{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TQqrOdkY6nsy"
   },
   "source": [
    "# **Klasyfikacja za pomocą naiwnej metody bayesowskiej z rozkładem normalnym**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlcfRFCPSXIj"
   },
   "source": [
    "# **Twierdzenie Bayesa**\n",
    "![bayes.svg](data:image/svg+xml;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rcpTnWjOh5dq"
   },
   "source": [
    "P(A) -- oznacza prawdopodobieństwo a-priori wystąpienia klasy A (tj. prawdopodobieństwo, że dowolny przykład należy do klasy A)\n",
    "\n",
    "P(B|A) -- oznacza prawdopodobieństwo a-posteriori, że B należy do \n",
    "klasy A\n",
    "\n",
    "P(B) -- znacza prawdopodobieństwo a-priori wystąpienia przykładu B "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Naiwny klasyfikator bayesowski jest _naiwny, ponieważ zakłada, że poszczególne cechy są niezależne od siebie**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSaJsYOhz8h8"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "0             5.1          3.5           1.4          0.2     Setosa\n",
       "1             4.9          3.0           1.4          0.2     Setosa\n",
       "2             4.7          3.2           1.3          0.2     Setosa\n",
       "3             4.6          3.1           1.5          0.2     Setosa\n",
       "4             5.0          3.6           1.4          0.2     Setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  Virginica\n",
       "146           6.3          2.5           5.0          1.9  Virginica\n",
       "147           6.5          3.0           5.2          2.0  Virginica\n",
       "148           6.2          3.4           5.4          2.3  Virginica\n",
       "149           5.9          3.0           5.1          1.8  Virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris_data = pd.read_csv(\"iris.csv\")\n",
    "iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7efc44f734f0>"
      ]
     },
     "execution_count": 106,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "setosa = iris_data[:50]\n",
    "versicolor = iris_data[50:100]\n",
    "virginica = iris_data[100:150]\n",
    "# ax.scatter(setosa['sepal.length'],np.arange(50), color='blue', lw=2)\n",
    "# ax.scatter(versicolor['sepal.length'],np.arange(50),  color='orange', lw=2)\n",
    "# ax.scatter(virginica['sepal.length'],np.arange(50),  color='green', lw=2) \n",
    "\n",
    "ax.scatter(setosa['petal.width'],np.arange(50), color='blue', lw=2)\n",
    "ax.scatter(versicolor['petal.width'],np.arange(50),  color='orange', lw=2)\n",
    "ax.scatter(virginica['petal.width'],np.arange(50),  color='green', lw=2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yabcm4Rei2ue"
   },
   "source": [
    "![GaussianNB.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dsf6FnlgjiOL"
   },
   "source": [
    "# Funkcja gęstości prawdopodobieństwa rozkładu normalnego \n",
    "![gestosc.svg](data:image/svg+xml;base64,<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="36.328ex" height="7.509ex" style="vertical-align: -3.171ex;" viewBox="0 -1867.7 15641.2 3233.2" role="img" focusable="false" xmlns="http://www.w3.org/2000/svg" aria-labelledby="MathJax-SVG-1-Title">
<title id="MathJax-SVG-1-Title">{\displaystyle f_{\mu ,\sigma }(x)={\frac {1}{\sigma {\sqrt {2\pi }}}}\,\exp \left({\frac {-(x-\mu )^{2}}{2\sigma ^{2}}}\right).}</title>
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "v0oeHebytjNp"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "fOYTA3VVtjNw"
   },
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   "source": [
    "class NaiveBayesClassifier():\n",
    "    def calc_prior(self, features, target):\n",
    "        '''\n",
    "        Wyliczenie prawdopodobieństwa a priori\n",
    "        '''\n",
    "        self.prior = (features.groupby(target).apply(lambda x: len(x)) / self.rows).to_numpy()\n",
    "\n",
    "        return self.prior\n",
    "    \n",
    "    def calc_statistics(self, features, target):\n",
    "        '''\n",
    "        Wyliczenie średnich i wariancji dla danych\n",
    "        ''' \n",
    "        self.mean = features.groupby(target).apply(np.mean).to_numpy()\n",
    "        self.var = features.groupby(target).apply(np.var).to_numpy()\n",
    "              \n",
    "        return self.mean, self.var\n",
    "    \n",
    "    def gaussian_density(self, class_idx, x):     \n",
    "        '''\n",
    "        Wyliczenie prawdopodobieństwa z rozkładu normalnego        \n",
    "        (1/√2pi*σ) * exp((-1/2)*((x-μ)^2)/(2*σ²))\n",
    "        μ -średnia\n",
