task5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bostons = datasets.load_boston()\n",
    "f, axarr = plt.subplots(5,2, sharex=True,sharey=True,figsize=(12,12))\n",
    "for i in range(0,5):\n",
    "    for j in range(0,2):\n",
    "        \n",
    "        bostons_X = bostons.data[:,np.newaxis,i*2+j]\n",
    "        \n",
    "        bostons_X_train = bostons_X[:-20]\n",
    "        bostons_X_test = bostons_X[-20:]\n",
    "        \n",
    "        bostons_Y_train = bostons.target[:-20]\n",
    "        bostons_Y_test = bostons.target[-20:]\n",
    "        \n",
    "        regr = linear_model.LinearRegression()\n",
    "        regr.fit(bostons_X_train, bostons_Y_train)\n",
    "                \n",
    "        axarr[i,j].scatter(bostons_X_test, bostons_Y_test,  color='red')\n",
    "        axarr[i,j].plot(bostons_X_test, regr.predict(bostons_X_test), color='blue', linewidth=1)\n",
    "        \n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg\n",
      "----------\n",
      "  mse=10.960410679423\n",
      "  wl1=27.44644771106094\n",
      "  wl2=18.30257967932424\n",
      "  w=[-1.05618627e-01  4.91381113e-02  3.21540897e-02  2.51293737e+00\n",
      " -1.76280019e+01  3.81765978e+00  1.05792271e-02 -1.43608998e+00\n",
      "  3.61498721e-01 -1.54712068e-02 -9.12347239e-01  9.85932090e-03\n",
      " -5.55092176e-01]\n",
      " \n",
      "Ridge\n",
      "----------\n",
      "  mse=11.195127507244013\n",
      "  wl1=22.455522943462604\n",
      "  wl2=13.721374175259173\n",
      "  w=[-1.02102273e-01  5.02013578e-02  8.80018592e-03  2.44957291e+00\n",
      " -1.28232742e+01  3.84148840e+00  6.44215775e-03 -1.37722226e+00\n",
      "  3.53296950e-01 -1.62693966e-02 -8.55851103e-01  9.89548869e-03\n",
      " -5.61106278e-01]\n",
      " \n",
      "Lasso\n",
      "----------\n",
      "  mse=15.16642047020635\n",
      "  wl1=0.8815286487451344\n",
      "  wl2=0.7836759400442239\n",
      "  w=[-0.          0.04108094 -0.          0.          0.          0.\n",
      "  0.04466578 -0.          0.         -0.00861893 -0.          0.0059101\n",
      " -0.7812529 ]\n",
      " \n",
      "ElNet\n",
      "----------\n",
      "  mse=13.894251181932834\n",
      "  wl1=4.906963287634066\n",
      "  wl2=2.348667810255262\n",
      "  w=[-0.08762685  0.05765551 -0.03325711  0.         -0.          1.79323781\n",
      "  0.01817113 -1.0161469   0.3820772  -0.02093352 -0.75754858  0.00875724\n",
      " -0.73155144]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "diabetes = datasets.load_boston()\n",
    "diabetes_X = diabetes.data\n",
    "diabetes_Y = diabetes.target\n",
    "\n",
    "diabetes_X_train = diabetes_X[:-50]\n",
    "diabetes_X_test = diabetes_X[-50:]\n",
    "\n",
    "diabetes_Y_train = diabetes_Y[:-50]\n",
    "diabetes_Y_test = diabetes_Y[-50:]\n",
    "\n",
    "regressors = {}\n",
    "regressors['LinReg'] = linear_model.LinearRegression()\n",
    "regressors['Ridge'] = linear_model.Ridge(alpha = .5)\n",
    "regressors['Lasso'] = linear_model.Lasso(alpha = 5.1)\n",
    "regressors['ElNet'] = linear_model.ElasticNet(alpha = .5,l1_ratio=0.5)\n",
    "\n",
    "fit_results = {}\n",
    "\n",
    "for key in regressors:\n",
    "    regr = regressors[key]\n",
    "    regr.fit(diabetes_X_train,diabetes_Y_train)\n",
    "    mse = np.mean((regr.predict(diabetes_X_test) - diabetes_Y_test) ** 2)\n",
    "    w = regr.coef_\n",
    "    wl1 = np.sum(np.abs(w))\n",
    "    wl2 = np.sqrt(np.sum(w**2))\n",
    "    fit_results[key] = {'mse': mse,'wl2': wl2,'wl1':wl1,'w':w}\n",
