SU1-2022-LAB2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sveučilište u Zagrebu  \n",
    "Fakultet elektrotehnike i računarstva  \n",
    "  \n",
    "## Strojno učenje 1 2022/2023  \n",
    "http://www.fer.unizg.hr/predmet/struce1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------\n",
    "\n",
    "### Druga laboratorijska vježba: Linearni diskriminativni modeli i logistička regresija\n",
    "\n",
    "*Verzija: 1.1 (ispravljena greška u tekstu prvog zadatka)  \n",
    "Zadnji put ažurirano: 1. 10. 2021.*\n",
    "\n",
    "(c) 2015-2022 Jan Šnajder, Domagoj Alagić  \n",
    "\n",
    "Rok za predaju: **6. studenog 2022. u 23:59h**\n",
    "\n",
    "------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upute\n",
    "\n",
    "Druga laboratorijska vježba sastoji se od šest zadataka. U nastavku slijedite upute navedene u ćelijama s tekstom. Rješavanje vježbe svodi se na **dopunjavanje ove bilježnice**: umetanja ćelije ili više njih **ispod** teksta zadatka, pisanja odgovarajućeg kôda te evaluiranja ćelija. \n",
    "\n",
    "Osigurajte da u potpunosti **razumijete** kôd koji ste napisali. Kod predaje vježbe, morate biti u stanju na zahtjev asistenta (ili demonstratora) preinačiti i ponovno evaluirati Vaš kôd. Nadalje, morate razumjeti teorijske osnove onoga što radite, u okvirima onoga što smo obradili na predavanju. Ispod nekih zadataka možete naći i pitanja koja služe kao smjernice za bolje razumijevanje gradiva (**nemojte pisati** odgovore na pitanja u bilježnicu). Stoga se nemojte ograničiti samo na to da riješite zadatak, nego slobodno eksperimentirajte. To upravo i jest svrha ovih vježbi.\n",
    "\n",
    "Vježbe trebate raditi **samostalno**. Možete se konzultirati s drugima o načelnom načinu rješavanja, ali u konačnici morate sami odraditi vježbu. U protivnome vježba nema smisla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Učitaj osnovne biblioteke...\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_2d_clf_problem(X, y, h=None):\n",
    "    '''\n",
    "    Plots a two-dimensional labeled dataset (X,y) and, if function h(x) is given, \n",
    "    the decision surfaces.\n",
    "    '''\n",
    "    assert X.shape[1] == 2, \"Dataset is not two-dimensional\"\n",
    "    if h!=None : \n",
    "        # Create a mesh to plot in\n",
    "        r = 0.04  # mesh resolution\n",
    "        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, r),\n",
    "                             np.arange(y_min, y_max, r))\n",
    "        XX=np.c_[xx.ravel(), yy.ravel()]\n",
    "        try:\n",
    "            Z_test = h(XX)\n",
    "            if Z_test.shape == ():\n",
    "                # h returns a scalar when applied to a matrix; map explicitly\n",
    "                Z = np.array(list(map(h,XX)))\n",
    "            else :\n",
    "                Z = Z_test\n",
    "        except ValueError:\n",
    "            # can't apply to a matrix; map explicitly\n",
    "            Z = np.array(list(map(h,XX)))\n",
    "        # Put the result into a color plot\n",
    "        Z = Z.reshape(xx.shape)\n",
    "        plt.contourf(xx, yy, Z, cmap=plt.cm.Pastel1)\n",
    "\n",
    "    # Plot the dataset\n",
    "    plt.scatter(X[:,0],X[:,1], c=y, cmap=plt.cm.tab20b, marker='o', s=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Linearna regresija kao klasifikator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "U prethodnoj laboratorijskoj vježbi koristili smo model linearne regresije za, naravno, regresiju. Međutim, model linearne regresije može se koristiti i za **klasifikaciju**. Iako zvuči pomalo kontraintuitivno, zapravo je dosta jednostavno. Naime, cilj je naučiti funkciju $f(\\mathbf{x})$ koja za pozitivne primjere predviđa vrijednost $1$, dok za negativne primjere predviđa vrijednost $0$. U tom slučaju, funkcija $f(\\mathbf{x})=0.5$ predstavlja granicu između klasa, tj. primjeri za koje vrijedi $h(\\mathbf{x})\\geq 0.5$ klasificiraju se kao pozitivni, dok se ostali klasificiraju kao negativni.\n",
    "\n",
    "Klasifikacija pomoću linearne regresije implementirana je u razredu [`RidgeClassifier`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html). U sljedećim podzadatcima **istrenirajte** taj model na danim podatcima i **prikažite** dobivenu granicu između klasa. Pritom isključite regularizaciju ($\\alpha = 0$, odnosno `alpha=0`). Također i ispišite **točnost** vašeg klasifikacijskog modela (smijete koristiti funkciju [`metrics.accuracy_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)). Skupove podataka vizualizirajte korištenjem pomoćne funkcije ``plot_clf_problem(X, y, h=None)`` koja je dana na početku ove bilježnice. `X` i `y` predstavljaju ulazne primjere i oznake, dok `h` predstavlja funkciju predikcije modela (npr. `model.predict`). \n",
    "\n",
    "U ovom zadatku cilj je razmotriti kako se klasifikacijski model linearne regresije ponaša na linearno odvojim i neodvojivim podatcima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, RidgeClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (a)\n",
    "\n",
    "Prvo, isprobajte *ugrađeni* model na linearno odvojivom skupu podataka `seven` ($N=7$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seven_X = np.array([[2,1], [2,3], [1,2], [3,2], [5,2], [5,4], [6,3]])\n",
    "seven_y = np.array([1, 1, 1, 1, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [1 1 1 1 0 0 0]\n",
      "accuracy: 1.0\n",
      "score: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "classifier = RidgeClassifier(alpha=0).fit(seven_X, seven_y)\n",
    "\n",
    "# model predict\n",
    "predicted = classifier.predict(seven_X)\n",
    "print(f\"predicted: {predicted}\")\n",
    "\n",
    "# score\n",
    "accuracy = accuracy_score(y_true=seven_y, y_pred=predicted)\n",
    "print(f\"accuracy: {accuracy}\")\n",
    "\n",
    "score = classifier.score(seven_X, seven_y)\n",
    "print(f\"score: {score}\")\n",
    "\n",
    "assert(accuracy == score)\n",
    "\n",
    "# show plot\n",
    "plot_2d_clf_problem(seven_X, seven_y, classifier.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kako bi se uvjerili da se u isprobanoj implementaciji ne radi o ničemu doli o običnoj linearnoj regresiji, napišite kôd koji dolazi do jednakog rješenja korištenjem isključivo razreda [`LinearRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). Funkciju za predikciju, koju predajete kao treći argument `h` funkciji `plot_2d_clf_problem`, možete definirati lambda-izrazom: `lambda x : model.predict(x) >= 0.5`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted lin reg: [ 1.   0.9  1.2  0.7  0.2  0.1 -0.1]\n",
      "predicted override: [ True  True  True  True False False False]\n",
      "accuracy: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "reg = LinearRegression().fit(seven_X, seven_y)\n",
    "\n",
    "# model predict\n",
    "predicted_reg = reg.predict(seven_X)\n",
    "print(f\"predicted lin reg: {predicted_reg}\")\n",
    "\n",
    "# override model predict\n",
    "def predict_override(X):\n",
    "    return reg.predict(X) >= 0.5\n",
    "\n",
    "predicted_override = predict_override(seven_X)\n",
    "print(f\"predicted override: {predicted_override}\")\n",
    "\n",
    "# score\n",
    "accuracy = accuracy_score(y_true=seven_y, y_pred=predicted_override)\n",
    "print(f\"accuracy: {accuracy}\")\n",
    "\n",
    "assert(accuracy == 1.0)\n",
    "\n",
    "# show plot\n",
    "plot_2d_clf_problem(seven_X, seven_y, predict_override)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Kako bi bila definirana granica između klasa ako bismo koristili oznake klasa $-1$ i $1$ umjesto $0$ i $1$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (b)  \n",
    "\n",
    "Probajte isto na linearno odvojivom skupu podataka `outlier` ($N=8$):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_X = np.append(seven_X, [[12,8]], axis=0)\n",
    "outlier_y = np.append(seven_y, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true: \t\t[1 1 1 1 0 0 0 0]\n",
      "predicted: \t[1 1 1 1 0 1 0 0]\n",
      "accuracy: 0.875\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "classifier = RidgeClassifier(alpha=0).fit(outlier_X, outlier_y)\n",
    "\n",
    "# model predict\n",
    "predicted = classifier.predict(outlier_X)\n",
    "print(f\"true: \\t\\t{outlier_y}\")\n",
    "print(f\"predicted: \\t{predicted}\")\n",
    "\n",
    "# score\n",
    "accuracy = accuracy_score(y_true=outlier_y, y_pred=predicted)\n",
    "print(f\"accuracy: {accuracy}\")\n",
    "\n",
    "# show plot\n",
    "plot_2d_clf_problem(outlier_X, outlier_y, classifier.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Zašto model ne ostvaruje potpunu točnost iako su podatci linearno odvojivi?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (c)  \n",
    "\n",
    "Završno, probajte isto na linearno neodvojivom skupu podataka `unsep` ($N=8$):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsep_X = np.append(seven_X, [[2,2]], axis=0)\n",
    "unsep_y = np.append(seven_y, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true: \t\t[1 1 1 1 0 0 0 0]\n",
      "predicted: \t[1 1 1 1 0 0 0 1]\n",
      "accuracy: 0.875\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "classifier = RidgeClassifier(alpha=0).fit(unsep_X, unsep_y)\n",
    "\n",
    "# model predict\n",
    "predicted = classifier.predict(unsep_X)\n",
    "print(f\"true: \\t\\t{unsep_y}\")\n",
    "print(f\"predicted: \\t{predicted}\")\n",
    "\n",
    "# score\n",
    "accuracy = accuracy_score(y_true=unsep_y, y_pred=predicted)\n",
    "print(f\"accuracy: {accuracy}\")\n",
    "\n",
    "# show plot\n",
    "plot_2d_clf_problem(unsep_X, unsep_y, classifier.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Očito je zašto model nije u mogućnosti postići potpunu točnost na ovom skupu podataka. Međutim, smatrate li da je problem u modelu ili u podacima? Argumentirajte svoj stav."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Višeklasna klasifikacija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Postoji više načina kako se binarni klasifikatori mogu se upotrijebiti za višeklasnu klasifikaciju. Najčešće se koristi shema tzv. **jedan-naspram-ostali** (engl. *one-vs-rest*, OVR), u kojoj se trenira po jedan klasifikator $h_j$ za svaku od $K$ klasa. Svaki klasifikator $h_j$ trenira se da razdvaja primjere klase $j$ od primjera svih drugih klasa, a primjer se klasificira u klasu $j$ za koju je $h_j(\\mathbf{x})$ maksimalan.\n",
    "\n",
    "Pomoću funkcije [`datasets.make_classification`](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html) generirajte slučajan dvodimenzijski skup podataka od tri klase i prikažite ga koristeći funkciju `plot_2d_clf_problem`. Radi jednostavnosti, pretpostavite da nema redundantnih značajki te da je svaka od klasa \"zbijena\" upravo u jednu grupu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Vaš kôd ovdje\n",
    "X, y = make_classification(n_samples=30, n_features=2, n_redundant=0, n_repeated=0, n_classes=3, n_clusters_per_class=1)\n",
    "plot_2d_clf_problem(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trenirajte tri binarna klasifikatora, $h_1$, $h_2$ i $h_3$ te prikažite granice između klasa (tri grafikona). Zatim definirajte $h(\\mathbf{x})=\\mathrm{argmax}_j h_j(\\mathbf{x})$  (napišite svoju funkciju `predict` koja to radi) i prikažite granice između klasa za taj model. Zatim se uvjerite da biste identičan rezultat dobili izravno primjenom modela `RidgeClassifier`, budući da taj model za višeklasan problem zapravo interno implementira shemu jedan-naspram-ostali.\n",
    "\n",
    "**Q:** Alternativna shema jest ona zvana **jedan-naspram-jedan** (engl, *one-vs-one*, OVO). Koja je prednost sheme OVR nad shemom OVO? A obratno?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 1 1 0 1 1 0 2 2 2 0 2 2 0 0 2 0 1 2 0 2 1 1 2 0 1 0 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "print(y)\n",
    "\n",
    "# class 1 (index 0)\n",
    "y1 = [1 if i == 0 else 0 for i in y]\n",
    "\n",
    "reg1 = LinearRegression().fit(X, y1)\n",
    "\n",
    "def predict1(X):\n",
    "    return reg1.predict(X) >= 0.5\n",
    "\n",