    "        σ² - wariancja\n",
    "        σ - odchylenie standardowe\n",
    "        '''\n",
    "        mean = self.mean[class_idx]\n",
    "        var = self.var[class_idx]\n",
    "        \n",
    "        numerator = np.exp((-1/2)*((x-mean)**2) / (2 * var)) # Licznik wzoru na gęstość rozkładu normalnego \n",
    "        denominator = np.sqrt(2 * np.pi * var) # Mianownik wzoru na gęstość rozkładu normalnego \n",
    "        prob = numerator / denominator\n",
    "        \n",
    "        return prob\n",
    "    \n",
    "    def classify(self, x):\n",
    "        '''\n",
    "        Wyliczenie prawdopodobieństwa a posteriori i zwrócenie klasy, dla której prawdopodobieństwo jest najwyższe\n",
    "        '''\n",
    "        posteriors = []\n",
    "        posteriors_no_log = []\n",
    "\n",
    "        # calculate posterior probability for each class\n",
    "        for i in range(self.count):\n",
    "            prior = np.log(self.prior[i]) # Do predykcji używane jest prawodopodobieństwo logarytmiczne\n",
    "            prior_no_log = self.prior[i] # Zwykłe prawdopodobieństwo liczymy, żeby zwrócić je z predykcjami\n",
    "\n",
    "            conditional = np.sum(np.log(self.gaussian_density(i, x))) \n",
    "            conditional_no_log = np.prod(self.gaussian_density(i, x))\n",
    "\n",
    "            posterior = prior + conditional\n",
    "            posterior_no_log = prior_no_log * conditional_no_log\n",
    "\n",
    "            posteriors.append(posterior)\n",
    "            posteriors_no_log.append(posterior_no_log)\n",
    "\n",
    "        # Zwracamy klasę o największym prawdopodobieństwie\n",
    "        return self.classes[np.argmax(posteriors)], np.max(posteriors_no_log)\n",
    "\n",
    "    def fit(self, features, target):\n",
    "        '''\n",
    "        Główna metoda trenująca model\n",
    "        '''\n",
    "        self.classes = np.unique(target)\n",
    "        self.count = len(self.classes)\n",
    "        self.feature_nums = features.shape[1]\n",
    "        self.rows = features.shape[0]\n",
    "        \n",
    "        self.calc_statistics(features, target)\n",
    "        self.calc_prior(features, target)\n",
    "        \n",
    "    def predict(self, features):\n",
    "        '''\n",
    "        Predykcja wartości dla każdego wiersza\n",
    "        '''\n",
    "        preds = [self.classify(f) for f in features.to_numpy()]\n",
    "        return preds\n",
    "\n",
    "    def accuracy(self, y_test, y_pred):\n",
    "        '''\n",
    "        Wyliczenie accuracy modelu\n",
    "        '''\n",
    "        accuracy = np.sum(y_test == y_pred) / len(y_test)\n",
    "        return accuracy\n",
    "\n",
    "    def visualize(self, y_true, y_pred, target):\n",
    "        '''\n",
    "        Narysowanie wykresu porównującego rozkład klas prawdziwych i przewidzianych\n",
    "        '''\n",
    "        tr = pd.DataFrame(data=y_true, columns=[target])\n",
    "        pr = pd.DataFrame(data=y_pred, columns=[target])\n",
    "        \n",
    "        \n",
    "        fig, ax = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(15,6))\n",
    "        \n",
    "        sns.countplot(x=target, data=tr, ax=ax[0], alpha=0.7, hue=target, dodge=False)\n",
    "        sns.countplot(x=target, data=pr, ax=ax[1], alpha=0.7, hue=target, dodge=False)\n",
    "        \n",
    "        ax[0].tick_params(labelsize=12)\n",
    "        ax[1].tick_params(labelsize=12)\n",
    "        ax[0].set_title(\"Prawdziwe wartości\", fontsize=18)\n",
    "        ax[1].set_title(\"Predykcje\", fontsize=18)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitność wody"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "5-riUAGntjN2",
    "outputId": "f87f047d-bc71-41ef-a43a-17b6f7cf84c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2948, 9) (2948,)\n",
      "(328, 9) (328,)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing danych\n",
    "\n",
    "# Uzupełnienie pustych wartości w kolumnach\n",
    "def fill_nan(df):\n",
    "    for index, column in enumerate(df.columns[:9]):\n",
    "        df[column] = df[column].fillna(df.groupby('Potability')[column].transform('mean'))\n",
    "    return df\n",
    "\n",
    "# Wczytywanie danych\n",
    "df = pd.read_csv(\"water_potability.csv\")\n",
    "\n",
    "df = fill_nan(df)\n",
    "\n",
    "# Zrandomizowanie kolejności danych w datasecie\n",
    "df = df.sample(frac=1, random_state=10).reset_index(drop=True)\n",
    "\n",
    "# Podział na atrybuty i przewidywane wartości\n",
