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   "metadata": {},
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   "source": [
    "### Dataset类\n",
    "其实这就表示，无论使用自定义的数据集，还是官方为我们封装好的数据集，其本质都是继承了Dataset类。而在继承Dataset类时，至少需要重写以下几个方法：\n",
    "\n",
    "* \\_\\_init\\_\\_()：构造函数，可自定义数据读取方法以及进行数据预处理；\n",
    "* \\_\\_len\\_\\_()：返回数据集大小；\n",
    "* \\_\\_getitem\\_\\_()：索引数据集中的某一个数据。"
   ],
   "id": "6f460291da56d507"
  },
  {
   "metadata": {
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     "end_time": "2025-06-12T12:58:38.694696Z",
     "start_time": "2025-06-12T12:58:38.690734Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "    # 构造函数\n",
    "    def __init__(self, data_tensor, target_tensor):\n",
    "        self.data_tensor = data_tensor\n",
    "        self.target_tensor = target_tensor\n",
    "\n",
    "    # 返回数据集大小\n",
    "    def __len__(self):\n",
    "        return self.data_tensor.size(0)\n",
    "\n",
    "    # 返回索引的数据与标签\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data_tensor[idx], self.target_tensor[idx]"
   ],
   "id": "1244f4325aad0ac5",
   "outputs": [],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T12:59:08.444392Z",
     "start_time": "2025-06-12T12:59:08.435084Z"
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   },
   "cell_type": "code",
   "source": [
    "# 生成数据\n",
    "data_tensor = torch.randn(10, 3)\n",
    "target_tensor = torch.randint(2, (10,)) # 二分类任务\n",
    "\n",
    "print(data_tensor)\n",
    "print(target_tensor)"
   ],
   "id": "e2f8ad8dc637791a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.1016,  0.8803,  1.3645],\n",
      "        [ 0.6674,  0.4500,  0.0367],\n",
      "        [ 1.3528,  0.2438,  0.4721],\n",
      "        [-1.3651, -1.1315,  0.0299],\n",
      "        [ 1.4538,  1.3622, -0.5165],\n",
      "        [ 0.7515,  0.6168, -0.9036],\n",
      "        [ 1.3542, -0.3779, -1.2439],\n",
      "        [-0.4588, -0.2233, -0.3531],\n",
      "        [-0.0515,  0.8951,  0.3544],\n",
      "        [ 0.0181,  0.4075, -2.0888]])\n",
      "tensor([0, 0, 1, 0, 0, 1, 1, 1, 1, 0])\n"
     ]
    }
   ],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T12:59:25.092189Z",
     "start_time": "2025-06-12T12:59:25.085985Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建数据集\n",
    "my_dataset = MyDataset(data_tensor, target_tensor)\n",
    "\n",
    "# 查看数据集大小\n",
    "print(\"dataset size：\",len(my_dataset))\n",
    "\n",
    "# 使用索引获取数据\n",
    "print(\"data:\", my_dataset[0])  # 获取第一个数据"
   ],
   "id": "cd8f86852f54b0c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset size： 10\n",
      "data: (tensor([-1.1016,  0.8803,  1.3645]), tensor(0))\n"
     ]
    }
   ],
   "execution_count": 67
  }
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