6_1_Nykyforov_FIT_4_6.ipynb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO5VTKhRaRUrHMBaffOGe+Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vovizzz/ml/blob/main/6_1_Nykyforov_FIT_4_6.ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "ZnfwD2H_DnCl",
        "outputId": "f438cf6e-48ce-4b79-9bcf-1055281bfe97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train MSE: 0.5179331255246699, Test MSE: 0.5558915986952442\n",
            "Train R^2: 0.6125511913966952, Test R^2: 0.575787706032451\n",
            "Ridge Test MSE: 0.5558879138674184\n",
            "Lasso Test MSE: 0.5482548967938964\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAG4CAYAAABl37IJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABPoklEQVR4nO3de1xUdf4/8NeZAQZRGBHkKhe5Jt4gDBTRvCDg1qplrdaa6LcoXbOSNnfbWkvdMjNNKze7SGK5SW1t9jMzFEVNMApDzYqbclMHBLkjA8zM7w9ycuTiMAJnZng9H4/z2M45n3PmfaazzKvP5zNnBI1GowERERERdYtE7AKIiIiITBFDFBEREZEBGKKIiIiIDMAQRURERGQAhigiIiIiAzBEERERERmAIYqIiIjIAAxRRERERAZgiCIiIiIyAEMUERERkQFED1Fbt26Ft7c3rK2tER4ejszMzE7b7tixA4Ig6CzW1tY6bRYtWtSuTWxsbLtzffXVVwgPD8eAAQNgb2+POXPm6OwvLi7GXXfdBRsbGzg5OeGZZ55Ba2trj1wzERERmT4LMV88OTkZCQkJ2LZtG8LDw7F582bExMQgJycHTk5OHR5jZ2eHnJwc7bogCO3axMbG4oMPPtCuy2Qynf2fffYZ4uPj8fLLL2PatGlobW3FTz/9pN2vUqlw1113wcXFBenp6bh06RIWLlwIS0tLvPzyy3pfn1qtxsWLF2Fra9thnURERGR8NBoN6urq4ObmBomki/4mjYjCwsI0y5Yt066rVCqNm5ubZt26dR22/+CDDzRyubzLc8bFxWlmz57d6f6WlhaNu7u75v333++0zb59+zQSiUSjUCi0295++22NnZ2dRqlUdvn61yspKdEA4MKFCxcuXLiY4FJSUtLl57xoPVHNzc3IysrCs88+q90mkUgQFRWFjIyMTo+rr6+Hl5cX1Go1br/9drz88ssYOXKkTpu0tDQ4OTnB3t4e06ZNw7/+9S84ODgAAE6ePIkLFy5AIpEgJCQECoUCwcHB2LBhA0aNGgUAyMjIwOjRo+Hs7Kw9Z0xMDJYuXYqzZ88iJCSkw9qUSiWUSqV2XaPRAABKSkpgZ2fXzXeIiIiIxFBbWwsPDw/Y2tp22U60EFVRUQGVSqUTVADA2dkZv/76a4fHBAYGIjExEWPGjEFNTQ1ee+01RERE4OzZsxg2bBiAtqG8e++9F8OHD0dBQQH+8Y9/YObMmcjIyIBUKsW5c+cAAC+++CI2bdoEb29vbNy4EVOmTEFubi6GDBkChULRYV0AoFAoOr2mdevWYfXq1e2229nZMUQRERGZmJtNxRF1TlR3TZgwARMmTNCuR0REYMSIEXjnnXewdu1aAMD8+fO1+0ePHo0xY8bA19cXaWlpmD59OtRqNQDgueeew9y5cwEAH3zwAYYNG4ZPP/0Ujz32mMH1Pfvss0hISNCuX0uyREREZH5E+3aeo6MjpFIpysrKdLaXlZXBxcVFr3NYWloiJCQE+fn5nbbx8fGBo6Ojto2rqysAICgoSNtGJpPBx8cHxcXFAAAXF5cO67q2rzMymUzb68TeJyIiIvMmWk+UlZUVQkNDkZqaqn28gFqtRmpqKh5//HG9zqFSqXDmzBn84Q9/6LRNaWkpKisrteEpNDQUMpkMOTk5iIyMBAC0tLSgsLAQXl5eANp6vF566SWUl5drvyV44MAB2NnZ6YQvIiKi3qRSqdDS0iJ2GWbH0tISUqn0ls8j6nBeQkIC4uLiMG7cOISFhWHz5s1oaGjA4sWLAQALFy6Eu7s71q1bBwBYs2YNxo8fDz8/P1RXV2PDhg0oKirCI488AqBt0vnq1asxd+5cuLi4oKCgACtXroSfnx9iYmIAtM1PWrJkCV544QV4eHjAy8sLGzZsAADcf//9AIDo6GgEBQXhoYcewquvvgqFQoHnn38ey5Yta/e4BCIiop6m0WigUChQXV0tdilma/DgwXBxcbmlRxCJGqLmzZuHy5cvY9WqVdpvye3fv187ibu4uFjn+QxVVVWIj4+HQqGAvb09QkNDkZ6eru0dkkqlOH36NJKSklBdXQ03NzdER0dj7dq1OuFnw4YNsLCwwEMPPYSrV68iPDwchw4dgr29vfY8e/fuxdKlSzFhwgQMHDgQcXFxWLNmTR++O0RE1F9dC1BOTk6wsbHhswZ7kEajQWNjI8rLywH8Ps3HEILm2vfwqcfV1tZCLpejpqaG86OIiEgvKpUKubm5cHJy0j6eh3peZWUlysvLERAQ0G5oT9/Pb9F/9oWIiIh+d20OlI2NjciVmLdr7++tzDljiCIiIjJCHMLrXT3x/jJEERERERmAIYqIiIjIAAxRRERE1CcKCwshCAKys7M7bZOWlgZBEEzi8Q4MUURERNQjFi1aBEEQIAgCLC0tMXz4cKxcuRJNTU0AAA8PD1y6dAmjRo0SudKeYVK/nUdt/m/H91CpNZBZSGD12yKzkEBmIW1bl+puv36bTrvftsssbzhGKtVuk0g4sZGIiPQXGxuLDz74AC0tLcjKykJcXBwEQcD69eshlUr1/mk3U8AQZYKO5V1Gi6pvHu9lIRF0w5g2kEm122Q3hLTf20l1ApqsXbCTdhj22gW937ZbSgV+W4WI+h2NRoOrLSpRXnuApbTbf3dlMpk2KHl4eCAqKgoHDhzA+vXrUVhYiOHDh+PHH39EcHAwAGDfvn146qmnUFJSgvHjxyMuLq7dOd977z2sWbMGlZWViImJwaRJk7BmzRqdIb89e/Zg9erV+Pnnn+Hm5oa4uDg899xzsLDovajDEGWCXrt/LJStajRfW1RqKFvUaFap2m1Tqq5r16qGslWFZtX169f982/br9eq1qC1WYXGZnH+D3w9QUDHPWpd9LxZ/dbuxpB2Y0CTWUrb7WvfiyfVCY3spSOivnC1RYWgVd+I8to/r4mBjZXhUeGnn35Cenq69rdpb1RSUoJ7770Xy5Ytw6OPPooffvgBTz/9tE6b48ePY8mSJVi/fj1mzZqFgwcP4p///KdOm2PHjmHhwoV44403MGnSJBQUFODRRx8FALzwwgsG138zDFEmaHawe6+dW6PR6ISsa/+sbL3xf1U6+3UCmartn7Vt2oU93eCmbOkk2KnUUKk119WG386rRl2vvQP6s5AIHQcyC90ets6CXfth1ht68To4t7Wlbg/etWMtJOylIyLjsHfvXgwaNAitra1QKpWQSCR46623Omz79ttvw9fXFxs3bgQABAYG4syZM1i/fr22zZtvvomZM2fir3/9KwAgICAA6enp2Lt3r7bN6tWr8fe//13bi+Xj44O1a9di5cqVDFHUdwRB+K3n5tZ/3bonqNQandCm7CDYtQUulTag6fSuXbf/xm1KnfOoOgyN+vTSNRhdL520g160G4djb+hZ++1/ba0tcIf3EIwZNhhS9rQRGYUBllL8vCZGtNfurqlTp+Ltt99GQ0MDXn/9dVhYWGDu3Lkdtv3ll18QHh6us23ChAk66zk5Objnnnt0toWFhemEqFOnTuH48eN46aWXtNtUKhWamprQ2NjYa09/Z4gioyaVCBhgJcUAK/FD3Y29dNf3mF1b1+l96zDstfW8tR9m1T3mWnDsPDR21UvXesvXamdtgYl+jpjkPxST/B3hMYQ/P0EkFkEQbmlIra8NHDgQfn5+AIDExESMHTsW27dvx8MPP9xrr1lfX4/Vq1fj3nvvbbfP2tq6117XdP6tEInM2HrpWlUdDKd2NGR6XS/bjcOy1/fSKVvVKKttQkZBJWqbWvH1Twp8/ZMCADDccSAm+beFqvE+Q2BrbSny1RORKZBIJPjHP/6BhIQEPPjgg+32jxgxAl9++aXOthMnTuisBwYG4vvvv9fZduP67bffjpycHG146ysMUUQmykIqgYVUAhurnj1vq0qN0xdqcCy3At/mX8bJ4mqcr2jA+YoG7MwogoVEQIjnYG0vFYf+iKgr999/P5555hls3boV9913n86+JUuWYOPGjXjmmWfwyCOPICsrCzt27NBps3z5ckyePBmbNm3CH//4Rxw6dAhff/21zjzQVatW4e6774anpyfuu+8+SCQSnDp1Cj/99BP+9a9/9dq18WGbRKTDQirB7Z72eDLKH58uicCPq2bg3YdC8dB4L3g72KBVrcH3hVXYdCAX9/w7HbevPYC/7MrCx5nFKLnSKHb5RGRkLCws8Pjjj+PVV19FQ0ODzj5PT0989tln+OKLLzB27Fhs27YNL7/8sk6biRMnYtu2bdi0aRPGjh2L/fv3Y8WKFTrDdDExMdi7dy9SUlJwxx13YPz48Xj99dc7/VZgTxE0Gk3fPHCoH6qtrYVcLkdNTQ3s7OzELoeoR5RcacSxvAocy7uMb/MrUNekOweLQ39Et6apqQnnz5/H8OHDe3U+jymLj4/Hr7/+imPHjhl8jq7eZ30/vzmcR0Td4jHEBg+Ge+LBcE+dob9jeZfxY0n7ob/bPe0R6e/IoT8iMthrr72GGTNmYODAgfj666+RlJSEf//732KXxZ6o3sSeKOpvaptacKKgUttTVVipO7wnH2CJiX4OmOQ/FJF+/NYfUUfYE9Xen/70J6SlpaGurg4+Pj5Yvnw5lixZckvn7ImeKIaoXsQQRf1dcWUjjuVfxrd5FRz6I9ITQ1Tf4HAeERk1Twcb/NnBC38O99J76G+SvyMiOfRHRCaAPVG9iD1RRJ3rztDfJH9HDLPn0B/1D9d6SLy9vTFgwACxyzFbV69e1f4gMnuiiMik2FlbInqkC6JHtv3a+7Whv2O5FTheUIGaqy3Yd0aBfWfaHvjp89vQX6T/UEzwdcAgGf98kXmytGwb1m5sbGSI6kWNjW3/4Xbt/TYEe6J6EXuiiAzT0dDf9T9zc/3Q36SAoRjtLufQH5mVS5cuobq6Gk5OTrCxseEPjPcgjUaDxsZGlJeXY/DgwXB1dW3XhhPLjQBDFFHPqG1qQUZBJb7l0B/1ExqNBgqFAtXV1WKXYrYGDx4MFxeXDgMqQ5QRYIgi6h03Dv3d+K0/n+u/9cehPzJhKpUKLS0tYpdhdiwtLSGVdv47qAxRRoAhiqj3ceiPiHoaQ5QRYIgi6nvXhv6O5bU9n6qjob9IP0ftoxQ49EdEN2KIMgIMUUTi49AfEXUXQ5QRYIgiMi6tKjVOldZoJ6hz6I+IOsIQZQQYooiM2/VDf8fyKlDEoT8iAkOUUWCIIjItHPojIoAhyigwRBGZrmtDf9cmqHc49Odlj8m/PUWdQ39E5oMhyggwRBGZDw79EfUfDFFGgCGKyHzddOhv6EBM8uPQH5EpYogyAgxRRP3D9UN/x/IqkN3F0N8k/6EYxaE/IqPGEGUEGKKI+qebDf0NtrHERF9H7aMU3AcPEKlSIuoIQ5QRYIgiIgAoqmzAsbwKfJvX+dDf5N9+PDnch0N/RGJjiDICDFFEdCMO/REZP4YoI8AQRUQ3o9fQn59j2yR1Dv0R9QmGKCPAEEVE3XVt6O9Y3mWkF1R2OfQ33scBAzn0R9TjGKKMAEMUEd2Kmw39WUoFhHhy6I+opzFEGQGGKCLqSTVX24b+vs3veujv2lPUOfRHZBiGKCPAEEVEvUln6C+/EnVKDv0R9QSGKCPAEEVEfUWfob/bPe0xOWAoIv0cOfRH1AWGKCPAEEVEYrk29Hcs7zK+zefQH1F3MEQZAYYoIjIWNxv68x06EJM49EcEgCHKKDBEEZExahv6q/4tVHU99DfJ3xEj3Tj0R/0LQ5QRYIgiIlNw/dDfsbwKFF/pfOhvkv9QuHHoj8wcQ5QRYIgiIlPEoT/q7xiijABDFBGZumtDf0dzK/BtfgV+LK7CdSN/HPojs8QQZQQYoojI3Nxs6M/+2m/9ceiPTBhDlBFgiCIic6fv0N/SKb5wtrMWqUqi7mGIMgIMUUTUn1w/9Hcs7zKyS6q1Q38jXO3w1fJISDjURyZA389vSR/W1KmtW7fC29sb1tbWCA8PR2ZmZqdtd+zYAUEQdBZra93/ulm0aFG7NrGxsTptvL2927V55ZVXtPsLCwvb7RcEASdOnOjZiyciMhMWUglCvYZgxYwAfP6XifhxVTS2LbgdtjIL/HKpFvt+uiR2iUQ9SvSvVCQnJyMhIQHbtm1DeHg4Nm/ejJiYGOTk5MDJyanDY+zs7JCTk6NdF4T2/2UTGxuLDz74QLsuk8natVmzZg3i4+O167a2tu3aHDx4ECNHjtSuOzg46HdhRET9nHyAJWJHuSJHUY/XD+Zi04FcxI50gYXUKP77neiWiX4nb9q0CfHx8Vi8eDGCgoKwbds22NjYIDExsdNjBEGAi4uLdnF2dm7XRiaT6bSxt7dv18bW1lanzcCBA9u1cXBw0GljaWl5axdMRNTP/F+kN+xtLHHucgO+yL4odjlEPUbUENXc3IysrCxERUVpt0kkEkRFRSEjI6PT4+rr6+Hl5QUPDw/Mnj0bZ8+ebdcmLS0NTk5OCAwMxNKlS1FZWdmuzSuvvAIHBweEhIRgw4YNaG1tbddm1qxZcHJyQmRkJL788ssur0epVKK2tlZnISLq72ytLbHkTl8AwJbUXDS3qkWuiKhniBqiKioqoFKp2vUkOTs7Q6FQdHhMYGAgEhMTsWfPHnz00UdQq9WIiIhAaWmptk1sbCx27tyJ1NRUrF+/HkeOHMHMmTOhUqm0bZ544gns3r0bhw8fxmOPPYaXX34ZK1eu1O4fNGgQNm7ciE8//RRfffUVIiMjMWfOnC6D1Lp16yCXy7WLh4eHoW8NEZFZWTjBG46DZCi5chWf/FAidjlEPULUb+ddvHgR7u7uSE9Px4QJE7TbV65ciSNHjuC777676TlaWlowYsQIPPDAA1i7dm2Hbc6dOwdfX18cPHgQ06dP77BNYmIiHnvsMdTX13c4fwoAFi5ciPPnz+PYsWMd7lcqlVAqldr12tpaeHh48Nt5REQAdhw/jxf/389wtpPhyDNTYW0pFbskog6ZxLfzHB0dIZVKUVZWprO9rKwMLi4uep3D0tISISEhyM/P77SNj48PHB0du2wTHh6O1tZWFBYWdtmmq3PIZDLY2dnpLERE1OaBcE+4ya1RVqvEru+KxS6H6JaJGqKsrKwQGhqK1NRU7Ta1Wo3U1FSdnqmuqFQqnDlzBq6urp22KS0tRWVlZZdtsrOzIZFIOv1G4LU2XZ2DiIg6J7OQ4onp/gCAfx/OR4Oy/TxUIlMi+iMOEhISEBcXh3HjxiEsLAybN29GQ0MDFi9eDKBtCM3d3R3r1q0D0PZYgvHjx8PPzw/V1dXYsGEDioqK8MgjjwBom3S+evVqzJ07Fy4uLigoKMDKlSvh5+eHmJgYAEBGRga+++47TJ06Fba2tsjIyMCKFSuwYMEC7bf4kpKSYGVlhZCQEADA559/jsTERLz//vt9/RYREZmNuaHD8PaRAhRVNmJHeiGWTfUTuyQig4keoubNm4fLly9j1apVUCgUCA4Oxv79+7WTzYuLiyGR/N5hVlVVhfj4eCgUCtjb2yM0NBTp6ekICgoCAEilUpw+fRpJSUmorq6Gm5sboqOjsXbtWu1cJ5lMht27d+PFF1+EUqnE8OHDsWLFCiQkJOjUtnbtWhQVFcHCwgK33XYbkpOTcd999/XRO0NEZH4spRKsiArAU8nZeOdIARaM94J8AB8dQ6aJP/vSi/izL0RE7anUGsRuPoq88no8Mc0PCdGBYpdEpMMkJpYTEVH/I5UISJgRAADY/u15XGloFrkiIsMwRBERUZ+LHeWCkW52aGhW4Z0jBWKXQ2QQhigiIupzgiDgr78N4yVlFKK8tknkioi6jyGKiIhEMSVwKG73HIymFjW2Hu78GXxExoohioiIRCEIAv4a09Yb9Z/MYpRWNYpcEVH3MEQREZFoInwdEeHrgBaVBm+msjeKTAtDFBERierp3+ZG/fdkKc5XNIhcDZH+GKKIiEhUoV72mHabE1RqDbYczBW7HCK9MUQREZHorj03as+pi8hR1IlcDZF+GKKIiEh0o9zlmDnKBRoN8PoB9kaRaWCIIiIio5AwIwCCAOw/q8CZ0hqxyyG6KYYoIiIyCv7OtpgT7A4A2HggR+RqiG6OIYqIiIzGk9P9IZUISMu5jB8Kr4hdDlGXGKKIiMhoeDsOxJ/GDQMAbEzh3CgybgxRRERkVB6f5g8rqQQZ5ypxPL9C7HKIOsUQRURERsV98AA8GO4JAHgtJQcajUbkiog6xhBFRERG5y9TfWFtKcGPxdU4nFMudjlEHWKIIiIio+Nka424CG8AwGvf5EKtZm8UGR+GKCIiMkpLJvtikMwCP1+qxf6zCrHLIWqHIYqIiIyS/UArPBw5HACw6UAuVOyNIiPDEEVEREbr4UnDIR9gifzyeuzJviB2OUQ6GKKIiMho2Vlb4rE7fQAAmw/moUWlFrkiot8xRBERkVFbFOENx0FWKL7SiP9mlYpdDpEWQxQRERk1GysL/GWKHwDgjdQ8NLWoRK6IqA1DFBERGb0Hwz3hKrfGpZomfJxZLHY5RAAYooiIyARYW0qxfJo/AGDr4QI0NreKXBERQxQREZmI+8cNg+cQG1TUK5GUXiR2OUQMUUREZBospRI8Ob2tN2rbkQLUNrWIXBH1dwxRRERkMuaEuMN36EDUXG1B4rfnxS6H+jmGKCIiMhlSiYCEGYEAgPePnUdVQ7PIFVF/xhBFREQmZeYoF4xwtUO9shXvHD0ndjnUjzFEERGRSZFIBPw1OgAAsCP9PMrrmkSuiPorhigiIjI5025zQrDHYDS1qPHvwwVil0P9FEMUERGZHEEQ8NfotrlR//muGBerr4pcEfVHDFFERGSSJvo5YLzPEDSr1HjzUL7Y5VA/xBBFREQmSRAEPP1bb9SnP5SgqLJB5Iqov2GIIiIik3WH9xDcGTAUrWoNthzME7sc6mcYooiIyKRdmxv1v+wLyCurE7ka6k8YooiIyKSNHiZHzEhnaDTA6wdzxS6H+hGGKCIiMnkJMwIhCMC+Mwr8dKFG7HKon2CIIiIikxfoYotZY90AAK8fYG8U9Q2GKCIiMgtPTveHVCIg9ddynCyuErsc6gcYooiIyCz4DB2Eube7AwA2puSIXA31BwxRRERkNp6Y7g9LqYDj+ZVIL6gQuxwycwxRRERkNobZ2+CBME8AwMaUXGg0GpErInPGEEVERGZl2VQ/yCwkyCqqQlruZbHLITPGEEVERGbF2c4acRHeANrmRrE3inoLQxQREZmdxyb7YKCVFD9dqMU3ZxVil0NmiiGKiIjMjsMgGf4vcjgAYNOBXKjU7I2inscQRUREZumRST6ws7ZAblk99p6+KHY5ZIYYooiIyCzJB1jisTt9AbQ9xbxFpRa5IjI3DFFERGS2FkV4w2GgFQorG/H5yVKxyyEzwxBFRERma6DMAkuntPVGvZGaD2WrSuSKyJwYRYjaunUrvL29YW1tjfDwcGRmZnbadseOHRAEQWextrbWabNo0aJ2bWJjY3XaeHt7t2vzyiuv6LQ5ffo0Jk2aBGtra3h4eODVV1/tuYsmIqI+sWC8F5ztZLhQfRW7M0vELofMiOghKjk5GQkJCXjhhRdw8uRJjB07FjExMSgvL+/0GDs7O1y6dEm7FBUVtWsTGxur0+bjjz9u12bNmjU6bZYvX67dV1tbi+joaHh5eSErKwsbNmzAiy++iHfffbdnLpyIiPqEtaUUj0/zBwC8dTgfV5vZG0U9Q/QQtWnTJsTHx2Px4sUICgrCtm3bYGNjg8TExE6PEQQBLi4u2sXZ2bldG5lMptPG3t6+XRtbW1udNgMHDtTu27VrF5qbm5GYmIiRI0di/vz5eOKJJ7Bp06aeuXAiIuoz88Z5YJj9AFyuU+LDE4Vil0NmQtQQ1dzcjKysLERFRWm3SSQSREVFISMjo9Pj6uvr4eXlBQ8PD8yePRtnz55t1yYtLQ1OTk4IDAzE0qVLUVlZ2a7NK6+8AgcHB4SEhGDDhg1obW3V7svIyMDkyZNhZWWl3RYTE4OcnBxUVVV1WJdSqURtba3OQkRE4rOykODJ6W29UW+nFaCuqUXkisgciBqiKioqoFKp2vUkOTs7Q6Ho+AmzgYGBSExMxJ49e/DRRx9BrVYjIiICpaW/f+siNjYWO3fuRGpqKtavX48jR45g5syZUKl+78J94oknsHv3bhw+fBiPPfYYXn75ZaxcuVK7X6FQdFjXtX0dWbduHeRyuXbx8PDo3htCRES95p4Qd/gMHYiqxhZ8cLxQ7HLIDFiIXUB3TZgwARMmTNCuR0REYMSIEXjnnXewdu1aAMD8+fO1+0ePHo0xY8bA19cXaWlpmD59OgAgISFB22bMmDGwsrLCY489hnXr1kEmkxlU27PPPqtz3traWgYpIiIjYSGVYEVUAJZ//CPeO3oOCyd4YbCN1c0PJOqEqD1Rjo6OkEqlKCsr09leVlYGFxcXvc5haWmJkJAQ5Ofnd9rGx8cHjo6OXbYJDw9Ha2srCgsLAQAuLi4d1nVtX0dkMhns7Ox0FiIiMh53jXbFbS62qFO24t2j58Quh0ycqCHKysoKoaGhSE1N1W5Tq9VITU3V6W3qikqlwpkzZ+Dq6tppm9LSUlRWVnbZJjs7GxKJBE5OTgDaeryOHj2Klpbfx80PHDiAwMDADiepExGR8ZNIBCTMCAAAfHC8EBX1SpErIlMm+rfzEhIS8N577yEpKQm//PILli