draw_demo.ipynb

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      "h=0.10000, numerical limit=2.30000\n",
      "h=0.01000, numerical limit=2.02999\n",
      "h=0.00100, numerical limit=2.00295\n",
      "h=0.00010, numerical limit=2.00033\n",
      "h=0.00001, numerical limit=2.00272\n",
      "=====\n",
      "[[1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n",
      "6.0\n",
      "=====\n",
      "[[ True  True  True]\n",
      " [ True  True  True]\n",
      " [ True  True  True]]\n",
      "[[1. 2. 3.]\n",
      " [2. 0. 4.]\n",
      " [3. 4. 5.]]\n",
      "[[ True  True  True]\n",
      " [ True  True  True]\n",
      " [ True  True  True]]\n",
      "===k==\n",
      "[-1. -2. -3. -4. -5. -6. -7. -8.]\n",
      "=====\n",
      "[[0. 1. 2.]\n",
      " [3. 4. 5.]\n",
      " [6. 7. 8.]]\n",
      "=====\n",
      "2.0\n",
      "=====\n",
      "[[15. 18. 21.]\n",
      " [42. 54. 66.]]\n",
      "[[0. 1. 2.]\n",
      " [3. 4. 5.]]\n",
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n",
      "[[ 3.  3.  3.  3.]\n",
      " [12. 12. 12. 12.]]\n"
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   "source": [
    "%matplotlib inline\n",
    "from matplotlib_inline import backend_inline\n",
    "from mxnet import np, npx\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "npx.set_np()\n",
    "\n",
    "\n",
    "def f(x, sigma):\n",
    "    p = 1 / math.sqrt(2 * math.pi * sigma**2)\n",
    "    return p * np.exp(-0.5 / sigma**2 * (x)**2)\n",
    "\n",
    "def use_svg_display():  #@save\n",
    "    \"\"\"使用svg格式在Jupyter中显示绘图\"\"\"\n",
    "    backend_inline.set_matplotlib_formats('svg')\n",
    "    \n",
    "    \n",
    "def set_figsize(figsize=(3.5, 2.5)):  #@save\n",
    "    \"\"\"设置matplotlib的图表大小\"\"\"\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    \n",
    "#@save\n",
    "def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):\n",
    "    \"\"\"设置matplotlib的轴\"\"\"\n",
    "    axes.set_xlabel(xlabel)\n",
    "    axes.set_ylabel(ylabel)\n",
    "    axes.set_xscale(xscale)\n",
    "    axes.set_yscale(yscale)\n",
    "    axes.set_xlim(xlim)\n",
    "    axes.set_ylim(ylim)\n",
    "    if legend:\n",
    "        axes.legend(legend)\n",
    "    axes.grid()\n",
    "\n",
    "#@save\n",
    "def plot(X, Y=None, xlabel=None, ylabel=None, legend=None, xlim=None,\n",
    "         ylim=None, xscale='linear', yscale='linear',\n",
    "         fmts=('-', 'm--', 'g-.', 'r:'), figsize=(3.5, 2.5), axes=None):\n",
    "    \"\"\"绘制数据点\"\"\"\n",
    "    if legend is None:\n",
    "        legend = []\n",
    "\n",
    "    set_figsize(figsize)\n",
    "    axes = axes if axes else plt.gca()\n",
    "\n",
    "    # 如果X有一个轴，输出True\n",
    "    def has_one_axis(X):\n",
    "        return (hasattr(X, \"ndim\") and X.ndim == 1 or isinstance(X, list)\n",
    "                and not hasattr(X[0], \"__len__\"))\n",
    "\n",
    "    if has_one_axis(X):\n",
    "        X = [X]\n",
    "    if Y is None:\n",
    "        X, Y = [[]] * len(X), X\n",
    "    elif has_one_axis(Y):\n",
    "        Y = [Y]\n",
    "    if len(X) != len(Y):\n",
    "        X = X * len(Y)\n",
    "    axes.cla()\n",
    "    for x, y, fmt in zip(X, Y, fmts):\n",
    "        if len(x):\n",
    "            axes.plot(x, y, fmt)\n",
    "        else:\n",
    "            axes.plot(y, fmt)\n",
    "    set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n",
    "def f(x):\n",
    "    return 3 * x ** 2 - 4 * x\n",
    "    \n",
    "for h in 10.0**np.arange(-1, -6, -1):\n",
    "    print(f'h={h:.5f}, numerical limit={(f(1+h)-f(1))/h:.5f}')\n",
    "    \n",
    "x = np.arange(9).reshape(3,3)\n",
    "print('=====')\n",
    "print(np.ones((4, 9)))\n",
    "print(np.linalg.norm(np.ones((4, 9))))\n",
    "print('=====')\n",
    "print(x==x.T.T)\n",
    "A = np.array([[1, 2, 3], [2, 0, 4], [3, 4, 5]])\n",
    "print(A)\n",
    "\n",
    "print(A == A.T)\n",
    "plot(x,  x, 'x', 'f(x)', legend=['f(x)', 'Tangent line (x=1)'])\n",