    "X, y = df.iloc[:, :-1], df.iloc[:, -1]\n",
    "\n",
    "# Normalizacja i skalowanie danych\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X.to_numpy())\n",
    "X = pd.DataFrame(X, columns=df.columns.values.tolist()[:-1])\n",
    "\n",
    "# Podział na dane trenujące i testowe, z uwzględnieniem równego rozłożenia danych\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=y, random_state=1)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "O82SGzK6tjN5"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>ph</th>\n",
       "      <th>Hardness</th>\n",
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       "      <td>0.232417</td>\n",
       "      <td>2.319505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3191</th>\n",
       "      <td>-0.587365</td>\n",
       "      <td>0.223203</td>\n",
       "      <td>-0.731867</td>\n",
       "      <td>0.397503</td>\n",
       "      <td>0.759893</td>\n",
       "      <td>0.330607</td>\n",
       "      <td>0.094379</td>\n",
       "      <td>0.282563</td>\n",
       "      <td>0.235024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.003078</td>\n",
       "      <td>-0.241037</td>\n",
       "      <td>0.773051</td>\n",
       "      <td>0.580019</td>\n",
       "      <td>1.334369</td>\n",
       "      <td>-0.049130</td>\n",
       "      <td>-1.121422</td>\n",
       "      <td>-0.200432</td>\n",
       "      <td>-0.946356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>-2.176058</td>\n",
       "      <td>1.443006</td>\n",
       "      <td>-1.626771</td>\n",
       "      <td>-4.164610</td>\n",
       "      <td>-0.033706</td>\n",
       "      <td>-1.050763</td>\n",
       "      <td>-0.391328</td>\n",
       "      <td>-0.398649</td>\n",
       "      <td>-0.298341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>0.213047</td>\n",
       "      <td>0.403036</td>\n",
       "      <td>-0.464729</td>\n",
       "      <td>0.070417</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.952776</td>\n",
       "      <td>-0.213330</td>\n",
       "      <td>0.111419</td>\n",
       "      <td>-0.235893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>0.003078</td>\n",
       "      <td>1.199106</td>\n",
       "      <td>-0.003483</td>\n",
       "      <td>-0.670308</td>\n",
       "      <td>-0.069513</td>\n",
       "      <td>0.185754</td>\n",
       "      <td>-0.466010</td>\n",
       "      <td>0.031975</td>\n",
       "      <td>0.676276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>-2.034004</td>\n",
       "      <td>-1.508135</td>\n",
       "      <td>0.255310</td>\n",
       "      <td>0.083839</td>\n",
       "      <td>-1.413707</td>\n",
       "      <td>0.694074</td>\n",
       "      <td>-1.110579</td>\n",
       "      <td>0.232996</td>\n",
       "      <td>2.544703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>-0.702987</td>\n",
       "      <td>-0.575677</td>\n",
       "      <td>0.755056</td>\n",
       "      <td>0.664695</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.489334</td>\n",
       "      <td>0.371852</td>\n",
       "      <td>-2.272990</td>\n",
       "      <td>-1.764684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>1.525943</td>\n",
       "      <td>0.497074</td>\n",
       "      <td>-0.714355</td>\n",
       "      <td>-1.024237</td>\n",
       "      <td>-1.022037</td>\n",
       "      <td>-0.327074</td>\n",
       "      <td>-1.107341</td>\n",
       "      <td>0.517432</td>\n",
       "      <td>-1.230528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>-0.043558</td>\n",
       "      <td>-0.882359</td>\n",
       "      <td>-0.456141</td>\n",
       "      <td>-0.770271</td>\n",
       "      <td>0.795189</td>\n",
       "      <td>0.560306</td>\n",
       "      <td>-1.086081</td>\n",
       "      <td>-1.356820</td>\n",
       "      <td>0.172521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2948 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ph  Hardness    Solids  Chloramines   Sulfate  Conductivity  \\\n",
       "1022  0.003078  0.688791  0.846257     1.428934 -0.858263      0.002792   \n",
       "3191 -0.587365  0.223203 -0.731867     0.397503  0.759893      0.330607   \n",
       "13    0.003078 -0.241037  0.773051     0.580019  1.334369     -0.049130   \n",
       "2068 -2.176058  1.443006 -1.626771    -4.164610 -0.033706     -1.050763   \n",
       "1484  0.213047  0.403036 -0.464729     0.070417  0.021560     -0.952776   \n",
       "...        ...       ...       ...          ...       ...           ...   \n",
