4. 机器学习简介.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5283380",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b7b2392",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 20, 5)\n",
    "y = 2 * x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5449b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0979dddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c99305aa90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "911d41c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151b45ba3a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, s = 200, c = 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd67a7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151b4682640>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim((0,30))\n",
    "plt.ylim((0,50))\n",
    "plt.scatter(x, y, s = 200, c = 'green', marker = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1ba100ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151b46d5730>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim((0,30))\n",
    "plt.ylim((0,50))\n",
    "\n",
    "x_ticks = np.linspace(0, 30, 11)\n",
    "plt.xticks(x_ticks)\n",
    "\n",
    "plt.scatter(x, y, s = 200, c = 'green', marker = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a23f8389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151b4774550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim((0,30))\n",
    "plt.ylim((0,50))\n",
    "\n",
    "x_ticks = np.linspace(0, 30, 11)\n",
    "plt.xticks(x_ticks)\n",
    "\n",
    "plt.xlabel('x0')\n",
    "plt.ylabel('x0')\n",
    "\n",
    "plt.scatter(x, y, s = 200, c = 'green', marker = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e23215d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b45f9ee0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 40)\n",
    "y = x * x + 1\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24e4c025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b47582e0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 40)\n",
    "y = x * x + 1\n",
    "y0 = -x * x\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.plot(x, y0, c = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6572b679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b4a092e0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 40)\n",
    "y = x * x + 1\n",
    "\n",
    "x2 = np.linspace(-10, 10, 5)\n",
    "y2 = 3 * x2 + 50\n",
    "\n",
    "plt.scatter(x2, y2, c = 'red')\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "859b9f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3]\n",
    "y = [5, 3, 4]\n",
    "plt.bar(x, y, color = 'r', width = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02b0aee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      " [0 1 2 3 4 5 6 7 8 9]]\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [2 2 2 2 2 2 2 2 2 2]\n",
      " [3 3 3 3 3 3 3 3 3 3]\n",
      " [4 4 4 4 4 4 4 4 4 4]\n",
      " [5 5 5 5 5 5 5 5 5 5]\n",
      " [6 6 6 6 6 6 6 6 6 6]\n",
      " [7 7 7 7 7 7 7 7 7 7]\n",
      " [8 8 8 8 8 8 8 8 8 8]\n",
      " [9 9 9 9 9 9 9 9 9 9]]\n"
     ]
    }
   ],
   "source": [
    "x0, y0 = np.meshgrid(np.arange(0, 10, 1), np.arange(0, 10, 1))\n",
    "print(x0)\n",
    "print(y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d7c7c50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151b4b7d2e0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0, y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2fa16d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-bdcc59306bff>:5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  plt.pcolormesh(X, Y, Z)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x151b5ba08e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 20)\n",
    "y = np.linspace(-5, 5, 20)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = (1 - X/2 + X**3 + Y**4)*np.exp(-X**2-Y**2)\n",
    "plt.pcolormesh(X, Y, Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc0a440f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x151b5c00490>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 20)\n",
    "y = np.linspace(-5, 5, 20)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = (1 - X/2 + X**3 + Y**4)*np.exp(-X**2-Y**2)\n",
    "plt.contourf(X, Y, Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb3d93d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -4.47368421, -3.94736842, -3.42105263, -2.89473684,\n",
       "       -2.36842105, -1.84210526, -1.31578947, -0.78947368, -0.26315789,\n",
       "        0.26315789,  0.78947368,  1.31578947,  1.84210526,  2.36842105,\n",
       "        2.89473684,  3.42105263,  3.94736842,  4.47368421,  5.        ])"
      ]
     },
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