    "plot_2d_clf_problem(X, y1, predict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S3BzS/1j9lI41nJJhSLYtxWIJvbnhfb3xxvuS1H+7ZdnaufOYduw4qoULpul//s1n5fEU36+tHbfUubUt5bjOLafkGc9pmmI2mAWifc2/ljW9xamRAlR8r2iA05KcSkkkLP3N//w3HW9sk9QbNvpYZ84AnHn76R9s3LRfjz/xG33lnmXDWW1eiBzukhKpk54dsxU91i3vRGZFik3f2ozBGEnNv4oRQQRIg3u0R7HmSFqzIu4qz4A/27hpnw4eah5UDbZt6/nnt2rFF65WZWXZoB4jX8VOxdI69SVDip2KEkSKzFC7ibKeo7ARRIA0+KaUqXtnR8pxrmCJ3KNKBvz5S7/ZJtM0+mc5MmXbtl57baduumn+oO4PZFNZY1TmjIMZ348FoiMbQQRIg8vvln9Whbp2hAYeZErlcyuTXuXS3BwadAiRJJdpqvVUeND3z1fuyhJF0nlabMk9auAZJzinb7t5bRqb8X1p/jWyEUSANJVeGJDhMhR+LySdeXmpLZl+lwILq1QyOvnVMuVlvv6FqINh2bb8/uJ7I/ZO9Cv8bnvKdSJGiSHvBJrG5ZsPWpoTKJA5ggiQJsMwVHpBQL6pZYoc7VYiFJNMQyXVXpXUeNPq93HVVTO1ecuBQddgWbauuvKCQd8/X5klpsrmBBXe0pZ0XNnlo7hiZhgN9lTKmew9e/qvWAEGgyACZMhwm/JNHtxi0WuWztI//3+vqKsr2t+wLF2maWju5ZNVVzd6UMfOd6VTyyVJ4XdOz4ycMeNkuA2VXV4pH4tUh03fAtH1W64e4iPVcsUKhoQgAuRQaalHDz7wOX39b55RImGlvV7ENA2NHVupv/6rT2e5QmeVTi2Xb6JfkcNdZ7d4ryuV4S7eZm5DMeiN2VggijxBEAFybM7sSfred2/Xj5/6T/3hD3v6w0gg4NOSj81SNBrTK6++p2g0LkkqL/fqhmWX69ZbPqJAoPjXRxhuU77TsyNILhDYI/t45m3ND2xazHoO5A3DznR+OIdCoZCCwaCOHj2qiooKp8sBhl1bW5cam9pU4napvn50f9fU7u6ojje2yTQNjR83qii7qWJoyo+vp5EX8laoq0ujb7lN7e3tKd+/eXUDHFRZ6Vdl5bnrHkpLPZo6pcaBipBLg10sau/ZQwhB0SCIAIAD+haLHti0OOP77jBZIIriQRABgBzra/5V3+RTvTJfq8HGbigmBBEAyEAgsEf2nswXiJ7pwG4WiwJ9CCIAkIb+vhvvXy2pdkiPRfMv4AMEEQAjymD6bkga0u6wAAZGEEFRsmKWrO6EDNOQWeZKq/06il/frMbBUOYLRGn+BWQHQQRFJdERU9euDkWOdElW722mz5RverlKZwTYp2QEG+oCUQDZQRBB0YidjKj9dy29+5Sc0abP6rHUtT2kaEOPgovH0Cq8wAUCg1soyu6wQH4iiKAo2HFLod+flOIDNwqOt0bV+U6bAvOqclgZhlPfdvOZ2GH2/ptggSiQnwgiKAqRI92yo1bqcYe6VHZppUwPsyKF5MwrVjINFPTcAPIbQQRFIXK0K72BlhRt7GE7+RzqCxFDxRUrQHEiiKAo2LH09260Y6lnTjA8+haI7jCvHtLjLDu+kStWgCJFEEFRMH2mZOisRaoDjvUW/mkZy7K15e0D2rHjiBIJW5MmjdFHr5opr7ckK8cbTO8Nc8bB/gWinB4BMBCCCIqCd1KZog09KccZbkOecaU5qCh7tm07rIf+fr2amtrlcvWGqkTC0vcfe1H/9UtL9alPXj5sxxpK340dW2pZIAogJYIIioJnnE9muVtWOJ50VqT0gsLuJbJt+xGt/tpPlEj0/p9MJD44zdTVFdH3Hn1e0Whcn1m+YMjH6gshv+7MfIGoxCJRAOkhiKAoGKah4NVj1P5as6yuxId+KMmWvBNLVXpRwJH6hoNt2/rud59TImHLtgdOW//0f36rpUsuVmVlmaTeQGHOOJj58Y7vUeJ9uokCyC6CCIqGq8ytymtr1XMgrJ59nb2BxJBKqr3yTS+XZ5yvoFu9b99xVEeOnkw5zrJsvfDiu7r1lkUfLBbdkvkmbcuOHx5MmQCQEYIIiorpMeWfGZB/ZqB/1qCQw0efssao9m88LNM0ZFnJV+Tatq33tx5R4EujWSwKIO8RRFC0iiGASL2XwNrH98i2mmWkc1mQJNs+ofUsFkUGOnviemH7SW093KG4ZWvSaJ9unFOtuiqf06WhyBFEgDx1VjfR44c1zVWhhNWY8n6mIU0rC9D8C2l7cftJffu5A4rFbcmQbFt6Y6/01IZG/dHl1br3ExPldhX+Ze/ITwQRIAsG03fjw/qvWDkdKOZPqVBNwKMTHckf27alG+eMGfLxMTK8vvuU/vZX+z+44fSkW98ZwF+93SzbtvXXN0zJfXEYEQgiwDDrWyA6VPVNZ1+xYhqG7r1+or7273uT9m7700VjVRv0Dvn4KH6WbevR3yRflGxL+tXWFt28YKwmjynsHjzITwQRYBj17Q6bre3mPzpjlL6xfJr+3/84oK6oJZfxQSAxJH1+0Th9cfGErBwbxWfLwZCaQqln71yG9Mu3m/WXn5iYg6ow0hBEgPMIBPZkfB97z55B7Q6bqaUXVWnR9KB+816rth3pVMKyNbm6VJ+cPUZVZdlp8Y7itL+5W8bpNSHJJGxpf3OaG0sCGSKIAGc4c4Fo5mpztkDUV+LSjXOqdeOc6pwcD8XJZRpp7c8kSW6zOK5CQ/4hiKAoDXax6IcXiALFbHZ9IK0cYhq9Y4FsyEkQeeyxx/TQQw+psbFRc+bM0aOPPqqFCxfm4tAYgfr6bmTqYGjxOQtEgWI2o9avC8eVaU9jWMn65BmG9Clm35AlWQ8izzzzjFatWqUf/OAHuuKKK/TII4/o+uuv1+7du1VTU5Ptw2OE+WCxaObtyetF8y+MPF+9YbL+4l92Khq3Bgwjd398okaXs/4I2WHYyXbPGgZXXHGFFixYoO9///uSJMuyVF9fr3vuuUerV69Oet9QKKRgMKijR4+qoqIim2XCYYPdmO3D1m/J3ToNoFi839SlNev3a++JbplG7wxIwpKCpW79xTX1umE2fWmQmVBXl0bfcpva29tTvn9ndUYkGo1q8+bNuv/++/tvM01T1157rd54441sHhoFpG+B6IFNi4f0ODtMmxACDMKMWr/++YuztLMhrHeO9LZ4nzjap6umV9JRFVmX1SDS0tKiRCKh2tqzd/6sra3Vrl27zhkfiUQUiUT6vw+FQtksD3mgr/lXfZNvyKdG2NgNGDzDMHTxhHJdPKHc6VIwwuTVVTNr1qzRN77xDafLwCAEAntk78l8geiB3Yuz1vwLAJD/shpExowZI5fLpaamprNub2pq0tix57bAvv/++7Vq1ar+70OhkOrr+Zyb7/oWiO4wa1MP/hB2hwWAkS2rQcTj8WjevHl6+eWXtXz5ckm9i1Vffvll3X333eeM93q98nrZI8Mpg+m90b87bNNbnBoBAGQs66dmVq1apRUrVmj+/PlauHChHnnkEYXDYd1xxx3ZPjTS1LdYNFMHQ4u1w6T5FwBg8LIeRG699VY1NzfrgQceUGNjoy677DI9//zz5yxghTP6QshguonWi1kQAMDQZL2PyFDQRyS5wWzM9mHZ3CkWADAy5U0fEWRP3wLRoSKEAACcRBApMGfuDssVJwCAQkcQcchgF4hKYndYAEDRIIg44MwFopladnwju8MCAIoGQWQIBtN3w5xxsL+lOYECADDSEUQGqW+PlIxxlQoAAP0IIoNQfnw9i0UBABgGIyqIlDVGZc44OKTHsPfsYbEoAADDZMQEkb5TKTu2DK2j67Ljh1nbAQDAMBkRQaSv+Vc9G7MBAJBXCiqIBAJ7ZO/JvK056zkAAMhPBRFEyk7EVB4+vUD0+OGM78+pFAAA8pPpdAHpMOyXWSAKAEARKogZkcQ+n5b5CSEAABSbgpgRAQAAxYkgAgAAHEMQAQAAjiGIAAAAxxTEYlUAyMSpcEy/f79NHZG4qvwlumpGpcp9vNwB+YjfTABFoyuS0CMvHtJLO04qYUumIVm25HEb+uy8Wn35YxPkdjERDOQTggiAotATS+i//2S3djeGZdm9t/X9GY3beubNRjWc6tE3PjNdLtNwrlAAZ+GjAYCi8O8bm7TrjBDyYbak1/e06dVdrTmtC0ByBBEABS9h2fr55hOyBwghfUxD+vmmE7kpCkBaCCIACl5TKKqWzljKcZYtbT/WKStVYgGQMwQRAAUvkUg/WNiSrIHO3wDIOYIIgIJXXVEirzu9Bahjgx6unAHyCL+NAAqer8SlZZeOkStFFjEkfWZuTU5qApAeggiAovCnHxmnUq9LA12Z6zJ7Z0NuvKw6t4UBSIogAqAojKv06tHPX6iqshJJ6g8kfX9OrPLpe5+/UAE6rAJ5hd9IAEVjWo1fz/zFbP1uT5te29Wq9u64RpeX6LpZY7RgaoVMg0ZmQL4hiAAoKiUuU9dcVKVrLqpyuhQAaeDUDAAAcAwzIkABiCUs/W5Pm17Z2Xu6oarMrWtnjdaiaZXsmwKgoBFEgDy370SXvvpve9TcEevfTdY0pN/uPKW6UV49dOsFmjDK53SZADAonJoB8lhTKKKv/HiXTp5uX/7hXWWPt0V0z493qb0r7lCFADA0BBEgjz39ZqPCkcSAO8ombKm1M6ZfvM1GbgAKE0EEyFPRuKX/eKdFqbZRsWxp3eYTstnIDUABIogAeao1HFN3zEp7bE+aYwEgnxBEgDzlzvBqGK6eAVCICCJAnhpdXqJxQY9SxQvTkC4c55fHza8zgMLDKxeQpwzD0Ofm16YcZ9nS5+alHgcA+YggAuSx5XNrdEld+YA7yhqGdOX0oK6dNTq3hQHAMCGIAHnM4zb1939ygW6cU92/ZqQvlHjchm5eUKtvfnY660MAFCw6qwJ5zlfi0l/dMFl3fqxOf9jbpo6euCr9bl01Y5TKvC6nywOAISGIAAUi6HfrhtljnC4DAIYVp2YAAIBjCCIAAMAxBBEAAOAYgggAAHAMQQQAADiGIAIAABxDEAEAAI4hiAAAAMcQRAAAgGMIIgAAwDFZCyLf+ta3dOWVV8rv96uysjJbhwEAAAUsa0EkGo3q5ptv1l133ZWtQwAAgAKXtU3vvvGNb0iS1q5dm61DAEBOROKW3m/sUjRhaVzQq3GVXqdLAooGu+8CwAC6own96PcN+uXbzeqMJPpvv3xiQF9cPEFz6gM5rSccSejF7Sf14vYWtXXFFfS79YlZo3X9JaNV7uPlHIUpr/7lRiIRRSKR/u9DoZCD1WAkOd4W0d4TXZKkC2r9qg3yiXek64om9JUf79L7TV2y7LN/9s6RDn3lx7v0v5ZP05ILq3JSz3sNnfrqM3vU0dMbiGxJx9oieq8hrH/+3TE9dMsFmjWhPCe1AMMpozUiq1evlmEYSb927do16GLWrFmjYDDY/1VfXz/oxwLSsb+5S3/1zG7d+vi7+vrP9urrP9urW/7xXa3+6R4dOtntdHlw0BOvHD1vCJEky5ZsW/rmL/erNRzLei2N7RGt+sludUYSstUbQs7UFUnovqd3q+FUT9ZrAYZbRjMi9913n26//fakY6ZOnTroYu6//36tWrWq//tQKEQYQdbsOh7WX/54l6Jx66zbbUkb9rVr6+EOPfaFizStxu9MgXBMOJLQr99tPm8I6WNLSli2fv1Os75w5fis1vPTjU3qiVkD1mPZUiRm6acbm/SV6yZltRZguGUURKqrq1VdXZ2tWuT1euX1MiWO7LNsWw+s26to/Pwv7pYt9cQsPbBun/71y5fIMIzcFwnHvH04pGg8SQo5zbKl13efymoQiZ8OO8lCkSQlbOnX77Zo5cfr5XbRIgqFI2trRA4fPqzW1lYdPnxYiURCW7dulSRNnz5d5eWcx4Sz3trfrsb2aNIxli0dae3R1sMdunxSRY4qQz7ojlqpB50WPmMR6/lE4pbe2Num5o6oSj0ufWRaUGPKPWk/fmdPXF1p1tMTs9TWHc/o8QGnZS2IPPDAA/rRj37U//3ll18uSXrllVe0ZMmSbB0WSMub+9rlMqVEitd3lylt2N9OEBlhqgPpvZGbhjR2gIXNtm3rxxsa9dQbx9UZScgweteVmIb0sQurdN/1k1RRmvol2OPObHbDm+F4wGlZ+xe7du1a2bZ9zhchBPkgEk/vE6YhQ9FY+p+OURxm15ertiJ1GLFs6VNzxpxzu23beuTFQ/rhq0f7L/u17Q/u89quVq38l53q6ImnPIbf49JF48pkpjg7aBjSzLF+BbiMFwWG6IwRwbZtReOW7NPvBuMqvf1vDMkkbFtjaV414piGoTuuTr7uw2VI9VU+LZ456pyfvXOkQ+u2NA94377Tfj/6fUNa9fzxgtqUa0RsW/rj+bVpPR6QT4jOKGq7jof1042NenXXKcUStnwlppZdMlpLLqw65xLI8zElXTdrdLbLRB765OxqNYdi+uffHZPL6F0MKkmGeq+YGRv06h/+5AKVnF4Yatu2uqOWbEn/vrEp5ak/y5bWb23Wf108Qb4SV9JaPn5xlTbsa9dLO04OOOaai6r0iUv4t4rCQxBB0Xr27RP6++cPnfWG0BOz9KutzVr/TovmT67QpgOhAQOJIemz82s1qqwkVyUjz6z46Hh9ZHpQv9h8Qn/Y16ZY3Nb4Sq9umlujay+uUqnHpZ5YQr/a2qyfbTqhhrbehox9YSWVrqil/Se6dXGKRmSmYehrN07RlOpSPfNmo9q7PzilU1Hq1i0LavX5ReNkcnXXsInELW3Y16aWjpj8HpeumBZUFa8FWUEQQVF6+1BIf//8IUnnfipN2JJsW1sOhrRwalBv7j974Wrf36+/dLTuuoY+NiPdzLFl+h+fmnLen3V0x3XvT3Zrb1PXWbenE0L6xFKdcznNZRq6bdE43bqwVlsPdyjUHVdFqVuXTQz0z8pg6Czb1lNnLjJW739PlyFdc/Fo3XvdRNbhDDOeTRSlpzY0yjSU/Ly6IY2tKNH3b7tQP998QjuPh2VIumRCuZbPrdGsCWX0D0FSf/ur/dp3oiuj4HEmQ9KEUZmtQSpxmVowJTjIIyKZvkXGvzhjfU/ff9uELb383kntPdGlf/zCRSrzJj+dhvQRRFB0Qt1xvbm/PeW4hCW9sKNVq5ZN1uwcb16Gwnf4ZLfe2Jf639lATEO6cnolPT/yyNuHO84KIR9m2dKhlm79/39o0F1LmS0dLsznoei0d6W+JLJPT8xKq4Mm8GG/3dma8pLagZiG5DYN3XH1hOEtCkPy800nlOosl2VLv3q7Oe0WAEiNIIKiU+5Lf8q0xGXI4+b0CzLX1hVPe3Go8aE//R6XHrr1As2oZR+jfLL5UChlk0NJ6owkdKCZTTGHC6dmUHRGlZXokgnleq+hM+kaEZcpLblwFOtAMCgVPnd/X5pkzNP9RoJ+t8o8Ll09c5Q+flHvFTfIL/FE+rOjmYxFcgQR5BXbtoclGPzJFWP1Nz/fm3RMwpI+RwMoDNLSi6q0No2GZJYt/c2np2rmuLIcVIWhqK/yat+J7pSLj01DGp/hImMMjCACxzW2R7Ruywk9926L2rvi8pWYWnLhKH1ufq0uGDu4F+/FM0fpC1eO07/84fg5V8/0Nadadf0kXTyeDRgxOFOqSzVvcoXePhQacObNZUgXjS8nhBSIz8yr1UPPHUw6xjSkj84YRU+RYcQaEThq88GQ/uyftuuZNxvV1hWXLak7ZunFHSd155Pv6dm3Twz6se/8WJ3+9rPTzwkb86ZU6Dv/ZaaWz60ZYvUY6R68aarqq3w63ySeaUhjK736fz47LfeFYVA+MatKk0b75BpgUrZvkfHtH03e/h+ZMex0TnI6JBQKKRgM6uS//asq/CzqKjaN7RF94YfbFU1YSfd9+Yc/uUDzh9g3oTUcU2dPXMHSEgX9TARi+IQjCf1sU5PWbTmhk50xSdKoMrc+M7dGn5tfS/OrAtPSGdVXn9mjvSe6+2dT+5qalXld+t+fm85u3GkIdXVp9C23qb29XRUVyZ8vfkPgmHWbTyiWIoSYhvTjDY1DDiJVZSVMpSIryrwu/dlV43XblePUGj4dRPwlcg322l44aky5R//ni7O0cX9Iz21rUVMoonKvS4tnVvW39cfwIojAMc9ta0m5o6hl956+OdkZ0+hyggTyl2kYNCcrEqZh6IppQV0xjQ62ucAaETjCtm21ZdB4rO+TJgCguBBE4AjDMOQrSf+fXzn7OgBAUSKIwDFLLxw14Or0PoakqdWlGhtkyhsAihFBBI757PxapWpOaEu6deFYup8CQJEiiMAxM8eWadX1kyRpwM3D5k4K6OMXj8phVQCAXCKIwFHL59bo27fMOG9vD0PSlkMd+ssf71ZHT/oLWwEAhYMgAse9fbhDbeFzg0bfWZtdx8N6YN2+3BYFAMgJgggc1dET1882NiXdZKqvl8iu4+Gc1QUAyA2CCBz12q5TiqWxnbbLlJ7f1pKDigAAuURnVTjqREdUpmkokaLFasKSmjuiOaoKg7G7MayfbWrSa7tOqSdmqbLMrU/NrtZNc6tVW8GW6QDOjyACR/k9LqWz76Jp9I5Ffvr55iY98uJhuQz1X5J9KhzXUxuO62ebmvTtWy/QnPqAs0UCyEucmoGjrppemXK/Gal3nchVMyqzXg8y9+a+dj3y4mFJOqcvjGVLkbilrz6zhxktAOdFEIGj6kf7tGBKRdIOq6YhjS4v0UcJInnpX95oGLAPjPRBGPnl2825KwpAwSCIwHH33zhFYwKe876ZuQzJ6zb1d388Q24X/1zzTVMoonePdKa1i/Kv3yGIADgXr+xw3Jhyj/7pjou1fG7NWRvhuQzpYxdW6Yd3XKyZ48ocrBADae1Mf1fkUxnstgxg5GCxKvJCpb9E9143SV9eUqcDzd2ybFv1VT5V+kucLg1JlHvTfwkpzWC35XzRGo7phW0tOnoqIo/b0PzJQX1kWlCuZOeiAGSEIIK84ve4NGtCudNlIE11VV7VV3l1tDWStCmdy5CuubgqZ3UNVcKy9cQrR/TTjU2y1LcXkqGfbTqh6kCJ/tfyabq0jquAgOFQeB9RAOQNwzB068KxSUOI1LtG5DNza3JS03D4zouH9PRbTUrYkm339rHp63VzsjOme5/aTadfYJgwI4K81RVN6KUdJ7V+a7NOdERVWuLSx2aO0k1zazS+kgZZ+eLTl1Vrx7GwntvWIkM6K5SYRm8I+esbJmtajd+pEjPyflNX0it8LLv3fx79zWE99oWLclcYUKQIIshLh092696f7FZLR6z/ze2U4nrmrUY981ajVn9qipZdOsbpMqHeWZHVn5qsS+vK9cxbjTp0sqf/Z5dPCui2ReM1b3KFgxVm5tm3T5zVmO18LFvadrRTB1u6NXlMae6KA4oQQQR5JxxJ6N6ndqs13HtFxpnvB32Xia5Zf0Bjyks0f0ow9wXiHIZh6MbLqvWpOWPU0BZRZySh0eUlGlPucbq0jO1sCCcNIWfa0xgmiABDRBBB3nl+W4tOdsaSrjswDOnJ/2wgiOQZwzA0YZTP6TIAFBAWqyLv/Gprc1qLH7cd7VTDqZ4UI4HMzBpflrTT75kuGEt/G2CoCCLIO03tkfTHhti/BMPrprk1KU/NmIY0u76c0zLAMCCIIO94Mmh85S3AJlnIb9Nq/EkvNTYNye0y9JfXTsxhVUDx4lUceefqC0alNTVe6XfrgtrCuCQUheUr103UbYvGyW0aMgzJbRr93VSrAx59708v5LQMMExYrIq885m5NSl3ajWM3nFshIdsMA1DX15Sp1sW1urF7SfV0BaR22Vo/uQKLZwalGnQ4h0YLgQR5J1pNX7d/fF6ff/lI+c0yJJ6Q8ic+oA+v2icE+VhBKn0l+iWhWOdLgMoagQR5KVbFo5VTYVHT/5ngw40d/ffHvC5tHxujVZcNV4eN7MhAFDoCCLIW0surNLHZo7SvhPdau6IqtRj6uLx5QSQ04629mj9O8060tqjEpepyycF9IlZo+X3uJwuDQDSRhBBXjMMQ9Nr/ZrOotR+8YSlf3jhkNa/09K/l4tpSL/d2arHXj6ir904RUsuLJydbgGMbHy0BArMt//joH79ToukD1re9/0ZiVl6cN0+bdjX5kxxAJAhgghQQPY2den57ScH7Dzbd/ujvzki205zwxQAcBBBBCggvzy9M2wytqQjrT3adrQzJzUBwFAQRIACsvdEd9o7w555tREA5CsWqyJnOnrieu9YWNGEpbpRPk2pZp+OTJkZfHQwTZpuAch/BBFkXag7ridePaIXtp1U9IyP8xeO8+vLH6vT/ClBB6srLHPqA9p+tLN/cWoyl9aVZ78gABgiTs0gq9q74vpvP3pPv36n5awQIkm7G7t039N79PJ7Jx2qrvD80WXVSrUG1TR6Qwg7wwIoBAQRZNX3fnNIDW2R836C73tD/d/rD+hUOJbbwgpUbdCrP19aN+DPTUPyuk3dt2xSDqsCgMEjiCBrToVj+u17rUlPI9iS4patX7/bkrO6Ct2ffmScVl0/SeW+3g6qLtNQ33KQqdWl+sc/u0hTq2kAB6AwZG2NyMGDB/XNb35Tv/3tb9XY2Kjx48frtttu09e//nV5PJ5sHRZ5ZMuhUFpXeNi29If323Qbm9ilbfncGn1y9hj95/ttOnKyR26XocsnBXTxeNaFACgsWQsiu3btkmVZeuKJJzR9+nRt375dd955p8LhsB5++OFsHRZ5JBK30h7bHUtksZLi5HGbuuYiWrkDKGxZCyLLli3TsmXL+r+fOnWqdu/erccff5wgMkLUVnjTGmca0oRKX5arAQDko5yuEWlvb1dVFZ/gRorLJgZUE0h9Gs6ypRsvG5ODigAA+SZnQWTv3r169NFH9ed//ucDjolEIgqFQmd9oXC5TENfWjwh6RjT6O0nsnAqvUQAYCTKOIisXr1ahmEk/dq1a9dZ9zl27JiWLVumm2++WXfeeeeAj71mzRoFg8H+r/r6+sz/HyGv3DB7jP7b6ctNz2z02ff36TV+ffuWC2QadAEFgJHIsDPcorO5uVknTyZvQDV16tT+K2MaGhq0ZMkSfeQjH9HatWtlJulRHYlEFIlE+r8PhUKqr6/XyX/7V1X4uRyxkB1s6dazW05ow/52xeKW6kf7dNPlNfrojEq5XVxFDgDFJNTVpdG33Kb29nZVVFQkHZvxYtXq6mpVV1enNfbYsWNaunSp5s2bpyeffDJpCJEkr9crrze9BY4oLJPHlOor103SV5wuBACQV7J21cyxY8e0ZMkSTZo0SQ8//LCam5v7fzZ27NhsHRYAABSQrAWRl156SXv37tXevXtVV3d2S+oMzwYBAIAilbWT87fffrts2z7vFwAAgMReMwAAwEEEEQAA4BiCCAAAcAxBBAAAOIYgAgAAHEMQAQAAjiGIAAAAxxBEAACAYwgiAADAMQQRAADgGIIIAABwDEEEAAA4hiACAAAcQxABAACOIYgAAADHEEQAAIBjCCIAAMAxBBEAAOAYgggAAHAMQQQAADiGIAIAABxDEAEAAI4hiAAAAMcQRAAAgGMIIgAAwDEEEQAA4BiCCAAAcAxBBAAAOIYgAgAAHEMQAQAAjiGIAAAAxxBEAACAYwgiAADAMQQRAADgGIIIAABwDEEEAAA4hiACAAAcQxABAACOIYgAAADHEEQAAIBjCCIAAMAxBBEAAOAYgggAAHAMQQQAADiGIAIAABxDEAEAAI4hiAAAAMcQRAAAgGMIIgAAwDEEEQAA4BiCCAAAcAxBBAAAOIYgAgAAHEMQAQAAjiGIAAAAx7idLiAZ27YlSaGuLocrAQAA6ep73+57H0/GsNMZ5ZCjR4+qvr7e6TIAAMAgHDlyRHV1dUnH5HUQsSxLDQ0NCgQCMgzD6XLyTigUUn19vY4cOaKKigqnyylIPIdDw/M3dDyHQ8PzN3TZeA5t21ZHR4fGjx8v00y+CiSvT82YppkySUGqqKjgF3CIeA6Hhudv6HgOh4bnb+iG+zkMBoNpjWOxKgAAcAxBBAAAOIYgUsC8Xq8efPBBeb1ep0spWDyHQ8PzN3Q8h0PD8zd0Tj+Heb1YFQAAFDdmRAAAgGMIIgAAwDEEEQAA4BiCCAAAcAxBpAgcPHhQX/rSlzRlyhSVlpZq2rRpevDBBxWNRp0uraB861vf0pVXXim/36/KykqnyykIjz32mCZPniyfz6crrrhCb731ltMlFYzXX39dn/70pzV+/HgZhqFf/OIXTpdUUNasWaMFCxYoEAiopqZGy5cv1+7du50uq2A8/vjjmj17dn8Ts0WLFum5555zpBaCSBHYtWuXLMvSE088oR07dug73/mOfvCDH+hrX/ua06UVlGg0qptvvll33XWX06UUhGeeeUarVq3Sgw8+qC1btmjOnDm6/vrrdeLECadLKwjhcFhz5szRY4895nQpBem1117TypUrtWHDBr300kuKxWK67rrrFA6HnS6tINTV1env/u7vtHnzZm3atEnXXHONbrrpJu3YsSPntXD5bpF66KGH9Pjjj2v//v1Ol1Jw1q5dq3vvvVdtbW1Ol5LXrrjiCi1YsEDf//73JfXuDVVfX6977rlHq1evdri6wmIYhtatW6fly5c7XUrBam5uVk1NjV577TUtXrzY6XIKUlVVlR566CF96UtfyulxmREpUu3t7aqqqnK6DBSpaDSqzZs369prr+2/zTRNXXvttXrjjTccrAwjVXt7uyTxujcIiURCTz/9tMLhsBYtWpTz4+f1pncYnL179+rRRx/Vww8/7HQpKFItLS1KJBKqra096/ba2lrt2rXLoaowUlmWpXvvvVdXXXWVLrnkEqfLKRjbtm3TokWL1NPTo/Lycq1bt04XX3xxzutgRiSPrV69WoZhJP368Iv+sWPHtGzZMt1888268847Hao8fwzmOQRQWFauXKnt27fr6aefdrqUgjJz5kxt3bpVb775pu666y6tWLFC7733Xs7rYEYkj9133326/fbbk46ZOnVq/98bGhq0dOlSXXnllfrhD3+Y5eoKQ6bPIdIzZswYuVwuNTU1nXV7U1OTxo4d61BVGInuvvturV+/Xq+//rrq6uqcLqegeDweTZ8+XZI0b948bdy4Ud/97nf1xBNP5LQOgkgeq66uVnV1dVpjjx07pqVLl2revHl68sknZZpMdkmZPYdIn8fj0bx58/Tyyy/3L7C0LEsvv/yy7r77bmeLw4hg27buuecerVu3Tq+++qqmTJnidEkFz7IsRSKRnB+XIFIEjh07piVLlmjSpEl6+OGH1dzc3P8zPp2m7/Dhw2ptbdXhw4eVSCS0detWSdL06dNVXl7ubHF5aNWqVVqxYoXmz5+vhQsX6pFHHlE4HNYdd9zhdGkFobOzU3v37u3//sCBA9q6dauqqqo0ceJEBysrDCtXrtRTTz2lZ599VoFAQI2NjZKkYDCo0tJSh6vLf/fff79uuOEGTZw4UR0dHXrqqaf06quv6oUXXsh9MTYK3pNPPmlLOu8X0rdixYrzPoevvPKK06XlrUcffdSeOHGi7fF47IULF9obNmxwuqSC8corr5z339uKFSucLq0gDPSa9+STTzpdWkH44he/aE+aNMn2eDx2dXW1/fGPf9x+8cUXHamFPiIAAMAxLCQAAACOIYgAAADHEEQAAIBjCCIAAMAxBBEAAOAYgggAAHAMQQQAADiGIAIAABxDEAEAAI4hiAAAAMcQRAAAgGMIIgAAwDH/FxD7MAEOk2idAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "# class 2 (index 1)\n",
    "y2 = [1 if i == 1 else 0 for i in y]\n",
    "\n",
    "reg2 = LinearRegression().fit(X, y2)\n",
    "\n",
    "def predict2(X):\n",
    "    return reg2.predict(X) >= 0.5\n",
    "\n",
