linearloss/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import  numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "file = open(\"q.txt\")\n",
    "data = file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zara\\AppData\\Local\\Temp\\ipykernel_2084\\3422459984.py:4: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  x.append(np.float(line.split(\",\")[0]))\n",
      "C:\\Users\\zara\\AppData\\Local\\Temp\\ipykernel_2084\\3422459984.py:5: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y.append(np.float(line.split(\",\")[1]))\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "y = []\n",
    "for line in data.split(\"\\n\"):\n",
    "    x.append(np.float(line.split(\",\")[0]))\n",
    "    y.append(np.float(line.split(\",\")[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x = np.array(x[0:150])\n",
    "y = np.array(y[0:150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "xy  = np.concatenate(([x],[y]),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa17d80160>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xy[0],xy[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def linear(x,y):\n",
    "    xybar = np.average( x*y)\n",
    "    xbar = np.average(x)\n",
    "    ybar = np.average(y)\n",
    "    x2bar = np.average(np.power(x,2))\n",
    "    k = (xybar -xbar*ybar)/(x2bar - xbar**2)\n",
    "    k = 0\n",
    "    b = ybar - k*xbar\n",
    "    return k,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def loss(x,y):\n",
    "    k,b = linear(x,y)\n",
    "    yy = k*x+b\n",
    "    l = np.average(np.power(y-yy,2))\n",
    "    return  l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "xy = np.sort(xy,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "startlinearloss = []\n",
    "endlinearloss = []\n",
    "for i in range(120):\n",
    "    startlinearloss.append([x[i+1],loss(x[0:i+2],y[0:i+2])])\n",
    "    endlinearloss.append([x[140-i-2], loss(x[140 - i -2:140],y[140 -i -2:140])])\n",
    "    # print(i,200-i-2,x[200-i-2],loss(x[200-i-2:200],y[200-i-2:200]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "startlinearloss = np.array(startlinearloss)\n",
    "endlinearloss = np.array(endlinearloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa1ab296d0>,\n",
       " <matplotlib.lines.Line2D at 0x1aa1ab29730>,\n",
       " <matplotlib.lines.Line2D at 0x1aa1ab29850>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xy[0],xy[1],startlinearloss[:,0],startlinearloss[:,1], endlinearloss[:,0], endlinearloss[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa1bdc6160>,\n",
       " <matplotlib.lines.Line2D at 0x1aa1bdc61c0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xy[0][100:149],xy[1][100:149]-200,endlinearloss[0:40,0],endlinearloss[0:40,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x292340f9f40>,\n",
       " <matplotlib.lines.Line2D at 0x292340f9fa0>,\n",
       " <matplotlib.lines.Line2D at 0x29234103100>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[0:150],y[0:150],startlinearloss[:,0],startlinearloss[:,1],endlinearloss[:,0],endlinearloss[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "startlinearlossdiff = startlinearloss[0:110] +startlinearloss[1:111]+startlinearloss[2:112]+startlinearloss[3:113]+startlinearloss[4:114]-10*startlinearloss[5:115]+startlinearloss[6:116]+startlinearloss[7:117]+startlinearloss[8:118]+startlinearloss[9:119]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa1abf2e80>,\n",
       " <matplotlib.lines.Line2D at 0x1aa1abf2ee0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(startlinearloss[5:55,0],startlinearlossdiff[0:50,1],xy[0][0:50],xy[1][0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "endlinearlossdiff =  endlinearloss[0:110] +endlinearloss[1:111]+endlinearloss[2:112]+endlinearloss[3:113]+endlinearloss[4:114]-10*endlinearloss[5:115]+endlinearloss[6:116]+endlinearloss[7:117]+endlinearloss[8:118]+endlinearloss[9:119]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa1acc5460>,\n",
       " <matplotlib.lines.Line2D at 0x1aa1acc54c0>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(endlinearloss[5:35,0],endlinearlossdiff[0:30,1],xy[0][100:150],xy[1][100:150]-230)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"numpy.float64\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\zara\\Documents\\WorkSpace\\linearloss\\notebook.ipynb Cell 21'\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/zara/Documents/WorkSpace/linearloss/notebook.ipynb#ch0000017?line=0'>1</a>\u001b[0m str_x \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/zara/Documents/WorkSpace/linearloss/notebook.ipynb#ch0000017?line=1'>2</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m150\u001b[39m):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/zara/Documents/WorkSpace/linearloss/notebook.ipynb#ch0000017?line=2'>3</a>\u001b[0m     str_x \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m x[i]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/zara/Documents/WorkSpace/linearloss/notebook.ipynb#ch0000017?line=3'>4</a>\u001b[0m     str_x \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/zara/Documents/WorkSpace/linearloss/notebook.ipynb#ch0000017?line=4'>5</a>\u001b[0m str_x \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m x[\u001b[39m150\u001b[39m]\n",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"numpy.float64\") to str"
     ]
    }
   ],
   "source": [
    "str_x = \"\"\n",
    "for i in range(150):\n",
    "    str_x += str(x[i])\n",
    "    str_x += \", \"\n",
    "str_x += x[150]\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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}