       "691   0.003078  1.199106 -0.003483    -0.670308 -0.069513      0.185754   \n",
       "1283 -2.034004 -1.508135  0.255310     0.083839 -1.413707      0.694074   \n",
       "2818 -0.702987 -0.575677  0.755056     0.664695  0.021560     -0.489334   \n",
       "1330  1.525943  0.497074 -0.714355    -1.024237 -1.022037     -0.327074   \n",
       "1926 -0.043558 -0.882359 -0.456141    -0.770271  0.795189      0.560306   \n",
       "\n",
       "      Organic_carbon  Trihalomethanes  Turbidity  \n",
       "1022        0.913790         0.232417   2.319505  \n",
       "3191        0.094379         0.282563   0.235024  \n",
       "13         -1.121422        -0.200432  -0.946356  \n",
       "2068       -0.391328        -0.398649  -0.298341  \n",
       "1484       -0.213330         0.111419  -0.235893  \n",
       "...              ...              ...        ...  \n",
       "691        -0.466010         0.031975   0.676276  \n",
       "1283       -1.110579         0.232996   2.544703  \n",
       "2818        0.371852        -2.272990  -1.764684  \n",
       "1330       -1.107341         0.517432  -1.230528  \n",
       "1926       -1.086081        -1.356820   0.172521  \n",
       "\n",
       "[2948 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "a3jkTMFLtjN6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andrzej/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3438: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Trenowanie modelu klasyfikatora\n",
    "x = NaiveBayesClassifier()\n",
    "x.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "CoC22aNgtjN9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predykcja wartości dla danych testowych\n",
    "predictions = x.predict(X_test)\n",
    "\n",
    "# Prawdopodobieństwa kolejnych predykcji\n",
    "probabilities = [p[1] for p in predictions]\n",
    "\n",
    "# Przewidziana wartość\n",
    "predictions = [p[0] for p in predictions]\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "JR06zodmtjN9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.280487804878049e-01"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wyliczenie accuracy modelu\n",
    "x.accuracy(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "1jW0QPootjN_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14084507042253522"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vEVogTmAtjOA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.609756\n",
       "1    0.390244\n",
       "Name: Potability, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "jCVOdBZytjOB"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.visualize(y_test, predictions, 'Potability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "aw8Tefprhjnn"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ph_val = X_test[\"ph\"]\n",
    "sulfate_val = X_test[\"Sulfate\"]\n",
    "hard_val = X_test[\"Hardness\"]\n",
    "carb_val = X_test[\"Organic_carbon\"]\n",
    "turb_val = X_test[\"Turbidity\"]\n",
    "ch_val = X_test[\"Chloramines\"]\n",
    "\n",
    "\n",
    "figure, axes = plt.subplots(nrows=3, ncols=2)\n",
    "\n",
    "axes[0, 0].plot(ph_val, predictions, 'bo')\n",
    "axes[0, 0].set_title(\"pH\")\n",
    "\n",
    "axes[0, 1].plot(sulfate_val, predictions, 'bo')\n",
    "axes[0, 1].set_title(\"Sulfate\")\n",
    "\n",
    "axes[1, 0].plot(hard_val, predictions, 'bo')\n",
    "axes[1, 0].set_title(\"Hardness\")\n",
    "\n",
    "axes[1, 1].plot(carb_val, predictions, 'bo')\n",
    "axes[1, 1].set_title(\"Organic carbon\")\n",
    "\n",
    "axes[2, 0].plot(turb_val, predictions, 'bo')\n",
    "axes[2, 0].set_title(\"Turbidity\")\n",
    "\n",
    "axes[2, 1].plot(ch_val, predictions, 'bo')\n",
    "axes[2, 1].set_title(\"Chloramines\")\n",
    "\n",
    "figure.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f716bdc1310>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X_test)\n",
    "X_pca = pca.transform(X_test)\n",
    "\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=predictions, s=50, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f716bda93d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_test, s=50, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Irysy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4) (105,)\n",
      "(45, 4) (45,)\n",
      "9.333333333333333e-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andrzej/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3438: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing danych\n",
    "\n",
    "\n",
    "# Wczytywanie danych\n",
    "df = pd.read_csv(\"iris.csv\")\n",
    "\n",