    "plot_2d_clf_problem(X, y2, predict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z2Xk4FzCVHn/thMY5dFemJf1uf7e6+6Iq8+elpS6cwS4ZYHJSuiLy8ssva/ny5Vq+fLkkacuWLVq+fLnuueeeVD4skDXaApGkrjMtqT3JazG1uB0DTE5KV0TWrVsnyxrv9RyA0eS5DcVHuy/zAV6Po+60AkBS+MkFONjq2aVyG+NfN60oTw3T8lNfEIZpqWu2uwQg4xFEAAf72MpqxcdZEDGMxHUeVxKJBVNmsEHVMjbQoApMAkEEcLDls0r0yYtrR/24y5Auqi/Sx1ePfg2mXqJBtZ1dMsAUIIgADnfr+nr9zYZZqiwaviMmP8+lP2mu0X2fnE9/SBoxLwSYWo6arArgXIZh6I9XVuu/La/SrsNBneyNqtDn1vJZxfJ73XaXBwCTQhABMoTbZWhlI0P+AGQX1nMBIEk0qAJTjyACAEkghACpQRABgHEQQoDUIYgAQJIIIcDUI4gAAADbEEQAAIBt2L4LTFBnb0Qtb3Tq/ZMD8rgNLWso1rqFFfIxVAwAJowgAiTJtCx995kjevSFtqH3GYb0i9c79c2nDusfN83WpReU2VcgAGQgggiQpG/vaNVPXmof/s7TB9KFBuL6x//Yr3/7+Dytnl2a/uKyyEA0rqffOaV97SEZMrRweqEun19u2xj7xLkybQoGN9ny+EC2I4gASTjSNXBuCDmLdfr/fPPJ9/XDT18ow5j8SbiWlUg5U/G5MsWv3ujUN598X30Rc+g04f//ZUtfz3fr81fP0pWLpqW1ntaa1Wpa1aZgcF5aHxfIJQQRIAk/39UhlyGZ1ujXWJJau8J6vbVXy2YWj/n5+iNx/fqtk9r+ygkdPTUgt9vQylklum5ZlTqCEW1/9YQOdfTLZSRWBD62qkaXzy+X25W9oeQXr3foy798b+jvsbOe7NBAXF94/KAMw9AVCyvSUg8hBEgPggiQhH1toTFDyCBD0oH2vjGDyImeiO58ZI+OnArL0OnVlLil3+/v1u/2dw99HkuJvpS3j/bqzSO9WjOnVP98/dysPGm3LxLXN588POrHLSWek6+2vKfLLijLyucAyFV8NwNJcE1gJWKsOylx09Lf/WSfjneHJQ21mEiSzLP+POz9p//y/MGA/vdTo/+yzmQ7dndpIGqOeY0lKTgQ1869p9JTFIC0IIgASVg0vUjJZBFL0sLphaN+/IV3AzrU0a94Eqsr53xuS/rFrg6dCkUn/o8dbs/xkNxJ/DTyuAztaQulvJ6WumY1luyUub8x5Y8F5DqCCJCETcsqx73GZUhzqwu0sG70IPKrNzuTCjSjMS3pt+90nf8nwLgSu2Ta1Vu3iZHuQBoQRIAk1JT4dPPaGaN+3GVIbpehz29sHHOXy4meSFK9JqNxuwx1ZeGKyLwav+Jj35mRlGhgnVczetCbLBpUgfQjiABJ+vM1dbr9ygYV5CW+bTwuY+h2Qk2JV9/4s/laPKNozM9RlO/WZPa9mJYlv9c9ic/gTB9ePC2pybRF+W5dvqA8DRUBSBd2zQBJMgxDH19dq+uWVenpPV2JEe8uQ8tmFmtlY4lcScz7WDu/XC8d6jnvGkxLWjsv+34R+31u3X5Vg77a8v6Y19354VmM0geyDEEEmKACr1sfuajqvP7thxdP03eePqL+SHzCt2jchrSysUQN0/LP67Gd7qPLqyVJD+xo1UDUPL3aZChuWvJ7XfqbDbO0YUnqBpq11DXr2qKdMvdvkGpT9jAAPoAgAqSR3+vWv35srv7usX2KmVbSYcRlSLVlPv3jdbNTW6DNPrq8Wh9ePE07dndpX1tIhqQF0wt1xcIK5eel7pZUS12zNq1oZ4w7YAOCCJBmy2eV6KFPLdK23x3Tc/tODYWRkny3rlo8TQNRU0/tPqlILPGBIp9b1y2v0p9/qE7FBdn/Lev3unXdsipJ57fqNFFnzpKhQRWwQ/b/VAMcaE61X/98/Vx190V1rDusPLdLs6blD00M/dyHZ+p4d1guw9CMch+TRFOE2zGA/QgigI3K/Hkq8+ed836/16051X4bKgKA9OJlFgAAsA1BBEBOStyWeU6WsYEJqoCNuDUDIOecaVBllwxgN1ZEAOQUDrQDnIUgAgAAbMOtGWSlvnBcJ4IReVyG6sp8ck/myFsAQMoQRJBVWk8O6N//cExP7e5SLJ4YCFZZlKfrV1brhtW1nFOS42hQBZyHIIKs8fbRXm358V5FYqbiZ41O7+yN6ns7j+q/DnTra5+cr4IsPL0W42OMO+BMvDxEVhiIxvX3P92n8AdCyCDLkt45FtIDO1rTXxxsl9gl084Yd8CBCCLICjt2d6mnf+wTbU1L+uWbnQr2x9JXGABgTAQRZIVn9nTJSKIfNRa39Id3A6kvCACQFIIIskJwIC5rjNWQQYakUDie8nrgHMwNAZyNZlVkhWlFeXIZGvPWjCRZksoLM//L3rQsvXyoR2+0BhW3LDVVFujy+RXy5fHa4mzskgGcL/N/IgOSrl5Sqef2dY97nd/r0po5ZSmvJ5V2HQ7qX584qLZARG6XIUNSzLT09d8c1mfX1+ujy6vtLtERGOMOZAZePiErXHJBmerLfXKP0yfyidW1Gb1q8HprUFt+vFftPRFJUty0FDu9DBQKx/XVlvf105fa7CwRACYkc38iA2fxuAzd98n5qiz2ntO0OjhUdcOSafrLS6env7gpYlmWvvKr92Ra1pj9MN/+7RGdCkXTVxgATAJBBFljeplP3795sT67vl51pYl+ALdLWjazWF/82Fz946amjB71/saRXh0+OTBuH4xpWfrFG53pKQoAJokeEWSV4nyP/vTiOv3pxXWKm5ZchmQks683A+w+2ptcQ66VuBYAMgFBBFkrk1c/RjJeADlbMluZgbOZUVPh9/sU7QhLpiV3SZ7ymwrlLuLXBFKLrzAgQ1xQ408qjLgMaW6NP/UFOVhi2+5OdswkaeBwn3pf7pLMs97ZNqD+vUH5mgpVtLxMRpYFezgHPSJAhljVVKLq4vFnYViWtGlpZRoqcqbBc2V66wghyQgf7Vfvix8IIVJi6I6k8KGQel87lfa6kDsIIkCGcBmG7rx6pqTEhNjR/NmaWtWU+tJTlMO01qxW06o2DrdLkmVZCr3ePe514UN9ivWwEwupQRABMshlF5TrC5vnqMCb+NZ1G4lbMS4j8ee/vKROt1xeb3OV9mGU+8RET4Rl9iVx5IEhDRwMpb4g5CR6RIAMs35hhdbMLdVTu7v0Zmuv4qalxqoCfeSiSlUU5tldHjJIPNlVDkuKB1gRQWoQRIAMlJ/n1qalVdq0tMruUhyjpa5Z1+o5u8vILBPZ2s76OVKEIAIg43GuzPnJq0z+IMC8ytzsO0LqpSXjPvDAA2psbFR+fr4uvvhivfjii+l4WAA5gAbV8+cp88pTnjd297MkGVJ+U2FaakLuSXkQeeyxx7Rlyxbde++9evXVV7V06VJdffXVOnHiRKofGkCWa6lrpkF1kopWlp85kGkUhUvL5Mp3p6ki5JqUB5Gvfe1ruuWWW3TTTTdp0aJF+s53viO/36/vf//7qX5oAMA4PGVela2vkrv0dKOzoaEVEsPrUtGqchXMLbKtPmS/lPaIRCIRvfLKK7r77ruH3udyuXTVVVfpD3/4QyofGgCQJE+ZV2VXVSvWFVG0MzI04t1bl89EVaRcSoNIZ2en4vG4ampqhr2/pqZGe/bsOef6cDiscDg89Peenp5UlgcggyXGuD/HBNUpYhiG8qb5lDeNplSkl6M2ZG3dulWlpaVDbw0NDXaXBMCBGOMOZI+UBpHKykq53W61t7cPe397e7tqa2vPuf7uu+9WIBAYemttbU1leQAyEA2qQHZJaRDxer1auXKlduzYMfQ+0zS1Y8cOrVmz5pzrfT6fSkpKhr0BAIDslfKBZlu2bNGNN96oVatWafXq1frGN76hUCikm266KdUPDQAAHC7lQeQTn/iEOjo6dM8996itrU3Lli1TS0vLOQ2sADCewQZVy9igUG3yU0EBOJdhWZZldxGj6enpUWlpqU7+5Icq8fvtLgeAjZigCmSOnp4e1dfXKxAIjNtm4ahdMwAwEkIIkL0IIgAAwDYEEQAAYBuCCABHY24IkN0IIgAci10yQPZL+fZdADgfiTHubQoGGeMOZDNWRAAAgG0IIgAAwDYEEQCOQ4MqkDsIIgAchQZVILfQrArAMWhQBXIPKyIAHIHbMUBuIogAAADbEEQAAIBtCCIAbEeDKpC7CCIAbEUIAXIbQQSAbQghAAgiAADANswRAZB1zIG4IscHZEZMufJd8k4vkCuP112AExFEAGQNM2oqtKtb4cN9kiXJUOK/rlMqmFsk/5JSGS7D5ioBnI0gAiArWDFTPTs7FOuOJsKHdOa/ptS/r1fx3piK10yTYRBGAKdgrRJAVug/0KvYqbNCyAgixwYUOdKfvqIAjIsVEQC2aKlr1qYV7VNyroxlWRo40JvUtf0HeuVr8E/6MQFMDVZEAKRd4nC7dgWD86bk85l9cZkDZlLXxk5GZFljLJsASCuCCIC0aq1ZraZVbVMWQiRJ5gSDBTkEcAyCCICM5ypwS+7kGlBdfjc7ZwAHIYgAyHiGx6X8Wf7Edt1x5M8pSn1BAJJGEAGQNi11zWos2Slzf+OUf+6C+cUyPMboYcRIrIbkNxVO+WMDOH8EEQBpMdig2lu3KSXnyrgLPSq9vEou3wd+rJ0OJu5ij0rXVcnl5cce4CRs3wWQcilpUB2Bp8yr8o/UKXK0X+Gj/bLCplwFbvlm+pVX42OQGeBABBEAWcVwGfI1+JkVAmQI1igBAIBtWBEBMkA0buq5fd16+p0uBfpjqij06KrF07RmTpncDt+K2lLXrGuLdsrcv0GqtbsaAE5DEAEc7t0TffofP9mnjmBULiMxu8tlSL9955Tqy336yifmaUZ5vt1ljmgqx7gDyE7cmgEcrL0nrL/+0R6d7I1KOjNAdPC/x7vDuuNHexToi9lU4eimeow7gOxEEAEc7NEX2hQKx0edYB63pK7eqH722on0FpakVMwLAZBdCCKAQ0Vipn75eqfi45yLYlrS9ldOcJAbgIxEEAEcqisUVX80uRNlu0JRDSR5bToMTlAFgPEQRACH8kxwN4xTds8kdsk8J8vYkJIJqgCyC7tmAIeaVpSnulKv2gKRMU+tdxnSvFq/vB77X1ckGlTb2CUDIGn2/+QCMCLDMPSxVTXjXmda0sdWjn9dqqXyQDsA2YsgAjjY5hXVWlJfpNHuuhiGdMncUl21eFp6CwOAKUIQARzM63Hpq5+cp01Lq4Z6RgZDiddj6IbmGv3z9XMd0x8CABNFjwjgcPl5bv3tNY265fJ6/f5At4IDMZX5Pbr0gnIV+tx2lyeJBlUA548gAmSIUr9H11xUaXcZ5xgMIb11NKgCmDhuzQA4b2evhADA+SCIAAAA2xBEAACAbQgiAM4LDaoApgJBBMCEEUIATBV2zQCYEMa4A5hKrIgAAADbEEQAAIBtCCIAAMA2BBEAAGCblAWRL37xi7rkkkvk9/tVVlaWqocBAAAZLGVBJBKJ6IYbbtCtt96aqocAkGYtdc1qLNkpc3+j3aUAyBIp2777hS98QZK0bdu2VD0EgDTK5W27VtxSrDsiK27JXeiRu5DJB8BU4bsJwLhaa1araVWbgsF5dpeSVlbMVN87QQ0c7JUVtYben1fllX9RqfKqfGmtx4yaCh/uU/j9PpnhuFw+t3wz/fLN8suVR8sfMpOjgkg4HFY4HB76e09Pj43VIJcc7w7rwIk+SdK8Gr9qStP7CwbOY8VMdT/bofip6Dkfi3ZGFNjZoeKLK+Sr96elnmhXRD2/65QVMYfeZ4biinVF1Pd2QCWXVSpvGl+3yDwTCiJ33XWXvvzlL495zTvvvKMFCxacVzFbt24duqUDpMPBjj59+7etevHgmdBrSFozt1S3XtGgWdMK7CsOtgq9GVC8+9wQIkk6vTgSfLFLeZU+ufLdKa0lHoqpZ2eHrJg14setqKXAc50qv6pG7iJHvb4ExmVYljXyV/YIOjo6dPLkyTGvmT17trzeM2dPbNu2TXfeeae6u7vH/fwjrYg0NDTo5E9+qBJ/el51IHfsOR7S5360R5GYKfMD3wUuQ8rPc+mBv1ioOdW5/bWXi+fKmFFTXT8/JpnjX+tfXCL/wpKU1tO7q1sD7/YOBaARGVL+nCIVLStLaS1AMnp6elRfX69AIKCSkrG/PyYUnauqqlRVVTWp4sbi8/nk87G0iNQzLUv3bD8wYghJfFwaiJq6Z/u7+uGnl8gwjPQX6QC52qAa7QgnFUIkKXy0P6VBxDItDbwXGjuESJIlDRwKqfCiUhmu3Px6RWZK2Rre4cOH1dXVpcOHDysej2vXrl2SpLlz56qoqChVDwsk5cWDAbUFImNeY1pSa9eAdh0Oavms1L7idaJcbVCVNOotkPO51opbihzvl9kfl+FxyVubL1dB8rdyrKgpJVtP3JIVNmVM4PMDdktZELnnnnv0gx/8YOjvy5cvlyQ9/fTTWrduXaoeFkjKC+8G5HZJ8XFe9bpd0vMHAzkXRBK3Y3bK3L9BqrW7mvRzT+AXuds/8rWWZal/b1D9e4PDdtzIkLwzClS0olwu7/g7XQz3BFc3Jno9YLOU7ffatm2bLMs6540QAicIx5JbdzdkKBJNco0eWcNT6U161SK/qfCc91mWpd7XutX3Vs/wECJJlhQ52q/A0ydkRsb/2jI8Lnkq8pKqxV2el1S4AZyEr1jkBMuyFImZGuzNrivzKZk27bhlqbaMvqVcYxiG/IvHWQUzJHeRR94Z5+6sinVGFD4YGv3fWlK8N6a+d5IbUZA/tzip6wrmctsbmYd9Xshqe46H9NOX2vTMnlOKxi3l57m0cck0rVtQMW7vn5RI6hsWT0t1mY6Si7tkRpLfWCizL66+3T2JPd0f3Fnld6tkbeVQY6hlWUP9In37gyP+m2EsKXwopMLFJTI8Y78m9DUUKNJWoMjh/lGv8dYXyDczt3d4ITMRRJC1Hn/thL7a8v6wXpCBqKmf7+rQE693alVjiV4+1DPq7wpD0vWralRemNyyeDbI1V0yo/EvKpG3Ll/97/YqcnxAMi25Cj0qmF0o30y/DI9LVszUwKGQ+g/0ygzFJ/T5rZilWCA67iAywzBU3Fyh/pKg+vf1DhtqZnhdKrigSAULinN2d1cqWHFLkbaB003GRqLJOMXzYnIVQQRZ6bX3e/TVlvclnduQGrckWZZefa9Hq2eX6oWDwxtXB/989YXTdOsVDekt3Ea53qA6Gk+5V8WrKkb8mBkxFdjZMfrgs2Qk2YJkGIb8C0pUMK9Y0Y6wrIgpw+tSXpWP7bpTaMwm44YCFS1LrskYySOIICs98nybXIZGnBEyxJBqS/L0rT9foP/7ygm9czwkQ9KSGUXavKJai2cU8goTYwq+2KV4YBIhRJrwJFTDZchbkz+px8TIBpuMR+zvsaRIa78C3VGVrq/mbJ8pRBBB1unpj+mFg4Fxr4ub0q/f7tKWjY26qCG5ZkBgUCwYVbRt4Pw/gSF56yY2UwSpFe0Ij99kHIyp/50eFV5Ulra6sh2RDlkn0BdL+tqBqKnIBIZXZSsaVCcu0tqfaCQ6X0aiBwXOMXCgd/z/TU9PsLXi/NyYKqyIIOsUTaChLM9tyOvJ7dsvNKieHzM8scbUsxl5horXTJOnjNDnJNGO8Pij9JU4ZDAWiCqvgv/9pgJBBFmnvDBPS2YUafex3jF7RNwuad2C8pzuA8nlMe6TZSTbsGhIriKPXD6XDI9LvhkF8jUUjLtlF+lnTWR2YfLnxWIcfCfAUSZwGPSYPnlx7diNqkr0iHxsVc2UPB5yj6/en9SrZ1lSyeoKla2rVulllcpvKiSEONREGofdhbyOnyo8k7BdWyCs7a+e0K/e6FSgL6b8PJfWLSjXx1bVaF7tueOzk7F2frn+4pI6/fvvj5+ze8ZtJLbwbrl6lhZNZxIlzo+nNE951b6xl/MNyVPhlaecJfxMUDCnUL2vdo990WCTMTNFpgyxHLZ65b0e/eX33tJjL7Spuy8mS1J/1NRv3j6pWx7ercdfO3Hen/uWy+v1L9fPPSdsrGwq0df/dL42r6ieZPWZraWuWY0lO2Xub7S7lIxVfHHFmK+iXX63Sj6UW5N5M5lvpl/uYs/YDas0GU85VkRgm7ZAWHf9dL8icfOc262Dw8W+2vK+ZpT5tKqp9LweY+38cq2dX66uUFS9AzGVFuSp1M+XPbtkpobL51bpFdUaONCr/nd7ZQ0kvnANn0sFc4qUP7eI4VcZxPC4VLq2SoHfdSbmw3xgTD9NxqnBT2TYZvsrJxQdIYSczWVIP3q+7byDyKCKwjxV5NCo9rGwS2ZqufJc8i8sUcGC4jNBJN+V003QmcxV4FbZVdWKtoc18F5oaMS7b0bB0Fh/TC2CCGzzqzc7x20oNa3E7ZuTvVFNKyJIwLkMw5DBcLKsYBiJs2W8tUywTQeiHWxhWZa6JzB4rCs0uTHaAABnIojAFoZhKH8CZzUU+XilORVoUAXgNAQR2Gb9gnK5x7mNbkiaXVWg2lKawyaLBlUATkQQgW2uX1Wj8Y5rsCR9YnUtjX+T1FLXrE0r2tVbt4kQAsBRCCKwzfzaQm25epakxO6YkayYVawrF5WnsarstNg0uB0DwJEIIrDV5hXV+rePXzDibA9D0qvvB/W5H+1VcCD5xlYAQOYgiMB2rx0Oqjt0btAYvGuz53hI92x/N71FAQDSgiACWwUHYvqPl9rHPDtscJbInuOhtNUFAEgPgghs9eyeU4qO17Eqye2SWt7sTENFAIB0YrIqbHUiGJHLZSg+zojVuCl1BCNpqiq7JLbt7pSlDSl9nNipiPoP9Cp8tF+KWTJ8LuU3Fip/TqHcnO8DYBT8dICt/F63rLEOmznNZSSuxcSk61yZ/gO9Cu3qHnZImBU21b8vqP53e1V6aaXyqnwprQFAZuLWDGx16dyycc+bkRJ9IpdeUJbyerJJa81qNa1qUzA4L6WPE2kbSIQQSec0+1iSYpYC/9WpeH88pXUAyEwEEdiqYVq+mptKxpyw6jKkaUV5uowgkrR0jnLv29Mz/kVxSwMHe1NeC4DMQxCB7e7e1KTKYu+IQ83chuTzuPSlP7lAHjdfrk4T74sp1plE744lDRxi1xOAc/GTHbarLPLqezct0uYV1cMOwnMb0uULKvTdmxZpfl2hjRViNOaAmfS1Vjj5awHkDppV4Qhl/jzduWGWPr2uXoc6+mValhoq8lXmz7O7tIwzeLhdb11qG1QlyZWX/BlAhifzzgsyB+IaeL9PZm9MchvKq/bJW5fP2UfAFCKIwFH8XrcWzyiyu4yMla5dMoNcRR65izyK944zgt+QfA3+tNQ0FSzLUuiNgAYO9CYabk/njoEDvXIVuFV8cYXyKtkFBEwFbs0AWSKdDaqDDMNQwbwkgqMl5c/JnIDZ+2q3Bvb3ntkFZGnoz2Z/XIGdHYp2MdcGmAqsiMCx+iJxPfn2ST2xq0MnghEV5Ll1+fxyfXRFtaaX8WrUKXxNhYp2RRR+r+/cD56eK1K0slye0sy4zRbrjig8XmOtKYVe71bZ+ur0FAVkMYIIHOnwyX7d+eO96gxGh2ZknVJMj73YpsdebNNd1zZp44WVdpcJJVZFilaWK2+aT/37gooHz9ymyavyqWB+sbw1+TZWODEDB0PDBrONJnYyolhPVJ6SzAhYgFMRROA4oXBcdz6yV12hqKThvw8Gh59tfeKQKovytKqpNP0FOtBgg6plbFCo1pv2xzcMQ/lNhfI1+mWG4rKiplz5brkKMm8abvRkZNwQMih2KkIQASaJHhE4TsubnTrZGx1z4qphSA//7lj6inKwRINqu3rrNtkSQs5mGIbcRR55yr0ZGUIApB9BBI7z810d474gNS3pzSO9OnZqIC01OVW6xrjnkrxp3qFdMuPxlNsb/IBsQBCB47QHwslf28POBUyt/DlF49+aMSRPpZfbMsAUIIjAcbx5yX9Z+iZwLZAMT2me8ueMM8nXkIqWlqWlHiDb8VMcjvNH88rHPARvUJnfo3k1mTMka6rZMTckVxQuK1PB/OIzt2gMDf3Z5Xer9PIqbssAU4RdM3CcP15Rrf98rWPMawwjcV2uHoSXzjHuucgwDBVeWKqCeUUKH+5LTI51GfJW+5RXy4h3YCoRROA4c6r9uv3KBn1rR+uI4xwMQ1raUKz/vqbOjvJsl+4x7rnM5XOr4IJiu8sAshpBBI708dW1qi7x6uHfHdOhjv6h9xfnu7V5RbVuvHS6vJ7cXA0BgGxCEIFjrVtQocvnl+vdE/3qCEZU4HVp0fQiAshp8d6YBg6FFA9GJVfiZNj8mX4ZPD8AMghBBI5mGIbm1vg1N4ebUs/WUtesj/h36sQvP6RQe9uwUeSRI/0KvR5QcXO5fPU8XwAyAy+dgAwx2KDa+e7FCrWfHuT2wQaauKXg812KHO8/598DgBMRRIAMMDjGvbtgg0Inxh/4FnojIMtK8sAUALARQQRwuLPnhQydDDuOeDCm2EmmzgJwPoIIkEFigWjyJ8MGoqktBgCmAM2qSJvgQEy7j4YUiZuqL89XU1WB3SVlngnM0WLmFoBMQBBByvX0x/TQM6369ZsnFYmfeTm/oM6vT19er1VNpTZW52yDDaqWsUGhWq/yOn2JWy5JrIp4Kn2pLxAAJolbM0ipQF9Mn/3Bbv3i9c5hIUSS9rb16fOP7tOO3Sdtqs7ZPhhCJCl/dmFyJ8NO42RYAJmBIIKU+t9Pva9j3WGZI/zyHNzU8a9PHNKpEP0MZxsphEiS2++R/8IxVpAMSW5DRSvKU18kAEwBgghS5lQoqt/u7hoxhAyyJMVMS794ozNtdWU6//xiFS4vk5F3ugnkrJNh3SV5KltXJU8pqyEAMkPKgsh7772nm2++WU1NTSooKNCcOXN07733KhJhS2GuePX9HsWT6GWwLOn3+7tTXk82KZhTpIpN01V8cYX8C0vkX1yi0iuqVf7hGnnKOJ4eQOZIWbPqnj17ZJqmHnroIc2dO1dvvfWWbrnlFoVCId13332pelg4SDhmJn1tfzSewkoyy2i3ZT7IcBvyNTDKHUBmS1kQ2bhxozZu3Dj099mzZ2vv3r168MEHCSI5oqYkuV0bLkOaUZaf4moyQ7IhBACyRVp7RAKBgCoqKtL5kLDRspnFqi4e/5epaUmbllWmoSJnGwwhvXWbCCEAckbagsiBAwd0//336zOf+cyo14TDYfX09Ax7Q+ZyuwzdvHbGmNe4jMQ8kdWzmSWy2DRkzJtndxkAkFYTDiJ33XWXDMMY823Pnj3D/s3Ro0e1ceNG3XDDDbrllltG/dxbt25VaWnp0FtDQ8PE/z+Co1xzUaU+u75eUiJ0DBr889xqv/7t4/PkYgwoAOQkw5rgEZ0dHR06eXLsAVSzZ8+W15tYWj527JjWrVunD33oQ9q2bZtcrtGzTzgcVjh85mTRnp4eNTQ06ORPfqgSP015mey9zn49/uoJPX8woGjMVMO0fH10ebUuu6BMHje7yKXECbtNq9oUDLIqAiCz9fT0qL6+XoFAQCUlJWNeO+Fm1aqqKlVVVSV17dGjR7V+/XqtXLlSDz/88JghRJJ8Pp98PsZSZ6PGygL99YZZ+mu7CwEAOErKds0cPXpU69at06xZs3Tfffepo6Nj6GO1tbWpelgAAJBBUhZEnnzySR04cEAHDhxQfX39sI9N8G4QkPUSO2Z2yty/QSKnA8ghKbs5/6lPfUqWZY34BuCM1prV2rSinW27AHISXYKAjWhQBZDrCCIAAMA2BBEAAGAbgghgk5a6ZjWW7JS5v9HuUgDANinbNQNgdC11zdq0ol3B4Ca7SwEAW7EiAqTZ4C4ZGlQBgCACAABsRBABAAC2IYgAaUSDKgAMRxAB0iQxxv05WcYGJqgCwGnsmgHSINGg2sYuGQD4AFZEgBTjdgwAjI4gAgAAbEMQAQAAtiGIAClEgyoAjI1mVSBFGOMOAONjRQRIgcGVEBpUAWBsBBEAAGAbgggAALANQQSYYjSoAkDyCCLAFCKEAMDEsGsGmCKMcQeAiWNFBAAA2IYgAgAAbEMQAaYIB9sBwMQRRIBJaqlrlvuCARpUAeA80KwKTMLgLpneOhpUAeB8sCICTMJi05Axb57dZQBAxiKIAAAA2xBEAACAbQgiAADANgQRAABgG4IIcJ5a6pqZHQIAk8T2XeA8cK4MAEwNVkSACWqtWa2mVW0KBtm2CwCTRRABJoDbMQAwtQgiAADANgQRAABgG4IIkKTBc2U43A4Apg67ZoAksEsGAFKDFRFgHDSoAkDqEEQAAIBtCCIAAMA2BBFgDDSoAkBq0awKjIIGVQBIPVZEgBG01DUzxh0A0oAgAgAAbEMQAQAAtiGIAB8w2KDK3BAASD2CCHAWdskAQHqxawY4jV0yAJB+rIgAAADbEEQAAIBtCCKAONgOAOxCEEHOo0EVAOzj6GZVy7IkST19fTZXgmx1pGql1s59V8eCayUNSD0DdpcEABkvGAxKOvN7fCyGlcxVNjly5IgaGhrsLgMAAJyH1tZW1dfXj3mNo4OIaZo6duyYiouLZRiG3eU4Tk9PjxoaGtTa2qqSkhK7y8lIPIeTw/M3eTyHk8PzN3mpeA4ty1IwGNT06dPlco3dBeLoWzMul2vcJAWppKSEb8BJ4jmcHJ6/yeM5nByev8mb6uewtLQ0qetoVgUAALYhiAAAANsQRDKYz+fTvffeK5/PZ3cpGYvncHJ4/iaP53ByeP4mz+7n0NHNqgAAILuxIgIAAGxDEAEAALYhiAAAANsQRAAAgG0IIlngvffe080336ympiYVFBRozpw5uvfeexWJROwuLaN88Ytf1CWXXCK/36+ysjK7y8kIDzzwgBobG5Wfn6+LL75YL774ot0lZYydO3fquuuu0/Tp02UYhn72s5/ZXVJG2bp1q5qbm1VcXKzq6mpt3rxZe/futbusjPHggw/qoosuGhpitmbNGv3qV7+ypRaCSBbYs2ePTNPUQw89pLfffltf//rX9Z3vfEf/8A//YHdpGSUSieiGG27QrbfeancpGeGxxx7Tli1bdO+99+rVV1/V0qVLdfXVV+vEiRN2l5YRQqGQli5dqgceeMDuUjLSs88+q9tuu03PP/+8nnzySUWjUW3YsEGhUMju0jJCfX29vvSlL+mVV17Ryy+/rCuuuEIf/ehH9fbbb6e9FrbvZqmvfOUrevDBB3Xw4EG7S8k427Zt05133qnu7m67S3G0iy++WM3NzfrWt74lKXE2VENDg+644w7dddddNleXWQzD0Pbt27V582a7S8lYHR0dqq6u1rPPPqu1a9faXU5Gqqio0Fe+8hXdfPPNaX1cVkSyVCAQUEVFhd1lIEtFIhG98soruuqqq4be53K5dNVVV+kPf/iDjZUhVwUCAUni5955iMfjevTRRxUKhbRmzZq0P76jD73D+Tlw4IDuv/9+3XfffXaXgizV2dmpeDyumpqaYe+vqanRnj17bKoKuco0Td1555269NJLtWTJErvLyRhvvvmm1qxZo4GBARUVFWn79u1atGhR2utgRcTB7rrrLhmGMebbB3/oHz16VBs3btQNN9ygW265xabKneN8nkMAmeW2227TW2+9pUcffdTuUjLK/PnztWvXLr3wwgu69dZbdeONN2r37t1pr4MVEQf7/Oc/r0996lNjXjN79uyhPx87dkzr16/XJZdcou9+97spri4zTPQ5RHIqKyvldrvV3t4+7P3t7e2qra21qSrkottvv11PPPGEdu7cqfr6ervLySher1dz586VJK1cuVIvvfSSvvnNb+qhhx5Kax0EEQerqqpSVVVVUtcePXpU69ev18qVK/Xwww/L5WKxS5rYc4jkeb1erVy5Ujt27BhqsDRNUzt27NDtt99ub3HICZZl6Y477tD27dv1zDPPqKmpye6SMp5pmgqHw2l/XIJIFjh69KjWrVunWbNm6b777lNHR8fQx3h1mrzDhw+rq6tLhw8fVjwe165duyRJc+fOVVFRkb3FOdCWLVt04403atWqVVq9erW+8Y1vKBQK6aabbrK7tIzQ29urAwcODP390KFD2rVrlyoqKjRz5kwbK8sMt912mx555BE9/vjjKi4uVltbmySptLRUBQUFNlfnfHfffbeuueYazZw5U8FgUI888oieeeYZ/frXv05/MRYy3sMPP2xJGvENybvxxhtHfA6ffvppu0tzrPvvv9+aOXOm5fV6rdWrV1vPP/+83SVljKeffnrEr7cbb7zR7tIywmg/8x5++GG7S8sIf/VXf2XNmjXL8nq9VlVVlXXllVdav/nNb2yphTkiAADANjQSAAAA2xBEAACAbQgiAADANgQRAABgG4IIAACwDUEEAADYhiACAABsQxABAAC2IYgAAADbEEQAAIBtCCIAAMA2BBEAAGCb/wfR5BEDrABl4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "# class 3 (index 2)\n",
