Thu May 11 06:42:00 UTC 2023 inscode

main.py

-import argparse
-import platform
-import socket
-import psutil
+import numpy as np
+import matplotlib.pyplot as plt
 
-def main():
-    parser = argparse.ArgumentParser(description='Description of your program')
-    parser.add_argument('-i', '--input', help='Input file path', required=False)
-    parser.add_argument('-o', '--output', help='Output file path', required=False)
-    args = parser.parse_args()
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
 
-    # 执行具体的操作逻辑
-    # ...
-
-# 定义一个获取本机操作系统信息的方法
-def get_os_info():
-    # 使用platform模块获取当前操作系统的名称
-    os_name = platform.system()
-    # 使用platform模块获取当前操作系统的版本号
-    os_version = platform.release()
-    # 将操作系统的名称和版本号拼接起来，返回结果字符串
-    return f'{os_name} {os_version}'
-
-# 定义一个获取本机IP地址的方法
-def get_ip_address():
-    # 使用socket模块获取本机的主机名
-    hostname = socket.gethostname()
-    # 使用socket模块获取本机的IP地址
-    ip_address = socket.gethostbyname(hostname)
-    # 返回获取到的IP地址字符串
-    return ip_address
-
-
-def get_system_info():
-    # 获取 CPU 信息
-    cpu_count = psutil.cpu_count()  # CPU 核心数
-    cpu_freq = psutil.cpu_freq().current  # CPU 当前主频
-
-    # 获取内存信息
-    mem = psutil.virtual_memory()
-    mem_total = mem.total // 1024 // 1024  # 总内存大小，单位 MB
-    mem_used = mem.used // 1024 // 1024  # 已使用内存大小，单位 MB
-
-    # 获取磁盘信息
-    disk_partitions = psutil.disk_partitions()
-    disk_usage = []
-    for partition in disk_partitions:
-        partition_usage = psutil.disk_usage(partition.mountpoint)
-        disk_usage.append(
-            {"mountpoint": partition.mountpoint,
-             "total_size": partition_usage.total // 1024 // 1024,  # 总大小，单位 MB
-             "used_size": partition_usage.used // 1024 // 1024,  # 已使用大小，单位 MB
-             "free_size": partition_usage.free // 1024 // 1024})  # 剩余大小，单位 MB
-
-    return {"cpu_count": cpu_count,
-            "cpu_freq": cpu_freq,
-            "mem_total": mem_total,
-            "mem_used": mem_used,
-            "disk_usage": disk_usage}
-
-
-if __name__ == '__main__':
-    main()
-    os_info = f'当前操作系统为：{get_os_info()}'
-    ip_info = f'当前IP地址为：{get_ip_address()}'
-    system_info = f'当前IP地址为：{get_system_info()}'
-    print(os_info)
-    print(ip_info)
-    print(system_info)
+def costFunction(theta, X, y):
+    m = len(y)
+    J = 0
+    grad = np.zeros(theta.shape)
     
+    h = sigmoid(np.dot(X, theta))
+    J = (-1/m) * np.sum(y*np.log(h) + (1-y)*np.log(1-h))
+    grad = (1/m) * np.dot(X.T, (h-y))
+    
+    return J, grad
 
-
-
-
-
+def gradientDescent(X, y, theta, alpha, num_iters):
+    m = len(y)
+    J_history = np.zeros(num_iters)
+    
+    for i in range(num_iters):
+        J_history[i], grad = costFunction(theta, X, y)
+        theta = theta - alpha*grad
+        
+    return theta, J_history
+
+# 生成样本数据
+np.random.seed(0)
+X = np.random.randn(100, 2)
+ones = np.ones((100, 1))
+X = np.hstack((ones, X))
+y = np.random.randint(0, 2, size=(100,1))
+
+# 初始化theta
+initial_theta = np.zeros((X.shape[1], 1))
+
+# 梯度下降
+alpha = 0.1
+num_iters = 1000
+theta, J_history = gradientDescent(X, y, initial_theta, alpha, num_iters)
+
+# 绘制决策边界
+x1 = np.arange(-3, 3, 0.1)
+x2 = -(theta[0]+theta[1]*x1)/theta[2]
+plt.plot(x1, x2, label='Decision Boundary')
+plt.scatter(X[:, 1], X[:, 2], c=y.flatten())
+plt.legend()
+plt.show()

