0]*100:.1f}%")
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