    "y3 = [1 if i == 2 else 0 for i in y]\n",
    "\n",
    "reg3 = LinearRegression().fit(X, y3)\n",
    "def predict3(X):\n",
    "    return reg3.predict(X) >= 0.5\n",
    "\n",
    "plot_2d_clf_problem(X, y3, predict3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "# argmax\n",
    "\n",
    "def predict_argmax(X):\n",
    "    predicted = list()\n",
    "    for r in [reg1, reg2, reg3]:\n",
    "        predicted.append(r.predict(X.reshape(1, -1)))\n",
    "    return np.argmax(predicted)\n",
    "\n",
    "plot_2d_clf_problem(X, y, predict_argmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "# ridge model\n",
    "classifier = RidgeClassifier(alpha=0).fit(X, y)\n",
    "\n",
    "plot_2d_clf_problem(X, y, classifier.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Logistička regresija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ovaj zadatak bavi se probabilističkim diskriminativnim modelom, **logističkom regresijom**, koja je, unatoč nazivu, klasifikacijski model.\n",
    "\n",
    "Logistička regresija tipičan je predstavnik tzv. **poopćenih linearnih modela** koji su oblika: $h(\\mathbf{x})=f(\\mathbf{w}^\\intercal\\tilde{\\mathbf{x}})$. Logistička funkcija za funkciju $f$ koristi tzv. **logističku** (sigmoidalnu) funkciju $\\sigma (x) = \\frac{1}{1 + \\textit{exp}(-x)}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (a)  \n",
    "\n",
    "Definirajte logističku (sigmoidalnu) funkciju $\\mathrm{sigm}(x)=\\frac{1}{1+\\exp(-\\alpha x)}$ i prikažite je za $\\alpha\\in\\{1,2,4\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "def sigm(x, alpha=1):\n",
    "    e = np.exp(-alpha*x)\n",
    "    return 1 / (1 + e)\n",
    "\n",
    "\n",
    "alpha_list = [1, 2, 4]\n",
    "x = np.arange(-5, 5, 0.1)\n",
    "\n",
    "# show plot\n",
    "for a in alpha_list:\n",
    "    s = sigm(x, a)\n",
    "    plt.plot(x, s)\n",
    "\n",
    "plt.legend(alpha_list, loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q**: Zašto je sigmoidalna funkcija prikladan izbor za aktivacijsku funkciju poopćenoga linearnog modela? \n",
    "</br>\n",
    "\n",
    "**Q**: Kakav utjecaj ima faktor $\\alpha$ na oblik sigmoide? Što to znači za model logističke regresije (tj. kako izlaz modela ovisi o normi vektora težina $\\mathbf{w}$)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (b) \n",
    "\n",
    "Implementirajte funkciju \n",
    "\n",
    "> `lr_train(X, y, eta=0.01, max_iter=2000, alpha=0, epsilon=0.0001, trace=False)` \n",
    "\n",
    "za treniranje modela logističke regresije gradijentnim spustom (*batch* izvedba). Funkcija uzima označeni skup primjera za učenje (matrica primjera `X` i vektor oznaka `y`) te vraća $(n+1)$-dimenzijski vektor težina tipa `ndarray`. Ako je `trace=True`, funkcija dodatno vraća listu (ili matricu) vektora težina $\\mathbf{w}^0,\\mathbf{w}^1,\\dots,\\mathbf{w}^k$ generiranih kroz sve iteracije optimizacije, od 0 do $k$. Optimizaciju treba provoditi dok se ne dosegne `max_iter` iteracija, ili kada razlika u pogrešci unakrsne entropije između dviju iteracija padne ispod vrijednosti `epsilon`. Parametar `alpha` predstavlja faktor L2-regularizacije.\n",
    "\n",
    "Preporučamo definiranje pomoćne funkcije `lr_h(x,w)` koja daje predikciju za primjer `x` uz zadane težine `w`. Također, preporučamo i funkciju `cross_entropy_error(X,y,w)` koja izračunava pogrešku unakrsne entropije modela na označenom skupu `(X,y)` uz te iste težine.\n",
    "\n",
    "**NB:** Obratite pozornost na to da je način kako su definirane oznake ($\\{+1,-1\\}$ ili $\\{1,0\\}$) kompatibilan s izračunom funkcije gubitka u optimizacijskome algoritmu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linalg\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# model\n",
    "def lr_h(x, w):\n",
    "    sigm_arg = np.dot(w, x)\n",
    "    s = sigm(sigm_arg)\n",
    "    return s\n",
    "\n",
    "# error\n",
    "def cross_entropy_error(X, y, w):\n",
    "    error = 0\n",
    "    for i in range(len(X)):\n",
    "        h = lr_h(X[i], w)\n",
    "        error += ((-y[i] * np.log(h)) - ((1 - y[i]) * np.log(1 - h)))\n",
    "    return (error / len(X))\n",
    "\n",
    "# train\n",
    "def lr_train(X, y, eta=0.01, max_iter=2000, alpha=0, epsilon=0.000001, trace=False):\n",
    "    \n",
    "    # Vaš kôd ovdje\n",
    "    w_list = np.zeros(X.shape[1] + 1)\n",
    "    w_iterations_list = [w_list]\n",
    "    \n",
    "    w_temp = np.zeros(X.shape[1])\n",
    "    \n",
    "    error_last_iter = epsilon\n",
    "    error_iter = 0\n",
    "    \n",
    "    for current_iter in range(max_iter):\n",
    "        if abs(error_iter - error_last_iter) < epsilon: break\n",
    "        error_last_iter = error_iter\n",
    "        error_iter = cross_entropy_error(X, y, w_temp)\n",
    "        \n",
    "        delta_w0 = 0\n",
    "        delta_w = np.zeros(X.shape[1])\n",
    "        for n in range(len(X)):\n",
    "            x = np.insert(X[n], 0, 1)\n",
    "            h = lr_h(x, w_list)\n",
    "            delta_w0 = delta_w0 + (h - y[n])\n",
    "            delta_w = delta_w + ((h - y[n]) * X[n])\n",
    "\n",
    "        w0_temp = w_list[0] - eta * delta_w0\n",
    "        w_temp = w_temp * (1 - eta * alpha) - eta * delta_w\n",
    "        w_list = np.insert(w_temp, 0, w0_temp)\n",
    "\n",
    "        w_iterations_list.append(w_list)\n",
    "\n",
    "    if trace:\n",
    "        return w_list, w_iterations_list\n",
    "    else:\n",
    "        return w_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (c)\n",
    "\n",
    "Koristeći funkciju `lr_train`, trenirajte model logističke regresije na skupu `seven`, prikažite dobivenu granicu između klasa te  izračunajte pogrešku unakrsne entropije. \n",
    "\n",
    "**NB:** Pripazite da modelu date dovoljan broj iteracija."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "seven_X = np.array([[2,1], [2,3], [1,2], [3,2], [5,2], [5,4], [6,3]])\n",
    "seven_y = np.array([1, 1, 1, 1, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E: 0.07751718252492557\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "w_list = lr_train(seven_X, seven_y)\n",
    "\n",
    "# h\n",
    "def h_override(x):\n",
    "    x = np.insert(x, 0, 1)\n",
    "    return lr_h(x, w_list) >= 0.5\n",
    "\n",
    "plot_2d_clf_problem(seven_X, seven_y, h_override)\n",
    "\n",
    "# error\n",
    "poly = PolynomialFeatures(degree=1)\n",
    "seven_X1 = poly.fit_transform(seven_X)\n",
    "E = cross_entropy_error(seven_X1, seven_y, w_list)\n",
    "print(f\"E: {E}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Koji kriterij zaustavljanja je aktiviran?\n",
    "\n",
    "**Q:** Zašto dobivena pogreška unakrsne entropije nije jednaka nuli?\n",
    "\n",
    "**Q:** Kako biste utvrdili da je optimizacijski postupak doista pronašao hipotezu koja minimizira pogrešku učenja? O čemu to ovisi?\n",
    "\n",
    "**Q:** Na koji način biste preinačili kôd ako biste htjeli da se optimizacija izvodi stohastičkim gradijentnim spustom (*online learning*)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (d)\n",
    "\n",
    "Prikažite na jednom grafikonu pogrešku unakrsne entropije (očekivanje logističkog gubitka) i pogrešku klasifikacije (očekivanje gubitka 0-1) na skupu `seven` kroz iteracije optimizacijskog postupka. Koristite trag težina funkcije `lr_train` iz zadatka (b) (opcija `trace=True`). Na drugom grafikonu prikažite pogrešku unakrsne entropije kao funkciju broja iteracija za različite stope učenja, $\\eta\\in\\{0.005,0.01,0.05,0.1\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import zero_one_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "# calculate weight\n",
    "w_list, w_iterations_list = lr_train(seven_X, seven_y, trace=True)\n",
    "assert(len(w_iterations_list) == 2001)\n",
    "\n",
    "# calculate iteration errors\n",
    "E_cross_list = list()\n",
    "E_zero_list = list()\n",
    "\n",
    "for w_iteration in w_iterations_list:\n",
    "    \n",
    "    # calculate cross entropy error\n",
    "    E_cross = cross_entropy_error(seven_X1, seven_y, w_iteration)\n",
    "    E_cross_list.append(E_cross)\n",
    "\n",
    "    # calculate zero-one error\n",
    "    def override_h(x, w):\n",
    "        x = np.insert(x, 0, 1)\n",
    "        return lr_h(x, w) >= 0.5\n",
    "    \n",
    "    # get predicted seven_y\n",
    "    seven_y_predicted = list()\n",
    "    for X in seven_X:\n",
    "        y = override_h(X, w_iteration)\n",
    "        seven_y_predicted.append(y)\n",
    "\n",
    "    # actual zero one loss calculation\n",
    "    E_zero = zero_one_loss(y_true=seven_y, y_pred=seven_y_predicted)\n",
    "    E_zero_list.append(E_zero)\n",
    "\n",
    "# show chart\n",
    "X_os = np.linspace(0, len(w_iterations_list), len(w_iterations_list))\n",
    "plt.plot(X_os, E_cross_list, E_zero_list)\n",
    "plt.legend([\"E cross\", \"E zero\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "eta_list = [0.005, 0.01, 0.05, 0.1]\n",
    "for eta in eta_list:\n",
    "    w_list, w_iterations_list = lr_train(seven_X, seven_y, eta=eta, trace=True)\n",
    "\n",
    "    # calculate cross entropy error\n",
    "    E_cross_list = list()\n",
    "    for w_iteration in w_iterations_list:\n",
    "        E_cross = cross_entropy_error(seven_X1, seven_y, w_iteration)\n",
    "        E_cross_list.append(E_cross)\n",
    "        \n",
    "    # add to chart\n",
    "    X_os = np.linspace(0, len(w_iterations_list), len(w_iterations_list))\n",
    "    plt.plot(X_os, E_cross_list)\n",
    "    \n",
    "# show chart\n",
    "plt.legend(eta_list, loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Q:** Zašto je pogreška unakrsne entropije veća od pogreške klasifikacije? Je li to uvijek slučaj kod logističke regresije i zašto?\n",
    "\n",
    "**Q:** Koju stopu učenja $\\eta$ biste odabrali i zašto?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (e)\n",
    "\n",
    "Upoznajte se s klasom [`linear_model.LogisticRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) koja implementira logističku regresiju. Usporedite rezultat modela na skupu `seven` s rezultatom koji dobivate pomoću vlastite implementacije algoritma.\n",
    "\n",
    "**NB:** Kako ugrađena implementacija koristi naprednije verzije optimizacije funkcije, vrlo je vjerojatno da Vam se rješenja neće poklapati, ali generalne performanse modela bi trebale. Ponovno, pripazite na broj iteracija i snagu regularizacije."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Vaš kôd ovdje\n",
    "\n",
    "clf = LogisticRegression(random_state=0)\n",
    "model = clf.fit(seven_X, seven_y)\n",
    "\n",
    "# show chart\n",
    "plot_2d_clf_problem(seven_X, seven_y, clf.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Analiza logističke regresije"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (a)\n",
    "\n",
    "Koristeći ugrađenu implementaciju logističke regresije, provjerite kako se logistička regresija nosi s vrijednostima koje odskaču. Iskoristite skup `outlier`. Prikažite granicu između klasa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_X = np.append(seven_X, [[12,8]], axis=0)\n",
    "outlier_y = np.append(seven_y, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "clf = LogisticRegression(random_state=0)\n",
    "model = clf.fit(outlier_X, outlier_y)\n",
    "\n",
    "# show chart\n",
    "plot_2d_clf_problem(outlier_X, outlier_y, clf.predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Zašto se rezultat razlikuje od onog koji je dobio model klasifikacije linearnom regresijom iz prvog zadatka?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (b)\n",
    "\n",