5dioaGBixevBgAsHDhQjz77LPa9mvWrEFKSgrOnTuHkydPYsGCBSgqKsIjjzwCoG3S+TPPPIMTJ06gsLAQqampmD17Nvz8/BATEwOgbdL45s2bcerUKZw7dw67du3CihUrsGDBAm1AevDBB2FlZYWHH34YZ8+eRXJyMrZs2aIzXEdERKZnRpAzxg6T42qLCm+nFYhdDpkw0edEzZs3D5cvX8aqVaugUCgQHByM/fv3aydxFxcXQyL5PetVVVUhPj4eCoUC9vb2CA0NRXp6OoKCggAAUqkUp0+fRlJSEqqrq+Hm5obo6GisXbtWO9dJJpNh9+7dePHFF6FUKjF8+HCsWLFCJyDJ5XKkpKRg2bJlCA0NhaOjI1atWoVHH320D98dIiLqaYIg4OnoQCxMzMSHJ4rwyKThcJUPELssMkGCRqPRiF2EuaqtrYVcLkdNTQ3nRxERGRGNRoN575xAZuEV/DncEy/dM1rsksiI6Pv5LfpwHhERUV9r641qmxuV/H0JSq40ilwRmSKGKCIi6pfCfRwwyd8RrWoNNh/ME7scMkEMUURE1G89HR0IAPjfj6XIL68XuRoyNQxRRETUbwV7DMaMIGeoNcDmg7lil0MmhiGKiIj6tWvPjdp7+hJ+vsjfPCX9MUQREVG/NsLVDnePaXsY86YD7I0i/TFEERFRv7diRgAkAnDwlzJkl1SLXQ6ZCIYoIiLq93yHDsK9tw8DAGxMyRG5GjIVDFFEREQAnpzuD0upgGN5FThxrlLscsgEMEQREREB8Bhig3l3eAAANqXkgj/oQTfDEEVERPSbx6f6w8pCgszCKziaVyF2OWTkGKKIiIh+4yK3xkPjvQC0zY1ibxR1hSGKiIjoOkun+MLGSorTpTU48HOZ2OWQEWOIIiIiuo7jIBkWT/QG0PbcKLWavVHUMYYoIiKiGzw6yRe21hb4VVGHvWcuiV0OGSmGKCIiohvIbSzx6CQfAMDmA7loValFroiMEUMUERFRBxZHDoe9jSXOVTTg8x8viF0OGSGGKCIiog4Mkllg6RRfAMCWg3lobmVvFOnSO0SVl5d3ub+1tRWZmZm3XBAREZGxeGi8N5xsZbhQfRXJP5SIXQ4ZGb1DlKurq06QGj16NEpKfr+hKisrMWHChJ6tjoiISEQDrKR4fJofAOCtQ3loalGJXBEZE71D1I0PHCssLERLS0uXbYiIiEzdvDs84D54AMpqlfjoRJHY5ZAR6dE5UYIg9OTpiIiIRCezkOKJ6W29Uf9OK0CDslXkishYcGI5ERHRTcy9fRi8HWxwpaEZHxw/L3Y5ZCT0DlGCIKCurg61tbWoqamBIAior69HbW2tdiEiIjJHFlIJVswIAAC8c/QcahpbbnIE9QfdmhMVEBAAe3t7DBkyBPX19QgJCYG9vT3s7e0RGBjYm3USERGJ6u4xbghwHoS6pla8/+05scshI2Chb8PDhw/3Zh1ERERGTSoRkDAjEEs+ykLit+exKMIbDoNkYpdFItI7RN155529WQcREZHRixnpjFHudvjpQi22HSnAc3cFiV0SiUjv4bzW1lYolUqdbWVlZVi9ejVWrlyJb7/9tseLIyIiMiaCIODp6LbpKzszilBW2yRyRSQmvUNUfHw8nnjiCe16XV0d7rjjDmzduhXffPMNpk6din379vVKkURERMZiSsBQjPOyh7JVjbcO5YtdDolI7xB1/PhxzJ07V7u+c+dOqFQq5OXl4dSpU0hISMCGDRt6pUgiIiJjcX1v1O7vi1FypVHkikgseoeoCxcuwN/fX7uempqKuXPnQi6XAwDi4uJw9uzZnq+QiIjIyEzwdcBEPwe0qDR481Ce2OWQSPQOUdbW1rh69ap2/cSJEwgPD9fZX19f37PVERERGalrvVGfnbyAc5f5+dcf6R2igoOD8eGHHwIAjh07hrKyMkybNk27v6CgAG5ubj1fIRERkRG63dMe029zgkqtweaD7I3qj/QOUatWrcKWLVvg6+uLmJgYLFq0CK6urtr9//vf/zBx4sReKZKIiMgYJUS3PcX8/52+iF8V/OWO/qZbz4nKyspCSkoKXFxccP/99+vsDw4ORlhYWI8XSEREZKxGuslx12hXfHXmEjal5OLdhePELon6kKDRaDRiF2GuamtrIZfLUVNTAzs7O7HLISKiXpBfXofo149CrQG+fHwixgwbLHZJdIv0/fzWuyfq6NGjerWbPHmyvqckIiIyeX5OtpgT7I7Pf7yAjSm5SPo/jsr0F3qHqClTpkAQBABtP0bcEUEQoFKpeqYyIiIiE/FklD++PHURR3Iv4/vCK7jDe4jYJVEf0Htiub29PTw8PPDPf/4TeXl5qKqqardcuXKlN2slIiIySl4OA3H/OA8AwIZvcjrtbCDzoneIunTpEtavX4+MjAyMHj0aDz/8MNLT02FnZwe5XK5diIiI+qPl0/xgJZUg8/wVHM+vFLsc6gN6hygrKyvMmzcP33zzDX799VeMGTMGjz/+ODw8PPDcc8+htbW1N+skIiIyam6DB+DP4z0BABtS2BvVH+gdoq7n6emJVatW4eDBgwgICMArr7yC2lo+H4OIiPq3pVN8McBSilMl1Uj9pVzscqiXdTtEKZVK/Oc//0FUVBRGjRoFR0dHfPXVVxgyhJPoiIiof3OytUZchDcAYOOBXKjV7I0yZ3qHqMzMTCxduhQuLi7YsGEDZs2ahZKSEnzyySeIjY3tzRqJiIhMxpI7fWArs8Avl2qx76dLYpdDvUjvh21KJBJ4enoiLi4OoaGhnbabNWtWjxVn6viwTSKi/mnzwVxsPpgHn6EDkfLUZFhIDZo9QyLR9/O7WyHqZvicKF0MUURE/VNdUwsmvXoY1Y0t2Hj/WMwNHSZ2SdQN+n5+6x2N1Wr1TRcGKCIiIsDW2hJL7vQFAGxOzUVzq1rkiqg3sH+RiIioFyyc4AXHQTKUXLmKT7NKxC6HegFDFBERUS+wsbLAsqltvVFvpuajqYWjNeaGIYqIiKiXPBjuCTe5NRS1Tdj1XbHY5VAPY4giIiLqJTILKZZP9wcAvJ2WjwYlf93DnDBEERER9aL7QofBy8EGFfXNSMooFLsc6kHdDlE+Pj6orGz/w4rV1dXw8fHpkaKIiIjMhaVUgqei2nqj3jlyDjVXW0SuiHpKt0NUYWFhh48yUCqVuHDhgkFFbN26Fd7e3rC2tkZ4eDgyMzM7bbtjxw4IgqCzWFtb67RZtGhRuzadPVVdqVQiODgYgiAgOztb5zpvPIcgCDhx4oRB10hERP3XrLHu8HMahJqrLdj+7Xmxy6EeYqFvwy+//FL7z9988w3kcrl2XaVSITU1Fd7e3t0uIDk5GQkJCdi2bRvCw8OxefNmxMTEICcnB05OTh0eY2dnh5ycHO26IAjt2sTGxuKDDz7Qrstksg7PtXLlSri5ueHUqVMd7j948CBGjhypXXdwcNDruoiIiK6RSgQkzAjAX3adROK357EowhtDBlqJXRbdIr1D1Jw5cwC0BZa4uDidfZaWlvD29sbGjRu7XcCmTZsQHx+PxYsXAwC2bduGr776ComJifj73//e4TGCIMDFxaXL88pkspu2+frrr5GSkoLPPvsMX3/9dYdtHBwcbnoeIiKim4kd6YKRbnY4e7EW7xwpwLN/GCF2SXSLuv3Eck9PT5SXl+s8qVypVCInJwd33313t168ubkZWVlZiIqK+r0giQRRUVHIyMjo9Lj6+np4eXnBw8MDs2fPxtmzZ9u1SUtLg5OTEwIDA7F06dJ287jKysoQHx+PDz/8EDY2Np2+1qxZs+Dk5ITIyEid3riOKJVK1NbW6ixEREQAIJEIeDo6AACQlFGI8tomkSuiW9XtOVHnz5+Ho6Ojzrbq6mqDXryiogIqlQrOzs46252dnaFQKDo8JjAwEImJidizZw8++ugjqNVqREREoLS0VNsmNjYWO3fuRGpqKtavX48jR45g5syZ2rlcGo0GixYtwpIlSzBu3LgOX2fQoEHYuHEjPv30U3z11VeIjIzEnDlzugxS69atg1wu1y4eHh7dfUuIiMiMTQ10QojnYDS1qPHvtAKxy6FbpemmV155RbN7927t+n333acRBEHj5uamyc7O7ta5Lly4oAGgSU9P19n+zDPPaMLCwvQ6R3Nzs8bX11fz/PPPd9qmoKBAA0Bz8OBBjUaj0WzZskUzceJETWtrq0aj0WjOnz+vAaD58ccfu3ythx56SBMZGdnp/qamJk1NTY12KSkp0QDQ1NTU6HUtRERk/o7nXdZ4/W2vxv8f+zSlVY1il0MdqKmp0evzu9s9Udu2bdP2sBw4cAAHDx7E/v37MXPmTDzzzDPdOpejoyOkUinKysp0tpeVlek9D8nS0hIhISHIz8/vtI2Pjw8cHR21bQ4dOoSMjAzIZDJYWFjAz88PADBu3Lh2872uFx4e3uXryGQy2NnZ6SxERETXi/BzxAQfBzSr1HgzNU/scugWdDtEKRQKbYjau3cv/vSnPyE6OhorV67E999/361zWVlZITQ0FKmpqdptarUaqampmDBhgl7nUKlUOHPmDFxdXTttU1paisrKSm2bN954A6dOnUJ2djays7Oxb98+AG3fFHzppZc6PU92dnaXr0NERKSPv8a0zY36NKsUhRUNIldDhtL723nX2Nvbo6SkBB4eHti/fz/+9a9/AWibZ9TR86NuJiEhAXFxcRg3bhzCwsKwefNmNDQ0aL+tt3DhQri7u2PdunUAgDVr1mD8+PHw8/NDdXU1NmzYgKKiIjzyyCMA2iadr169GnPnzoWLiwsKCgqwcuVK+Pn5ISYmBgDg6empU8OgQYMAAL6+vhg2bBgAICkpCVZWVggJCQEAfP7550hMTMT777/f7WskIiK6XqjXEEwNHIrDOZex+WAuNs8PEbskMkC3Q9S9996LBx98EP7+/qisrMTMmTMBAD/++KN2WKw75s2bh8uXL2PVqlVQKBQIDg7G/v37tZPNi4uLIZH83mFWVVWF+Ph4KBQK2NvbIzQ0FOnp6QgKCgIASKVSnD59GklJSaiuroabmxuio6Oxdu3aTp8V1Zm1a9eiqKgIFhYWuO2225CcnIz77ruv29dIRER0o4QZgTiccxl7Tl3EX6b6IcDZVuySqJsEjUaj6c4BLS0t2LJlC0pKSrBo0SJtT83rr78OW1tbbY8QAbW1tZDL5aipqeH8KCIiamfJh1nYf1aBmaNc8PaCULHLod/o+/nd7RBF+mOIIiKiruSW1SFm81FoNMDe5ZEY5S6/+UHU6/T9/O72xHIA+PDDDxEZGQk3NzcUFRUBADZv3ow9e/YYVi0REVE/FOBsi9lj3QAAG1NybtKajE23Q9Tbb7+NhIQEzJw5E9XV1drJ5IMHD8bmzZt7uj4iIiKz9lRUAKQSAYdzLiOr6IrY5VA3dDtEvfnmm3jvvffw3HPPQSqVarePGzcOZ86c6dHiiIiIzJ2340DcH9r2zfDXvskVuRrqDoN+9uXaZPLryWQyNDTwWRdERETdtXy6P6ykEmScq0R6foXY5ZCeuh2ihg8fjuzs7Hbb9+/fjxEj+IvURERE3eU+eAAeCGt7kPVrKTngd75Mg94has2aNWhsbERCQgKWLVuG5ORkaDQaZGZm4qWXXsKzzz6LlStX9matREREZmvZVD9YW0pwsrgah3PKxS6H9KD3Iw6kUikuXboEJycn7Nq1Cy+++CIKCtp+gdrNzQ2rV6/Gww8/3KvFmho+4oCIiLpj3b5f8M7RcxjpZof/93gkJBJB7JL6pR5/TpREIoFCoYCTk5N2W2NjI+rr63W20e8YooiIqDuuNDRj8quHUa9sxdt/vh0zR/P3WsXQK8+JEgTdRGxjY8MARURE1EOGDLTC/0UOBwBsPJALlZpzo4xZt347LyAgoF2QutGVK3zGBRERkaEejhyOpPRC5JfX48tTF3BPyDCxS6JOdCtErV69GnI5H0lPRETUW+QDLPHoZB9s+CYHmw/m4e4xbrCUGvQDI9TLuhWi5s+fz+E7IiKiXrZ4ojc+OH4eRZWN+G9WKR4I8xS7JOqA3tH2ZsN4RERE1DNsrCywdIofAOCN1Dw0tahErog6oneI4oO/iIiI+s6fwz3hYmeNSzVN2J1ZLHY51AG9Q5RareZQHhERUR+xtpRi+fS23qi3DhegsblV5IroRpypRkREZKTuD/WAx5ABqKhXYmdGkdjl0A0YooiIiIyUlYUET04PAABsO1KAuqYWkSui6zFEERERGbF7QtzhO3QgqhtbsP3b82KXQ9dhiCIiIjJiUomAFTPaeqO2HzuPqoZmkSuiaxiiiIiIjNwfRrlihKsd6pStePfYObHLod8wRBERERk5iUTA07/1Ru04XojyuiaRKyKAIYqIiMgkTB/hhLEeg3G1RYW30wrELofAEEVERGQSBEHAX6PbeqN2nSjGxeqrIldEDFFEREQmItLPEeHDh6BZpcabh/LFLqffY4giIiIyEYIg4OnoQADApz+UoKiyQeSK+jeGKCIiIhMSNnwIJgcMRatagy2peWKX068xRBEREZmYa3OjvvjxAvLL60Supv9iiCIiIjIxY4YNRnSQM9Qa4PUD7I0SC0MUERGRCUqIDoAgAF+duYSzF2vELqdfYogiIiIyQbe52OGPY9wAAJtSckWupn9iiCIiIjJRT0X5QyIAqb+W42Rxldjl9DsMUURERCbKZ+ggzL19GAD2RomBIYqIiMiEPTHdH5ZSAd/mVyCjoFLscvoVhigiIiIT5jHEBvPv8AQAbEzJgUajEbmi/oMhioiIyMQ9Ps0PMgsJfiiqwpHcy2KX028wRBEREZk4ZztrLJzgBQDYmJLL3qg+whBFRERkBpbc6QsbKynOXKjBN2fLxC6nX2CIIiIiMgMOg2T4v4nDAQCbDuRApWZvVG9jiCIiIjIT8ZN9YGdtgdyyeuw9fVHscsweQxQREZGZkA+wxKOTfQAArx/IRatKLXJF5o0hioiIyIwsmjgcQwZaobCyEZ+fvCB2OWaNIYqIiMiMDJJZ4C9TfAEAW1LzoGxViVyR+WKIIiIiMjMLxnvByVaGC9VXkfx9idjlmC2GKCIiIjNjbSnF8ml+AIA3D+XjajN7o3oDQxQREZEZmneHJ9wHD8DlOiU+PFEodjlmiSGKiIjIDFlZSPBklD8A4O20AtQrW0WuyPwwRBEREZmpe0Pc4eM4EFWNLfjg2/Nil2N2GKKIiIjMlIVUgqdmBAAA3j12DjWNLSJXZF4YooiIiMzY3aNdEehsi7qmVrx7rEDscswKQxQREZEZk0gEJES39UZ9cLwQFfVKkSsyHwxRREREZi46yBljhsnR2KzC22nsjeopDFFERERmThAEPB0dCAD48EQRFDVNIldkHowiRG3duhXe3t6wtrZGeHg4MjMzO227Y8cOCIKgs1hbW+u0WbRoUbs2sbGxHZ5PqVQiODgYgiAgOztbZ9/p06cxadIkWFtbw8PDA6+++uotXysREZEYJvs74g5vezS3qvHW4TyxyzELooeo5ORkJCQk4IUXXsDJkycxduxYxMTEoLy8vNNj7OzscOnSJe1SVFTUrk1sbKxOm48//rjDc61cuRJubm7tttfW1iI6OhpeXl7IysrChg0b8OKLL+Ldd981/GKJiIhEIggC/vpbb9TuzBKUXGkUuSLTJ3qI2rRpE+Lj47F48WIEBQVh27ZtsLGxQWJiYqfHCIIAFxcX7eLs7NyujUwm02ljb2/frs3XX3+NlJQUvPbaa+327dq1C83NzUhMTMTIkSMxf/58PPHEE9i0adOtXTAREZFIwn0cMMnfEa1qDbaksjfqVokaopqbm5GVlYWoqCjtNolEgqioKGRkZHR6XH19Pby8vODh4YHZs2fj7Nmz7dqkpaXByckJgYGBWLp0KSorK3X2l5WVIT4+Hh9++CFsbGzaHZ+RkYHJkyfDyspKuy0mJgY5OTmoqqrqsC6lUona2lqdhYiIyJhcmxv1+clSFFyuF7ka0yZqiKqoqIBKpWrXk+Ts7AyFQtHhMYGBgUhMTMSePXvw0UcfQa1WIyIiAqWlpdo2sbGx2LlzJ1JTU7F+/XocOXIEM2fOhErV9gOMGo0GixYtwpIlSzBu3LgOX0ehUHRY17V9HVm3bh3kcrl28fDw0O+NICIi6iPBHoMRNcIZag3w+oFcscsxaaIP53XXhAkTsHDhQgQHB+POO+/E559/jqFDh+Kdd97Rtpk/fz5mzZqF0aNHY86cOdi7dy++//57pKWlAQDefPNN1NXV4dlnn+3R2p599lnU1NRol5KSkh49PxERUU9I+O0p5ntPX8IvlzhqYihRQ5SjoyOkUinKysp0tpeVlcHFxUWvc1haWiIkJAT5+fmdtvHx8YGjo6O2zaFDh5CRkQGZTAYLCwv4+fkBAMaNG4e4uDgAgIuLS4d1XdvXEZlMBjs7O52FiIjI2AS52eGuMa4AgE3sjTKYqCHKysoKoaGhSE1N1W5Tq9VITU3FhAkT9DqHSqXCmTNn4Orq2mmb0tJSVFZWatu88cYbOHXqFLKzs5GdnY19+/YBaPum4EsvvQSgrcfr6NGjaGn5/XeGDhw4gMDAwA4nqRMREZmSFVEBkAjAgZ/LkF1SLXY5Jkn04byEhAS89957SEpKwi+//IKlS5eioaEBixcvBgAsXLhQZ9htzZo1SElJwblz53Dy5EksWLAARUVFeOSRRwC0TTp/5plncOLECRQWFiI1NRWzZ8+Gn58fYmJiAACenp4YNWqUdgkIaOvW9PX1xbBhwwAADz74IKysrPDwww/j7NmzSE5OxpYtW5CQkNCXbw8REVGv8HMahHtC2j7zNqbkiFyNabIQu4B58+bh8uXLWLVqFRQKBYKDg7F//37tJO7i4mJIJL9nvaqqKsTHx0OhUMDe3h6hoaFIT09HUFAQAEAqleL06dNISkpCdXU13NzcEB0djbVr10Imk+ldl1wuR0pKCpYtW4bQ0FA4Ojpi1apVePTRR3v2DSAiIhLJk9P9sSf7Ao7lVeC7c5UI93EQuySTImg0Go3YRZir2tpayOVy1NTUcH4UEREZpef+dwa7vitGmPcQJD82HoIgiF2S6PT9/BZ9OI+IiIjE8/g0P1hZSJBZeAXH8irELsekMEQRERH1Y67yAVgQ7gWgbW4UB6j0xxBFRETUz/1lqi8GWEpxqrQGB34uu/kBBIAhioiIqN9zHCTD4oneANqeG6VWszdKHwxRREREhEcn+8BWZoFfFXX46swlscsxCQxRREREhME2Voif7AOg7Tf1WlVqkSsyfgxRREREBABYPNEb9jaWOFfRgP/9eEHscoweQxQREREBAGytLbHkTl8AwJbUPDS3sjeqKwxRREREpLVwgjeG2spQWnUVyT+UiF2OUWOIIiIiIq0BVlI8PtUPAPDWoTw0tahErsh4MUQRERGRjvlhHnAfPABltUp8dKJI7HKMFkMUERER6ZBZSPHE9LbeqLfTCtCgbBW5IuPEEEVERETt3Hv7MHg72KCyoRk70gvFLscoMUQRERFRO5ZSCZ6KCgAAvHOkADVXW0SuyPgwRBEREVGH/jjWDQHOg1Db1Ir3j50TuxyjwxBFREREHZJKBCTMaOuNSvz2PCrrlSJXZFwYooiIiKhTMSNdMMrdDg3NKrxzlL1R12OIIiIiok4JgoCnowMBAEnphSirbRK5IuPBEEVERERdmhIwFKFe9lC2qrH1cL7Y5RgNhigiIiLqUltvVNvcqI8zi1Fa1ShyRcaBIYqIiIhuKsLXERP9HNCi0uCN1DyxyzEKDFFERESkl4QZbXOjPjt5Aecu14tcjfgYooiIiEgvoV72mHabE1RqDbawN4ohioiIiPR37blRX566iBxFncjViIshioiIiPQ2yl2OP4x2gUYDbDqQI3Y5omKIIiIiom5ZERUAQQC+OVuGM6U1YpcjGoYoIiIi6hZ/Z1vcE+wOAHgtpf/2RjFEERERUbc9GeUPqUTAkdzL+L7witjliIIhioiIiLrNy2Eg/jRuGADgtW9yoNFoRK6o7zFEERERkUGWT/OHlVSC785fwfH8SrHL6XMMUURERGQQt8ED8GC4J4C2uVH9rTeKIYqIiIgM9pepvrC2lCC7pBqHfi0Xu5w+xRBFREREBnOytcaiiOEAgNdScqFW95/eKIYoIiIiuiWPTfbBIJkFfrlUi69/UohdTp9hiCIiIqJbYj/QCg9HtvVGbTqQA1U/6Y1iiCIiIqJb9vCk4RhsY4mCyw344scLYpfTJxiiiIiI6JbZWVviscm+AIDNqbloUalFrqj3MUQRERFRj4iL8ILjIBlKrlzFpz+Uil1Or2OIIiIioh5hY2WBZVPbeqPePJSHphaVyBX1LoYoIiIi6jEPhHnCVW6NSzVN+M93xWKX06sYooiIiKjHWFtKsXyaPwDg32n5aGxuFbmi3sMQRURERD3q/nHD4DnEBhX1zdiRXih2Ob2GIYqIiIh6lKVUgqei2nqj3jlyDrVNLSJX1DsYooiIiKjHzQ52h5/TINRcbcH2Y+fFLqdXMEQRERFRj5NKBCTMCAAAbP/2PKoamkWuqOcxRBEREVGviB3pgiBXO9QrW7HtaIHY5fQ4higiIiLqFRKJgKej23qjktILUV7XJHJFPYshioiIiHrNtNucEOI5GE0tavz7sHn1RjFEERERUa8RBAF/jQ4EAPznu2JcqL4qckU9hyGKiIiIelWErwPG+wxBs0qNtw7liV1Oj2GIIiIiol51fW/UJz+UorCiQeSKegZDFBEREfW6cd5DMCVwKFRqDbakmkdvFEMUERER9YmnZ7T1Rn2RfQF5ZXUiV3PrjCJEbd26Fd7e3rC2tkZ4eDgyMzM7bbtjxw4IgqCzWFtb67RZtGhRuzaxsbE6bWbNmgVPT09YW1vD1dUVDz30EC5evKjdX1hY2O4cgiDgxIkTPXvxRERE/cToYXLEjnSBRgNsOpArdjm3TPQQlZycjISEBLzwwgs4efIkxo4di5iYGJSXl3d6jJ2dHS5duqRdioqK2rWJjY3VafPxxx/r7J86dSo++eQT5OTk4LPPPkNBQQHuu+++duc5ePCgznlCQ0Nv/aKJiIj6qRUzAiAIwNc/KfDThRqxy7klooeoTZs2IT4+HosXL0ZQUBC2bdsGGxsbJCYmdnqMIAhwcXHRLs7Ozu3ayGQynTb29vY6+1esWIHx48fDy8sLERER+Pvf/44TJ06gpUX3RxIdHBx0zmNpadkzF05ERNQPBbrYYtZYNwCm3xslaohqbm5GVlYWoqKitNskEgmioqKQkZHR6XH19fXw8vKCh4cHZs+ejbNnz7Zrk5aWBicnJwQGBmLp0qWorKzs9HxXrlzBrl27EBER0S4kzZo1C05OToiMjMSXX37Z5fUolUrU1tbqLERERKTrqagASCUCDv1ajqyiKrHLMZioIaqiogIqlapdT5KzszMUCkWHxwQGBiIxMRF79uzBRx99BLVajYiICJSWlmrbxMbGYufOnUhNTcX69etx5MgRzJw5EyqVSudcf/vb3zBw4EA4ODiguLgYe/bs0e4bNGgQNm7ciE8//RRfffUVIiMjMWfOnC6D1Lp16yCXy7WLh4eHIW8LERGRWRvuOBD33T4MALAxJUfkagwnaDQajVgvfvHiRbi7uyM9PR0TJkzQbl+5ciWOHDmC77777qbnaGlpwYgRI/DAAw9g7dq1HbY5d+4cfH19cfDgQUyfPl27vaKiAleuXEFRURFWr14NuVyOvXv3QhCEDs+zcOFCnD9/HseOHetwv1KphFKp1K7X1tbCw8MDNTU1sLOzu+m1EBER9RelVY2Y+loaWlQa/OeRcET4OYpdklZtbS3kcvlNP79F7YlydHSEVCpFWVmZzvaysjK4uLjodQ5LS0uEhIQgPz+/0zY+Pj5wdHRs18bR0REBAQGYMWMGdu/ejX379nX57bvw8PAuX0cmk8HOzk5nISIiovaG2dvgwTBPAMBrKTkQsU/HYKKGKCsrK4SGhiI1NVW7Ta1WIzU1VadnqisqlQpnzpyBq6trp21KS0tRWVnZZRu1Wg0AOj1JN8rOzu7yHERERKS/ZVP9ILOQ4GRxNdJyLotdTrdZiF1AQkIC4uLiMG7cOISFhWHz5s1oaGjA4sWLAbQNobm7u2PdunUAgDVr1mD8+PHw8/NDdXU1NmzYgKKiIjzyyCMA2iadr169GnPnzoWLiwsKCgqwcuVK+Pn5ISYmBgDw3Xff4fvvv0dkZCTs7e1RUFCAf/7zn/D19dWGt6SkJFhZWSEkJAQA8PnnnyMxMRHvv/9+X79FREREZsnJzhpxEd549+g5vJaSgymBQzudUmOMRA9R8+bNw+XLl7Fq1SooFAoEBwdj//792snmxcXFkEh+7zCrqqpCfHw8FAoF7O3tERoaivT0dAQFBQEApFIpTp8+jaSkJFRXV8PNzQ3R0dFYu3YtZDIZAMDGxgaff/45XnjhBTQ0NMDV1RWxsbF4/vnntW0AYO3atSgqKoKFhQVuu+02JCcnd/gsKSIiIjLMkjt9setEEc5erMX+nxSYOdp0RnxEnVhu7vSdmEZERNSfbUrJwRuH8uHvNAj7n5oMqUTc3iiTmFhORERE9PAkH9hZWyCvvB7/79TFmx9gJBiiiIiISFTyAZZ47E5fAMDrB3PRolKLXJF+GKKIiIhIdIsivOEw0ApFlY34LKv05gcYAYYoIiIiEt1AmQWWTmnrjXojNQ/KVtVNjhAfQxQREREZhQXjveBiZ42LNU34+Ltiscu5KYYoIiIiMgrWllI8Ps0PAPDW4QJcbTbu3iiGKCIiIjIafxrngWH2A1BRr8TOjEKxy+kSQxQREREZDSsLCZ6KCgAAvH2kAHVNLSJX1DmGKCIiIjIqc4Ld4DN0IKobW5D4baHY5XSKIYqIiIiMioVUghW/9Ua9f+wcqhubRa6oYwxRREREZHTuGu2K21xsUadsxTtHz4ldTocYooiIiMjoSCQCno4OBADsOF6Iy3VKkStqjyGKiIiIjFLUCCeM9RiMqy0qvJ1WIHY57TBEERERkVESBAF/jW6bG/XRd0W4VHNV5Ip0MUQRERGR0Yr0c0TY8CFoblXjzUP5YpejgyGKiIiIjFZbb1Tb3KhPvi9BcWWjyBX9jiGKiIiIjFrY8CGYHDAUrWoNNqfmil2OFkMUERERGb2nZ7TNjfrixwvIL68TuZo2DFFERERk9MZ6DMaMIGeoNcDrB/PELgcAQxQRERGZiKejAyAIwFenL+HsxRqxy2GIIiIiItNwm4sd7h7jBgB4/YD4c6MYooiIiMhkPBXlD4kAHPylHD8WV4laC0MUERERmQzfoYMw9/ZhAICNKeL2RjFEERERkUl5Yro/LKUCvs2vwM8Xa0Wrw0K0VyYiIiIygMcQGzx/VxBGutkhyM1OtDoYooiIiMjkxEV4i10Ch/OIiIiIDMEQRURERGQAhigiIiIiAzBEERERERmAIYqIiIjIAAxRRERERAZgiCIiIiIyAEMUERERkQEYooiIiIgMwBBFREREZACGKCIiIiIDMEQRERERGYAhioiIiMgAFmIXYM40Gg0AoLa2VuRKiIiISF/XPrevfY53hiGqF9XV1QEAPDw8RK6EiIiIuquurg5yubzT/YLmZjGLDKZWq3Hx4kXY2tpCEIQO29xxxx34/vvvu7WvtrYWHh4eKCkpgZ2dXY/W3Ju6ulZjfi1Dz9Xd4/Rtr0+7/nRfAX13b/G+6rqNud1b/em+MuTYnrq3jPG+0mg0qKurg5ubGySSzmc+sSeqF0kkEgwbNqzLNlKptNN/+V3tAwA7OzuT+oN0s+sx1tcy9FzdPU7f9vq060/3FdB39xbvq67bmNu91Z/uK0OO7al7y1jvq656oK7hxHKRLVu2zKB9pqgvr6cnX8vQc3X3OH3b69OuP91XQN9dE++rrtuY273Vn+4rQ47tqXvLlO8rDueZoNraWsjlctTU1JjUf9WRceN9Rb2F9xb1BmO4r9gTZYJkMhleeOEFyGQysUshM8L7inoL7y3qDcZwX7EnioiIiMgA7IkiIiIiMgBDFBEREZEBGKKIiIiIDMAQRURERGQAhigiIiIiAzBEmbl77rkH9vb2uO+++8QuhUzc3r17ERgYCH9/f7z//vtil0Nmgn+jqKeVlJRgypQpCAoKwpgxY/Dpp5/22mvxEQdmLi0tDXV1dUhKSsJ///tfscshE9Xa2oqgoCAcPnwYcrkcoaGhSE9Ph4ODg9ilkYnj3yjqaZcuXUJZWRmCg4OhUCgQGhqK3NxcDBw4sMdfiz1RZm7KlCmwtbUVuwwycZmZmRg5ciTc3d0xaNAgzJw5EykpKWKXRWaAf6Oop7m6uiI4OBgA4OLiAkdHR1y5cqVXXoshSkRHjx7FH//4R7i5uUEQBHzxxRft2mzduhXe3t6wtrZGeHg4MjMz+75QMnm3eq9dvHgR7u7u2nV3d3dcuHChL0onI8a/YdQbevK+ysrKgkqlgoeHR6/UyhAlooaGBowdOxZbt27tcH9ycjISEhLwwgsv4OTJkxg7dixiYmJQXl6ubRMcHIxRo0a1Wy5evNhXl0EmoCfuNaIb8b6i3tBT99WVK1ewcOFCvPvuu71XrIaMAgDN//73P51tYWFhmmXLlmnXVSqVxs3NTbNu3bpunfvw4cOauXPn9kSZZAYMudeOHz+umTNnjnb/k08+qdm1a1ef1Eum4Vb+hvFvFHXG0PuqqalJM2nSJM3OnTt7tT72RBmp5uZmZGVlISoqSrtNIpEgKioKGRkZIlZG5kafey0sLAw//fQTLly4gPr6enz99deIiYkRq2QyAfwbRr1Bn/tKo9Fg0aJFmDZtGh566KFerYchykhVVFRApVLB2dlZZ7uzszMUCoXe54mKisL999+Pffv2YdiwYfzjRe3oc69ZWFhg48aNmDp1KoKDg/H000/zm3nUJX3/hvFvFHWHPvfV8ePHkZycjC+++ALBwcEIDg7GmTNneqUei145KxmNgwcPil0CmYlZs2Zh1qxZYpdBZoZ/o6inRUZGQq1W98lrsSfKSDk6OkIqlaKsrExne1lZGVxcXESqiswR7zXqDbyvqDcY233FEGWkrKysEBoaitTUVO02tVqN1NRUTJgwQcTKyNzwXqPewPuKeoOx3VcczhNRfX098vPztevnz59HdnY2hgwZAk9PTyQkJCAuLg7jxo1DWFgYNm/ejIaGBixevFjEqskU8V6j3sD7inqDSd1XvfrdP+rS4cOHNQDaLXFxcdo2b775psbT01NjZWWlCQsL05w4cUK8gslk8V6j3sD7inqDKd1X/O08IiIiIgNwThQRERGRARiiiIiIiAzAEEVERERkAIYoIiIiIgMwRBEREREZgCGKiIiIyAAMUUREREQGYIgiIiIiMgBDFBEREZEBGKKIiK6TlpYGQRBQXV2t9zEvvvgigoODe60mIjJODFFE1C9lZGRAKpXirrvuErsUIjJRDFFE1C9t374dy5cvx9GjR3Hx4kWxyyEiE8QQRUT9Tn19PZKTk7F06VLcdddd2LFjR6dtd+zYgcGDB+OLL76Av78/rK2tERMTg5KSknZtP/zwQ3h7e0Mul2P+/Pmoq6vT7tu/fz8iIyMxePBgODg44O6770ZBQUFvXB4R9RGGKCLqdz755BPcdtttCAwMxIIFC5CYmAiNRtNp+8bGRrz00kvYuXMnjh8/jurqasyfP1+nTUFBAb744gvs3bsXe/fuxZEjR/DKK69o9zc0NCAhIQE//PADUlNTIZFIcM8990CtVvfadRJR77IQuwAior62fft2LFiwAAAQGxuLmpoaHDlyBFOmTOmwfUtLC9566y2Eh4cDAJKSkjBixAhkZmYiLCwMAKBWq7Fjxw7Y2toCAB566CGkpqbipZdeAgDMnTtX55yJiYkYOnQofv75Z4waNao3LpOIehl7ooioX8nJyUFmZiYeeOABAICFhQXmzZuH7du3d3qMhYUF7rjjDu36bbfdhsGDB+OXX37RbvP29tYGKABwdXVFeXm5dj0vLw8PPPAAfHx8YGdnB29vbwBAcXFxT10aEfUx9kQRUb+yfft2tLa2ws3NTbtNo9FAJpPhrbfeMvi8lpaWOuuCIOgM1f3xj3+El5cX3nvvPbi5uUGtVmPUqFFobm42+DWJSFzsiSKifqO1tRU7d+7Exo0bkZ2drV1OnToFNzc3fPzxx50e98MPP2jXc3JyUF1djREjRuj1upWVlcjJycHzzz+P6dOnY8SIEaiqquqRayIi8bAnioj6jb1796KqqgoPP/ww5HK5zr65c+di+/bt2LBhQ7vjLC0tsXz5crzxxhuwsLDA448/jvHjx2vnQ92Mvb09HBwc8O6778LV1RXFxcX4+9//3iPXRETiYU8UEfUb27dvR1RUVLsABbSFqB9++AGnT59ut8/GxgZ/+9vf8OCDD2LixIkYNGgQkpOT9X5diUSC3bt3IysrC6NGjcKKFSs6DGtEZFoETVff6yUi6ud27NiBp556qls/A0NE/QN7ooiIiIgMwBBFREREZAAO5xEREREZgD1RRERERAZgiCIiIiIyAEMUERERkQEYooiIiIgMwBBFREREZACGKCIiIiIDMEQRERERGYAhioiIiMgA/x+Krb+5Y7RqOgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#1\n",
        "data = fetch_california_housing()\n",
        "X, y = data.data, data.target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "#2\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred_train = model.predict(X_train)\n",
        "y_pred_test = model.predict(X_test)\n",
        "train_mse = mean_squared_error(y_train, y_pred_train)\n",
        "test_mse = mean_squared_error(y_test, y_pred_test)\n",
        "train_r2 = r2_score(y_train, y_pred_train)\n",
        "test_r2 = r2_score(y_test, y_pred_test)\n",
        "\n",
        "print(f\"Train MSE: {train_mse}, Test MSE: {test_mse}\")\n",
        "print(f\"Train R^2: {train_r2}, Test R^2: {test_r2}\")\n",
        "\n",
        "#4\n",
        "ridge = Ridge()\n",
        "params_ridge = {'alpha': [0.1, 1, 10, 100]}\n",
        "ridge_cv = GridSearchCV(ridge, params_ridge, cv=5)\n",
        "ridge_cv.fit(X_train, y_train)\n",
        "\n",
        "lasso = Lasso()\n",
        "params_lasso = {'alpha': [0.01, 0.1, 1, 10]}\n",
        "lasso_cv = GridSearchCV(lasso, params_lasso, cv=5)\n",
        "lasso_cv.fit(X_train, y_train)\n",
        "\n",
        "y_pred_ridge = ridge_cv.predict(X_test)\n",
        "y_pred_lasso = lasso_cv.predict(X_test)\n",
        "\n",
        "ridge_mse = mean_squared_error(y_test, y_pred_ridge)\n",
        "lasso_mse = mean_squared_error(y_test, y_pred_lasso)\n",
        "\n",
        "print(f\"Ridge Test MSE: {ridge_mse}\")\n",
        "print(f\"Lasso Test MSE: {lasso_mse}\")\n",
        "\n",
        "#5\n",
        "alphas_ridge = [0.1, 1, 10, 100]\n",
        "mse_ridge = []\n",
        "for alpha in alphas_ridge:\n",
        "    ridge = Ridge(alpha=alpha)\n",
        "    ridge.fit(X_train, y_train)\n",
        "    y_pred = ridge.predict(X_test)\n",
        "    mse_ridge.append(mean_squared_error(y_test, y_pred))\n",
        "\n",
        "plt.plot(alphas_ridge, mse_ridge, label=\"Ridge\")\n",
        "plt.xscale(\"log\")\n",
        "plt.xlabel(\"Alpha\")\n",
        "plt.ylabel(\"Test MSE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ]
}