AIC_Project.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SBU-AIC/summer-project/blob/main/AIC_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Project Description\n",
        "# @markdown Feel free to ask any questions about this project from your TAs. ;)\n",
        "from IPython.display import IFrame\n",
        "link_id = \"1x2-V9q9agw8F3nCaTkv2uy6jyDE5y3LgKrisKXTjIDg/edit?usp=sharing\"\n",
        "print(f\"If you want to download the slides: https://docs.google.com/document/d/{link_id}/\")\n",
        "IFrame(src=f\"https://docs.google.com/document/d/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=1200, height=480)"
      ],
      "metadata": {
        "colab": {
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        "id": "9iO1T97CWPWu",
        "outputId": "702a795f-e82c-4feb-ab6b-0728d55e2dc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "If you want to download the slides: https://docs.google.com/document/d/1x2-V9q9agw8F3nCaTkv2uy6jyDE5y3LgKrisKXTjIDg/edit?usp=sharing/\n"
          ]
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              "        <iframe\n",
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              "            \n",
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              "        "
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          "metadata": {},
          "execution_count": 9
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hN_HeMVgtSFT",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @markdown import necessaries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Figure Settings\n",
        "import logging\n",
        "logging.getLogger('matplotlib.font_manager').disabled = True\n",
        "\n",
        "import ipywidgets as widgets  # interactive display\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "metadata": {
        "cellView": "form",
        "id": "lLUMSW7LRN48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7izED7wADVm"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
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      "execution_count": null,
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        "id": "9x9pOwGdADVn",
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        },
        "outputId": "935090af-2a71-41e6-f872-c7e7ab0043c4",
        "cellView": "form"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
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          "metadata": {}
        }
      ],
      "source": [
        "# @markdown load data\n",
        "from tqdm.notebook import tqdm\n",
        "import os\n",
        "\n",
        "urls = ['https://raw.githubusercontent.com/SBU-AIC/summer-project/main/USDGBP.csv',\n",
        "        'https://raw.githubusercontent.com/SBU-AIC/summer-project/main/USDJPY.csv']\n",
        "\n",
        "progress = tqdm(total=len(urls))\n",
        "\n",
        "for url in urls:\n",
        "  !wget $url -q\n",
        "\n",
        "  progress.set_description(f\"Downloading files\")\n",
        "  progress.update(1)\n",
        "\n",
        "progress.close()\n",
        "\n",
        "usd_gbp = pd.read_csv('USDGBP.csv', parse_dates=[0])\n",
        "usd_jpy = pd.read_csv('USDJPY.csv', parse_dates=[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 206
