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XWL.py

@@ -1,34 +1,236 @@
-print(" 题目1")
-s = "Hello"
-for c in s:
-    print(f"'{c}' 的ASCII码: {ord(c)}")
-print(f"ASCII码65对应的字符: {chr(65)}")
+# 安装jieba
+import subprocess
+subprocess.run(['pip', 'install', 'jieba', '-q'])
+
+print("jieba安装完成!")
+import jieba
+
+print("=" * 50)
+print("jieba分词演示")
+print("=" * 50)
+
+text = "我喜欢深度学习和人工智能"
+
+print(f"原文: {text}")
+print()
+
+# 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)}")
+
+# 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("题目3 ")
-A = [3, 4]
-B = [1, 2]
-A_plus_B = [A[0] + B[0], A[1] + B[1]]
-print("A + B =", A_plus_B)
-two_times_A = [2 * A[0], 2 * A[1]]
-print("2 × A =", two_times_A)
-A_norm = math.sqrt(A[0] ** 2 + A[1] ** 2)
-print("A 的模 =", A_norm)
-print(" 题目4 ")
-A = [1, 2, 3]
-B = [4, 5, 6]
-dot_product = sum(a * b for a, b in zip(A, B))
-print("A·B =", dot_product)
-def vector_norm(v):
-    return math.sqrt(sum(x ** 2 for x in v))
-norm_A = vector_norm(A)
-norm_B = vector_norm(B)
-cos_sim = dot_product / (norm_A * norm_B)
-print("余弦相似度 =", cos_sim)
-A_new = [1, 0]
-B_new = [0, 1]
-dot_product_new = sum(a * b for a, b in zip(A_new, B_new))
-norm_A_new = vector_norm(A_new)
-norm_B_new = vector_norm(B_new)
-cos_sim_new = dot_product_new / (norm_A_new * norm_B_new)
-print("A=[1,0], B=[0,1] 的余弦相似度 =", cos_sim_new)
-print("原因：两个向量正交,点积为0,因此余弦相似度为0")
+
+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})")

XWL2.py

@@ -0,0 +1,28 @@
+docs = [
+    "Python 是 编程 语言",
+    "Java 是 编程 语言",
+    "Python Python Python"
+]
+all_words = []
+for doc in docs:
+    words = doc.split()
+    all_words.extend(words)
+vocab = sorted(list(set(all_words)))
+print("词表（手动实现）：", vocab)
+bow_vectors = []
+for doc in docs:
+    words = doc.split()
+    vector = [words.count(word) for word in vocab]
+    bow_vectors.append(vector)
+print("\n每个文档的BoW向量(手动实现):")
+for i, vec in enumerate(bow_vectors):
+    print(f"Doc{i+1}: {vec}")
+from sklearn.feature_extraction.text import CountVectorizer
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(docs)
+print("\n词表(sklearn实现):", vectorizer.get_feature_names_out())
+print("\n每个文档的BoW向量(sklearn实现):")
+for i, vec in enumerate(X.toarray()):
+    print(f"Doc{i+1}: {vec}")
+#6#
+print("1忽略词序信息:无法区分语序不同但词频相同的文本,会丢失语义逻辑。2不理解词语语义关联:将词视为独立符号，无法捕捉同义词、近义词的关系。")