    "    print(\"{}\\n----------\\n  mse={}\\n  wl1={}\\n  wl2={}\\n  w={}\\n \".format(key,mse,wl1,wl2,w))\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiTpP4s+QlJVF5O8AOwHrgN2VtWpJUsmaaE8qiVJi2cNlaTFs4aqFam67LEVkiRJkiRJvTLJERJJkiRJkqRW2MCQJEmSJEm9ZwND6rkkleSDsevrk/yaZG9zfVuSvUm+SXI6yb5mflWSC0lOjL2e6ep9SFKfJTmXZEUz3pnklyTfdZ1LkoZgtoYmWZnkiyRnkpxK8mLX2TRdFv0QT0mt+RN4MMnNVXUBeAL4YWx9K3Cgqt4CSLJmbO1sVa1tL6okTYX3gG3A+x3nkKShuQi8XFXHkywHjiU5UFWnuw6m6eAODGkYPgOeasZPA7vG1m4HZmYvqupki7kkaRCSvJpkSzN+M8nnzfjxJB+O/25VHQTOdxBTknrpWmtoVf1UVceb8R/AGeDOLjJrOtnAkIbhE2BTkpuANcBXY2tvAzua7XqvJbljbG31nCMkG9oMLUk9chCYrYHrgWVJbgAeAQ51lkqShmHBNTTJKuAhLr1vlSZiA0MagGZXxSpGuy/2zVnbD9wLvAM8AHyd5NZm+WxVrR17eZMu6f/qGLCu2dL8N3CY0U34BmxgSNLVLKiGJlkG7AZeqqrf2wyq6WYDQxqOPcDrXHp8BICqOl9VH1fVZuAI8Gjb4SSpz6rqH+Ac8CzwJaMb7seA1Yy2OEuSrmAhNbTZmbEb+KiqPm03qaadDQxpOHYCW6vq2/HJJBuT3NKMlzP6R/J9B/kkqe8OAq80Pw8BzwMnqqo6TSVJw3DVGpokwA7gTFW90UlKTTUbGNJAVNXM7DeNzLEOOJrkJKPtfO9W1ZFmbe4zMLa0FliS+ucQowcfH66qn4G/mH/r8y5G9fT+JDNJnms3piT10rXU0IeBzcDGsfvPJ1vOqSkWP3SQJEmSJEl95w4MSZIkSZLUezYwJEmSJElS79nAkCRJkiRJvWcDQ5IkSZIk9Z4NDEmSJEmS1Hs2MCRJkiRJUu/ZwJAkSZIkSb33LyvE9BEcWw53AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#utworzenie wykresów błedu regresji\n",
    "groups = 3 \n",
    "index = np.arange(groups)\n",
    "bar_width = .2\n",
    "opacity = 0.4\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,9))\n",
    "\n",
    "t=0\n",
    "for key in regressors:\n",
    "    results = fit_results[key]\n",
    "    res_val = (results['mse'],results['wl1'],results['wl2'])\n",
    "    plt.bar(index+ bar_width*t, res_val, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color=np.random.rand(1,3),\n",
    "                 label=key)\n",
    "    t+=1\n",
    "plt.title('Porownanie modeli regresji: MSE, wl1, wl2')\n",
    "plt.xticks(index + (t-2)*bar_width, ('MSE', 'wl1', 'wl2'))\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "malzenstwo = pd.read_csv('malzenstwo.csv', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "malzenstwo_X_train = malzenstwo.ludnosc[:10]\n",
    "malzenstwo_X_test = malzenstwo.ludnosc[10:]\n",
    "malzenstwo_Y_train = malzenstwo.malzenstwa[:10]\n",
    "malzestwo_Y_test = malzenstwo.malzenstwa[10:]\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "\n",
    "regr.fit(malzenstwo_X_train.values.reshape(-1,1), malzenstwo_Y_train.values.reshape(-1,1))\n",
    "\n",
    "plt.scatter(malzenstwo_X_test.values, malzestwo_Y_test.values,  color='red')\n",
    "plt.plot(malzenstwo_X_test, regr.predict(malzenstwo_X_test.values.reshape(-1,1)), color='blue', linewidth=1)\n",
    "plt.title('Regresja liniowa ilosci malzenstw do ludnosci')\n",
    "plt.xlabel('Ludnosc')\n",
    "plt.ylabel('Malzenstwa')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wydatki_csv = pd.read_csv('wydatki.csv', sep=' ')\n",
    "dochody = wydatki_csv.dochody\n",
    "wydatki = wydatki_csv.wydatki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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_train = dochody[:6]\n",
    "X_test = dochody[6:]\n",
    "Y_train = wydatki[:6]\n",
    "Y_test = wydatki[6:]\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "\n",
    "regr.fit(X_train.values.reshape(-1,1), Y_train.values.reshape(-1,1))\n",
    "\n",
    "plt.scatter(X_test.values, Y_test.values,  color='red')\n",
    "plt.plot(X_test, regr.predict(X_test.values.reshape(-1,1)), color='blue', linewidth=1)\n",
    "plt.title('Regresja liniowa ilosci wydatkow do dochodow')\n",
    "plt.xlabel('Dochody')\n",
    "plt.ylabel('Wydatki')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = np.random.uniform(0,1,size=(2,100)) # size zwraca nam a*b(w tym wypadku 2 listy po 100 punktow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Srednie X:  0.47890618542191626\n",