requirements.txt

+numpy
\ No newline at end of file

test.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "今天的日期是:  2023-05-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "today = datetime.date.today()\n",
+    "print(\"今天的日期是: \", today)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'numpy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msigmoid\u001b[39m(z):\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def sigmoid(z):\n",
+    "    return 1 / (1 + np.exp(-z))\n",
+    "\n",
+    "def costFunction(theta, X, y):\n",
+    "    m = len(y)\n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "    \n",
+    "    h = sigmoid(np.dot(X, theta))\n",
+    "    J = (-1/m) * np.sum(y*np.log(h) + (1-y)*np.log(1-h))\n",
+    "    grad = (1/m) * np.dot(X.T, (h-y))\n",
+    "    \n",
+    "    return J, grad\n",
+    "\n",
+    "def gradientDescent(X, y, theta, alpha, num_iters):\n",
+    "    m = len(y)\n",
+    "    J_history = np.zeros(num_iters)\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        J_history[i], grad = costFunction(theta, X, y)\n",
+    "        theta = theta - alpha*grad\n",
+    "        \n",
+    "    return theta, J_history\n",
+    "\n",
+    "# 生成样本数据\n",
+    "np.random.seed(0)\n",
+    "X = np.random.randn(100, 2)\n",
+    "ones = np.ones((100, 1))\n",
+    "X = np.hstack((ones, X))\n",
+    "y = np.random.randint(0, 2, size=(100,1))\n",
+    "\n",
+    "# 初始化theta\n",
+    "initial_theta = np.zeros((X.shape[1], 1))\n",
+    "\n",
+    "# 梯度下降\n",
+    "alpha = 0.1\n",
+    "num_iters = 1000\n",
+    "theta, J_history = gradientDescent(X, y, initial_theta, alpha, num_iters)\n",
+    "\n",
+    "# 绘制决策边界\n",
+    "x1 = np.arange(-3, 3, 0.1)\n",
+    "x2 = -(theta[0]+theta[1]*x1)/theta[2]\n",
+    "plt.plot(x1, x2, label='Decision Boundary')\n",
+    "plt.scatter(X[:, 1], X[:, 2], c=y.flatten())\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test.py

+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+def costFunction(theta, X, y):
+    m = len(y)
+    J = 0
+    grad = np.zeros(theta.shape)
+    
+    h = sigmoid(np.dot(X, theta))
+    J = (-1/m) * np.sum(y*np.log(h) + (1-y)*np.log(1-h))
+    grad = (1/m) * np.dot(X.T, (h-y))
+    
+    return J, grad
+
+def gradientDescent(X, y, theta, alpha, num_iters):
+    m = len(y)
+    J_history = np.zeros(num_iters)
+    
+    for i in range(num_iters):
+        J_history[i], grad = costFunction(theta, X, y)
+        theta = theta - alpha*grad
+        
+    return theta, J_history
+
+# 生成样本数据
+np.random.seed(0)
+X = np.random.randn(100, 2)
+ones = np.ones((100, 1))
+X = np.hstack((ones, X))
+y = np.random.randint(0, 2, size=(100,1))
+
+# 初始化theta
+initial_theta = np.zeros((X.shape[1], 1))
+
+# 梯度下降
+alpha = 0.1
+num_iters = 1000
+theta, J_history = gradientDescent(X, y, initial_theta, alpha, num_iters)
+
+# 绘制决策边界
+x1 = np.arange(-3, 3, 0.1)
+x2 = -(theta[0]+theta[1]*x1)/theta[2]
+plt.plot(x1, x2, label='Decision Boundary')
+plt.scatter(X[:, 1], X[:, 2], c=y.flatten())
+plt.legend()
+plt.show()