    "Trenirajte model logističke regresije na skupu `seven` te na dva odvojena grafikona prikažite, kroz iteracije optimizacijskoga algoritma, (1) izlaz modela $h(\\mathbf{x})$ za svih sedam primjera te (2) vrijednosti težina $w_0$, $w_1$, $w_2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K9kPxITVslB1Xt86LjN8IS4TYzEaP/g1bS0zn1V0I0SXY3V7K69yUNgoX5YGA0ThwlAdaNRSlba+v12qIthqJbSFcNCkPlIUZdd2ipaKzSRjphc7VnuONI2+w5uQabIG1QCIMEdw44EZuHngzWXFZnVcZv18dz1F0oOmjtqD58zVaiMmAhKGQMETdxg+GuIHqpFtCiB5NURQq7R5Kap2U1LgoqXUF90vr92tdlNS4cHja1nqh1UBsmJG4MBPxEeo2LtxIfLiJ+PCG5/WhI6KbdIF0BxJGepGc6hz+cuAvfHj6w+C6L/2j+jN/6HxuHHAjVkMHz02hKFCTD3k7IX8n5O1SWz/cdc2fH5sJScMhcZgaOBKGqqFDulSE6HF8foXyuqbh4vz90sDD7Wv92AuTXquGiQgT8WHGYLiICwQMNWioISPGakSnlXARChJGeoHTVaf50/4/8fGZj4Mr3k5NncqCYQuYmjoVraaD+iLd9kDo2AH5u9UQUld04Xk6EyQNg+RsSB6pbpOGy6BRIXoIt9dPSa2TomonhdWNtjWO4POSWhc+f+v7SWKsBhIjzCRGmkiIMKn7ESYSIxv24yNM0i3STUgY6cHKHGW8sOcF1pxcEwwhM9Nn8r2R32NE/Ij2f0NXLZzbBme+gLNfqAHk/HVSNDo1aPQZD2njIW0sxA0CnfwoCtEdOT0+imvOCxnVgZARKC+rc7VqPIZGA3FhJpIiTWqwCISNxAgTCU32TZj0cht9TyKfAD2Q3WPnb4f/xl8P/hVHYCXW2emz+f6o77fveBCvG859CSfXw5nPoWAvKOf10UakQvrEhvCRMkqmKheiG6l2eMivdJBf5SCv0h7cz69ykF/poNzmbtXrGHVakqJMpERaSI4ykxJlbrS1kBxpJj7ciF7Xs+8aEc2TMNLDbDq3iV9t+xVFNrU7ZGT8SB6c8CBjEse0zxtU5Kjh4+QGyPkUAgNgg6L7Qr+rIGMa9Juq3korTaRCdEmKolBW5w4Gi/wqO3mVjobAUemg1nXpSbfMBi2pURaSIi8MGfXPY61GtDIeQ7RAwkgPUWwrZvn25azPXQ9AWngaPxn3E+b2m3tl/aWKona3HPkXHP03lJ9sejwsEQZeA5kzod80dbIwIUSX4XD7yK2wk1th52y5jXMVds4GnudXOlo1ZXhsmJG0aIv6iLHQJ6ZhPy3aQpTFIOMyxBWRMNLNKYrCeyff49c7fo3NY0On0XHX8Lv4/qjvY9Ff5vonfh+c3QJHPlADSE1+wzGtHtInw8DZMHAOJGXLkvNChJCiKJTWucgttwdDR/3+2Qo7pbUXXyVao4GkCHMwWDQOG31iLKRGW7Aa5aNCdCz5CevGKp2VPL71cTbkbgBgZMJIlk1expDYIZf3gkUHYf8qOPCuuhJtPWM4DLoWsm5UA4jM5yFEp1IUhdJaF6fLbOQEHqdL1VaO3Ar7JefTiDTr6RcXRt9YK33jrOo21kp6jJXkKHOPn91TdH0SRrqprQVbefjzhyl1lKLX6lkyZgl3DbsLXVsXaqsrVQPIvreg+EBDuTkahl6vBpDMWTK3hxCdoMbp4UyjsJHTKHzUXWTshlYDKVEW+sZa6RdnJT2w7RtrpV9sGFFWQyd+FUK0nYSRbkZRFF45+Aq/3/17FBQyozJZPn152+6SURT1Ftwdf4FD7zXcfqs1wOC5MOpbakuI3tQhX4MQvZnPr5BXaedEcR0nS+vICYSO02U2yupa7lLRaiA91kr/+LDgo761Iy3aIq0boluTMNKN2Dw2Hv3iUT45+wkAtw66lZ9P/Hnrx4Z4nLD/Ldj+ctNWkNSxMGY+DL8FrLEdUHMheh+vz8/ZikDoKKnlREkdJ4rrOFVad9FBowkRJvrHh5HZKHRkJoSRHmuVuTVEjyVhpJsoshWxeP1iTladxKA18L+T/pfbBt/Wuoud1bBzJXz5krrqLYDeDNm3wfhF6sRjQojL4vb6ySmzcaKkNtjacbK4jtNldXh8zc/0ZdRrGZAQzsDEcDIDYSMzPpyMeCsRZulSEb2PhJFu4HjlcRavX0yJvYQESwLPzXqOUQmjLn2hvQK2/EHtjnHVqGWRfWDy92H0fGkFEaINFEWhpNbFkcIajhbVcjSwPVlSh7eFacytRh0DE9XQMSgxgkGB/fRYq6yBIkQjEka6uB1FO7h/4/3UemoZEDWAFV9ZQXJY8sUvctvUVpAvfg+uarUsYShM+4naGqKTv7yEuBinx8fx4lqOFtZypKiGo4W1HC2qodLuafb8CJOeQUlq4BiYGM7ApHAGJYaTGmWRib6EaAUJI13YloIt/Hjjj3H5XIxNHMvvZ/+eKFNUyxf4fbDrr7Dp12ArUcuSRsDMpTDkOpkPRIhmVNjcHCqo5kB+NYfyazhSVMOZMhvNNXZoNZCZEM7Q5AiyUiIZmhzB0JRIUqPMMumXEFdAwkgX1TiIzOwzk2dmPoNJd5G7W3K3wX9+BkWBgakxGTDrERhxq4QQIQJKa10czK9WHwXVHMyvIb/K0ey5sWFGslIiGJocGQwfAxPDMRtkEKkQ7U3CSBf0ZeGXTYLIszOfxdBS14qtDD5ZBntfV5+bo9QQMu5u0Bs7rc5CdDXFNU7259W3eKjho7im+VtnM+KsjEiLYkRaFMNSIhmaEkFCuElaO4ToJBJGupjD5Ye5f+P9rQsiRz6AD34C9jL1+Zhvw5zHISy+0+orRFdgd3vZn1fN3nNV7M2tYu+5KopqnBecp9HAgIRwRqRGNoSP1Egi5Q4WIUJKwkgXcq72HIvXL8butTMpZRK/nfnb5oOIoxI++rk6ZwhA4jC48XeQPrFzKyxECPj8CidL6th7rpK956rYk1vF8eLaC8Z4aDUwKDEiEDoiyU6LIislkjCT/NoToquR/5VdRKWzksXrF1PhrGBIzBCen/k8Rl0z3SzntsM7d6uL12m0MO1+dYCqzJYqeqhqh4fduZXsPFPB7rNV7M+rwua+cC2W5Egzo9OjGd03mtHp0WSnRUnwEKKbkP+pXYDX7+X/ffr/OFtzltSwVF6a8xLhxvCmJykKbFsB6x4BvxdiB8DX/wTpE0JTaSE6SEGVgx1nKth5ppIdZyo4VlyLcl6rh9WoIzstitF9oxmTHs3o9BiSo2T9JCG6KwkjXcDvdv+ObYXbsOgtvHDNCyRYE5qe4LbBez+Aw++pz4d/Hb72BzBFdHpdhWhPPr/C8eJadp6pYMeZSnadrWz27paMOCvj+sUyrl8MY/pGMygxHL1O7hIToqeQMBJiH5/5mFcPvQrAE9OeYFDMoKYn1BbDm7dDwR51Ibu5v4KJ96kj8YToZvx+hSNFNWw9Vc6Xp8vZllNBrbPparQ6rYbhqZGM7xfLhIwYxmXEkBghrR5C9GQSRkLoXM05HvviMQC+M+I7XJtxbdMTSo7C69+A6lywxMK33oB+U0JQUyEuj6IoHC+uY+upMrYGwkfVebOYhhl1jO0Xw7h+MUzIiGV0erSM9RCil5H/8SHi9XtZ+vlS7F47YxPH8uMxP256Qt4ueO3r6iJ3sZkw/12IGxCaygrRSoqicLrMxpZT5XwZaP0ot7mbnBNm1DGhfyxTMuOYMiCOYSmR0uUiRC8nYSREXt7/MvtK9xFhiOCp6U+h0zaa1TF3G7x2K7hroc9EuGMVhMWFrrJCXES13cMXp8r47EQpnx4vu2DMh9mgZXy/WKYMUMNHdloUBgkfQohGJIyEwIHSA/xp/58AeHjyw6SGpzYcPLtF7Zpx10G/q+DOt8AU3sIrCdH5vD4/+/Kq+fR4KZ+dKGXvuaomc3wYdVrG9otmSmY8UwbEMSo9CpNeplAXQrRMwkgn8/g9/N/W/8On+JiXMY/rM69vOFi4D17/phpEMmfCt94EozVkdRWiXmG1g03HSvn0eClfnCyj5rxBpwMTw5k+KJ6rBycwqX8sVqP8ahFCtJ78xuhk/zj8D45XHifKFMVDkx5qOFB+qqFrJmO62jVjsISuoqJX8/sV9udXs/FIMeuPlHC4sKbJ8SiLgasGxjN9UDzTByeQFi0/q0KIyydhpBOdqz3HS3tfAuDB8Q8Sa45VD9SVwj++DrZSSB6p3jUjQUR0MpvLy2cnyth4tJiNR0spq2tYVE6jgTHp0cwYnMjVg+MZ2ScanVZuLxdCtA8JI53oNzt+g9PnZGLyRG4acJNa6HXD2wuh6izE9Idv/xPMkaGtqOg1CqocfHK4mA1HS/jyVDlunz94LNykZ8bgBGYPTWTmkATiwmXJASFEx5Aw0km2F25n07lN6DQ6Hp78cMPS5B//HHK3gCkS7nwbwhNDWk/R850ps/HxoSI+OljEvnNVTY71i7NyzdAkrslKZEJGLEa93PUihOh4EkY6gc/v4+mdTwPwzSHfJDMqUz2w86+wcyWggVv/AgmDQ1dJ0WPVTzz20cFCPj5YxNGi2uAxjQbG94vhK8OSmD00iQEJYQ1BWQghOomEkU7wwekPOFpxlAhDBItHLVYLiw/BRz9X9695FAbPDV0FRY+jKAoH82v4TyCA5JTZgsd0Wg1TB8Qxd3gy1w5PkqnWhRAhJ2Gkg7l9bl7Y8wIA9428jxhzDHgc8O4i8Llg0LVw1QMhrqXoKU6W1PKvvQV8sL+wSQAx6rVcPSier45IYU5WItFWYwhrKYQQTUkY6WBrTqyh2F5MojWRO7LuUAvXPQKlRyAsEW76oyx6J65IXqWdD/YV8q99BRxpdAuu2aBl9tBE5o1IYdbQRMJlvRchRBclv506kNvn5uUDLwOwaMQiTDoTnN4MO/6invD1FRCeEMIaiu6qrM7Fh/sLeX9vPrtzq4Lleq2Gqwcn8LVRqXxlWJIsOCeE6BbkN1UHeu/ke2qriCWRWwffqnbPfHC/enD8Ihh4TWgrKLoVl9fHxiMlvLsrj03HS/EF5mDXaGBy/zi+NjqVeSOSpQtGCNHtXFYYefHFF3n66acpKipi1KhR/OEPf2DixIktnv/888/z0ksvkZubS3x8PLfddhtPPfUUZnPPHTjn8XmCrSLfyf6O2iqycRlU5kBEKsz5v9BWUHQLiqJwIL+ad3fl8a99BVTZPcFjo/pE8bXRadwwMoWkyJ77f0kI0fO1OYy89dZbPPDAA6xYsYJJkybx/PPPM3fuXI4dO0Zi4oVzZLzxxhs89NBDrFy5kqlTp3L8+HHuvvtuNBoNzz77bLt8EV3R2rNrKbIVEW+J57bBt0HRQdiiDmTlhmdlYjNxUSU1TtbsyefdXXmcKKkLlidFmrhlbB9uHduHgYmygKIQomdocxh59tln+e53v8s999wDwIoVK/jwww9ZuXIlDz300AXnb9myhWnTpnHnnXcCkJGRwR133MG2bduusOpdl6Io/OPwPwC4Y+gdmLRGWLsUFB8MuwmGzAtxDUVX5PX52XSslDe35/LfYyXBlXBNei1zhydz27g+TBsYL9OwCyF6nDaFEbfbza5du1i6dGmwTKvVMmfOHLZu3drsNVOnTuW1115j+/btTJw4kdOnT/Of//yHBQsWtPg+LpcLl6thXYyampoWz+2Kdpfs5nD5YUw6E98Y/A049hHkfAo6E3zll6GunuhiCqsdrNp+jrd3nqOw2hksH98vhlvH9eH6kSlEmg0hrKEQQnSsNoWRsrIyfD4fSUlJTcqTkpI4evRos9fceeedlJWVcdVVV6EoCl6vl+9///v87//+b4vv89RTT/H444+3pWpdSn2ryA2ZNxCjD1Nv5QWY8gOI6RfCmomuwudX2Hy8hDe25bLxaEMrSIzVwG3j+vCtiX0ZkCDdMEKI3qHD76bZtGkTTz75JH/84x+ZNGkSJ0+e5P777+eXv/wljz76aLPXLF26lAceaJgIrKamhvT09I6uarsoqCvgv+f+C8CCYQtg11+h4hSEJcjkZoLSWhdvbs9l1fZcChq1gkzqH8udk/ry1RHJmPS6ENZQCCE6X5vCSHx8PDqdjuLi4iblxcXFJCcnN3vNo48+yoIFC7j33nsByM7Oxmazcd999/Hwww+j1V64EJfJZMJk6p4rhL538j38ip+JyRMZEJYKn/1WPTBzqQxa7cX251Xx6hdn+Pf+wuDKuNFWA7eO7cMdE/vKYFQhRK/WpjBiNBoZN24cGzZs4OabbwbA7/ezYcMGfvSjHzV7jd1uvyBw6HTqX36KolxGlbsun9/HmpNrALh10K2w61WoK4aodBjT8hgZ0TN5fH4+PljEq1vOsOtsZbB8TN9oFk7px7wRKZgN0goihBBt7qZ54IEHuOuuuxg/fjwTJ07k+eefx2azBe+uWbhwIWlpaTz11FMA3HjjjTz77LOMGTMm2E3z6KOPcuONNwZDSU+xtXArRbYiokxRXJM6FVb/RD0w/Wegl4moeovyOrUr5h9fnqW4Rh2IbdBpuGFkKndNzWB0enRoKyiEEF1Mm8PI7bffTmlpKcuWLaOoqIjRo0fz8ccfBwe15ubmNmkJeeSRR9BoNDzyyCPk5+eTkJDAjTfeyK9+9av2+yq6iNUnVgNwY+aNmPauamgVGT0/xDUTnSGnzMbLn53m3V15uL1qV0x8uIn5k/oyf1JfEmViMiGEaJZG6QZ9JTU1NURFRVFdXU1kZNccd1HhrOCat6/Bq3j55w3vMPgf34CqXLj+tzDh3lBXT3SgPbmV/PnT03x8qIj6/00j+0Rxz7QMrstOkQGpQoheq7Wf37I2TTtZd2YdXsXL8LjhDC4+pgYRSyyMujPUVRMdwO9X2HS8hBWbT7M9pyJYfs3QRL43YwATMmLQyGrMQgjRKhJG2slHOR8BMK//PNj6olo4YREYrSGslWhvHp+ff+0t4E+fnuJ4sTpNu0Gn4ebRadx3dSaDkiJCXEMhhOh+JIy0gyJbEbtLdqNBw1dNaXBuG2gN0j3Tg7i9ftbsyePF/54it8IOQLhJz/xJfblnWn+So2Q8iBBCXC4JI+1g7Zm1AIxNGkvS/nfUwuzbIKL5uVdE9+Hy+nhnZx4vbTpFfpUDgPhwI4uuymT+5L4yTbsQottTPB685eXo4+LQGELzO03CSDsIdtGkzYD3A4sFTvhuCGskrpTT4+OtHed4adMpimrUmVITIkx8f8YA7pzYF4tRBqUKIbo2v9uNt6QUb0lJw6OsDG9paZOHr7ISFIX+77+PecjgkNRVwsgVOldzjkPlh9BpdHyltga8TkgcDmljQ101cRlcXh9vbsvlj5tOUVKrzhGSHGlm8cwB3D4hXSYpE0KEnOL14i2vwFtSHAwZnmDgCISP4mJ8VVWtf1Gdrm3ntzMJI1eofh2a8Unjid3/rlo4diHInRTditfnZ/WefH63/kSwOyYt2sLimQP4xvg+cnuuEKLDKX4/vqqqJi0ZnuLiC1s3ysvB72/Va2oMBvSJieojIaHpI7FhXxcTg6aZ5Vk6i4SRK7QpbxMAs6IGQdE7oDPCyG+GtlKi1fx+hY8PFfHbdcc4VWoD1JaQJdcM5Bvj0jHqQ/efUwjRc/hdLrzFxXiKivAWFeEpamjV8BYX4yktwVtaBh5P615Qp0MfH6+GjKREDMHAkdgQPhIT0EVHd4tpBiSMXIFqVzW7i3cDMKPkrFqYdSNYY0NYK9EaiqLw6Ykynl57lIP5NQDEWA38YOZAFkzpJ90xQohW8zscasgIho1iPMVFeAuL1JaNoiJ1XEYr6eLigmFCDRlJwef6RDV46GJj0fSgJVUkjFyBz/M/x6f4GBg9kD5H1TtqZJKzrm/fuSqe/M8RtgUmKwsz6rh3eib3Tu9PhNwdI4RoxG+zBQOFp7BIDRn1YaMoEDSqq1v1WhqzGUNyMvrkZAxJSeiTki5s2YiLQ2PsfWuZSRi5ApvObQJgZngG2DeCNQ4yZ4SySuIi8qsc/Objo7y/twAAo17Lwsn9WDxzAHHhphDXTgjR2RS3G09xMZ6CQjwFBXgKC/AWFuIpLMJbrHal+GtrW/VaGqsVQ3JyQ9hITkKfFNgGyrWRkd2iyyQUJIxcJo/Pw+f5nwMws6pMLRx2E+jkL+uuptbp4Y+bTvHK5zm4vX40Gvj6mDQevHYIqdGWUFdPCNFBfDU1eAoL8eSrQcNTEAgbgfDhLS2FVizPpg0Px5CSjD4pGX1yEob6bXJKMGxow8MlaFwBCSOXaU/JHuo8dcSaY8k+sVktHHFraCslmvD6/KzacY7nPjlOuc0NwOTMWB65fhgj0qJCXDshxJVQfD68paVqi0ajlg1PQQHegkI8hYX46+ou+ToakwlDSgqG1BT0KSkYUlIxpKQEwkYy+qQkdOHhnfAV9W4SRi7Tl4VfAjA1PAOtcy9EpEDfKaGtlAj67EQpv/jgMCdK1F9GmfFhLL0uizlZifLXixDdgOL14ikqxpOfjycvD09+nrqfrwYOT0kJeL2XfB1dTIwaNtJS1bCRmqoGjlR1XxcbK78TugAJI5epPoxMtqu3g5L1NdD2nJHN3VV+lYNffnCYjw8VAeodMj+ZM5g7J/XFoJPbdIXoKhS/H29pWUPIyMvDnZenho28PDxFRZcOG3q9Ok6jvmUjNTWwn6aGjeRktFZZrLQ7kDByGapd1RwqPwTA5Ny9auHQ60JXIYHT4+Mvn53mhf+exOnxo9NqWDilHz+5ZjBRVhnHI0RnUxQFX1VVoFWjUdjIyw+0cOSjuN0XfQ2NwaC2ZPTpgyEtLbANtGykpaKPj+9Rt7f2ZhJGLsPOop34FT+Z1hSSareBKQr6TQt1tXqtjUeLefyDw5wtV1fTndg/ll/cNJyhyZEhrpkQPZviduMpKMB97hzus7l4zuXizj0XDCB+u/3iL6DVqi0bffoEg4axfr9PH/QJCSGdFVR0Hgkjl2Fr4VYAJmvC1IJBc+QumhA4V2Hn8Q8Osf5ICQBJkSb+97osvjYqVfqAhWgnfpsNd14e7rNn8Zw7p4aNc7lq+CgsvOS05PqEhBbDhiEpKWSrxIquRcLIZQiOFynPUwuGSBdNZ/L6/LzyeQ7PrT+O0+NHr9Ww6Kr+LLlmEOEm+ZEWoi2C3Slnz6otHLm5eHLVrfvcOXxlZRe9XmM2Y0xPx9C3b2Cbrm7T+mBITUFrNnfSV9L5fD4fntZO395DGQwGdO3QVSa/uduoxF7C2ZqzaNEyvugEaPUw8JpQV6vX2J9XxUP/PMDhQnUK98mZsTxx8wgGJkaEuGZCdG3eykrcZ87gPnNW3Z49izv3LJ7cc5e8BVYXFaWGjb59A2GjL8a+agDRJyT0upZIRVEoKiqiKoSr3HYl0dHRJCcnX9HPgYSRNqpfi2aIOZ4I5Qz0nQSWmNBWqhewubz8dt1xXt2Sg1+BaKuBh6/L4rZxfXrdL0IhWuK329WQceZM8OE6cwbPmbOXnLJcn5TUTNjoh7FvOrpIGX/VWH0QSUxMxGq19trfQYqiYLfbKSlRu8pTUlIu+7UkjLTR7hI1jIz1BPpJM2eFsDa9w8ajxTz63iHyqxwA3Dw6lUduGEa8TOEueiHF7cadl98kcNQHEG9x8UWv1aekYMzohzEjA2O/fhgDYcPQp0+P7k5pTz6fLxhE4uLiQl2dkLNY1FmsS0pKSExMvOwuGwkjbbSnZA8AY8py1YLMmaGrTA9XaXPz2L8O8a996loyfWIsPHHzCGYOSQxxzYToWIqi4KusxH3qFK5Tp3HnnMYVCB6evHzw+Vq8VhcTo4aN+sCRkYGxfwbGvn3RWmT5gytVP0bEKvOXBNV/Lzwej4SRzlDrruVYxTEAxtZWqLf0po4Jca16pnWHivjfNQcpq3Oh1cC90zP5yZxBWI3yIyt6DsXvV2+NbRw6Tp3GferURbtVNFYrxox+mDIyMPRTt/XhQxcd3XlfQC/WW7tmmtMe3wv5zd4G+0r3oaCQro8gweeHQdNBJ9/C9lRt9/D4B4dYvScfgIGJ4fz2G6MYlR4d2ooJcQX8Lpc6cPR0IHScPo3r9GncOTkoLlfzF2k0GNLSMGb2x9Q/E2NmZrDFQ5/Y+waNip5NPknboH7w6pjgeJGZoatMD7TxaDEP/fMAJbVqa8h3r87kp3MGYzbIDIuie/Db7bhOncJ1/ASuU6eCocOTl9fifBwao1ENGZmZmDIzMQ4IbDMypFtF9BoSRtqgfvDquAp1DIOEkfZR4/Twyw8O884udd6WzIQwnvnGKMb2lbuURNfkd7nUoHHypBo8Tp7EdeKEGjpaoI2MVENGZiamAZnB8GHo00emNBcdzufzMX36dJKTk1m9enWwvLq6mhEjRrBw4UJ+9atf8eMf/5gvvviCgwcPkpWVxd69ezulfhJGWsnr93K4/DAAoxw2CE+GuIEhrlX3t+10OQ+8vY/8KgcaDdx7VX9+du0QaQ0RXYLi8eA+exbXiRO4TqiBw3XyJO6zZ1ts6dDFx2MaOFB9DMjEmDkAU2Z/dPHx0rUiQkan0/Hqq68yevRoXn/9debPnw/AkiVLiI2N5bHHHgue+53vfIdt27axf//+TqufhJFWOl19GofXgVWjJ8PjhUGTQX6xXDaPz8/z64/zx02nUBToG2vl2W+OYnxGbKirJnohxe/Hk5+P69ixpsHjzBloYYZNbVQUpkGB0DFoUPChj5EWPdE1DR48mOXLl7NkyRJmz57N9u3bWbVqFTt27MBoNALw+9//HoDS0lIJI13RoTJ1ld5higEdQN8pIa1Pd5ZTZuMnq/awL0+9W+Ab4/rw2NeGy1TuolP4bTacx4/jOnYc57GjuI4ew3X8OH6brdnztVYrxvNDx8BBMohUBCmKgsPT8u3WHcli0LXp53DJkiWsWbOGBQsWcODAAZYtW8aoUaM6sIatI7/9W+lg2UEARtRVqQV9J4euMt2Uoii8vfMcj39wGLvbR5TFwFO3ZHNd9uXP2idESxRFwZNfgOvYUZxHjwbDhyf3HCjKBedrDAaMAwdiHjwIY33wGDgIQ2qKrBwrLsrh8TFs2dqQvPfhX8xt05QHGo2Gl156iaysLLKzs3nooYc6sHatJ2GklQ6Wq2FkuL0WjOGQNCLENepequxuHvrnAT4+VATAlMw4nr19FClRcreAuHJ+hwPXiRNq6Dh6DOfxY7iOHcdfW9vs+bqEeMxDhmIeOgTTkMCjf39ZQVb0CitXrsRqtZKTk0NeXh4ZGRmhrpKEkdZw+9wcrzwOwAiXC/peLfOLtMHu3EqWvLGH/CoHBp2GB68dwnenZ6LVShO3aDtfXR2uI0dwHj4cfLhOnW5+QKnBgGnAAMxDBmNqFD70Mo23aEcWg47Dv5gbsvduiy1btvDcc8+xbt06nnjiCRYtWsT69etD3uUon6itcKziGF6/l2j0pHl9Ml6klRRF4ZXPc1j+0VG8foWMOCsv3DmWEWlRoa6a6Ca8lZVNg8ehw+qdLM3QxcVhHjIE09ChavgYOlRt7QgMzBOio2g0mm4xO7Tdbufuu+9m8eLFzJo1i/79+5Odnc2KFStYvHhxSOvW9b97XUCwi8bjRQPqSr3ioqrsbh58Zz/rj6gLd10/MoXlt2QTYZZmcNE8b1lZIHAcCgYPT0FBs+fqU1MwDxvW5GFIlDWLhLiYpUuXoigKy5cvByAjI4NnnnmGBx98kHnz5pGRkcHJkyepq6ujqKgIh8MRnGdk2LBhwTtuOoKEkVaov5NmRF1grQhZj+ai9uRW8qNAt4xRp+XRG4fx7Ul9Q94MKLoOX1UVjgMHcR48gGP/AZyHDuENLEN+PkPfvk2Dx/BhcvusEG20efNmXnzxRTZt2tRkkb/vfe97rF69Othdc++997J58+bg8TFj1M+7nJycDh1bImGkFY5VqovjZbnd6kRnZulmaM753TL94qy8KN0yvZ7f4cB55AiO/ftxHjiI48ABPLm5F56o0WDMzGwaPLKGoouM7PxKC9HDzJgxA6/X2+yxtWsb7gTatGlTJ9WoKQkjl+DxezhVdQqAwW43DBgb4hp1TTaXl/95dz8fHigE4PrsFJ66NZtI6ZbpVRSvF9fJk02Ch+vEiWaXvDf064sleySW7BGYs7MxDxmCNiwsBLUWQoSahJFLOFN9Bo/fQxhadfCqdNFcIKfMxvf+sZPjxXXotRoevWEYC6f0k26ZHk5RFLyFhTj27sWxbz+OAwdwHj6M4nRecK4uIV4NHiOzMY/IxjJiuCx1L4QIkjByCfW39A7yeNGChJHzbDhSzE/e2kut00tChImX5o+VKd17KL/bjfPQIRx79+HYswfH3r3NjvPQhodjzh6BZUQ25pHZWLKz0SclSTgVQrRIwsgl1I8XGeKwgUYLKSNDXKOuwe9X+P3GEzy//gQA4/rF8Mf5Y0mKNIe4ZqK9eIqLcezZq7Z87Nmjtnqcv06LTod56FAso0ZhGTUSc/ZIjBn9ZMZSIUSbSBi5hOMVasvIYLcHEoaCUfq0a5weHnhrL+uPqH8VL5jcj0dvGIZRLx9A3ZXiduM8elQNHnv3Yt+zF29h4QXn6WJjsYwejWXMaKyjR2MeMQKtRWbRFUJcGQkjl1DfTaMOXpUumtOlddz7t52cLrNh1Gv51c0j+Mb49FBXS7SR325XQ8fOXdh37cKxb9+FYz20WkxDhmAZPQrrmDFYRo/GkJ4u3S1CiHYnYeQiKpwVlDpK0RBoGUkZHeoqhdTnJ8r4weu7qHF6SY0ys2LBOEb2iQ51tUQreCsrcezerYaPnTtxHj58wR0uuqioYKuHZfQYLNkj5O4WIUSnkDByEccq1PEi6T4Fq6JAcu9dHO8fW8/wfx8cxudXGNs3mj8tGE9ChCnU1RIt8BQUYN+1K9DysRP3yVMXnKNPTcE6bjzWceOwThiPMTNTWj2EECEhYeQiTlSqgzMHOR1qQWJWCGsTGl6fn1/8+zB/36quB3LLmDSevCUbcxsXZxIdy52Xh33bNmzbtuHYuavZadSNAwYEg4d13DgMqakhqKkQQlxIwshFnK4+DcAAtwci+4Cld01BXW338MM3dvP5yTI0Gvh/c4eweMYA+eu5C/AUFQXCx3bsX355YfjQ6TBnZWEdPx7r+HFYxo2TKdSFEF2WhJGLyKnOASDT44HU4SGuTedqPFDVatTx3O2jmTs8OdTV6rW8ZWXYtm3Dvm07tm1f4jl73nTqej2W7GyskyZinTAB6+jRMt5DCBHk8/mYPn06ycnJrF69OlheXV3NiBEjWLhwId/85jdZvnw5n3/+OWVlZWRkZPD973+f+++/v8PrJ2HkIupbRjI9HkgaFuLadJ7tORV89+87qXZ4SI0y8/Jd4xmeKuvLdCZvZSX27TvU1o/t2y4c86HVYh4+nLBJE7FOmoR17FgJH0KIFul0Ol599VVGjx7N66+/zvz58wFYsmQJsbGxPPbYY7z22mskJiby2muvkZ6ezpYtW7jvvvvQ6XT86Ec/6tD6SRhpQYWzgipXFRoFMjxeSOodg1c/2FfAz97eh9vnZ1R6NC8vHEdihExk1tEUtxv7nr3YvvgC25YtOA8dAkVpco4pK4uwiYHwMWE8uoiIENVWCNEdDR48mOXLl7NkyRJmz57N9u3bWbVqFTt27MBoNPKd73ynyfmZmZls3bqV1atXSxgJldNVaqtIqs+HRVEgqWd30yiKwsufnebJ/xwF4NphSfzuW2OwGGWgakdQFAX36dPYvviCui++wL5jJ4rd3uQc48ABhE2aHOx6kTEfQnRRigIe+6XP6wgGK7RhHN+SJUtYs2YNCxYs4MCBAyxbtoxRo0a1eH51dTWxsR2/xIeEkRbk1KjjRfq73aA1QNzAENeo4/j8Co9/cCh4x8zdUzN49IZh6LQyULU9eSsqsG3diu2LLdi++AJvcXGT47q4OMKmTiVs2lTCpkzFkJQYopoKIdrEY4cnQ3R32v8WtGlmcI1Gw0svvURWVhbZ2dk89NBDLZ67ZcsW3nrrLT788MP2qOlFSRhpQX3LSH9PYBp4nSHENeoYDrePJW/uYf0R9YPxkeuzWHRVf7ljph0oXi+OvXup2/wpti++UCcaa0RjMmEdN46wadMImzYV0+DBsqaLEKLDrVy5EqvVSk5ODnl5eWRkZFxwzsGDB7npppt47LHHuPbaazu8ThJGWtDkTpq0njm/SFmdi0V/28m+c1UY9Vqe++Zorh+ZEupqdWvesjLqPvucuk83Y/tiC/6amibHTUOHBls/rOPGoTXLeBwhuj2DVW2hCNV7t8GWLVt47rnnWLduHU888QSLFi1i/fr1Tf4APXz4MNdccw333XcfjzzySHvXuFkSRloQvJPG7YWEwSGuTfs7U2Zj4crt5FbYibYaeHnheCZkdHy/YE+j+P04Dx6kbvOn1H36Kc4DB5oc10VHEzZ9OuHTryJsyhT0CQkhqqkQosNoNN1iEVW73c7dd9/N4sWLmTVrFv379yc7O5sVK1awePFiAA4dOsTs2bO56667+NWvftVpdZMw0gy7x06hTV2xNNPjgfieFUYOFVRz18odlNW5SI+18Oo9ExmQEB7qanUbvqoq6r74Atunn1L32ef4KiqaHDcPH074jKsJv/pqzNnZaHQyCFgIEXpLly5FURSWL18OQEZGBs888wwPPvgg8+bNo66ujtmzZzN37lweeOABioqKAPW24IQO/kNKwkgzztScASDW5yfa7+9RYeTL0+V89287qXV5yUqJ5G/fmSC37raC++xZajdspHbjBhy794DfHzymDQ8nbNo0wq++mvCrp0vrhxCiy9m8eTMvvvgimzZtwmpt6Nr53ve+x+rVq1m0aBFXXXUVpaWlvPbaa7z22mvBc/r168eZM2c6tH6XFUZefPFFnn76aYqKihg1ahR/+MMfmDhxYovnV1VV8fDDD7N69WoqKiro168fzz//PNddd91lV7wj5daos1tmeNyg0UJsZohr1D4+OVzMD9/YjdvrZ2JGLC/fNZ4oS88cmHulFL8f5/79agD578YLJh0zDRpI+IwZhF19NdYxY9AY5PsohOi6ZsyYgdfrbfbY2rVrg/uPP/54Z1WpiTaHkbfeeosHHniAFStWMGnSJJ5//nnmzp3LsWPHSEy88FZEt9vNV77yFRITE3n33XdJS0vj7NmzREdHt0f9O0RurRpG0j1eiO4H+u6/Ou07O8/x0OoD+PwKc7KSeOHOMbLY3Xn8Lhe2rVup27CR2k3/xVda1nBQr8c6YTwRs68hYvYsDGlpoauoEEL0MG0OI88++yzf/e53ueeeewBYsWIFH374IStXrmz2fuWVK1dSUVHBli1bMAT+emzuNqKupL5lJN3bMwav/mnzKZ76SJ3M7LZxfVh+SzZ6ndxCCur4j9pNm6jbsJG6L75oMvGYNiyMsKunEzH7GsJnXI0uMjKENRVCiJ6rTWHE7Xaza9culi5dGizTarXMmTOHrVu3NnvNv/71L6ZMmcIPf/hD3n//fRISErjzzjv5+c9/jq6FgX0ulwuXyxV8XnPe7ZEd7VztOQD6erwQP6hT37s9KYrC8o+O8qdP1TuD7rs6k6Xzhvb6OUS8lZXUrl9P7dp12L78Eho1XeqTkgifPYuI2ddgnTQRrdEYwpoKIUTv0KYwUlZWhs/nIykpqUl5UlISR48ebfaa06dPs3HjRubPn89//vMfTp48yQ9+8AM8Hg+PPfZYs9c89dRTIeu3goZumu4cRnx+hYfXHGDVDjVYLZ03lO/NGBDiWoWOt6yM2vXrqVm7Fvv2HeDzBY+ZBg8mYs41hM++BvPwYb0+rAkhRGfr8Ltp/H4/iYmJ/PnPf0an0zFu3Djy8/N5+umnWwwjS5cu5YEHHgg+r6mpIT09vaOrCqi39ZY51LECfbzd87Zer8/Pz97Zx/t7C9BqYPktI/nmhM75/nUlnuISaj/5hNqPP8a+a1eThefMw4YRMXcuEdd+BVP//iGspRBCiDaFkfj4eHQ6HcXnralRXFxMcnJys9ekpKRgMBiadMlkZWVRVFSE2+3G2EwzuMlkwmQKzaDR+i6aaJ+PKL8Ccd2rZcTt9fPjN/fw8aEi9FoNz39rNDeMDNGaCSHgKS6h9uOPqFm7Dsfu3U2OmUeOJHLutURcey3GTgq3QgghLq1NYcRoNDJu3Dg2bNjAzTffDKgtHxs2bGhxeeFp06bxxhtv4Pf70QbW3Th+/DgpKSnNBpFQazJexBwNYfGhrVAbOD0+vv/aLjYdK8Wo0/LH+WOZMyzp0hd2c97KSmrXfULNhx9i37GjSQuIZcwYIuZeS+RXviJ3wAghRBfV5m6aBx54gLvuuovx48czceJEnn/+eWw2W/DumoULF5KWlsZTTz0FwOLFi3nhhRe4//77WbJkCSdOnODJJ5/kxz/+cft+Je0keFuv1wtxA9q0NHMo2Vxe7v3bTraeLsds0PLywvFMH9RzJ9/y22zUbtxIzb8/pO6LL5oMQrWMHUvkV79KxNxrMST1/DAmhBDdXZvDyO23305paSnLli2jqKiI0aNH8/HHHwcHtebm5gZbQADS09NZu3YtP/3pTxk5ciRpaWncf//9/PznP2+/r6IdBW/r9XghqXuMJahxerjnrzvYdbaScJOelXdPYGL/nrfOjN/txvbZZ9R8+CG1G/+L4nQGj5mysoi6/joi582TFhAhhOhmLmsA649+9KMWu2U2bdp0QdmUKVP48ssvL+etOl2wm8brgdiuH0YqbW4WrtzOgfxqIs16/r5oEqPTo0NdrXaj+P3Yd+6k+l//onbdJ01WwTX060vU9TcQef11mAb03juFhBCiu5O1ac7TZPbVmIzQVuYSSmqdLPjLdo4V1xIXZuQfiyYxLLVnTMzlPnuW6vffp/r9f+HJzw+W65OSiJw3j8jrr8c8YrjchiuEEK3g8/mYPn06ycnJrF69OlheXV3NiBEjWLhwIQ888ADz589n//79lJeXk5iYyE033cSTTz5JZAdP+ihhpBGXz0WxTb1TSA0jXbdlpLjGyR1//pLTZTaSIk28fu8kBiZGhLpaV8RXU0PNRx9T/d57OPbsCZZrw8OJnPdVIm+4EeuE8Wi0MnusEEK0hU6n49VXX2X06NG8/vrrzJ8/H4AlS5YQGxvLY489hs1m46abbuKJJ54gISGBkydP8sMf/pCKigreeOONDq2fhJFGCusKUVCw+P3E+v1dtmWkcRBJi7bwxncn0S8uLNTVuiyK14vtiy+oeu896jZsRHG71QNaLWHTphF1801EXHMNWrOsLCyEEFdi8ODBLF++nCVLljB79my2b9/OqlWr2LFjB0ajEaPRyOLFi4Pn9+vXjx/84Ac8/fTTHV43CSONFNgKAEj1etHoTBCREuIaXai4xsm3/vwlOYEgsuq+yaTHWi99YRfjOnWKqnf/SfUHH+Ara1iQzjRoEFE330zkDTdgSLpw4UUhhOhqFEXB4XWE5L0tekubuquXLFnCmjVrWLBgAQcOHGDZsmWMGjWq2XMLCgpYvXo1M2bMaK/qtkjCSCOFdYUApHh9aqtIF+sOKKp2csfL3TeI+O12aj5eS9W77zaZkEwXG0vkDdcTffPNmLKyZByIEKJbcXgdTHpjUkjee9ud27AaWv85oNFoeOmll8jKyiI7O7vZBW7vuOMO3n//fRwOBzfeeCN/+ctf2rPKzZIw0kh+nTpQMs3rhdiM0FbmPN05iDgOHqLq3Xeo+feH+Ovq1EKdjvCZM4m+9RbCp09HE1jRWQghRMdauXIlVquVnJwc8vLyyMjIaHL8ueee47HHHuP48ePB5Vn++Mc/dmidJIw0UmirbxnxdqnbehsHkT4xFt78btcPIr6aGqr//W+q3n0X1+EjwXJD375E33YbUTffhCFRumGEEN2fRW9h253bQvbebbFlyxaee+451q1bxxNPPMGiRYtYv359kxbp5ORkkpOTGTp0KLGxsUyfPp1HH32UlJSOG7ogYaSRgrr6MSO+LjN4tbDawR1//pIz5Xb6xKgtIn1iumYQURQFx969VK16i5q1a4OTkmkMBiKuvZbob3wD68QJcjeMEKJH0Wg0beoqCRW73c7dd9/N4sWLmTVrFv379yc7O5sVK1Y0GbjamN/vB8DlcnVo3SSMNNJ4AGtXuK23uwQRv91O9b//TeWbq3AdaWgFMQ0aRPQ3vkHkjTegj4kJYQ2FEEIsXboURVFYvnw5ABkZGTzzzDM8+OCDzJs3j8OHD1NcXMyECRMIDw/n0KFD/L//9/+YNm3aBV057U3CSIDH76HEXgLUh5GMkNansNrBt/78JWfL7aTHql0zXS2IuE7nULnqTarXvIe/thYAjclE5HXXEfOt2zGPHCmDUYUQogvYvHkzL774Ips2bcJqbfgs+d73vsfq1atZtGgRjzzyCC+//DI//elPcblcpKenc8sttzQ7yLW9SRgJKLGX4Ff8GBSFOJ8fYvqFrC5dOYgoXi+1GzdS+eab2Lc2TPFv6NeXmG/dQfTXb0YXHR26CgohhLjAjBkz8DZaULSxtWvXBve3bNnSWVVqQsJIQMN4ES/asAQwtG1QULvVo8rBHS83BJFV900hLTo0dWnMW1ZG5dtvU/XW23iL1Vlq0WoJnzmTmDvuIGzaVBkLIoQQ4rJIGAmoDyMpXi9E9QlNHarUFpHcCjt9Y628ed/kkAcR55EjVPzt79R8+CGKxwOo84JE33YbMbd/U1bIFUIIccUkjAQ0DF71QUJ6579/Fwoiis9H3ebNVLz6N+zbtwfLLaNGEfPtbxMx91q0RmNI6iaEEKLnkTAS0LibhqjODSP5VepdM/VBZNV9k0kNQRDx1dmoXr2aitdew5Orrl6MTkfk3LnE3rUQSwtTBgshhBBXQsJIQP1U8Kmd3E3TFYKIOy+fytdeo+rdd4MzpGqjooj55jeIufNODB040Y0QQgghYSQg2E3j8XVaGMmvcvCtP2/lXIWDfnFW3vxu5wYRx6FDlP/lL9SuXQeBiW2M/fsTe9dCor72NbTWrnEHjxBCiJ5NwgjgV/zBqeBTvV6I7vhumlAFEUVRsG3ZQsUrr2DbsjVYHjZtGrF3LSTsqqvkrhghhBCdSsIIUGovxev3olMUEny+Dh8zkldp546XvwwGkVX3TSYlqmODiOL1UrtuHeV/eQXn4cNqoU5H5HXXEXfvIsxDhnTo+wshhBAtkTBCwwJ5yV4fer0FrHEd9l6dHUT8TifVa9ZQvvKveM6dA0BjsRB9223E3X2X3JorhBAi5CSM0MwcIx00hXlepZ1v/flL8iodZMSpt+92VBDx1dZS+fobVPzjH/jKywHQRUcT8+1vEzP/TlkrRgghRJchgwM4b4G8Dhq82llBxFdVRenvf8/J2ddQ+vzz+MrLMaSmkvTwwwzcuIGEH/1QgogQQvQyPp+PqVOncssttzQpr66uJj09nYcffrhJeXl5OX369EGj0VBVVdXh9ZOWERpu6032dsydNJ0RRLwVFVT89VUqX38dv90OgHHgAOK/9z0iv/pVNAZDu76fEEKI7kOn0/Hqq68yevRoXn/9debPnw/AkiVLiI2N5bHHHmty/qJFixg5ciT5+fmdUj8JI5zfMtK+g1fPVahjROqDyKr7ppAcZW631/eWllK+8q9UrlqF4nAAYBo6lPjFi4n4yhy5M0YIIQQAgwcPZvny5SxZsoTZs2ezfft2Vq1axY4dOzA2mlX7pZdeoqqqimXLlvHRRx91St0kjNDQMpLi9bXrbb0dGUQ8xcWU/+UVqt5+G8XlAsA8YgTxP1hM+KxZaDpo3IsQQoimFEUJ/jHY2TQWS5t+3y9ZsoQ1a9awYMECDhw4wLJlyxjVaHbtw4cP84tf/IJt27Zx+vTpjqhys3p9GFEUpUPGjHRUEPEUl1D+pz9R9c47wYXrLKNHE/+DxYRNny4hRAghOpnicHBs7LiQvPeQ3bvQtGGCSo1Gw0svvURWVhbZ2dk89NBDwWMul4s77riDp59+mr59+0oY6SwbfvACYboIvho2nDVZO0nxtU8YOV1ax50vb6OoxtluQcRbUUH5y3+h8o03gi0h1vHjif/hD7BOniwhRAghRKusXLkSq9VKTk4OeXl5ZGRkALB06VKysrL49re/3el16tVhJN6YTJw5iT6OQ8R5fZgUIPLK5t04VlTL/L9so6zOxcDEcF6/dxJJkZcfRHw1NZT/9a9U/u3vwYGpljFjSLj/fsImT7qiugohhLhyGouFIbt3hey922LLli0899xzrFu3jieeeIJFixaxfv16NBoNGzdu5MCBA7z77ruA2nMAEB8fz8MPP8zjjz/e7vWv16vDiIL6jdb5tWoXTXgy6E2X/XoH8qpZsHIbVXYPWSmRvLZoInHhl/d6vjobla/9g/KVf8VfUwOAefhwEn5yvzplu7SECCFEl6DRaNrUVRIqdrudu+++m8WLFzNr1iz69+9PdnY2K1asYPHixfzzn//E0Wjsy44dO/jOd77DZ599xoABAzq0bhJGAL2iC0x41v+yX2vX2QruXrmDWpeXUenR/P2eiURZ2347rd/ppPLNVZT/+c/4KisBMA0aSPyPf0zEnDkSQoQQQlyWpUuXoigKy5cvByAjI4NnnnmGBx98kHnz5l0QOMrKygDIysoiOjq6Q+vWu8NIoAlKp2jp7/FCwuV10Ww5Vca9f9uJ3e1jYkYsr9w9nghz24KI4vNR/d77lP7+93iLiwEw9utH/JIlRM77Khqd7rLqJoQQQmzevJkXX3yRTZs2YW3UivO9732P1atXN+muCYXeHUYCLSNaRcsAj+ey5hj55HAxP3pjNy6vn+mD4vnzgvFYjK0PDoqiYPv0U0qe+S2uEycA0KemkPDDHxJ1001o9L36n0gIIUQ7mDFjBl6vt9lja9eubbZ85syZwT/aO1qv/qRT8AOgQ8sQt7vNd9K8tSOXpasP4FdgTlYiL9w5FrOh9UHEceAgJc88g33bNgC0UVHEf+97xMy/E63p8seuCCGEEN1Jrw4jgYYRwv0atZumlWFEURT+uOkUT689BsA3x/fhya9no9e1brZT97lzlD73PDX/+Q8AGqORmAXfJv6++9BFRbX96xBCCCG6sV4dRuq7aSJ9oIFWhRG/X+EX/z7Mq1vOAPCDmQP4f3OHtKqfzVddTemLL1L55irweECjIeprXyPh/h9jSE29gq9ECCGE6L56eRjxB/YCLRqXGDPi9Pj42Tv7+HC/On38shuG8Z2rLn0HjuL1Uvn225T9/g/4Aqsfhl11FYkP/gzz0KGXW30hhBCiR+jlYaR+qwO9GaxxLZ5bUuvkvr/vYu+5Kgw6Db/95mi+NurSrRm2rVspfvKp4OBU06CBJD70EOHTprXHlyCEEEJ0e706jARpNBDdT90240hhDYte3UFBtZNoq4GX5o9jyoCWgwuAOzeX4t/8hrr1GwDQRUURf/+PifnmN+UOGSGEEKKRXv2pWD9mBEULCYObPWf94WJ+vGoPdrePzIQwVt41gYz4sBZf02+3U/bSCipefVVdyE6nI+aOO0j40Q/RdfCkMUIIIUR31KvDCPUNIRotxA9pcsjnV/jd+uP8fuNJAKYNjOOPd4676KyqtRs3UvTEE3gL1DElYdOmkbT0IUwDB3ZI9YUQQoieoJeHkfpbcbX4UsdSP0NIhc3N/av28NkJdSrchVP68egNwzC0cOuuJz+fol89Sd3GjQAYUlNJeuRhwmfNkunbhRBCiEvo3WFEqwYFD3qKk2eSCuw9V8UPXttFQbUTs0HL8ltGcvOY5qeJVzweKv7+d0pfeBHF4QC9nrh77iF+8ffRdoNFk4QQQoiuoHWzdPVwfo2eo8V1vLDxBLe9tIWCaif948N474fTWgwizmPHyLn9dkqefgbF4cAyfhyZa1aT+LMHJIgIIYToUnw+H1OnTuWWW25pUl5dXU16ejoPP/wwEFiB+LzHqlWrOrx+vbxlpH6j4Tuv7gwWX5+dwvJbs5td7E7xeCj7858pW/En8HjQRUWR+POfE/X1m6VLRgghRJek0+l49dVXGT16NK+//jrz588HYMmSJcTGxvLYY48Fz/3rX//KV7/61eDzjl6xF3p7GAmEh/oMEWHS8/hNw/n6mLRmg4Xz2DEKHlqK68gRAMKvuYaU/3sMfUJCp1VZCCGEuByDBw9m+fLlLFmyhNmzZ7N9+3ZWrVrFjh07MBqNwfOio6NJTk7u1Lr16jCiCYwZ0Wu0/O5bo5k5OLHZu2UURaHyzTcpWf5rFLcbXVQUSY8+SuT110lriBBC9HKKouB1+y99YgfQG7Vt+hxasmQJa9asYcGCBRw4cIBly5YxatSoJuf88Ic/5N577yUzM5Pvf//73HPPPR3+Wderwwg6LfjVO3xvGt382BBfdTWFjzxK7SefABA+YwYpT/xSWkOEEEIA4HX7+fP9m0Py3vf9bgYGU+tXi9doNLz00ktkZWWRnZ3NQw891OT4L37xC2bPno3VamXdunX84Ac/oK6ujh//+MftXfUmenkY0YEfUNRke37ycx4/Tt73F+MpKACDgaQHf0bMwoXSGiKEEKLbWrlyJVarlZycHPLy8sjIyAgee/TRR4P7Y8aMwWaz8fTTT0sY6UgavQ48alJU7HY0YQ0zq9q2bSfvRz/CX1uLIT2dtGefxZI9IoS1FUII0RXpjVru+92MkL13W2zZsoXnnnuOdevW8cQTT7Bo0SLWr1/f4h/ZkyZN4pe//CUulwuTydQeVW5W7w4jgUnMNGjwVVWhDYSR6n9/SOHSpSgeD5axY0n/44sylbsQQohmaTSaNnWVhIrdbufuu+9m8eLFzJo1i/79+5Odnc2KFStYvHhxs9fs3buXmJiYDg0i0MvDSP2kZ2g0eCsq0aemUrFyJSVPPwNAxLXXkvr0b9B28D+CEEII0dGWLl2KoigsX74cgIyMDJ555hkefPBB5s2bx4EDByguLmby5MmYzWY++eQTnnzySR588MEOr5uEEUCj0eI6dpTq99+n8rXXAIi9ayGJP/85Gq3MCyeEEKJ727x5My+++CKbNm3C2mhizu9973usXr2aRYsW8eCDD/Liiy/y05/+FEVRGDhwIM8++yzf/e53O7x+vTqM1N/aq0FD4SOBQTsaDYk//x/i7r47dBUTQggh2tGMGTPwer3NHlu7dm1wf968eZ1VpSZ6dRihvtWjfvIzo5HU3/yayEYzzwkhhBCiY/XqMKINtIxodXqib7+dmDvvwDxkSIhrJYQQQvQuvTqMUH83jVZHyuP/F9q6CCGEEL1Urx6dqQ3e2iuEEEKIULmsMPLiiy+SkZGB2Wxm0qRJbN++vVXXrVq1Co1Gw80333w5b9vudMZAw5AS2noIIYQQvVmbw8hbb73FAw88wGOPPcbu3bsZNWoUc+fOpaSk5KLXnTlzhgcffJDp06dfdmXbm96kLoqnkTQihBBChEybw0j9Pcf33HMPw4YNY8WKFVitVlauXNniNT6fj/nz5/P444+TmZl5RRVuT/rAZGYaySJCCCFEyLRpAKvb7WbXrl0sXbo0WKbVapkzZw5bt25t8bpf/OIXJCYmsmjRIj777LNLvo/L5cLlcgWf19TUtKWaraY3GwHnZQ0aqasopywvl7qKcly2OgA0Wi3WyCjComOISkomIi5BFtUTQgghLqFNYaSsrAyfz0dSUlKT8qSkJI4ePdrsNZ9//jmvvPIKe/fubfX7PPXUUzz++ONtqdplMZiNQOuyiKIoFJ44xpHP/8upXdupLSu95DUmaxjxfTNIHZJF32HZpA0djsFsvsJaCyGEED1Lh97aW1tby4IFC3j55ZeJj49v9XVLly7lgQceCD6vqakhPT293etntJpxA1q0+LxedPrmvx15hw/y2Zt/o+D4kWCZRqMlJjWNiLh4LBGRAPh9Puw1VdgqK6kuKcJlt5F/9BD5Rw+x4/130ep0pA0dzsAJUxg4YTKR8Qnt/jUJIYQQ3U2bwkh8fDw6nY7i4uIm5cXFxSQnJ19w/qlTpzhz5gw33nhjsMzv96tvrNdz7NgxBgwYcMF1JpOpw1cIBDCEm3ADOo0Ol92BNTKiyXGP08nm11ay75P/qHU2GBk0eRpZ02aQljUco9nS4mv7vB4q8vMozjlF3uGDnDu8n5rSEs4d2s+5Q/v576t/InngYLKumsXQaVdjjYzqyC9VCCFEL+bz+Zg+fTrJycmsXr06WF5dXc2IESNYuHAhv/rVrwB49dVXefbZZzl+/DiRkZF84xvf4MUXX+zQ+rUpjBiNRsaNG8eGDRuCt+f6/X42bNjAj370owvOHzp0KAcOHGhS9sgjj1BbW8vvfve7DmntaAtjmAkboNXocdTam4SRusoK1ix/nJIzpwAYec1XmXLbHYTHxrXqtXV6Awn9+pPQrz8jZs4BoKqokJM7v+TE9q0UHD9C0cnjFJ08zuZ/vELm2AkMnzmH/qPHtdhCI4QQQlwOnU7Hq6++yujRo3n99deZP38+AEuWLCE2NpbHHnsMUG9S+e1vf8vTTz/NpEmTsNlsnDlzpsPr1+ZPvQceeIC77rqL8ePHM3HiRJ5//nlsNhv33HMPAAsXLiQtLY2nnnoKs9nMiBEjmlwfHR0NcEF5KOjN6pev0+hx2uzB8prSEt56/CFqSkuwRkVz3ZIH6Zc9+orfLzo5hfE3fJ3xN3wdW1Ulx7Z+xqHNGyjJOcXJHVs5uWMr4TGxjPzKPEZe81XComOu+D2FEEIIgMGDB7N8+XKWLFnC7Nmz2b59O6tWrWLHjh0YjUYqKyt55JFH+OCDD7jmmmuC140cObLD69bmMHL77bdTWlrKsmXLKCoqYvTo0Xz88cfBQa25ublotd1jYleNQQcEumlsDgActTW8++QyakpLiE5O4db//SXRSRd2QV2psOgYxs77GmPnfY3S3DMc2ryBI5/9l7rKCra8/Tpf/vMtBk+exui5N5A6eKjclSOEEF2Uoih4G90B2pn0JlObPh+WLFnCmjVrWLBgAQcOHGDZsmWMGjUKgE8++QS/309+fj5ZWVnU1tYydepUfvvb33Z4T4ZGUZQuP8tGTU0NUVFRVFdXExkZ2W6v6ymxU/zsLlw+B/brwhkxYzL/fOoxzu7fQ0RcAnf88mki4lo/8PZKeT0eTmz7gj1r/03h8Ya7kxL7D2D8DV9nyJTpaHW6TquPEEKIppxOJzk5OfTv3x9z4O5Ij9PJ7++6LST1+fHf3m3zXZpHjx4lKyuL7Oxsdu/ejT4wNGD58uUsW7aMzMxMfve73xEVFcUjjzxCXl4e+/fvx2g0Nvt6zX1P6rX287tXD07Q6NUWHJ1GT01pCTs+WM3Z/XvQG03c8tBjnRpEAPQGA1lXzSTrqpkUnz7J3nUfcvTzzZTknOI/f3iGz978G+Ouu5nsa6696OBZIYQQoiUrV67EarWSk5NDXl4eGRkZgDoG1OPx8Pvf/55rr70WgDfffJPk5GT++9//Mnfu3A6rU+8OI4aGMHJ69xbKco8BMOvu+4jvmxHCmkFS5kDmfv9+rp5/D3vXfciej/9NbVkpm/7+Mlv/+QajvnIdY+d9TcaVCCFEiOlNJn78t3dD9t5tsWXLFp577jnWrVvHE088waJFi1i/fj0ajYaUlBQAhg0bFjw/ISGB+Ph4cnNz27Xe5+vdYUSn9rNpNBrKco7ix8/gKdPJnn1tiGvWwBIRyZRb72D8jbdwePNGdn24hsrCAra/9w67/r2GYVfPZsLXbiUmJS3UVRVCiF5Jo9F0iwkt7XY7d999N4sXL2bWrFn079+f7OxsVqxYweLFi5k2bRoAx44do0+fPgBUVFRQVlZGv379OrRu3WOkaQepbxkB9fbeqMQkrr3vR11ysKjBaGLUV+Zx97Mv8bWf/S8pg4fi83o5sHEdf/3pYv79u99Qmnsm1NUUQgjRRS1duhRFUVi+fDkAGRkZPPPMM/zP//wPZ86cYfDgwdx0003cf//9bNmyhYMHD3LXXXcxdOhQZs2a1aF169UDWBW/Qv7/fg7AVv2XfGXJgg65c6aj5B89zPb33+H07h3BssxxE5n89dtJGTQkhDUTQoie6WKDNbuyzZs3c80117Bp0yauuuqqJsfmzp2L1+tl/fr11NbW8tOf/pTVq1ej1WqZMWPGJecFa48BrL06jACcW/oZGgUKRicx8VuD2/W1O0vJmdNse+8djn/5OQT+OfuOGMWkr99O+vDsLtnSI4QQ3VF3DSMdSe6maQeKVoPGp+Cxu0NdlcuWmJHJjT/5ORUF89n+3rsc+fy/5B7cR+7BfaQMHsrkr99O/zHjJZQIIYToknr1mBEAAoNY3XZviCty5WJT+/DVH/yERb97mdFzr0dnMFB4/Chrfv04//j5jzm29TP8fl+oqymEEEI00evDSP1cI66a7tsycr7IhESu+c5ivvvCSsbfeAsGs4XSszn8+/lf8+rPfsjBTevxebt/+BJCCNEzSBgxqT1V7tqeE0bqhUXHMOPb3+G7L65kym13YA4Lp7Igj7UvPc8r93+XPR9/gMcdmimMhRBCiHq9PozowtQw4rd58fu7/Fjey2IJj2DqN+bz3RdXMv3Ou7FGRVNbVsrGv/6Jv/xoEdveeweX3RbqagohhOilen0Y0YcZANCh4OiBrSONGS1WJt50G/e+8ArXLPoBkQlJ2Kur+PzNv/HnH9zD56v+jr26KtTVFEII0cv0+rtpdFY1jBg0GuoqXYRFtW1q3e7IYDQx+trryJ59Lce2fsb2996hPC+XbWveZteH75M9+1rG3/h1IuMTQ11VIYQQvUCvDyMas7oKrkEDdZVOkjLadx6Trkyn1zNs+iyyps3g5K5tbF/zNkWnTrDn4w/Y98l/yLpqFhNuupW4tI5dOloIIUTv1uvDiNasfgsMGqgucYS4NqGh0WoZNGEKA8dPJvfgPra/9za5B/dzaPN6Dn26gUETpzDp5m+SlDkw1FUVQgjRA0kYsdSHEQ2VRb17EKdGo6Ff9mj6ZY+m8MQxtr33Dqd2fsmJbVs4sW0L/UaOYdLXv0mfrBEygZoQQoh2I2EkMIDVpIGCYnuIa9N1pAwaws3/7xHKcs+w/V//5OgXmzm7fw9n9+8hZdAQJtx4KwMmTEKr1YW6qkIIIS7B5/Mxffp0kpOTWb16dbC8urqaESNGsHDhQgYNGsQ999zT7PXFxcUkJnbcOMJevzaN82QVZX85QK1P4QsfLPrtdPmrvxnVJUXs+NdqDm76BJ/HA0B0Ugpjr7+JETPmdIvls4UQ4kp157Vpjh8/zujRo3n55ZeZP38+AAsXLmTfvn3s2LEDn89HdXV1k2vuvvtunE4nmzZtavF1ZW2adqCLVu+esWjBVevFWefBEmEMca26nqjEZObc+wOm3HYHe9f+m73r/kNVcSEbV65gy9uvM/ra6xg99wbComNCXVUhhOhUiqKgePwheW+NQdvqP6AHDx7M8uXLWbJkCbNnz2b79u2sWrWKHTt2YDSqn3sWiyV4fmlpKRs3buSVV17pkLo3JmEkUv0H0Gs06DVQWWSTMHIRYdExTLt9ARNv+gYHN69n14fvUV1cxJer32LHv/5J1vTZjL/hZuL69A11VYUQolMoHj8Fy7aE5L1TfzEVjbH13eVLlixhzZo1LFiwgAMHDrBs2TJGjRrV7Ll///vfsVqt3Hbbbe1V3Rb1+jCiNerQWvX47V4sGijLs5E6SP66vxSD2cyYuTcw6ivzOLVjGzs++CeFJ45x8L/rOPjfdWSOncD4G75On2HZ0u0lhBBdhEaj4aWXXiIrK4vs7GweeuihFs995ZVXuPPOO5u0lnSUXh9GAHRRJjWMaDWU59WGujrdilarY9CkqQyaNJX8Y0fY+cFqTu78ktO7d3B69w4SMjIZ+9UbGTptBnqjtDgJIXoejUFL6i+mhuy922rlypVYrVZycnLIy8sjIyPjgnO2bt3KkSNH+Mc//tEOtby0Xj+AFaDs74dxHi5nv92HLSWMbyyd0O7v0ZtUFuaz6z//4tCm9XgDC/FZIiIZOeerjPrKdUTExYe4hkIIcXm68wBWgC1btjBjxgzWrVvHE088AcD69esvaMFetGgRu3fvZs+ePZd8zfYYwNrr16YBMCRaAYjQQXmBDb8vNAOReoqYlDTmLFrMfX/8K9PvvJuI+AQctTVsW/M2L//oO3zw3HLyjh6iG+RgIYToMex2O3fffTeLFy9m1qxZvPLKK2zfvp0VK1Y0Oa+uro63336bRYsWdVrdpJsG0CepYSRSr8Xn8FJV4iA2JSzEter+LBGRTLzpNsbf8HVO7drGno8+4NzhAxz/8nOOf/k5iRkDGPPVG6QLRwghOsHSpUtRFIXly5cDkJGRwTPPPMODDz7IvHnzgt01b731Fl6vl29/+9udVjfppgHc+XWU/GEPHg38p9LDtYuGM2hCUru/j4DSsznsWftvjnz6X7wedZVkS0Qk2bOvJfuarxKdlBziGgohRMu6azfN5s2bueaaa9i0aRNXXXVVk2Nz587F6/UGu2umTp1K//79ef3111v12u3RTSNhBPC7fRQ8tgUU+Kjaw4hr+zHl6wPa/X1EA0dtDQc2rmPvug+pLStVCzUaMkaOYeRX5jFg7ES0OpndVQjRtXTXMNKRZNKzdqI16tDFmPFVOInUaSg5WxPqKvV453fh7PvkI87u38OZfbs5s2834TGxjJh9LdmzryUyvuOmIBZCCBF6EkYCjKlhOCqcROk05OXWovgVNFqZH6OjaXU6Bk2cyqCJU6kqLuLAho858N9PqKus4Mt/rmLb6rfpP2YcI+fMo/+YcbIWjhBC9EASRgIMaRE4DpYTY9Bwqs5LdamD6MDAVtE5opOSmX7n3Uz95nxO7viSfZ98xLlD+4NzlkTEJzBi5lcYMXMOkQnSWiKEED2FhJEAY59wAOKMOsBH8ZkaCSMhotMbGDJlOkOmTKeiIJ/9Gz7m0Kb11JaVsvXdN9j6zzfpO3wkI2bOYeDEKRhM0m8rhBDdmYSRAEOqGkbMfgW9BorP1DBkktzZEWqxqWnMXLCIq25fwIltX3Bw03pyD+4LPowWK0OnXs3wmXNIGTREpp4XQnSKbnDvR6dpj++FhJEAXZgBXYwJX6WLaJ2GkjMyiLUr0RuNZE2fRdb0WVSXFHNo8wYObd5ATWkx+zd8zP4NHxObls6ImXMYdvVsWT1YCNEhDAYDoE4g1hlrtnQHdrsdaPjeXA65tbeR8teP4DhQxiGHj9M+hfuen4FOL5PUdlWK38+5wwc5tOkTjm/bEpx6XqPVkjFyDFnTZzFw/GQMcvudEKIdFRYWUlVVRWJiIlartde2yCqKgt1up6SkhOjoaFJSUi44R+YZuZz32XSOmo/PUORX2Fbj5dafjyO5f1SHvZ9oPy67jWNbP+PgpvUUHj8aLDeYzAycMJmsq2bSb+QYmbtECHHFFEWhqKiIqqqqUFelS4iOjiY5ObnZUCZh5DK4cqop/dN+PFoN/6lwM/WWgYy5tm+HvZ/oGBUFeRz5fBNHPt9EdXFRsNwSGcWQKdMZNn0WyQMH99q/ZoQQ7cPn8+HxeEJdjZAyGAzoLvJHnoSRy6B4fOT/31bwKXxS4yFxRDzX/2Bkh72f6FiKolB44hhHPt/Esa2f4aipDh6LTkph6FUzybpqBrGpfUJYSyGE6LkkjFymkj/uxZ1byy6bl1KDjkXPTJfJz3oAn9fL2QN7OPLZJk7u/BKvyxU8Ft83g8GTpzF48lXEpaWHrpJCCNHDyHTwl8nYLxJ3bi0JJh15dV7KC2zEB+YgEd2XTq8nc8wEMsdMwO10cGrHlxz5YjNn9++hLPcMZbln2PL268Sn92PwlKskmAghRCeSlpHzOA6WUf7aERx6LevKXEy/fRAjZ8mHUk/lrKvj5M4vOb71M84e2Ivf5wsei0/vx+DJgWDSR34GhBCiraSb5jL5at0U/mobCvCfag/9xiTw1fuyO/Q9RdcQDCZffs7Z/Xvx+7zBY3F9+jJwwhQGTphMUuZAGfwqhBCtIN00l0kXYUQXZ8ZX7iRWp6HgRJUsmtdLmMPDGTFzDiNmzsFZV8epXds4tvUzzu7fS3leLuV5uWxb8xbhsXEMGD+ZgeMnkT48G53+8if6EUIIIS0jzap45zj2XcWc9Pg5ZPPxzYcnkJAe0eHvK7omZ10dOXt2cHLHl+Ts243H6QgeM1qs9B8znoETJtN/9HhMVlnPSAgh6kk3zRWw7S6m8u3j2A1aPil1MfnmTMZ9NaPD31d0fV63m9xD+zi540tO7dyGvboqeEyr05M+PJsB4yeROWY8UYmytpEQoneTMHIFfDUuCp/cjgJ8VO0hYWA0X//Z2A5/X9G9KH4/hSePcXLnNk7t+JKKgrwmx2NT+9B/zHj6jxlPn6zh0p0jhOh1JIxcoaJnd+ItcbDd5qXYB4t+Ox2jRYbYiJZVFOSpXTl7d5J/9DCK3x88ZjBb6Jc9Sg0no8cTERcfwpoKIUTnkDByhSrfP4ltayH5Wg07K9zM+342maMTOuW9RffntNWRe2Avp/fsJGfPzibdOQAJfTPoP2Y8GaPHkTp4qLSaCCF6JAkjV8hxqIzyfxzBbdLxUbGTYdNTmTV/aKe8t+hZFL+fkjOnydmzk9N7dlB48jg0+m9nMJnpM2wE/bJH0y97NHHp/eTWYSFEjyC39l4hU2Y0aMDo8mHWwNn9ZSh3yC2+ou00Wi1JmQNJyhzI5Fu/hb2mmrP7dnN6z05yD+7DXl1FTqAFBSAsOoa+gWDSL3s04bFxIf4KhBCiY0nLyEXUr1NzwOPntM3Hrf8zjuTMqE57f9HzKX4/pblnOHtgL2f37yH/yCG8HneTc+L69FWDycgxpA0dLrcPCyG6DemmaY/3/W8uNWvPUhtmYGO+nVFz0rnqtkGd9v6i9/G63RQcP8LZ/Xs4e2AvxTmnmnTpaDRakjIH0GdYNunDskkbOgyTNSyENRZCiJZJGGkHniIbxc/vRtFp+LDcjTXWzIJfTZH+fNFpHLU15B7cz9kDe8g9uI/q4qImxzUaLYn9B5A+PJs+WSPokzVcwokQosuQMNIOFEWh6Nc78FW52OHyUeDw842l40ns13l1EKKxmrJS8o4c5NyhA+QdPkBVcWGT42o4yQy0nIwgdXAWlgj5eRVChIaEkXZSf4tvRYSRz87ZGHNtX6beMrBT6yBES2rLy8g7fIBzgUdVUeEF58SmpZM2JIvUIcNIG5JFdHKqtO4JITqFhJF24jxeSdnKgyhmHf8qchIWZWThU9PQyl01oguqrSgj7/BBzh0+QN6RQ1SeNyssgDUqmtTBQ4PhJLH/QPQGmedECNH+JIy0E8Xrp+CJL1GcPrZ5oajOw41LRtF3uNxuKbo+e001BcePUnDsMPnHjlB86jg+r7fJOTqDgeQBg0gdMozUwVmkDBxMWHRMiGoshOhJZJ6RdqLRa7EMi8O+u4ShyRaKTno4urVQwojoFqyRUQwcP4mB4ycB4PV4KD59MhhOCo4dxlFbQ/7Rw+QfPRy8LjIhkeQBg0kZOJjkgYNJyhyIwWQO1ZchhOjhLiuMvPjiizz99NMUFRUxatQo/vCHPzBx4sRmz3355Zf5+9//zsGDBwEYN24cTz75ZIvnd0XWUQnYd5cQZfOgAU7vLcNp82AOk6Zt0b3oDQbShmSRNiSLCaiDtCsLC4LhpOjkMcrycqkpLaGmtITjX34OqBO3xaf3I2XgEJIHDSZlwGBi+6Sj1epC+wUJIXqENnfTvPXWWyxcuJAVK1YwadIknn/+ed555x2OHTtGYmLiBefPnz+fadOmMXXqVMxmM7/+9a9Zs2YNhw4dIi0trVXvGcpuGgDF56fwV9vw270cMOs5XeRg+u2DGDkrvdPrIkRHczvsFJ06SdGp4xSeOEbRyWPUVVZccJ7BbCF5wCCSBwwisf8AkjIHEp2YjEarDUGthRBdUYeNGZk0aRITJkzghRdeAMDv95Oens6SJUt46KGHLnm9z+cjJiaGF154gYULF7bqPUMdRgAq15zAtq0IV1o4Hx+qJDrJyp2PTZLp4UWvUFtRRtGJ4xSePEbRyeMUnTqBx+W84DyjxUpS/wFqOOk/gMTMgcSkpEoLihC9VIeMGXG73ezatYulS5cGy7RaLXPmzGHr1q2teg273Y7H4yE2NrYtbx1ylpEJ2LYVYS5zYDHrqCq2k3u4gn4jZOyI6PkiYuOJmBTPoElTAfD7fVTknaPw5HGKT5+gOOcUpWdzcDvswduM6xlMZhIyMknKHEBS/4Ek9h9AXFo6Wp0EFCGEqk1hpKysDJ/PR1JSUpPypKQkjh492qrX+PnPf05qaipz5sxp8RyXy4XL5Qo+r6mpaUs1O4SpfxS6WDO+CidjhsawZW8Z+/97TsKI6JW0Wh3xfTOI75tB9uxrAfB5vVTkn6M45xQlOacoPn2SkrOn8bicFBw7TMGxhgGyeoOR+H4ZJPTNIL5vfxL79Se+bwbm8PBQfUlCiBDq1Ltpli9fzqpVq9i0aRNmc8sj85966ikef/zxTqzZpWm0GsImJFGz9izJHh9oIPdQBeUFdcSlyi9QIXR6PQn9+pPQrz/MVP/Y8Pt9VBbkBwLKSYpPn6LkzCncDofa3XPyeJPXiIhPIKFvRvB14vtmSDePEL1Am8aMuN1urFYr7777LjfffHOw/K677qKqqor333+/xWufeeYZnnjiCdavX8/48eMv+j7NtYykp6eHdMwIgK/aReHy7aDA8bQIjhyqYNCEJK5dNDxkdRKiu1H8fiqLCik9m0NZbg4lZ3Moyz1DTWlJs+frDUbi0vuR0C8QUvpmEJfeD2ukrKAtRFfXIWNGjEYj48aNY8OGDcEw4vf72bBhAz/60Y9avO43v/kNv/rVr1i7du0lgwiAyWTCZDK1pWqdQhdlwjwkFufRCrISzBwBTuwsZsL1GcQky+JkQrSGRqslNjWN2NQ0hky5KljutNVRlnuG0twzalA5e4bSc2fwulzquJTTJ5q8jjUqmri0dOLS+xLXpx9xfdKJ69NXQooQ3VCbu2keeOAB7rrrLsaPH8/EiRN5/vnnsdls3HPPPQAsXLiQtLQ0nnrqKQB+/etfs2zZMt544w0yMjIoKlJXHQ0PDye8G/YPh01Kxnm0Av/RCgZkx3HqQDk7PzrDV+6R1hEhroQ5LDyw8vCIYJni91NVXBgIKGeCrSnVJcXYq6uwV1c1GSwLElKE6I7aHEZuv/12SktLWbZsGUVFRYwePZqPP/44OKg1NzcXbaN5Bl566SXcbje33XZbk9d57LHH+L//+78rq30ImIfEoo+34C1zMKpfOKcOlHNiezFjvtKP+D7dL1wJ0ZVptFpiUtKISUlj8KRpwXKP00l5/jnK83KbPFoTUmL79A20zPQhNrUPEXHxMjeKECEma9NchrovC6h67xS6ODP7Is2c2lNKn6ExfO3+0bIaqhAh5HE6qSjIo+zc2QtCSkv0RhMxySnEpPYhNjVN3aaoW5PV2om1F6LnkbVpOpB1bBLVa8/iK3cy4ao0cg6UkXe0krMHyskYGR/q6gnRaxnMZpIyB5KUObBJeeOQUpF/joqCfCoL86ksLMDrdqndQLlnLni9sOgYYlP7EBNoSYlJTSM2pQ+RCYkyT4oQ7UhaRi5T9cdnqN10DkOfcE6nhrN73Tmik6x865GJ6AzS5CtEd+D3+aguLaayIJ+Kgjx1W5hHRX4e9uqqFq/T6nREJSYRnZRCdHIq0ckp6iMphajEJHR6WbdKCOjA6eBDoSuGEV+tm6Lf7EDx+Im6YyjvvH4MR42b8ddlMOlrmaGunhDiCrnstoaAUpBPZUEeFYVqi4rP42nxOo1GS0R8AtHJKcTUB5TkFGKSUohKSpbVj0WvImGkE1R9lEPd5jwMqWHUTUvj45cPodVq+Mb/TpDBrEL0UIrfT21FOVVFhVQVF6jbokKqigqoKi5qds2exsJj4xpaURKSiEpMIjKwDYuOkcG0okeRMNIJfDYPRb/egeL2EfftLDZ9UcjpPaUk9I3g1p+PQ6eTXypC9CaKomCvrqKy6MKQUlVUgMtuu+j1OoNBDSYJiY1CSjJRCYlEJiZhiYiUQfKiW5Ew0kmq156h9r/n0CdYCL9nOKue2IHL7mXMV/oy9daBl34BIUSvoCgKzrraJgGluqSYmtJiqkuLqS0rQ1H8F30Ng9kSCClqWIlKSA4+j4xPxBQWJmFFdCkSRjqJ3+ml6Omd+G0eom7IpDTMyEd/Uuc3uP6HI8nIlrtrhBCX5vN6qS0vo7qkiJrSEqpLiqkuKaK6tJia0hJslRWXfA2D2UJkfAIR8QlExqnbiLj4QFkiEXFxMrhWdCoJI52obnshVatPojHrSH5wPF/8+wwHNuVhDjNw20PjiUqwhLqKQohuzuN2UVNaQk1JMdWlJWpoKVFbVapLS3DWtmJ1c42GsOiYhqASn0BkXLy6jU8kIj5BuoJEu5Iw0okUv0LJH/bgKbQRNiGZiK9lsuaZ3ZScrSUm2cqtPx+PySJTugghOo7H5aS2vIyaslJqy0upLStV9wPPa8pKL3oXUD29wUhEfDzhMXGEx8UTHhtHeEwcEbFxhMfFER4bR1hUjMyzIlpFwkgnc52ppnTFfgDi7x2BL97KO8t3YqtykT4slut/OFIGtAohQkZRFBy1NYGQUnJeWCmjpry0VV1BoN6+HBYdrQaV2EBgiQ0ElsDziNg4DGa5jbm3kzASApXvncT2ZSG6GBNJPxlHebGd1c/swuv2M2h8InO+MxytVpo/hRBdk9fjoa6inNry0sC2jLrKcuoq6h8V1FWWo/gvPtC2nskaFgwq9S0sYTExhEXHEBYdS3hMDNboGAzGrrdKu2gfEkZCwO/yUfzcLnxVLsKmpBBz00DOHCjjo5cO4PcrDJ2SzOwFWWgkkAghuim/34e9urpRQCmntqLsvOfleJyOVr+myRoWCCgxhMXENt1GxxAeE4s1OgZzWLiMZ+lmJIyEiPNEJWWvHAQgbuEwLMPiOLW7hLV/OYTiVxgyOZlZC4ZKl40Qokdz2e0NAaVSbWWxVVVgq6zEVlUZ3Pd63K1+TZ3BoAaUqJhAC0t9cFFDizUqGmtkNNaoKJnptouQMBJCVR+cou6LAjQWPUn3j0Efbeb4jiLW//UIil+h34g45n53BAaTDAATQvReiqLgdtipqwyElOpKbJUValip3wb2nba6Nr22wWzBGhXVJKCERUVjiYwmLDoaa2TgWFS02uIiM992CAkjIaR4/ZSs2Icnrw5j3wgS7huJRq/lzP4y1r58EK/HT2JGJNd9P5uwaOkrFUKIS/F6PNirKtXgUlWBraoq0LpSH1qqsNdUYa+uatVdQ41ptNom4UQNMI2fRwUDjSUySsa4tIGEkRDzVjgp/v0eFKcX67gkYm4bhEajoeh0Nf9+YR8uuxdLpJGvfncEqYOiQ11dIYToEdTWFocaTIIBpRp7dVWjssDz6qo2t7gA6E0mLBGRWCIisUZGBfctEZFYIiOxnF8WEdlrb4WWMNIFOI9VUPbqIVAg8qsZRM5MB6CqxM7HfzpAeb4NrVbD5JsHMGpOutxpI4QQnczn9eCoqcFWXYWjugpbddOw0mS/uhq/z3tZ72MOC8cSGYm5xRAT1RBmIiIxWXvG1P4SRrqIui0FVP3rFACxdwzFOioBAI/Lx39fO8qJHcUApAyM4pq7hslsrUII0UUpioLH6cBeU4OjthpHbQ2OmhocNYH9wEM9rj6cdbVwGR+zGq0Wc3gElvAIzOERmMPDsUREYg4PxxwWKIuIOO+cCIwWS5cKMRJGupDK909i21oIWg1x387CMiwOUH+wj3xRyOfvnMDj8qE36Zh8UybZM9LQyt02QgjR7fn9Ppx1dWpoaRxgagPPa2qwNymradNt0edrLsSYwyOwRES0EGLCMYdHdliIkTDShSh+hcq3j2HfWwo6DfF3Dcc8OCZ4vKbMwYa/HaHgRBUAsalhTL99MH2GxLTwikIIIXoqj9uFs7YWp60OZ22NGmbqanE2edThqFOPOetqcdbWtuk26fNptFq++eiT9Bk2oh2/EgkjXY7iU6h48wiOg+Wg1xB3x1AswxtW9FX8Coc+L2Db+6dx2tSR4JmjE5h4Y3/i0sJDVW0hhBDdhMftCgaVVoeYujq8bhcAC379exIzMtu1ThJGuiDF66f8jaM4D5eDBmJuGUTYhOQm5zhtHrb96zSHPs0PdjMOHJfIhOv7E5saFoJaCyGE6MnqQ4w1Mgqd3tCury1hpItSfAqVa05g36kOXI38Sj8iZqdf0FdXUWBj+79zOLW7JFjWLzuOUdek02dITJcaoCSEEEI0R8JIF6YoCtUfn6Fucx4Alux4Ym4bjLaZGVnL8urY8e8cTu8tDZbFpYUxYkYfBo1PxGRt3xQrhBBCtBcJI91A3fZCqt4/BT4FQ3IYsfOHYkiwNntuVbGd/f/N48jWQrwuHwA6g5bM0QkMnZJMn6GxMk+JEEKILkXCSDfhOlNN+WtH8Nd50Bi1RN84AOv4pBa7YZw2D0e3FnJkSyEVBbZguSXSSOboBAaMTiB1SLQsxCeEECLkJIx0I75qFxVvHcN1uhpQu22ibxqALtzY4jWKolCaW8vRLYUc31GMy94wK6DJqicjO56+I2JJHxqLJaLl1xFCCCE6ioSRbkbxK9R+mkfNurPgV9Ba9URdn4l1bOIlB6v6vH7yj1Vyam8pOXtLcdQ2XSQqoW8E6VmxpGfFkJQZhcHYO9dIEEII0bkkjHRT7rxaKv95Ak+h2gVjGhhN9I2ZGJJad1uv369QdKqKnH1lnDtSSXl+00WgtFoN8enhpAyIJnlAFCkDowiLkhUohRBCtD8JI92Y4vNT93k+1Z/kgtcPWgibkEzknH7o2tjlYqt2kXe0knOHK8g7VomtynXBOeGxJhLSI0joGxHcWqOMcvuwEEKIKyJhpAfwljuo+k8OzkPlAGhMOiKmpxE+LQ2tRd/m11MUhdoKJ0WnqikMPCry65pdw8kSYSAhPYKYlDBiU8KISbYSkxKGOUxuJRZCCNE6EkZ6ENfpaqo+PI0n0OWiMekIn5ZK+LQ0dFcYDtwOL6Xnaik7V0dpbi2l52qpLLS1uMikJdJIbLKVmOQwohItRMZbiEpQt4Zm5kkRQgjRe0kY6WEUv4LjQBk1G3PxFtsB0Bh1hE1IInxKKvp4S7u9l8ftozy/jvK8OioL7VQW2agotFFXeWEXT2PWSGOjcGImIs5MeIyZ8BgT4TFmCStCCNHLSBjpoRS/gvNwOTUbcoODXNGAeUgs4VNTMQ2MRtNBk5+5nV4qi+xUFtqoLLJTXeqgpkx9NL61uCUmq57wGBNh0WbCY02ER6shJSzKiCXSiDXSiCXCKJO3CSFEDyFhpIdTFAXXiSrqvsjHeawyWK6LNRM2NhHr2CT0seZOq4/T5qGmzBEMKNWlDuoqXdRVOKmrdOEJzBp7SRqwhBuw1oeTSCPWCCPWSBPWSAOWSCOWcCOmMD3mMAMGk04G2gohRBclYaQX8ZQ5sG0pwLarGKXRh74pMwrr2EQsw+LQhngNG5fDS12lE1ulSw0plc7g1l7jxl7jxlHngTb+NOr0WsxheszhBsxh6sMUbsASZmhSZg43YLLqMVr0mCx6dAathBghhOhgEkZ6Ib/bh+NQOfZdxbhOVTV8sGs1mAZGY82Oxzws7ooHvXYUv8+Po86Do9YdDCj2GjeOxvu1bpx1Hpw2Lz6v/7LfS6vXYLI0hJPGQcVoNWCy6DBamgYYk1WPwaTDaNZjMOvQ6WXKfSGEuBgJI72ct8qJfVcJ9v2lwQGvAGjBlBGFeWgs5sEx6JOs3bKFQFEUPC4fTpsHl80bCCgeHIGt0+bBWefB1ajM7fDidnhbvFOorbR6DUaTGkyMZh0Gkw6DWY/RpMNgbrpfH2Dqz2+yb9JhMOrQ6jXd8t9CCCFaImFEBHlK7TgOluE4UIan0eJ6ALooI+bBsZiHxGAaEH1Z85d0J/UhxmVXg4nL4cVtV7dNyzy4HD7cDo9a7vThsnvwOH14PZffInMxGq0GvVGLwahDb9JhMGrRG9WwojcGngf3dRhM6vGGcxpfG3je6HwJO0KIziZhRDTLW+7AcbQC1/FKnKeq1Rle62nAkByGqX8UpswojP2jumyXTij5fX48Lh9upw+P04fb5cVz3r7bGShz+XC7fHic9eU+PC5vk2v93s75L6jRatAbtOgMWvQGNcg07GvRG3RNjuuM6vMLzjVo0Rl0gWuaHlO3Dce0snq0EL2ahBFxSYrHhyunBuexCpzHK/GWOi44R59oxZQRibFPBMa+EegTrR1263Bv5ff58bj9eN1qeFG3TZ973WoA8rgDz11+PE2OB67xBMpcvuBr+n2h+y+u1WrQ1YcWgxpagsFFr+7r9PX7GvT688qbHFe3Ta/VoDPoAtvmjmvR6qRFSIhQkTAi2sxX68aVU60+Tlc3HWsSoDFqMaSFY0yPUANKWji6GLMElC7M5/PjdamBxuup3/rxNdlvxTGPH687cKx+36sGnuB5gbIuRUMwpGj1WjXwBEPOhSFGW39cp2l4rtOoW32gTNdoX69Bp9M23TeoIag+DNUHKm3gGp0uEJLk/43o4Vr7+d2zBwiINtFFGLGOTMA6MgEAn82D+0w17txa3OdqcefVobh9uHNqcOfUBK/TGHUYkq0YUsIwJIcFt1qz/Hh1BTqdFp1Vi8naOe+n+BW8Xj++QJjxenzBoOL1+PB51eDi8yr4PD58XkUNM8Hyhq3X68dfv19/jdeHz6Oo5wXK/cHj6rVNWoMU1NfroLE+V0Kr1aA1nBd2dNrzgk8LAag+1DQTgOqvUx/qdVpdQ5mu0b5W31x5/TWB6/QatFppYRIdRz4tRIt0YQYsw+OxDI8HAh8ypXbc5+pw56kBxVNkUwNKbi3u3Nqm18eYMCRa0SdY0SdaMMSrW22YQX6p9WAarUYdYGsM3fT/ir9pWGkagM4LPPXPG4chrxpofF4/fm/gtXwK/vrzvAp+X3048jfdP+/c+tfxeRUUf9OGaL9fwe/ycen5i7sGrfZiAUbT0Iqkaxx+zgs2Ok3gHO1FX+v8QNXkuL6ZskYPjVYt12iblmu16jH5/dP1SBgRrabRajAkhWFICiNsfBIAik/BW2bHU2TDUxh4FNnwVbvxVbrwVbqg0QyxABqzHkOiBX28BX2iFX2sOfjQWPTyi0JcMfXOJPVOIlOoK9OI36+GmGDACYaaZgJOINSoLT2tPLd+36s0hCJf4D0bbeuv9/sUfMFj6jXBff+FPfh+f6C8C7YytYVWq0HTKKA0bLVoAmFJoz3veCDc6HSBaxuV1+9rdBp0wdfWNjrn/NfTNglITQLUBXUKvG7jcNUkZDV9/+B+N/s9KmFEXBGNriGgMKqh3G/3qAGlxIG31I6n1IG3zIGv0oni9DbbkgKgMevQx6jBRBdnbtiPNaOLMqEN4V/bQlwprVaDVquDbnCTmqIoDcEkGIaaCTEtBBtf42DTuDwQxhquaXTc20xZM+/fJGTVv5a/IUT5fQpKC4EK1FCFX8Hn6eRvaifSaAiGK/XnTtP8fn1Q0mqYvTCL+D7hIamvhBHRIbRWA6bMaEyZ0U3KFY8Pb7kTT4kdbyCgeCuceCsc+Gs9KE5fsIWl+dfVo4s0oYs2oYsyoosyNTyiTeijjGgMEliEuFIajSYwRgWge/6fUhS1a6xxSGlo+fE3HPOdF2T8auBRziv3+xsC0oXX+i8MQ8HnalhSAkGraZ2avmbD8aatVMFjPqVp+GrhbjlFQR1M3oY+QK+nlWuIdQAJI6JTaQw6dZBrctgFx/xuH75KZyCcOPFVNNqvdKK4/fjtXvx2L56i5sMKqIFFG2FEF3io+wZ1P7xhX7qEhOjZNJr6LpNQ16RjNQQd/wXhq/HzC/ebBqiYpE4a5d4MCSOiy9AadWjru3zOoygKitOHr9qFt9qFr8qFr9qljk2pDuxXuVA8DYGluVuTm9Bp0IUb0dYHlTADujAD2jADWqsBbZheLbOqZRqzrBAshOh6tFoNaNXb1LsrCSOiW9BoNGgserQWfbOtKhAILA4vvho3vlr14a/1qPt1bvy1bny1Hvx1bvx2L/iUYJBpVdexVqO2ugTCii6sYV8bqJvWog/WM/jcJCFGCCEuRsKI6DE0Gg0aqxoOWgos9RSvH1+dJxBQAsHF7sFv8+K3efDZPIHnapni9oFfwV/nwV/XxlFvGpqGFHMLwcWsQ2PSozXp0Jh0aM1qkNGadGhkhWAhRA8mYUT0Shq9Fn20CaJbd+On2v3TOKR4g2HFZ/Pgd3hRHF789Q+nusWrgILaEmP3ctnDw/QatCY9GrOuIaw0eR4IMeZAeX2IMWrRGHVojDq0jfZl5k8hRFciYUSIVtAYtMG7dtpC8fjwO3z4HZ5ASPGpwcXe9Lnf4UVxevG7fCguH36XF8XpQ6mfz8Gr4Pd6wOa5/EDTmF6Dtj6YBEJKk+eGRmHGoIYfjVGrnmOoLw8cM2jR6OufBx6yQJ4Qog0kjIj/397dB0VVvXEA/95Fd4EUEJFdUEBQwhJERVmx1CZ3BLTUbCYiJtFM07BsMCMstfrjB8WkzpipzfjSjCnmjOKMmQ0ipOaqQaDhCyOEYsmCLy3gC8Kyz++PZS97YRFJcHfh+czscPecc8+ex8Pe+3jZe5Z1I6Gvk+mL3Nzk/2l/aiLQAwOMDU2g+iZTsmKZtNQ3merrLZIYc3mD6WFsMIrbMN8FaCAYDaarNd1CBgh9nKQJysOSlz4Wda0ffWRAn+Y2fUzrJpi3TeUWzx1wsSfGGCcjjNk1wanlczCPi4hMSUhDS6JCDUaL58bm5KVlu22b5rpG01UbajSCDEZxW2RESwL0pFkkK0IfwWoiA4t6SSLTfFVHsm/zwlEw7y9r2YZMEPtFc7v2tjlRYqx9nIww1ksIggD0bb7976muXwLUlOy0JCbSRMV68iIpf1j7JjJtNxlNr2EwggwEMhiB1os+NRGoyXSlyO7IrCQrfUzLfItJVOskp3kbTkJLYmROipqXCIcMpj+Nmfu3/Cmz8typ1U+Z0Hb/VvtZ7ZuTK9ZFOBlhjHUJU7Lj9MRXwCUjNScg5iTFlKi0JC3NiYuVRIYMRsC83dTquaElETL3j+YVMFu2rdQbTCt4wmBlZczmxabQaIT1dTMdjAwWiYsMghNMP9skOhY/BXOyY7FvqzIIFvs2J1uWZZCh1X7tl3Xcl3kMkCZhVsogoFVf0jIxQRPQUscJ2yPhZIQx5tDEk4OdLfhERIARgLH5yo7BWjIjTWweXk9Ak0ViZU5smlr9bF6NU/KzdbnR1Nejt20ndTLC1AYEoIckWF1NgClBMict5ucyAULrpMUyARIst1v2aemnpS9JEtdO/2IC1nrb4vX7PT8YfTydbfLP9J+SkY0bNyIjIwM6nQ7h4eHYsGEDIiMj222/d+9erFq1CleuXEFwcDC+/PJLTJ8+/T8PmjHG7J0gCKavdHFyguAAX4z3MESmW9SlyYvR9Nkgo9F6ImQtUWoigCzaNvcptqfmtubXslJm2s/cF1r6MlqMscO+qCWRIstxWZSJ/cK0n2WiZlHWYQZGzW1h+kB66yp74jJ6kOMkI3v27EFycjI2b94MtVqN9evXIzo6GiUlJfD29m7T/uTJk4iPj0daWhpeeukl7Nq1C7Nnz8Yff/yB0NDQLgmCMcZY95H8L52vp0u0SWzMz8kiQbJIdEDNCZJFMiNJpKhlH0niI7az6F+y3dI/jJCMxbKdOBZz/+JrAU5unVu6oCsJRNSp5EytVmP8+PH45ptvAABGoxF+fn5477338PHHH7dpHxcXh7t37+LgwYNi2YQJEzB69Ghs3rz5kV6ztrYW7u7uqKmpgZubW2eGyxhjjDEbedTzd6f+yNrQ0ICCggJoNJqWDmQyaDQaaLVaq/totVpJewCIjo5utz0APHjwALW1tZIHY4wxxnqmTiUjN2/eRFNTE5RKpaRcqVRCp9NZ3Uen03WqPQCkpaXB3d1dfPj5+XVmmIwxxhhzIPb18fNmqampqKmpER/Xrl2z9ZAYY4wx1k069VEkLy8vODk5oaqqSlJeVVUFlUpldR+VStWp9gCgUCigUNjugzSMMcYYe3I6dWVELpcjIiICOTk5YpnRaEROTg6ioqKs7hMVFSVpDwDZ2dnttmeMMcZY79Lpm7SSk5ORmJiIcePGITIyEuvXr8fdu3cxf/58AMDcuXMxePBgpKWlAQCWLVuGKVOm4Ouvv8aMGTOQmZmJ/Px8fPfdd10bCWOMMcYcUqeTkbi4ONy4cQOrV6+GTqfD6NGjcfjwYfFDqhUVFZDJWi64TJw4Ebt27cKnn36KlStXIjg4GFlZWbzGCGOMMcYA/Id1RmyB1xlhjDHGHE+3rDPCGGOMMdbVOBlhjDHGmE1xMsIYY4wxm+JkhDHGGGM2xckIY4wxxmzKIb4M2nzDD39hHmOMMeY4zOftjm7cdYhkpK6uDgD4C/MYY4wxB1RXVwd3d/d26x1inRGj0Yjr16+jf//+EAShy/qtra2Fn58frl271mPXL+npMXJ8jq+nx8jxOb6eHmN3xkdEqKurg6+vr2RB1NYc4sqITCbDkCFDuq1/Nze3HvkLZqmnx8jxOb6eHiPH5/h6eozdFd/DroiY8QdYGWOMMWZTnIwwxhhjzKZ6dTKiUCiwZs0aKBQKWw+l2/T0GDk+x9fTY+T4HF9Pj9Ee4nOID7AyxhhjrOfq1VdGGGOMMWZ7nIwwxhhjzKY4GWGMMcaYTXEywhhjjDGb6tXJyMaNGzF06FA4OztDrVbjzJkzth5Sh9LS0jB+/Hj0798f3t7emD17NkpKSiRtXnjhBQiCIHksXrxY0qaiogIzZsyAq6srvL29sWLFChgMhicZSrs+++yzNuMfMWKEWF9fX4+kpCQMHDgQ/fr1w6uvvoqqqipJH/Yc39ChQ9vEJwgCkpKSADjm/B07dgwvv/wyfH19IQgCsrKyJPVEhNWrV8PHxwcuLi7QaDS4fPmypM3t27eRkJAANzc3eHh4YMGCBbhz546kzblz5zBp0iQ4OzvDz88PX331VXeHBuDh8TU2NiIlJQVhYWF46qmn4Ovri7lz5+L69euSPqzNe3p6uqSNPcYHAPPmzWsz9piYGEkbe54/oOMYrb0nBUFARkaG2MZe5/BRzgtdddzMy8vD2LFjoVAoMHz4cOzYsaNrgqBeKjMzk+RyOW3bto3Onz9PCxcuJA8PD6qqqrL10B4qOjqatm/fTsXFxVRUVETTp08nf39/unPnjthmypQptHDhQqqsrBQfNTU1Yr3BYKDQ0FDSaDRUWFhIhw4dIi8vL0pNTbVFSG2sWbOGRo4cKRn/jRs3xPrFixeTn58f5eTkUH5+Pk2YMIEmTpwo1tt7fNXV1ZLYsrOzCQDl5uYSkWPO36FDh+iTTz6hffv2EQDav3+/pD49PZ3c3d0pKyuLzp49SzNnzqTAwEC6f/++2CYmJobCw8Pp1KlTdPz4cRo+fDjFx8eL9TU1NaRUKikhIYGKi4tp9+7d5OLiQlu2bLFpfHq9njQaDe3Zs4cuXbpEWq2WIiMjKSIiQtJHQEAAffHFF5J5tXzf2mt8RESJiYkUExMjGfvt27clbex5/og6jtEytsrKStq2bRsJgkBlZWViG3udw0c5L3TFcfOvv/4iV1dXSk5OpgsXLtCGDRvIycmJDh8+/Ngx9NpkJDIykpKSksTnTU1N5OvrS2lpaTYcVedVV1cTAPr111/FsilTptCyZcva3efQoUMkk8lIp9OJZZs2bSI3Nzd68OBBdw73kaxZs4bCw8Ot1un1eurbty/t3btXLLt48SIBIK1WS0T2H19ry5Yto2HDhpHRaCQix5+/1gd6o9FIKpWKMjIyxDK9Xk8KhYJ2795NREQXLlwgAPT777+LbX7++WcSBIH++ecfIiL69ttvacCAAZIYU1JSKCQkpJsjkrJ2ImvtzJkzBICuXr0qlgUEBNC6deva3cee40tMTKRZs2a1u48jzR/Ro83hrFmz6MUXX5SUOcoctj4vdNVx86OPPqKRI0dKXisuLo6io6Mfe8y98s80DQ0NKCgogEajEctkMhk0Gg20Wq0NR9Z5NTU1AABPT09J+Q8//AAvLy+EhoYiNTUV9+7dE+u0Wi3CwsKgVCrFsujoaNTW1uL8+fNPZuAduHz5Mnx9fREUFISEhARUVFQAAAoKCtDY2CiZuxEjRsDf31+cO0eIz6yhoQE7d+7EW2+9JfkSSEefP0vl5eXQ6XSSOXN3d4darZbMmYeHB8aNGye20Wg0kMlkOH36tNhm8uTJkMvlYpvo6GiUlJTg33//fULRPJqamhoIggAPDw9JeXp6OgYOHIgxY8YgIyNDcgnc3uPLy8uDt7c3QkJCsGTJEty6dUus62nzV1VVhZ9++gkLFixoU+cIc9j6vNBVx02tVivpw9ymK86bDvFFeV3t5s2baGpqkvyjA4BSqcSlS5dsNKrOMxqN+OCDD/Dcc88hNDRULH/jjTcQEBAAX19fnDt3DikpKSgpKcG+ffsAADqdzmrs5jpbU6vV2LFjB0JCQlBZWYnPP/8ckyZNQnFxMXQ6HeRyeZuDvFKpFMdu7/FZysrKgl6vx7x588QyR5+/1sxjsjZmyznz9vaW1Pfp0weenp6SNoGBgW36MNcNGDCgW8bfWfX19UhJSUF8fLzkS8fef/99jB07Fp6enjh58iRSU1NRWVmJtWvXArDv+GJiYjBnzhwEBgairKwMK1euRGxsLLRaLZycnHrU/AHA999/j/79+2POnDmSckeYQ2vnha46brbXpra2Fvfv34eLi8t/HnevTEZ6iqSkJBQXF+PEiROS8kWLFonbYWFh8PHxwdSpU1FWVoZhw4Y96WF2WmxsrLg9atQoqNVqBAQE4Mcff3ysX3Z7tHXrVsTGxsLX11csc/T5680aGxvx2muvgYiwadMmSV1ycrK4PWrUKMjlcrzzzjtIS0uz+2XGX3/9dXE7LCwMo0aNwrBhw5CXl4epU6facGTdY9u2bUhISICzs7Ok3BHmsL3zgr3rlX+m8fLygpOTU5tPEldVVUGlUtloVJ2zdOlSHDx4ELm5uRgyZMhD26rVagBAaWkpAEClUlmN3Vxnbzw8PPD000+jtLQUKpUKDQ0N0Ov1kjaWc+co8V29ehVHjhzB22+//dB2jj5/5jE97P2mUqlQXV0tqTcYDLh9+7bDzKs5Ebl69Sqys7M7/Cp2tVoNg8GAK1euALD/+CwFBQXBy8tL8jvp6PNndvz4cZSUlHT4vgTsbw7bOy901XGzvTZubm6P/R/FXpmMyOVyREREICcnRywzGo3IyclBVFSUDUfWMSLC0qVLsX//fhw9erTNJUFrioqKAAA+Pj4AgKioKPz555+Sg4f54Pnss892y7gfx507d1BWVgYfHx9ERESgb9++krkrKSlBRUWFOHeOEt/27dvh7e2NGTNmPLSdo89fYGAgVCqVZM5qa2tx+vRpyZzp9XoUFBSIbY4ePQqj0SgmY1FRUTh27BgaGxvFNtnZ2QgJCbH5JX5zInL58mUcOXIEAwcO7HCfoqIiyGQy8c8b9hxfa3///Tdu3bol+Z105PmztHXrVkRERCA8PLzDtvYyhx2dF7rquBkVFSXpw9ymS86bj/0RWAeVmZlJCoWCduzYQRcuXKBFixaRh4eH5JPE9mjJkiXk7u5OeXl5ktvL7t27R0REpaWl9MUXX1B+fj6Vl5fTgQMHKCgoiCZPniz2Yb6Fa9q0aVRUVESHDx+mQYMG2c2tr8uXL6e8vDwqLy+n3377jTQaDXl5eVF1dTURmW5R8/f3p6NHj1J+fj5FRUVRVFSUuL+9x0dkunvL39+fUlJSJOWOOn91dXVUWFhIhYWFBIDWrl1LhYWF4t0k6enp5OHhQQcOHKBz587RrFmzrN7aO2bMGDp9+jSdOHGCgoODJbeG6vV6UiqV9Oabb1JxcTFlZmaSq6vrE7k19GHxNTQ00MyZM2nIkCFUVFQkeV+a70I4efIkrVu3joqKiqisrIx27txJgwYNorlz59p9fHV1dfThhx+SVqul8vJyOnLkCI0dO5aCg4Opvr5e7MOe56+jGM1qamrI1dWVNm3a1GZ/e57Djs4LRF1z3DTf2rtixQq6ePEibdy4kW/t7QobNmwgf39/ksvlFBkZSadOnbL1kDoEwOpj+/btRERUUVFBkydPJk9PT1IoFDR8+HBasWKFZJ0KIqIrV65QbGwsubi4kJeXFy1fvpwaGxttEFFbcXFx5OPjQ3K5nAYPHkxxcXFUWloq1t+/f5/effddGjBgALm6utIrr7xClZWVkj7sOT4iol9++YUAUElJiaTcUecvNzfX6u9lYmIiEZlu7121ahUplUpSKBQ0derUNrHfunWL4uPjqV+/fuTm5kbz58+nuro6SZuzZ8/S888/TwqFggYPHkzp6ek2j6+8vLzd96V57ZiCggJSq9Xk7u5Ozs7O9Mwzz9D//vc/ycncXuO7d+8eTZs2jQYNGkR9+/algIAAWrhwYZv/uNnz/HUUo9mWLVvIxcWF9Hp9m/3teQ47Oi8Qdd1xMzc3l0aPHk1yuZyCgoIkr/E4hOZAGGOMMcZsold+ZoQxxhhj9oOTEcYYY4zZFCcjjDHGGLMpTkYYY4wxZlOcjDDGGGPMpjgZYYwxxphNcTLCGGOMMZviZIQxxhhjNsXJCGOMMcZsipMRxhhjjNkUJyOMMcYYsylORhhjjDFmU/8HsljlR+mN9i8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "w_list, w_iterations_list = lr_train(seven_X, seven_y, trace=True)\n",
    "\n",
    "# model h\n",
    "for X in seven_X:\n",
    "    h_list = list()\n",
    "    \n",
    "    # calculate zero-one error\n",
    "    def override_h(x, w):\n",
    "        x = np.insert(x, 0, 1)\n",
    "        return lr_h(x, w)\n",
    "    \n",
    "    for w_iteration in w_iterations_list:\n",
    "        h_out = override_h(X, w_iteration)\n",
    "        h_list.append(h_out)\n",
    "        \n",
    "    # add to chart\n",
    "    plt.plot(X_os, h_list)\n",
    "    \n",
    "# show chart of iterations\n",
    "plt.legend([f\"X{i}\" for i in range(1, len(seven_X)+1)], loc=\"best\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# iteration weights\n",
    "w0_list = list()\n",
    "w1_list = list()\n",
    "w2_list = list()\n",
    "\n",
    "for w_iteration in w_iterations_list:\n",
    "    w0_list.append(w_iteration[0])\n",
    "    w1_list.append(w_iteration[1])\n",
    "    w2_list.append(w_iteration[2])\n",
    "\n",
    "# show w chart\n",
    "X_os = np.linspace(0, len(w0_list), len(w0_list))\n",
    "plt.plot(X_os, w0_list)\n",
    "plt.plot(X_os, w1_list)\n",
    "plt.plot(X_os, w2_list)\n",
    "plt.legend([\"w0\", \"w1\", \"w2\"], loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (c)\n",
    "\n",
    "Ponovite eksperiment iz podzadatka (b) koristeći linearno neodvojiv skup podataka `unsep`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsep_X = np.append(seven_X, [[2,2]], axis=0)\n",
    "unsep_y = np.append(seven_y, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "w_list, w_iterations_list = lr_train(unsep_X, unsep_y, trace=True)\n",
    "\n",
    "# model h\n",
    "for X in unsep_X:\n",
    "    h_list = list()\n",
    "    \n",
    "    # calculate zero-one error\n",
    "    def override_h(x, w):\n",
    "        x = np.insert(x, 0, 1)\n",
    "        return lr_h(x, w)\n",
    "    \n",
    "    for w_iteration in w_iterations_list:\n",
    "        h_out = override_h(X, w_iteration)\n",
    "        h_list.append(h_out)\n",
    "        \n",
    "    # add to chart\n",
    "    plt.plot(X_os, h_list)\n",
    "    \n",
    "# show chart of iterations\n",
    "plt.legend([f\"X{i}\" for i in range(1, len(unsep_X)+1)], loc=\"best\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# iteration weights\n",
    "w0_list = list()\n",
    "w1_list = list()\n",
    "w2_list = list()\n",
    "\n",
    "for w_iteration in w_iterations_list:\n",
    "    w0_list.append(w_iteration[0])\n",
    "    w1_list.append(w_iteration[1])\n",
    "    w2_list.append(w_iteration[2])\n",
    "\n",
    "# show w chart\n",
    "X_os = np.linspace(0, len(w0_list), len(w0_list))\n",
    "plt.plot(X_os, w0_list)\n",
    "plt.plot(X_os, w1_list)\n",
    "plt.plot(X_os, w2_list)\n",
    "plt.legend([\"w0\", \"w1\", \"w2\"], loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Usporedite grafikone za slučaj linearno odvojivih i linearno neodvojivih primjera te komentirajte razliku."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Regularizirana logistička regresija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trenirajte model logističke regresije na skupu `seven` s različitim faktorima L2-regularizacije, $\\alpha\\in\\{0,1,10,100\\}$. Prikažite na dva odvojena grafikona (1) pogrešku unakrsne entropije te (2) L2-normu vektora $\\mathbf{w}$ kroz iteracije optimizacijskog algoritma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vaš kôd ovdje\n",
    "\n",
    "alpha_list = [0, 1, 10, 100]\n",
    "\n",
    "# cross entropy error chart\n",
    "for alpha in alpha_list:\n",
    "    w_list, w_iterations_list = lr_train(seven_X, seven_y, alpha=alpha, trace=True)\n",
    "    \n",
    "    # calculate cross entropy error\n",
    "    E_cross_list = list()\n",
    "    for w_iteration in w_iterations_list:\n",
    "        E_cross = cross_entropy_error(seven_X1, seven_y, w_iteration)\n",
    "        E_cross_list.append(E_cross)\n",
    "        \n",
    "    # add to chart\n",
    "    X_os = np.linspace(0, len(w_iterations_list), len(w_iterations_list))\n",
    "    plt.plot(X_os, E_cross_list)\n",
    "\n",
    "# show chart\n",
    "plt.legend(alpha_list, loc=\"best\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# L2 chart\n",
    "for alpha in alpha_list:\n",
    "    w_list, w_iterations_list = lr_train(seven_X, seven_y, alpha=alpha, trace=True)\n",
    "    \n",
    "    # L2 norm\n",
    "    L2_norm_list = list()\n",
    "    for w_iteration in w_iterations_list:\n",
    "        L2_norm = norm(w_iteration)\n",
    "        L2_norm_list.append(L2_norm)\n",
    "        \n",
    "    # add to chart\n",
    "    X_os = np.linspace(0, len(w_iterations_list), len(w_iterations_list))\n",
    "    plt.plot(X_os, L2_norm_list)\n",
    "    \n",
    "# show chart\n",
    "plt.legend(alpha_list, loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Jesu li izgledi krivulja očekivani i zašto?\n",
    "\n",
    "**Q:** Koju biste vrijednost za $\\alpha$ odabrali i zašto?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Logistička regresija s funkcijom preslikavanja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proučite funkciju [`datasets.make_classification`](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html). Generirajte i prikažite dvoklasan skup podataka s ukupno $N=100$ dvodimenzijskih ($n=2)$ primjera, i to sa dvije grupe po klasi (`n_clusters_per_class=2`). Malo je izgledno da će tako generiran skup biti linearno odvojiv, međutim to nije problem jer primjere možemo preslikati u višedimenzijski prostor značajki pomoću klase [`preprocessing.PolynomialFeatures`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html), kao što smo to učinili kod linearne regresije u prvoj laboratorijskoj vježbi. Trenirajte model logističke regresije koristeći za preslikavanje u prostor značajki polinomijalnu funkciju stupnja $d=2$ i stupnja $d=3$. Prikažite dobivene granice između klasa. Možete koristiti svoju implementaciju, ali se radi brzine preporuča koristiti `linear_model.LogisticRegression`. Regularizacijski faktor odaberite po želji.\n",
    "\n",
    "**NB:** Kao i ranije, za prikaz granice između klasa koristite funkciju `plot_2d_clf_problem`. Funkciji kao argumente predajte izvorni skup podataka, a preslikavanje u prostor značajki napravite unutar poziva funkcije `h` koja čini predikciju, na sljedeći način:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "#plot_2d_clf_problem(X, y, lambda x : model.predict(poly.transform(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Vaš kôd ovdje\n",
    "\n",
    "# generate dataset\n",
    "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_clusters_per_class=2, n_redundant=0)\n",
    "\n",
    "# move to feature space d = 2\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "X1 = poly.fit_transform(X)\n",
    "\n",
    "clf = LogisticRegression()\n",
    "model = clf.fit(X1, y)\n",
    "\n",
    "# show chart\n",
    "plot_2d_clf_problem(X, y, lambda x : model.predict(poly.transform(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# move to feature space d = 3\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "X1 = poly.fit_transform(X)\n",
    "\n",
    "clf = LogisticRegression()\n",
    "model = clf.fit(X1, y)\n",
    "\n",
    "# show chart\n",
    "plot_2d_clf_problem(X, y, lambda x : model.predict(poly.transform(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q:** Koji biste stupanj polinoma upotrijebili i zašto? Je li taj odabir povezan s odabirom regularizacijskog faktora $\\alpha$? Zašto?"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}