    "# Zrandomizowanie kolejności danych w datasecie\n",
    "df = df.sample(frac=1, random_state=10).reset_index(drop=True)\n",
    "\n",
    "# Podział na atrybuty i przewidywane wartości\n",
    "X, y = df.iloc[:, :-1], df.iloc[:, -1]\n",
    "\n",
    "# Normalizacja i skalowanie danych\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X.to_numpy())\n",
    "X = pd.DataFrame(X, columns=df.columns.values.tolist()[:-1])\n",
    "\n",
    "# Podział na dane trenujące i testowe, z uwzględnieniem równego rozłożenia danych\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)\n",
    "\n",
    "\n",
    "# Trenowanie modelu klasyfikatora\n",
    "x = NaiveBayesClassifier()\n",
    "x.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# Predykcja wartości dla danych testowych\n",
    "predictions = x.predict(X_test)\n",
    "\n",
    "# Prawdopodobieństwa kolejnych predykcji\n",
    "probabilities = [p[1] for p in predictions]\n",
    "\n",
    "# Przewidziana wartość\n",
    "predictions = [p[0] for p in predictions]\n",
    "\n",
    "\n",
    "# Wyliczenie accuracy modelu\n",
    "print(x.accuracy(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9326599326599326"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, predictions, average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal.length  sepal.width  petal.length  petal.width\n",
      "106     -0.900681     0.558611     -1.169714    -0.920548\n",
      "65      -1.143017     1.249201     -1.340227    -1.447076\n",
      "49       1.038005     0.558611      1.103783     1.712096\n",
      "74       2.128516    -0.131979      1.615320     1.185567\n",
      "110     -0.173674     1.709595     -1.169714    -1.183812\n",
      "..            ...          ...           ...          ...\n",
      "123     -1.021849    -0.131979     -1.226552    -1.315444\n",
      "121     -0.052506    -1.052767      0.137547     0.000878\n",
      "85      -0.537178     1.939791     -1.169714    -1.052180\n",
      "13      -1.143017    -0.131979     -1.340227    -1.315444\n",
      "125     -1.870024    -0.131979     -1.510739    -1.447076\n",
      "\n",
      "[105 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f716bd1e820>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(X_test)\n",
    "X_pca = pca.transform(X_test)\n",
    "\n",
    "df_pred = pd.DataFrame(predictions).replace({'Virginica': 0, 'Versicolor': 1, \"Setosa\": 2}, regex=True)\n",
    "df_pred = np.array(df_pred).reshape(1, -1)\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=df_pred[0], s=50, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zc+ONN95oyXFpN7VfbiamZHPx8jWcHWvuRiktr6KnqwMx4b5GvwBt2K+nqwNBPh4cOl5oeF3bt7btjtTT6HR61GoVQ+5yMHxJbEkMy2aPaPQl8mDvHhbd0GAqflP72lFJHmGYY3XnXlhwb5M3KFjaHpqek2s2ZTY5lrH553tnD6PL09MLjWzdtJthjreUPDrIhNpTy7a+ZNdebtQpulK15JLdzTInWkNnPmadOWdu9CU7U/NBft1cCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKYNNWG0pJSeH9999Hr9dTUVHBvffey6pVq0y2v3LlCl988QWzZs1qqxCbtWZTJjtST6PT6VGrVYQF92b2ZD+jbXennyExJZuLl6/R09WBIB8PDh0vNLyOCfdlVIAXAE++lkLR1UpDXyd7DY6OdkbbNjWuna2aymodej0Wx/eATxcCAlr1cIlW1hlySMka5kRt3pla3hRT7xWmxmrqvcXSsQBeWfMjh09eMsQz2LsHy2aPaNXj0lrt61Lp9Xq9VVFaoLCwkMjISJKTk+nVqxd6vZ7jx4/j6+trss/Zs2eJjo4mNTX1RodnVEVFBVlZWQwYMAB7e3vWbMpk+/7cRu0eHtan0Zv+7vQzvL3xMBVVWpPj29tqiJ0ymE+/zqpXjJpqCzQ7rrXx2WpUzJnmb/bE6ejS09MJsLACN5wTbakj5hC07zGzhLGcsLfVMCbwdr5PO9toeeyUwXSl0OgcMvVeMdi7B8dzixuN5dPHpV7xqPXwsD4AFo0VO2UwOw/mGh3PkqJUmx+mjkvslMFG3yuaa9/cfGiTM6SLFy9iY2ODi4sLACqVypBIhw8fZuXKlZSVlQEwZ84cRo0axZIlS7h69SpRUVE4ODiwYcMGcnNzWbRoEUVFRdjY2DB37lxCQkK4du0a8+fP5+TJk9jY2HDnnXfy5ptvcuHCBV588UXKysqoqKhg5MiRzJs3z6p92JF62uTyhm/4iSnZzRaNiiotiSnZzRajum1r/28Jc+Or0upJTMm+aQpSR9MZckjJjOVERZXWcGbScHliSjZ/Cu9hdCxT7xXGikRFldbo8qbGaWqsxJRsLly+Znaf5pg6LqbeKyxt31CbFCQfHx8GDRrEqFGjCA4OZsiQIURFRaHRaHj11Vf54IMP8PDwoLCwkMmTJ7Nt2zYWLVpEdHQ0W7ZsMYwTFxfH1KlTmTJlCidPnuTxxx8nJSWF9PR0rly5wvbt2wEoKSkBoFu3brz33ns4OTlRVVXFzJkz2bt3LyEhIWbHnpWVBdBoUtbS6fSkp6fXW2ZqQjRkbjtL29ZlSXwXLl9r1LYz60j72pFzCH7PI6UylROm8r62vbE5ZKqPpawZp7n3CUvmfHp6usXvFS19b2mTgqRWq3n33Xc5ceIEhw4dYufOnXz88cfMmzePs2fP1rvGrVKpyM3NxdXVtd4YpaWlZGdnEx0dDYC3tze+vr5kZmbi4+PDf//7XxYvXszQoUMZNWoUAFqtlhUrVpCRkYFer+fixYscP37comSqPbVUb8gzOkHUalWj03b3lEtmFRB3VwezC427qwNgeWGyJD53VweLL2N1VC25ZNceOnIOAYq/ZGcqJ9RqldG8r81HY3PI1HuFpdRqFWBZYWruPcXcOV+bH5a+VzTXvrkcatO77Pr168fjjz/Op59+SteuXdHr9fTv358tW7YY/u3Zs4eBAweaPaZKpcLLy4vt27dz//33s3//fqKioqioqODTTz/lypUrbNy4ka+//pqxY8dSUVFhVexhwb3NXh4T7ou9rabJ8extNcSE++LW1a7Zbde2NWdca+Oz1aiICTf9fYRQho6cQ0pmLCfsbTWEBfc2urypXDH1XjHYu4fRsQZ7G7/0Fxbc2+KxYsJ9TY5nanlTTB0XU/tvafuG2qQgFRQUkJGRYXh9/vx5ioqK8Pb2Jjc3lwMHDhjWHTlyBL1ej