完成作业4.23

1.py

@@ -1,41 +1,240 @@
-s = "Hello"
-for char in s:
-    print(f"字符'{char}'的ASCII码为: {ord(char)}")
+# 安装jieba
+import subprocess
+subprocess.run(['pip', 'install', 'jieba', '-q'])
 
+print("jieba安装完成！")
+import jieba
 
-print(f"ASCII码65对应的字符为: {chr(65)}")
-##图像是规则数值矩阵易处理，文本是离散符号序列，需理解语义更难
-# 题目3
-A = [3, 4]
-B = [1, 2]
-def vector_add(a, b):
-    return [x + y for x, y in zip(a, b)]
+print("=" * 50)
+print("jieba分词演示")
+print("=" * 50)
 
-def scalar_multiply(scalar, vector):
-    return [scalar * x for x in vector]
+text = "我喜欢深度学习和人工智能"
 
-def vector_norm(vector):
-    return sum(x**2 for x in vector) ** 0.5
+print(f"原文: {text}")
+print()
 
-print("题目3:")
-print("A + B =", vector_add(A, B))
-print("2 × A =", scalar_multiply(2, A))
-print("|A| =", vector_norm(A))
-A4 = [1, 2, 3]
-B4 = [4, 5, 6]
-def dot_product(a, b):
-    return sum(x * y for x, y in zip(a, b))
-def cosine_similarity(a, b):
-    norm_a = vector_norm(a)
-    norm_b = vector_norm(b)
-    if norm_a == 0 or norm_b == 0:
-        return 0.0
-    return dot_product(a, b) / (norm_a * norm_b)
+# 精确模式（默认）
+words精确 = list(jieba.cut(text, cut_all=False))
+print(f"精确模式: {' / '.join(words精确)}")
 