        },
        "id": "9_C2iFmAADVo",
        "outputId": "47b214d5-26e8-4784-df23-0d142e80f35c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  USDJPY  USDGBP\n",
              "0 2017-05-26  111.33  0.7809\n",
              "1 2017-05-25  111.84  0.7727\n",
              "2 2017-05-24  111.49  0.7708\n",
              "3 2017-05-23  111.78  0.7716\n",
              "4 2017-05-22  111.30  0.7693"
            ],
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              "      <th>Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>2017-05-26</td>\n",
              "      <td>111.33</td>\n",
              "      <td>0.7809</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-05-25</td>\n",
              "      <td>111.84</td>\n",
              "      <td>0.7727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-05-24</td>\n",
              "      <td>111.49</td>\n",
              "      <td>0.7708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-05-23</td>\n",
              "      <td>111.78</td>\n",
              "      <td>0.7716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-05-22</td>\n",
              "      <td>111.30</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "usd_dataset = usd_jpy.merge(right=usd_gbp, how='left', on='Date')\n",
        "usd_dataset = usd_dataset.dropna(axis=0, how='any') # drop missing values\n",
        "usd_dataset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NR8ybDcADVp"
      },
      "source": [
        "# Preprocess Dataset and plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "XiZj3vazADVq",
        "outputId": "614f1c26-61af-4673-e22e-a3c8dc33e664"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'USDGBP')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AAQAAAFlmGOa0oNAPhx6PuXMgXxXaeoF8QICJfEMPIAAAACCLgkGptlaqrJRqaszX2lrzeD4qtPUCACIjAAIAAACyxDC69waRzK+nTMnNmmIptPUCAKIjAAIAAACypKkp8nhzyTze2Jjd9cRTaOsFAERHAAQAAABkSSCQ3vlsK7T1AgCiIwACAAAAsqS8PL3z2VZo6wUAREcABAAAAGSJxyP5fJHP+Xz5NzGo0NYLAIiOAAgAAADIEpdLmj+/e6ji85nH802hrRcAEF1xrhcAAAAAOInbLfn9ZgPlQMAso8rnnTT5tl7DMJtTh9bi8ZhBFQAgNgIgAAAAIAe83vwOfrrKh/UGg93H0od2I7nduVoVABQGSsAAAAAA5D3D6B7+SObXU6bkZk0AUEgIgAAAAADkvaam7uFPSF2dWaIGAIiOAAgAAABA3gsE0jsPAE5HAAQAAAAg75WXp3ceAJyOAAgAAABwOMMwS6jq681Xw8j1irrzeLqPow/x+XLfoBoA8h0BEAAAAOBgwaBUWytVVko1NeZrba15PJ+4XOa0r64hUGgKGAAgNsbAAwAAAA4Vb7KW35/YPZqazB485eXmTh2Xy5r1ut3mmhobjzyPnT8AkBgCIAAAAMChEpmsFStgCQa7B0ihHTludyZX2pnXS/ADAMmiBAwAAABwqHQma8XbPQQAyC8EQAAAAIBDpTNZK5HdQwCA/EEABAAAADhUOpO10tk9BADIPgIgAAAAwKHSmayVzu4hAED20QQaAAAAcLBUJ2uFdg9FKgOLt3sIAJB97AACAAAAbMgwzFCnvt58NYzY13u9UnV14sFNOruHAADZxw4gAAAAwGayNZ491d1DAIDsYwcQAAAAYCO5GM+e7O4hAED2EQABAAAAMSRbSpVrVo9nL7TvRzx2+/0AQDQEQAAAAHCkRH7wDwal2lqpslKqqTFfa2vN4/nKyvHsyX4/8j1cKcQ/XwBIFQEQAAAAbCPRwCGRH/xzUUqVCVaNZ0/2+5Hv4Uqh/vkCQKoIgAAAAGALiQYOif7gb3UplVVC49kjSWc8ezLfj0IIVwr1zxcAUkUABAAAgIKXTOCQ6A/+VpZSWcmq8ezJfD8KIVwp1D9fAEgVARAAAAAKXjKBw5Ytse8V+sHfqlKqbAiNZ29okJYsMV/9/vRGwCfz/SiEcKWQ/3wBIBUEQAAAACh4yQQO7e2xrz140Hy1qpQqmzI5nj2Z70chhCt2+PMFgGQQAAEAAKDgJRM4FBfHvrakxHy1qpSqUCXz/SiEcIU/XwBOE+f//QEAAAD5LxQ4RCoD6xo4nHRS7Ht1DItCpVSNjeYuovLy/AgvciXR70coXOnalynfwhX+fAE4icswog3HBDpraWlR+Wf/iygQCKisrCzHKwIAADgiGIweOHTsfWMY5nSwaGGR32/1Sp2FcAUAkmfFz9/sAAIAAIAt2G13il14vQQ/AJAPCIAAAABgK4kEDpT+AEiFYZhTB0P/3fB4zFAZKAQEQAAAAHAsdqcASFSiZaZAvmIKGAAAAOBQhmHugqqvN1/pDgpEZhjdwx/J/HrKlNysCUgWARAAAADgQMGg2Qy7slKqqTFfa2vN4wA6a2qK3DheMo83NmZ3PUAqCIAAAAAAh8nWbgZ2GMEuAoH0zgP5gAAIAAAAcJhs7GZghxHs5LNp3CmfB/IBARAAAADgMO+/H/t8vN0M8Xb20C8FduPxmA2fI/H5aCaPwkAABAAAADjMe+/FPv/Pf5qvkYKeSDt7zjlHWrPmSBBEv5QjKIOzB5fLnPbVNQQKTQEDCgFj4AEAAACH+fjj2Oc/+ST6yOuDB80wo6O1a80QKPTDcCL9UpywY4Kx4fbidkt+vxnkBQJm2ZcT/h7DPtgBBAAAADjMF74Q+/wZZ0Qv4eoa/nQ9P2UK/VIkyuDszOuVqqsJf1B4CIAAAACABNmlnOfCC6VBgyKfGzRI+vzno5dwxVNXZ35fnN4vhTI4APmGAAgAAABIgJ2mWhUVmWVbXUOgQYPM4+mOtG5poV8KY8MB5Bt6AAEAAABxxCvn8futeWZT05FeIx6P2Yg2U5/7j/+QPvzQ/D1s3CideeaRwKatLb21l5fTL4UyOAD5hh1AAAAAQBzZLudJdbdRKp/z+aQf/rDzbp1YI6/j6Vri5dR+KYwNB5BvCIAAAACAOLJZzpNq8+BMNh2ONvJ6zJjYnxszxjklXvEwNhxAvqEEDAAAAIgjm+U8iew2irR7JNXPRROphMvjMXcURXrOmDHSsmWJ398JnF4GByC/sAMIAAAAiCOb5Typ7jayapdSxxKuWLtaFi1K7f5O4NQyOAD5hR1AAAAAQByh4KNriZUV5Typ7jbK1i4ldrUAQGEiAAIAAIAlUp1ila+yFXyEdhtFKrOKtdso1c+lyusl+AGAQkIJGAAAADIu1SlWhSCZch7DMAOj+nrz1TDifybV5sE0HS58qfx9AYBEsQMIAAAAGRVvGpXfn5t1ZVswGL1kzO2O/dlUdxtRnlW40vn7AgCJcBkGuTIS09LSovLPiscDgYDKyspyvCIAAJCPGhvNHT/RNDTYP5QwjOjTsnw+54RgSAx/XwB0ZcXP35SAAQAAIKOsmkZVSBIZyQ6E8PcleZTLAckjAAIAAEBGZWsaVT4jBEMynPL3JVOhjZ17jAFWIgACAABARoWmUUVixTSqfEQIhmQ44e9LpkKbeD3GAERHAAQAAICMYhpVdkMwSmEKn91D00yGNpTLAakjAJL0/vvva+bMmfJ6vSotLdWAAQNUVVWlWbNmae/evRl5xr/+9S/dcsstOv3009WvXz+VlpbqlFNO0bXXXqu///3vcT8/ffp0uVyuhH699957GVkzAABAqkLTqBoapCVLzFe/3znTjLIVglEKYw/Z+PuSy6Awk6GNU8rlACs4fgz84sWLNXXqVLW2toaP7d27V+vWrdO6des0d+5c+f1+VVRUpPyMOXPm6IYbbtCBAwc6Hd+8ebM2b96sJ598Ug899JCuv/76lJ8BAACQj7zewt+9kCqrR7LH21XB5Kj8YxhmGBL6++DxmOGPZO3fl1yPmE8ktEn09+qEcjnAKo4OgDZs2KDJkyerra1Nffv21e23367x48erra1NCxYs0BNPPKHm5mZNnDhR69atU79+/ZJ+xoIFC3TNNddIko455hjNnDlTX/7yl9WzZ09t2LBBDzzwgDZv3qwbb7xRxx13nL75zW/GvN+QIUP05z//OeY1J554YtLrBAAAgDWsCsES2VWRD+FbrNDDSRINYTL99yUfgsJMhjahcrlIf/ftUC4HWMnRAdBNN92ktrY2FRcXa8mSJTr33HPD57785S/rlFNO0a233qrm5mY99NBDuueee5K6/969e3XTTTdJkvr27auVK1fq9NNPD58fMWKEJk+erPPOO0+bNm3SjTfeKJ/Pp759+0a9Z0lJSad7AAAAwJkyuavCKrneeZIvchnC5ENQmMnQJlQuF+3vFYDoHNsDaM2aNVqxYoUk6aqrruoU/oTMnDlTlZWVkqTZs2fr4MGDST2jrq5OH374oSQzbIoU3Bx99NF6+OGHJUnbt2/X008/ndQzAAAA4Ez5XgrDtKYj4oUwa9ZY9+x86JmT6R5HTu8xBqTKsQHQiy++GH5/xRVXRLymqKhI3/nOdyRJwWBQS5cuTeoZ69atC7+fMGFC1OvGjRunXr16SZKef/75pJ4BAAAAZ8r3yVFMazoiXshiZStQq4PCRJtLWxHaeL1SdXXu/64DhcKxAdDKlSslSaWlpRo+fHjU68aOHRt+v2rVqqSesXPnzvD7448/Pup1xcXFGjBggCRp9erVam9vT+o5AAAAcJ5sTRpLVT7sPMkX8UKWtWutC8SsDApTmUJHaAPkjmMDoIaGBklSRUWFioujt0LydvgvU+gzierYy+fTTz+Nep1hGOEpZAcOHNDmzZujXrtz506NHTtWAwcOVM+ePXXCCSfoq1/9qh555JG0R9a3tLTE/LVt27a07g8AAIDMyudSmHwvUcsmj0caOTL2NVYFYlYFhZT4AYXHkU2g9+3bp48++kiSVFZWFvPa/v37q7S0VHv27FEgyf8qh/oHSdKyZcui7jTasGGDdu/eHf56y5YtnYKnjnbv3q3ly5eHv/7ggw/0wQcfaMmSJfr5z3+u//mf/9GoUaOSWmdIuZP+vzAAAICNWDVpLB1MazrC5ZJ+9SvpnHOiX2Pl/xS3YsR8PjSXBpAcR+4A2rVrV/h9rIlbIaWlpZLUKaRJxIQJE8K7ix5++OFw6NTR4cOHdeedd0ZdX4jL5dKXvvQl3XvvvXrppZf0t7/9Ta+99poef/xxjfzsnxO2bt2qmpoabdiwIal1AgAAAJmW7yVqifauyZSqqtz3bMpk+RUlfkDhcewOoJAePXrEvb5nz56SpLa2tqSeU15eru9+97t65JFHtHXrVo0ePVoPPPCAxo8frx49eujNN9/UPffcoz//+c/q0aOHDhw4EPU5//3f/y13hL285557rq6++mr96Ec/0n333ac9e/ZoxowZWrdunVwuV1LrjbfDadu2beGwCQAAAIjHip0nmZCL8fR2G19OiR9QeBwZAIUmbkkKhy6x7N+/X5LUu3fvpJ/14IMP6p///Kfq6urU3NysSy65pNs1I0aMUFVVlX79619Lkvr169ftmkjhT4jL5dK9996rN954Q6+88kp4d9Do0aOTWmu8cjgAAAAgFflUohavd43fb92z8zUQSwUlfkDhcWQJWMeAJZGyrj179khKrFysq549e2rx4sV64okndNZZZ3XalXPcccfpzjvv1IoVK2R02HPav3//pJ8jSddcc034/bJly1K6BwAAAPJLtkuV7C4fxtPbYRJWvpf4AejOsTuABg4cqJ07d6qlpSXmtZ988kk4AEq1SXJRUZFmzJihGTNmaNeuXdq+fbv69OmjwYMHq6jIzODefffd8PWnnnpqSs/p+LmtW7emdA8AAADkj1yUKtldIr1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\n"