      "Srednie Y:  0.5181507681913547\n"
     ]
    }
   ],
   "source": [
    "#obliczenie srednich dla x i y\n",
    "avg_X = numbers[0,:].mean()\n",
    "avg_Y = numbers[1,:].mean()\n",
    "print('Srednie X: ',avg_X)\n",
    "print('Srednie Y: ',avg_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nastepnie dla kazdego x odejmujemy nasz avg_X(tak samo dla y)\n",
    "roznica_X = np.array([x - avg_X for x in numbers[0,:]]) #uzycie LC szybkiego obliczenia wartosci\n",
    "roznica_Y = np.array([y - avg_Y for y in numbers[1,:]])\n",
    "# print('Roznice w X',roznica_X)\n",
    "# print('Roznice w Y',roznica_Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#teraz musimy policzyc kwadrat tych wartosci\n",
    "roznica_X_kwadrat = np.square(roznica_X)\n",
    "roznica_Y_kwadrat = np.square(roznica_Y)\n",
    "# print('Kwadrat roznicy X: ',roznica_X_kwadrat)\n",
    "# print('Kwadrat roznicy Y: ',roznica_Y_kwadrat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#musimy rowniez obliczyc wyrazenie:(x−x¯)∗(y−y¯) \n",
    "iloczyn_roznic = roznica_X_kwadrat * roznica_Y_kwadrat\n",
    "# print('Iloczyn roznic: ',iloczyn_roznic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#utworzenie zbioru DataFrame ze wszystkimi danymi\n",
    "dane = pd.DataFrame(data = {'X': numbers[0,:],\n",
    "                           'Y': numbers[1,:],\n",
    "                           'X-avg_X': roznica_X,\n",
    "                           'Y-avg_Y': roznica_Y,\n",
    "                           'Kwadrat_roznic_X': roznica_X_kwadrat,\n",
    "                           'Kwadrat_roznic_Y': roznica_Y_kwadrat,\n",
    "                           'Iloczyn_roznic': iloczyn_roznic})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X-avg_X</th>\n",
       "      <th>Y-avg_Y</th>\n",
       "      <th>Kwadrat_roznic_X</th>\n",
       "      <th>Kwadrat_roznic_Y</th>\n",
       "      <th>Iloczyn_roznic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.355678</td>\n",
       "      <td>0.328686</td>\n",
       "      <td>-0.123228</td>\n",
       "      <td>-0.189465</td>\n",
       "      <td>0.015185</td>\n",
       "      <td>0.035897</td>\n",
       "      <td>0.000545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.974395</td>\n",
       "      <td>0.854939</td>\n",
       "      <td>0.495489</td>\n",
       "      <td>0.336788</td>\n",
       "      <td>0.245509</td>\n",
       "      <td>0.113426</td>\n",
       "      <td>0.027847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.517837</td>\n",
       "      <td>0.964440</td>\n",
       "      <td>0.038931</td>\n",
       "      <td>0.446290</td>\n",
       "      <td>0.001516</td>\n",
       "      <td>0.199174</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.308234</td>\n",
       "      <td>0.574149</td>\n",
       "      <td>-0.170672</td>\n",
       "      <td>0.055998</td>\n",
       "      <td>0.029129</td>\n",
       "      <td>0.003136</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.242333</td>\n",
       "      <td>0.747859</td>\n",
       "      <td>-0.236573</td>\n",
       "      <td>0.229708</td>\n",
       "      <td>0.055967</td>\n",
       "      <td>0.052766</td>\n",
       "      <td>0.002953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.348582</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>-0.130324</td>\n",
       "      <td>-0.514683</td>\n",
       "      <td>0.016984</td>\n",
       "      <td>0.264899</td>\n",
       "      <td>0.004499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.662447</td>\n",
       "      <td>0.550095</td>\n",
       "      <td>0.183541</td>\n",
       "      <td>0.031944</td>\n",
       "      <td>0.033687</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.459706</td>\n",
       "      <td>0.691595</td>\n",
       "      <td>-0.019200</td>\n",
       "      <td>0.173444</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.030083</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.985834</td>\n",
       "      <td>0.387732</td>\n",
       "      <td>0.506928</td>\n",
       "      <td>-0.130419</td>\n",
       "      <td>0.256976</td>\n",
       "      <td>0.017009</td>\n",
       "      <td>0.004371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.922685</td>\n",
       "      <td>0.686398</td>\n",
       "      <td>0.443779</td>\n",
       "      <td>0.168247</td>\n",
       "      <td>0.196940</td>\n",
       "      <td>0.028307</td>\n",
       "      <td>0.005575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           X         Y   X-avg_X   Y-avg_Y  Kwadrat_roznic_X  \\\n",
       "0   0.355678  0.328686 -0.123228 -0.189465          0.015185   \n",
       "1   0.974395  0.854939  0.495489  0.336788          0.245509   \n",
       "2   0.517837  0.964440  0.038931  0.446290          0.001516   \n",
       "3   0.308234  0.574149 -0.170672  0.055998          0.029129   \n",
       "4   0.242333  0.747859 -0.236573  0.229708          0.055967   \n",
       "..       ...       ...       ...       ...               ...   \n",
       "95  0.348582  0.003468 -0.130324 -0.514683          0.016984   \n",
       "96  0.662447  0.550095  0.183541  0.031944          0.033687   \n",
       "97  0.459706  0.691595 -0.019200  0.173444          0.000369   \n",
       "98  0.985834  0.387732  0.506928 -0.130419          0.256976   \n",
       "99  0.922685  0.686398  0.443779  0.168247          0.196940   \n",
       "\n",
       "    Kwadrat_roznic_Y  Iloczyn_roznic  \n",
       "0           0.035897        0.000545  \n",
       "1           0.113426        0.027847  \n",
       "2           0.199174        0.000302  \n",
       "3           0.003136        0.000091  \n",
       "4           0.052766        0.002953  \n",
       "..               ...             ...  \n",
       "95          0.264899        0.004499  \n",
       "96          0.001020        0.000034  \n",
       "97          0.030083        0.000011  \n",
       "98          0.017009        0.004371  \n",
       "99          0.028307        0.005575  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Licznik :  0.6253511197430257\n"
     ]
    }
   ],
   "source": [
    "#obliczenie wartosci a (suma iloczynu roznic X - avg_X oraz Y - avg_Y) podzielona przez kwadrat roznic X - avg_X\n",
    "licznik = dane['Iloczyn_roznic'].sum()\n",
    "print('Licznik : ',licznik)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mianownik:  8.45355012074348\n"
     ]
    }
   ],
   "source": [
    "mianownik = dane['Kwadrat_roznic_X'].sum()\n",
    "print('Mianownik: ',mianownik)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wartosc a:  0.07397497037469826\n"
     ]
    }
   ],
   "source": [
    "wartosc_a = licznik/mianownik\n",
    "print('Wartosc a: ',wartosc_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wartosc b: 0.48272369731250875\n"
     ]
    }
   ],
   "source": [
    "wartosc_b = avg_Y - wartosc_a * avg_X\n",
    "print('Wartosc b:', wartosc_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "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(numbers[0,:], numbers[1,:],  color='red')\n",
    "plt.plot(numbers[0,:], wartosc_a * numbers[0,:] + wartosc_b, color='blue', linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}