Create KMeans_example.py

2022.03.16_frequently_used_python_package/python_example.py

@@ -1,9 +1,60 @@
-a = [1, 2] # 数组
+a = [1, 2] # list列表
 print(a)
-print(len(a)) # 数组长度
+print(type(a)) # 对象类型
-a.append(3) # 增加元素
+print(id(a))  # 对象唯一标识符
+print(len(a)) # 列表长度
+a.append(3) # list列表增加元素
 print(a)
-b = range(5)  # 数列（从0开始）
+print(sum(a))  # 求和
-print(b)
+print(max(a)) # 最大值
-for i0 in b:
+print(min(a)) # 最小值
-    print(i0)
+print(abs(-3.14)) # 绝对值
+b1 = [2, -1, 3]
+b2 = sorted(b1) # 排序，不改变原数列
+print(b1)
+print(b2)
+b3 = list(reversed(b1)) # 反向并转为list列表
+print(b1)
+print(b3)
+c = range(5)  # 数列，从0开始
+print(c)
+for i0 in c:
+    print(i0)
+d1 = [1, 2, 3, 3, 2, 1, 1]
+d2 = set(d1) # 转成集合，去除重复元素
+print(d1)
+print(d2)
+print(list(d2))
+
+print()
+dict_data = {"name": "张三", "age": 30, "city": "北京"} # dict字典
+print(dict_data)
+print(type(dict_data))
+print(dict_data.items())
+for key, value in dict_data.items():
+    print(f'打印字典内容 {key} {value}')
+
+print() # 打印空一行
+print(all([True, True, False])) # 所有元素为真，结果为真
+print(all([1, 2, True]))
+print(any([True, True, False])) # 有一个是真，结果为真
+print(any([0, None, ""]))
+
+print()
+e = 'abc'
+print(e)
+print(hash(e)) # 哈希值（如果是多次运行，对于相同的对象，返回的哈希值是不同的）
+print(hash(e)) # 哈希值（同一个运行中多次调用 hash()，对于相同的对象，返回的哈希值是相同的）
+
+print()
+for i0 in range(3):
+    exec(f'''
+a{i0} = {i0}
+print(a{i0})
+''')  # 执行动态创建的代码
+f = eval('1+2') # 执行表达式并返回值
+print(f)
+
+f = open('a.txt', 'w') # 打开文件
+f.write('test') # 写入文件
+f.close() # 关闭文件

2025.01.13_KMeans/KMeans_example.py

@@ -0,0 +1,30 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/44839
+"""
+
+import os
+os.environ["OMP_NUM_THREADS"] = "1" # KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting this environment variable
+
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+
+X, y = make_blobs(n_samples=300, centers=4, random_state=42) # 生成示例数据（四类）
+print(X.shape)
+print(y.shape)
+plt.scatter(X[:, 0], X[:, 1]) # 显示数据
+plt.show()
+
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis') # 通过颜色显示数据原有的标签
+plt.show()
+
+kmeans = KMeans(n_clusters=3, random_state=42) # 进行 KMeans 聚类（这里分为三类）
+kmeans.fit(X)
+labels = kmeans.labels_  # 获取聚类的标签
+print(labels.shape)
+
+plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis') # 绘制聚类结果
+plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', marker='X') # 绘制聚类中心
+plt.title('KMeans Result')
+plt.show()