7OzM9evX6e6uhoAZ2dnfH19SU5OBiAnJ4fjx48zePBgzp8/j0ajYezYsfz1r3+lqKiI4uJirl69iru7O/b29hQUFPD9999bvQ+zJ/vx8LA+hk8tarXK6A0DAKMCvIidMhh3VwdU1Hw6eHhYn3qva7/k++dr4Y2KkpO9xmjb5sa1t1WjqgnP4vgihrrI90cK1hlySMmM5UTslMHMnuxndHlTuWLqvWLZ7BFGx1o2e4TJ9xZLxxoV4MWy2SMaFR9r77IzdVxM7b+l7Rtqk7vs8vLyWLhwIXl5eXTp0gWdTsfjjz/O9OnTOXLkCG+88QYlJSVUVVXh5eXFe++9h1qt5pVXXiE9PZ3u3bs3+YXsnj17DLe/6nQ6IiMjeeaZZ8jLy+P555+nurqaW265BScnJ+644w7+/Oc/NxtzR7k7qLVYcwmrI+tod9l1xByCzp1HnTlnbtS+NTcf2qQgdUSdOZGM6czJZUxHK0gdVWc+Zp05Z9qrIMmTGoQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIlhdkGbOnMmGDRvqLdPr9YSGhhITE0NaWprFYx49epSXXnqp2XYFBQXMmDHD4vGFUBLJISHqs7G2Y3R0NGvXrmX69OmGZampqdjY2JCYmGi0T3V1NTY2pjc5cOBAVq1a1ey2PT09+eyzzywP+ia3O/0MiSnZXLx8DWdHWwBKy6vo6epAnx4q3kn5jouXr9HT1YEgHw8OHS80vI4J92VUgJfRcV9Z8yOHT14yvNaoQaej2X514+np6sCtPRw5+t8idDp9o7aDvXuwbPYIo+Os2ZTJjtTT6HR61GoVYcG9mT3Zz8Kj0/pjNUdyyDwN50ndOWVqnamfozU/34bzu3YubjtYxJINW42O9cf4nZwpLDP08fJw4t35Y03G29Q+ttWxVAKVXq9vnP1mqKys5IEHHuDzzz/H29sbgHnz5nHXXXfx008/8dRTTzF69GgWLFiAk5MTv/32G5cvXyYpKYnVq1ezfft2XFxcGDp0KPv37ycpKYnU1FTi4+NJSkri7NmzREdHM336dPbs2cO1a9f429/+RmBgoGFdamoqABkZGaxYsYKysjJDHCNGjCA+Pp6DBw9SVVWFq6sry5cv57bbbjNr/yoqKsjKymLAgAHY29tbc4gUZXf6Gd7eeJiKKq1V/e1tNcROGdxo8jZMVnP7WROPsaK0ZlMm2/fnNmr78LA+Tb7RpKenExAQYNFYrT0nOnsOQcvzyNg8qZ1TgNF1Pn1cjM5JLw+nekWiVlNzxdT8dutqR9HVSqNjHc25aHQ7bl3tKLuubRTvmMDb+T7trNF9bM1i0dSxbLgdY/nRGpqbD1ZfsrOzsyMiIoKkpCQASktL2blzJxMnTmzUNiMjg4SEBJKSkvjhhx/YtWsXW7Zs4YsvviA3t/EbQK3i4mL8/Pz46quv+NOf/sTKlSuNtomNjeUvf/kLW7duJTk5mYEDBwIwa9YsNm/ezNatWxk/frzR/jeLxJRsq4sRQEWVlsSU7EbLmypGTfWzJh5j29qRetpoW1PLm9KaY5lDcqh5xuZJ7Zwytc7UnDRWJKDpn6+psYwVo9qxTG2n6Gql0Xh3pJ42uY+tqaljqRRWX7IDmDx5Mk8//TQvvvgiKSkpBAQE4Onp2ahdWFgYjo6OQM0lifDwcMPrCRMm8O677xod39HRkdGjRwPg5+dHfHx8ozaZmZn07duXIUOGAKDRaOjevTsAe/fuZf369ZSXl1NdXW3VPmZlZVnVT2kuXL7WKmOkp6e3Sj9r42k4jrHLe7XLm4u1Ncey1s2QQ2B9HpmaJ60xn2u15s/X1Byypo+1+WZKU8fS2HZu1JxvSosKko+PD+7u7uzbt4/Nmzfzhz/8wWi72sSBmi9tVSqVWePb2dkZ/q9Wq40mhKkrjnl5efz9739n06ZNeHl58e9//5u4uDiztltXZ7lk555yqcVJ7O7q0Pg0fv1Zq/pZG0/DcdQb8owmtFqtavKSg7FLEs2NVXu5oTXdDDkE1ueRqXni7uoAtE5hanKumDG/G45laVEy1cdovrVAU8ey4XZu9CU7U1p823d0dDQJCQn89ttvhIaGNts+ODiYHTt2cO3aNXQ6HVu3bm3R9v39/cnJySEjIwMArVZLSUkJpaWl2Nra4u7ujk6na3Q3080mJtwXe1uN1f3tbTXEhPs2Wj7Yu4dV/ayJx9i2woJ7G21ranlTWnMsS0gOmWZsntTOKVPrTM1JLw8no8ub+vmaGsutq53R5WHBvU1ux62rndF4w4J7m9zH1tTUsVSKFp0hAURERLBixQqmTZtW79OYKWPGjCEjI4OoqCg8PT0ZPHgwJSUlVm/fxcWFhIQEXn/9dcrLy1Gr1cyfP5/hw4cTFhbGI488wq233kpQUJBFt9HWfmqsrDR+rbijGTbAAxUDSNp9kqKS6zg51Pzoy65V49a9C7e5qMgr1lNUch237l0Y1LcHR3IuGV5PGuXNfQM8qKioqDfuwqeCWLUujezcy4Zl6v93l11T/RrG49a9C54uDhw/U4zeyKdF3z6uvPREYKNxnorwxc5Gz57Mc+h1elRqFSP9buWJcN9GbRuydKzauWDlfUAmddYcgpbnkbF5UjunABPrerEu5ZiRn+M9JpabnivG5nftXHxz3Y9knbneaCyAhe/9RP6lckOfXj0cWfrc/RzIyjca7z13uhjdx+bmcGsdS2Pbac1t12ouh6y+y64lSktLcXZ2RqfT8fLLL+Ph4cHcuXPbOowmXb16lRMnTrR3GEKB+vXrR9euXds1ho6QQyB5JIwzlUPtUpD+9Kc/kZeXx/Xr17n33ntZvHgxzs7ObR1Gk3Q6HWVlZdja2pp9vV50bnq9nqqqKpycnFCr2/chJx0hh0DySNTXXA61S0ESQgghGpJn2QkhhFAEKUhCCCEUQQqSEEIIRZCCJIQQQhGkIAkhhFAEKUhCCCEUQQqSEEIIRWjxo4NEx3bq1CkWLFhAcXExLi4uxMfHc8cdd7R3WDfM5cuXmTdvHqdPn8bOzo4+ffqwZMkS3Nzc2js00UF01pxRQm7IL8be5GJiYoiOjiYqKootW7awefNmk3+ttDMoLi7ml19+ITg4GID4+HhKSkpYvnx5O0cmOorOmjNKyA25ZHcTu3TpEseOHWP8+PEAjB8/nmPHjlFUVNTOkd04Li4uhoSDmr8RdO7cuXaMSHQknTlnlJAbUpBuYvn5+Xh6eqLR1DySXqPR4OHhQX5+fjtH1jZ0Oh3/93//Z9affBACbp6caa/ckIIkblpLly7F0dGRJ554or1DEUJR2is35KaGm1ivXr0oKChAq9Wi0WjQarUUFhbSq1ev9g7thouPjyc3N5f33nuv3Z/cLTqOmyFn2jM3JBNvYj169MDX15dt27YBsG3bNnx9fTv9HWerV68mKyuLd955x6w/iCdErc6eM+2dG3KX3U0uJyeHBQsWcOXKFbp160Z8fDx33XVXe4d1w/z666+MHz+eO+64gy5dugBw++23884777RzZKKj6Kw5o4TckIIkhBBCEeSSnRBCCEWQgiSEEEIRpCAJIYRQBClIQgghFEEKkhBCCEWQgiSEEEIRpCAJIYRQBClIQgghFEEKkhBCCEWQgiSEEEIRpCAJIYRQBClIQgghFEEKkhBCCEWQgiSEEEIRpCDd5BISEoiLizO6LjU1lZCQkDaOqEZTcQmhZK0xd7du3cpTTz1lcv2MGTPYuHGjyfULFixg9erVLYqhPUhBUqjQ0FAGDRqEv78/w4cP569//StlZWXN9mtuoipRexY+cfPoSDkVGRnJJ598YlbbpKQkHn300RscUduQgqRg7733HhkZGSQnJ3P06FHWrFnT3iEJ0aFJTimbFKQOwNPTkwceeIBff/0VgMzMTKZPn05gYCCRkZGkpqYCsHr1atLS0liyZAn+/v4sWbIEgGXLljFy5EiGDBnCpEmTSEtLsyqOgoIC/vznP3PfffcRGhpKYmKiYV1CQgLPP/888+bNw9/fn0ceeYSjR48a1v/nP/9hwoQJ+Pv7M2fOHF544QVWr15NeXk5s2bNorCwEH9/f/z9/SkoKACgqqrK5HhCtER75dQTTzzBt99+C0BaWhr9+/dnz549APz8889ERUUBjc96fvrpJ8LCwggICGDJkiXU/qHvnJwcXn31VTIzM/H39ycwMNDQ58qVKzzzzDP4+/szZcoUTp8+3ZJD1iakIHUA+fn57N27F19fXwoKCnj22WeZPXs2Bw8eZP78+cyZM4eioiLmzp1LYGAgixYtIiMjg0WLFgEwcOBAvvrqKw4ePMj48eN5/vnnqaiosCgGnU7H7Nmz6d+/P3v37uWf//wn//znP9m3b5+hzQ8//MAjjzxCWloaoaGhLF26FIDKykpiY2OZOHGiIYadO3cC4OjoyIcffoiHhwcZGRlkZGTg6enZ5HhCtFR75VRQUBAHDx4EagqSl5eX4fWhQ4cICgpq1KeoqIg///nPvPDCCxw4cIDevXvz73//G4C+ffuyePFi/Pz8yMjIqFcYv/nmG2JjYzl06BC9e/fuEN8pSUFSsD/96U8EBgby2GOPERQUxHPPPceWLVsICQlh5MiRqNVq7r//fgYMGGD4lGVMVFQUrq6u2NjY8NRTT1FZWcmpU6csiuXo0aMUFRURGxuLnZ0dXl5eTJ06le3btxvaBAQEMHLkSDQaDVFRURw/fhyAw4cPU11dTUxMDLa2tjz44IMMHDiw2W2aGk8Ia7V3Tg0dOrReAXr22Wc5dOiQ4fXQoUMb9dm7dy/e3t6EhYVha2vLk08+Sc+ePZvd1rhx4xg0aBA2NjZERkaSnZ3dbJ/2ZtPeAQjT3nnnHYYPH15v2blz59ixYwe7du0yLKuuriY4ONjkOJ988gkbN26ksLAQlUpFaWkply9ftiiWvLw8CgsL610S0Gq19V7XTZIuXbpQUVFBdXU1hYWFeHp6olKpDOt79erV7DZNjWdjI9NWWKe9c8rPz4/ffvuNixcvcvz4cdasWcNbb71FUVERR44cqZdPtQoLC7nlllsMr1UqlVX5U15e3myf9iaZ3cH06tWLqKgoli1bZlb7tLQ0PvzwQ9auXcvdd9+NWq0mKCjIcA3aku3efvvtfPfddxbH7O7uTkFBAXq93lCU8vPz8fLyAqhXqIRoa22ZUw4ODtx7770kJiZy9913Y2dnh7+/P2vXrqV37964ubk16uPu7s758+cNr/V6Pfn5+YbXnSl/5JJdBxMZGcmuXbvYt28fWq2WiooKUlNTDRO2Z8+enDlzxtC+rKwMjUaDm5sb1dXVvP3225SWllq83UGDBuHs7MwHH3zA9evX0Wq1nDhxgiNHjjTb18/PD41Gw7p166iurmbnzp31blDo0aMHxcXFXL161eK4hGipts6poUOHsm7dOsP3RcHBwfVeNzRy5Eh+/fVXvvvuO6qrq0lMTOTixYuG9T169KCgoIDKykprdl9RpCB1ML169eLdd9/l/fffZ9iwYYwcOZKPP/4YnU4HQExMDN9++y1BQUEsW7aMESNGEBISwkMPPURoaCj29vZmne43pNFoWLNmDcePH2fMmDHcd999vPLKK2Ylop2dHQkJCWzatImgoCC2bt3KqFGjsLOzA2q+mH3kkUcYO3YsgYGBhrvshGgLbZ1TQUFBlJWVGQpQw9cNubm58eabb7Jq1SqCg4PJzc1lyJAhhvX33Xcf3t7ejBgxosnLjB2BSm/ptRshWsGUKVOYPn060dHR7R2KEEIh5AxJtImDBw9y4cIFqqurSU5O5pdffuGBBx5o77CEEAoiNzWINnHq1CleeOEFysvL8fLy4q233sLDw6O9wxJCKIhcshNCCKEIcoZkgk6no6ysDFtb2051W6Wwnl6vp6qqCicnJ9RqudptDskjUVdzOSQFyYSysjJOnDjR3mEIBerXrx9du3Zt7zA6BMkjYYypHGqyIM2cOZNx48Yxffp0wzK9Xs+YMWOIj483eZuiOQoKCoiLi+Ozzz6zqv/Zs2eJjo42PASxtdna2gI1B6729uTWlJWVxYABA1plrANZ+STtPklRyXXcundh0ihv7htg+a3dWVlZZJ5RsyfzHHrd71dyVWoVep2+0dh1t1u37S2uDpy/fM2sMcyJqbWOU0tVVlZy4sQJw9wwl+RR6+RRW88FY3kFNFp28sxlQ86o1CpG+t0K0GjZE+H3sC7lWKPlBZfKyc79/SkPvn1cud/vtlbJ6bqUkEvN5VCTBSk6Opq1a9fWS6TU1FRsbGzMSiKdTodKpTJ6qu7p6Wl1ErWEVqtFo9E02642Zjs7O+zt7W9ILK0x7u70M7y9MYuKKi0AxWVlJGzMQo+GUQFeFo217WARaSdNP16k7thAve3WdbmJX3C1Jr4bdfytZemlJ8mj1sujtpoLxvLq//viCHo9aP/fB63isjJW/d9hGn4L/9W+xk/V/mrfadJ/ucSZwrJm2+4/dpH9x37/xdeW5HRDSsklUznU5IXwsWPHkpuby8mTJw3LkpKSmDRpEh9++CGTJ09m4sSJPPfcc1y4cAH4/a8l/vGPfyQqKori4mJee+01wsLCiIyMNCTl2bNn6/0SV0ZGBo8++iiRkZFERkby448/AnDkyBGmTZtGREQE06ZNM/lkgL179zJhwgQiIiJ48sknyc3NBWoSPyoqiqVLlzJ16lT27t1r7jHrEBJTshsVhYoqLYkplj9IMT2n+Wdd1Y5tbLvmsja+jkryqOMxNr+rtXpDMaplyS1hDYuRJW6WnGnyDMnOzo6IiAiSkpKYN28epaWl7Ny5k4ULF/Lvf/+bL7/8ErVazfr163n99ddZtWoVUPOsp6SkJNzc3Dh27Bj79+8nJSUFtVpNSUlJo+0UFxcTGxtLQkICQ4YMQavVUlpaSmVlJXPmzGH58uUMHz6c/fv3M2fOnEbPU7t06RLz5s1j3bp1eHt7s3HjRuLi4gx/5fHEiRO89tprLFy40OIDlJWVZXEfc6Wnp7d4jAuXr5lcbun45iaXqW1awpL4WuM4tSfJo9bLo7aaC60xx1ubNTndkNJzqdmbGiZPnszTTz/Niy++SEpKCgEBAezevZusrCwmTpwI1Jy+Ozs7G/qEhIQYHhLo5eWFVqvl5ZdfJjg4mNGjRzfaRmZmJn379jU8DkOj0dC9e3d++eUXbG1tDU/nHTZsGLa2tpw6dQonJydD/8OHD+Pj44O3d8013ujoaBYvXmx4rE2fPn3w9/e36gANGDDghpzmpqenExAQ0OJx3FMuGU0ed1cHi8dX/d9Zs4qSu6sD0LKkNTe+1jpOraGiosLqN1bJo5bnUVvOBVN51Z6syem6lJBLzeVQs/eu+vj44O7uzr59+9i8eTPR0dHo9Xpmz57Nli1b2LJlC9u2bWPDhg2GPnUnedeuXfnmm28IDw/nl19+4ZFHHjFclqhl6leh6j4duq6Gy0y1q+Xo6NjcbnZYMeG+2NvWv5Zvb6shJtzX4rEC+jZ/nGrHNrZdc1kbX0cmedSxGJvfNhoVGnX942PJ14leHk7NNzLhZskZs36ZIjo6moSEBH777TdCQ0MJDQ1l/fr1hssGlZWVJv94WlFREdevXyckJIS4uDi6du1a78m5AP7+/uTk5JCRkQHUfFIsKSnhrrvuorKykgMHDgBw4MABqqurueOOOxr1z87OJicnB4Dk5GTuueeeep82O6tRAV7EThmMu6sDKmo+RcVOGWzVl5/jh7rx8LA+qBskXe3rumPX3W7Dtl4eTmaNcbORPOo4jOXV89P8eWG6f71lLz46pF7OqNUqHh7Wx+iyd+ePNbp8sHePetse7N2Dlx4b0io53dGY9XtIERERrFixgmnTpmFnZ8eECRMoLi7miSeeAGo+WT366KP4+Pg06pufn8/ChQuprq5Gq9USEhKCn58f586dM7RxcXEhISGB119/nfLyctRqNfPnz2f48OG89dZb/O1vf6O8vBxHR0fefPPNRrePurm5sWLFCuLi4qiursbNzY033nijJcelQ6ktEK1h9mQ/Zk/2a/Pt3gwkjzoWU/O74bJRAV7MnuzX6JKYsTyyNL9uNvLoIBNqr3Uq/Tuk1iQxNe1Gz4nOqDWPmZLmgjESX/Oamw/y/BMhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhtVpBSUlKYMGECUVFRhIWF8dJLLzXZ/sqVK3z44YdtFJ0Qyic5JDo7m7bYSGFhIYsXLyY5OZlevXqh1+s5fvx4k32uXLnCRx99xKxZs9oiRLOs2ZTJjtTT6HR6s9qr1SrCgnsze7Ifu9PPkJiSzcXL1+jp6sADPl0ICLjBAYtOo7PkEEDES1saLft6VVSjZXXzTa1WMeQuB5M50zC/YsJ9GRXg1dqhixusTQrSxYsXsbGxwcXFBQCVSoWvry8Ahw8fZuXKlZSVlQEwZ84cRo0axZIlS7h69SpRUVE4ODiwYcMGcnNzWbRoEUVFRdjY2DB37lxCQkK4du0a8+fP5+TJk9jY2HDnnXfy5ptvcuHCBV588UXKysqoqKhg5MiRzJs3z6p9WLMpk+37cy3qo9Pp2b4/l7wLpRzPLaaiSgvAhcvX+Prgde6884wkjTBLZ8ghMF6MapfXLUoN802n05N2spw1mzKZPdmvXt/d6Wd4e+Phevn19sbDAJJfHUybFCQfHx8GDRrEqFGjCA4OZsiQIURFRaHRaHj11Vf54IMP8PDwoLCwkMmTJ7Nt2zYWLVpEdHQ0W7b8PoHj4uKYOnUqU6ZM4eTJkzz++OOkpKSQnp7OlStX2L59OwAlJSUAdOvWjffeew8nJyeqqqqYOXMme/fuJSQkxOJ92JF62ur9P3zyUqNlVVo9iSnZkjDCLJ0hhyxhKt92pJ5uVJASU7INxahWRZVW8qsDapOCpFareffddzlx4gSHDh1i586dfPzxx8ybN4+zZ8/Wu6SgUqnIzc3F1dW13hilpaVkZ2cTHR0NgLe3N76+vmRmZuLj48N///tfFi9ezNChQxk1ahQAWq2WFStWkJGRgV6v5+LFixw/ftyiZMrKygIw+zKdJS5cvkZ6enqrj9sSSosHlBlTW+vIOQS/51FT6v6cTeWbTqdvNB8uXL5mtG175JfS56rS42uTglSrX79+9OvXj8cff5yHH34YvV5P//79+fzzzxu1PXv2rFljqlQqvLy82L59OwcOHGDv3r2sXr2ar7/+mk8//ZQrV66wceNG7O3tWbhwIRUVFRbFPGDAAOzt7VFvyGv1ouTu6kCAgr5ISk9PV1Q8oKyYKioqzHpjvZE6Yg7B73nEetMx1f05m8o3tVrVaD64p1wyWpTaOr+UNFeNUUJ8zeVQm9xlV1BQQEZGhuH1+fPnKSoqwtvbm9zcXA4cOGBYd+TIEfR6Pc7Ozly/fp3q6moAnJ2d8fX1JTk5GYCcnByOHz/O4MGDOX/+PBqNhrFjx/LXv/6VoqIiiouLuXr1Ku7u7tjb21NQUMD3339v9T6EBfe2uu9g7x7Y22rqLbPVqIgJ97V6THFz6Qw5ZAlT+WZseUy4b6P8srfVSH51QG1yhlRdXU1CQgJ5eXl06dIFnU7HCy+8wD333MO7777LG2+8wfLly6mqqsLLy4v33nsPFxcXIiIiiIiIoHv37mzYsIGVK1eyaNEi1q5di42NDStWrMDNzY09e/awatUqAHQ6Hc888wyenp7MmDGD559/ngkTJnDLLbcwbNgwq/eh9rp1a95lJ9e3hbk6Qw5Bzd105txl1zDfau+ya/j9Efx+44LcZdfxqfR6fet/OdIJ1J5aGi41tDIlnD43JDE17UbPic6oNY+ZkuaCMRJf85qbD/KkBiGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIKNtR1nzpzJuHHjmD59umGZXq9nzJgx3H777cyZM4fAwECLxjx69Chr165l1apVTbYrKCggLi6Ozz77zKrYRdvYnX6GxJRsLly+hlqtQqfTY2+rprJah15fv+1g7x4smz3CaB/Wn63XtnZ5V0dbAK6WV9VbFxbcm9mT/cyO7+Lla/R0dSAm3Beg0bJRAV4tOxAmSA4JU9ZsymRH6ml0Or1hTvve2cPo3HxlzY8cPnnJ0Lc2lxo6cqqMd1K+q9d/58Fco32N5caXO3/hTGGZoa2XhxMD+/Y0O05zqPT6hm8N5tm+fTtr167lyy+/NCw7cOAAixYt4rvvvjPap7q6Ghsbq2tgm6qoqCArK4sBAwZgb2/f6uOnp6cTEBDQ6uO2RGvGtDv9DG9vPExFldbsPl4eThRevm5RH1MeHtanyaJkLD6NWoVKBdXa31PC3lZD7JTBjArwavU50dlzCFo3j5SYM3W1VnxrNmWyfX9uo+UqFfU+yNnbavBw7VKvSNRqWJR2p5/hrS8yqNI2/3bf0jw0fJCsE6e5OWT1JbuxY8eSm5vLyZMnDcuSkpKYNGkSM2bMYNeuXQAsWLCApUuXMnPmTKZOnQrA6tWrGTduHFOmTOGNN95g0qRJAKSmphr+f/bsWYKDg1m9ejUTJkzgoYceIi0trd66WhkZGTz66KNERkYSGRnJjz/+CEB8fDzR0dFERkby5JNPkpeXZ+3uCgslpmRbPKHPFJa1SjEC2JF6usn1xuLT6vT1ihFARZWWxJTsVompIckhYYypudvw1KGiSmu0GAH1znqgZr6bU4yg5XlYtxiBZTlk9UctOzs7IiIiSEpKYt68eZSWlrJz505SUlL46aef6rXNyMhg3bp1ODo68sMPP7Br1y62bNlCly5dmDNnjsltFBcX4+fnx9y5c9m6dSsrV65kw4YNjdrExsaSkJDAkCFD0Gq1lJaWAjBr1izmz58PwMaNG1m5ciWrV6+2aD+zsrIsam+J9PT0Gza2tVorpguXr7XKONbS6fRN7osl8V24fO2G/KxulhyC1ssjJeZMXa0RX8M3dGvVjaW989HcHGrRuf/kyZN5+umnefHFF0lJSSEgIABPT89G7cLCwnB0dARqPsGFh4cbXk+YMIF3333X6PiOjo6MHj0aAD8/P+Lj4xu1yczMpG/fvgwZMgQAjUZD9+7dAdi7dy/r16+nvLyc6upqq/ZRLtlZxz3lUrsmgVqtanJfLInP3dWBgIAAw+WG1nQz5BC0Th4pMWfqaq341BvyWqUo1Y2lvfPR3Bxq0V12Pj4+uLu7s2/fPjZv3kx0dLTRdrWJAzVf2qpUKrPGt7Oz+z1QtdpoQpj6CiwvL4+///3vrFq1im3btrF8+XIqKyvN2q5ouZhwX+xtNRb18fJwsriPKWHBvZtcbyw+jVqFjab+3LS31RhudrgRJIdEQ6bmbsMfub2tBi8PJ6NtB3v3qPc6JtwXW415c6aleahWW59DLf52NDo6moSEBM6dO0doaGiz7YODg0lISODJJ5/E3t6erVu3tmj7/v7+vPLKK2RkZODv72+43FBaWoqtrS3u7u7odLpGlymaU5ukNzIBKyoqbtjY1mqtmIYN8EDFAJJ2n6So5DoqtQq9To+djZpKrQ4avAf69nHlpScCOZCV36hPQ7XLnRxqpm/Ztep660b63coT4b5N7kvD+Ny6d2HSKG+ARsvuG+BBRUWFYS5YeR+QSZ01h6D180iJOVNXa8T3VIQvdjZ69mSeQ6/TG+a0t5erkbnZi1Xr0sjOvWzoX5tLdWMZNsCDs2dcOZRTUa//T5l5RvvWzcPatt/8+F/yL5Ub2vbq4Uj/Pq5mxmleDrW4IEVERLBixQqmTZtW79OYKWPGjCEjI4OoqCg8PT0ZPHgwJSUlVm/fxcWFhIQEXn/9dcrLy1Gr1cyfP5/hw4cTFhbGI488wq233kpQUJDhC11zVFXV3Ep84sQJq2Nrzo38fsparRmTMxAzytWibVvaxzidWfvReFs1XwQ3XJaVVf8L4qqqKrp06dLCGH/XWXMIWj+PlJgzdbVWfH5e4Od1S50lOuCS0bn5kF8XHvLr1Wwcvl6O+Ho51lliuq+x3Jg2ojvQvcGoOrPjrMtUDll923dLlJaW4uzsjE6n4+WXX8bDw4O5c+e2dRhN0ul0lJWVYWtra/blEdG56fV6qqqqcHJyQq1u398p7wg5BJJHor7mcqhdfqFh/vz55OXlcf36de69915mzZrVHmE0Sa1W07Vr1/YOQyhMa54ZtURHyCGQPBKNNZVD7XKGJIQQQjQkz7ITQgihCFKQhBBCKIIUJCGEEIogBUkIIYQiSEESQgihCFKQhBBCKIIUpHa0ZcsWIiIiuOeee1i3bl27xXHq1CmmTZvGQw89xLRp0/jtt9/aLZZa8fHxhIaG0r9//xv6tAzRsSglZ+pSYv7U1ZFySQpSO/L19WX16tWMHz++XeN49dVXeeyxx/j222957LHHWLRoUbvGAzWPx/n888+57bbb2jsUoSBKyZm6lJg/dXWkXJKC1I769euHt7d3uz6G5tKlSxw7dsyQ4OPHj+fYsWMUFRW1W0wAgYGB9OrVq/mG4qaihJypS6n5U1dHyiVl/FRFu8nPz8fT0xONpuZx8xqNBg8PD/Lz89s5MiGUT/KndbXLs+xuFhMnTuTcuXNG1/3888+GSSyEqCE5c3OTgnQDJScnt3cIzerVqxcFBQVotVo0Gg1arZbCwsIOc4ovOpeOkDN1Sf60Lrlkd5Pr0aMHvr6+bNu2DYBt27bh6+uLm5tbO0cmhPJJ/rQuedp3O9q2bRsrVqzgypUr2Nra4uDgwCeffIK3t3ebxpGTk8OCBQu4cuUK3bp1Iz4+nrvuuqtNY2ho2bJlfPfdd1y8eBFXV1dcXFz45ptv2jUm0f6UkjN1KTF/6upIuSQFSQghhCLIJTshhBCKIAVJCCGEIkhBEkIIoQhSkIQQQiiCFCQhhBCKIAVJCCGEIkhBEkIIoQhSkIQQQijC/w/UrNLGYUBnnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sep_len = X_test[\"sepal.length\"]\n",
    "sep_with = X_test[\"sepal.width\"]\n",
    "pet_len = X_test[\"petal.length\"]\n",
    "pet_with = X_test[\"petal.width\"]\n",
    "\n",
    "figure, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "axes[0, 0].plot(sep_len, predictions, 'bo')\n",
    "axes[0, 0].set_title(\"Sepal lenght\")\n",
    "\n",
    "axes[0, 1].plot(sep_with, predictions, 'bo')\n",
    "axes[0, 1].set_title(\"Sepal width\")\n",
    "\n",
    "axes[1, 0].plot(pet_len, predictions, 'bo')\n",
    "axes[1, 0].set_title(\"Petal length\")\n",
    "\n",
    "axes[1, 1].plot(pet_with, predictions, 'bo')\n",
    "axes[1, 1].set_title(\"Petal width\")\n",
    "\n",
    "figure.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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