Fri May 19 07:45:00 UTC 2023 inscode

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 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",
-    "    \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": 1,