    "A = np.arange(6).reshape(2, 3)\n",
    "B = A.copy()  # Assign a copy of `A` to `B` by allocating new memory\n",
    "A, A + B\n",
    "print('===k==')\n",
    "print(np.arange(-1, -9, -1))\n",
    "\n",
    "# print(A.shape), \n",
    "# print(A.sum(axis=[1]))\n",
    "# print(A.sum())\n",
    "print('=====')\n",
    "print(x)\n",
    "print('=====')\n",
    "print(math.sqrt(4))\n",
    "print('=====')\n",
    "print(np.dot(A,x))\n",
    "B = np.ones(shape=(3, 4))\n",
    "print(A)\n",
    "print(B)\n",
    "print(np.dot(A, B))\n",
    "\n",
    "# def load_mnist(path, kind='train'):\n",
    "#     import os\n",
    "#     import gzip\n",
    "#     import numpy as np\n",
    "\n",
    "#     \"\"\"Load MNIST data from `path`\"\"\"\n",
    "#     labels_path = os.path.join(path,\n",
    "#                                '%s-labels-idx1-ubyte.gz'\n",
    "#                                % kind)\n",
    "#     images_path = os.path.join(path,\n",
    "#                                '%s-images-idx3-ubyte.gz'\n",
    "#                                % kind)\n",
    "#     labels_path_train = os.path.join(path,\n",
    "#                                't10k-labels-idx1-ubyte.gz')\n",
    "#     with gzip.open(labels_path, 'rb') as lbpath:\n",
    "#         labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\n",
    "#                                offset=8)\n",
    "\n",
    "#     with gzip.open(images_path, 'rb') as imgpath:\n",
    "#         images = np.frombuffer(imgpath.read(), dtype=np.uint8,\n",
    "#                                offset=16).reshape(len(labels), 784)\n",
    "        \n",
    "#     with gzip.open(labels_path_train, 'rb') as lbtrainpath:\n",
    "#         labels_train = np.frombuffer(lbtrainpath.read(), dtype=np.uint8,\n",
    "#                                offset=8)\n",
    "\n",
    "#     return images, labels,labels_train\n",
    "\n",
    "\n",
    "# images, labels,labels_train = load_mnist('')\n",
    "# # print(labels.shape)\n",
    "# print(images[1])\n",
    "# # print(labels_train.shape)\n",
    "# text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "#                    'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "# for label in labels_train:\n",
    "#     print(text_labels[int(label)])\n",
    "\n",
    "\n",
    "# def convert(imgf, labelf, outf, n):\n",
    "#     f = open(imgf, \"rb\")\n",
    "#     o = open(outf, \"w\")\n",
    "#     l = open(labelf, \"rb\")\n",
    "\n",
    "#     f.read(16)\n",
    "#     l.read(8)\n",
    "#     images = []\n",
    "\n",
    "#     for i in range(n):\n",
    "#         image = [ord(l.read(1))]\n",
    "#         for j in range(28*28):\n",
    "#             image.append(ord(f.read(1)))\n",
    "#         images.append(image)\n",
    "\n",
    "#     for image in images:\n",
    "#         o.write(\",\".join(str(pix) for pix in image)+\"\\n\")\n",
    "#     f.close()\n",
    "#     o.close()\n",
    "#     l.close()\n",
    "\n",
    "# convert(\"train-images-idx3-ubyte\", \"train-labels-idx1-ubyte\",\n",
    "#         \"mnist_train.csv\", 60000)\n",
    "# convert(\"t10k-images-idx3-ubyte\", \"t10k-labels-idx1-ubyte\",\n",
    "#         \"mnist_test.csv\", 10000)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 创建数据\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "y1 = np.sin(x)\n",
    "y2 = np.tan(x)\n",
    "\n",
    "# 创建一个包含两个子图的图表\n",
    "fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(8, 6))\n",
    "\n",
    "# 在第一个子图中绘制正弦函数\n",
    "axes[0].plot(x, y1, color='blue')\n",
    "axes[0].set_title('Sine Function')\n",
    "\n",
    "# 在第二个子图中绘制余弦函数\n",
    "axes[1].plot(x, y2, color='red')\n",
    "axes[1].set_title('Cosine Function')\n",
    "\n",
    "# 调整子图之间的间距\n",
    "plt.tight_layout()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26af3e6b",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bd8dbb1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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