-print("\n题目4:")
-print("A · B =", dot_product(A4, B4))
-print("余弦相似度 =", cosine_similarity(A4, B4))
-A5 = [1, 0]
-B5 = [0, 1]
-print("\n题目4 第3问:")
-print("A = [1, 0], B = [0, 1] 的余弦相似度 =", cosine_similarity(A5, B5))
+# 全模式
+words全 = list(jieba.cut(text, cut_all=True))
+print(f"全模式:   {' / '.join(words全)}")
+
+# 搜索引擎模式
+words搜索 = list(jieba.cut_for_search(text))
+print(f"搜索模式: {' / '.join(words搜索)}")
+# 更多分词示例
+import jieba
+
+print("=" * 50)
+print("更多分词示例")
+print("=" * 50)
+
+examples = [
+    "今天天气真不错",
+    "人工智能是未来的发展方向",
+    "Python是一门非常流行的编程语言",
+    "小明毕业于清华大学计算机系",
+    "我今天在京东买了一部iPhone手机"
+]
+
+for i, text in enumerate(examples):
+    words = list(jieba.cut(text))
+    print(f"{i+1}. {text}")
+    print(f"   → {' / '.join(words)}")
+    print()
+    import jieba.posseg as pseg
+
+print("=" * 50)
+print("jieba词性标注演示")
+print("=" * 50)
+
+text = "我喜欢深度学习和人工智能"
+
+print(f"原文: {text}")
+print()
+
+words = pseg.cut(text)
+print("分词 + 词性标注：")
+for word, flag in words:
+    print(f"  {word}: {flag}")
+import jieba
+
+print("=" * 50)
+print("停用词处理演示")
+print("=" * 50)
+
+# 常见停用词列表
+stopwords = set(['的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这'])
+
+text = "人工智能是未来的发展方向，也是当前科技领域的热门话题"
+
+print(f"原文: {text}")
+print()
+
+# 不使用停用词
+words_all = list(jieba.cut(text))
+print(f"不使用停用词: {' / '.join(words_all)}")
+
+# 使用停用词
+words_filtered = [w for w in words_all if w not in stopwords]
+print(f"使用停用词:   {' / '.join(words_filtered)}")
+print()
+
+# 更完整的停用词表可以从网上下载
+print("提示：实际项目中可以从以下地方获取停用词表：")
+print("  - 哈工大停用词表")
+print("  - 百度停用词表")
+print("  - 四川大学机器学习实验室停用词表")
+# 实战：完整的文本预处理流程
+import jieba
+
+print("=" * 50)
+print("完整的文本预处理流程")
+print("=" * 50)
+
+# 示例文档集合
+docs = [
+    "今天天气真不错！适合出去玩。",
+    "Python是一门很棒的编程语言。",
+    "人工智能和机器学习是未来的发展方向。",
+    "今天在咖啡馆喝了一杯很好喝的拿铁。"
+]
+
+# 停用词表
+stopwords = set(['的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这', '！', '。', ','])
+
+def preprocess_text(text):
+    """完整的文本预处理流程"""
+    # 1. 分词
+    words = jieba.cut(text)
+    
+    # 2. 去除停用词
+    words = [w for w in words if w not in stopwords and len(w) > 0]
+    
+    # 3. 去除空格
+    words = [w for w in words if w.strip()]
+    
+    return words
+
+print("预处理结果：")
+for i, doc in enumerate(docs):
+    words = preprocess_text(doc)
+    print(f"\nDoc{i+1}: {doc}")
+    print(f"  → {' / '.join(words)}")
+# 实战：jieba分词 + TF-IDF完整流程
+import jieba
+import math
+
+print("=" * 50)
+print("实战：jieba分词 + TF-IDF完整流程")
+print("=" * 50)
+
+def simple_tfidf_tokenized(docs, stopwords=None):
+    """
+    结合分词的TF-IDF实现
+    参数:
+        docs: 原始文档列表
+        stopwords: 停用词集合
+    返回:
+        vocab, tfidf_matrix
+    """
+    # 1. 分词
+    tokenized = []
+    for doc in docs:
+        words = jieba.cut(doc)
+        if stopwords:
+            words = [w for w in words if w not in stopwords and len(w) > 1]
+        else:
+            words = [w for w in words if len(w) > 1]
+        tokenized.append(words)
+    
+    # 2. 构建词表
+    vocab_set = set()
+    for doc in tokenized:
+        vocab_set.update(doc)
+    vocab = sorted(list(vocab_set))
+    
+    # 3. 构建TF矩阵并计算IDF
+    n_docs = len(tokenized)
+    tf_matrix = []
+    df_dict = {word: 0 for word in vocab}
+    
+    for doc in tokenized:
+        vec = [0] * len(vocab)
+        for word in doc:
+            if word in vocab:
+                idx = vocab.index(word)
+                vec[idx] += 1
+        tf_matrix.append(vec)
+    
+    # 计算DF
+    for vec in tf_matrix:
+        for j, count in enumerate(vec):
+            if count > 0:
+                word = vocab[j]
+                df_dict[word] += 1
+    
+    # 计算IDF
+    idf = []
+    for word in vocab:
+        df = df_dict[word]
+        idf_j = math.log(n_docs / (df + 1)) + 1
+        idf.append(idf_j)
+    
+    # 计算TF-IDF
+    tfidf = []
+    for vec in tf_matrix:
+        tfidf_vec = [vec[i] * idf[i] for i in range(len(vec))]
+        tfidf.append(tfidf_vec)
+    
+    return vocab, tfidf, tokenized
+
+# 示例文档集合
+docs = [
+    "Python是一门很棒的编程语言",
+    "人工智能是未来的发展方向",
+    "深度学习是机器学习的一个分支",
+    "Python和Java都是很流行的编程语言"
+]
+
+# 停用词
+stopwords = set(["的", "是", "一个", "很", "和", "在", "了"])
+
+vocab, tfidf_matrix, tokenized = simple_tfidf_tokenized(docs, stopwords)
+
+print("文档集合：")
+for i, doc in enumerate(docs):
+    print(f"  Doc{i+1}: {doc}")
+print()
+
+print(f"分词结果：")
+for i, words in enumerate(tokenized):
+    print(f"  Doc{i+1}: {' / '.join(words)}")
+print()
+
+print(f"词表（共{len(vocab)}个词）:")
+print(f"  {vocab}")
+print()
+
+print("TF-IDF矩阵：")
+for i, vec in enumerate(tfidf_matrix):
+    # 只显示非零值
+    nonzero = [(vocab[j], round(vec[j], 4)) for j in range(len(vec)) if vec[j] > 0]
+    print(f"  Doc{i+1}: {nonzero}")
+
+print()
+
+# 找每个文档最重要的词
+print("每个文档最重要的词（TF-IDF值最高）：")
+for i, vec in enumerate(tfidf_matrix):
+    max_idx = max(range(len(vec)), key=lambda j: vec[j])
+    max_score = vec[max_idx]
+    if max_score > 0:
+        print(f"  Doc{i+1}: '{vocab[max_idx]}' (TF-IDF={max_score:.4f})")
+#############
+######词表是所有文档中出现过的不重复词语的集合.
+#######Doc1:[1, 0, 1, 1, 1];Doc2:[0, 1, 1, 1, 1];Doc3:[3, 0, 0, 0, 0]
+########BoW 只统计词的出现频率，完全不考虑词在句子中的位置和顺序。如：语义理解任务；BoW 把每个词视为独立的 “符号”，无法体现词的语义相似度。如：信息检索 / 文本分类。