          },
          "metadata": {
            "image/png": {
              "width": 576,
              "height": 432
            }
          }
        }
      ],
      "source": [
        "days = 200\n",
        "inp_feature = 'USDJPY'\n",
        "out_feature = 'USDGBP'\n",
        "X = preprocessing.normalize(usd_dataset[inp_feature].values.reshape(-1, 1)[-days:], axis=0, norm='max')\n",
        "y = preprocessing.normalize(usd_dataset[out_feature].values.reshape(-1, 1)[-days:], axis=0, norm='max')\n",
        "\n",
        "plt.scatter(X, y, marker='o', color='blue', s=3)\n",
        "plt.xlabel(inp_feature)\n",
        "plt.ylabel(out_feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "srny6Lv2ADVq"
      },
      "source": [
        "# Linear Regression best model prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81eaW5KUADVr",
        "outputId": "4bb03843-9532-4e1c-8fde-8f629acdc315"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient : 1.7761 \n",
            "Intercept : -0.719\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X, y)\n",
        "print('Coefficient :', np.round(model.coef_[0][0],decimals=4),\n",
        "      '\\nIntercept :', np.round(model.intercept_[0], decimals=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "cellView": "form",
        "id": "czttaTZlADVr",
        "outputId": "db6b8e22-f0fc-454a-8a7e-b1ef9474c616"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'USDGBP')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 576,
              "height": 432
            }
          }
        }
      ],
      "source": [
        "# @markdown plot the result\n",
        "line = model.predict(X)\n",
        "plt.scatter(X, y, marker='o', color='blue', s=3)\n",
        "plt.plot(X, line, color='red')\n",
        "plt.xlabel(inp_feature)\n",
        "plt.ylabel(out_feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTViT4gSADVs"
      },
      "source": [
        "# Genetic algorithm Implementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gH_muodYADVs"
      },
      "outputs": [],
      "source": [
        "class Genetic:\n",
        "\n",
        "  def __init__(self, generations, population):\n",
        "    self.generations = 100\n",
        "    self.population = population\n",
        "\n",
        "  def get_generations(self):\n",
        "    return self.generations\n",
        "\n",
        "  def fitness(solution):\n",
        "      ############################################################################\n",
        "      ## TODO: Calculate the fitness of the solution using the Ordinary Least Squares (OLS) method\n",
        "      ## Fill in function and remove\n",
        "      raise NotImplementedError(\"Please complete the fitness exercise\")\n",
        "      ############################################################################\n",
        "      fitness_val = ...\n",
        "\n",
        "      return fitness_val\n",
        "\n",
        "  def genPopulation(self):\n",
        "      '''Generate population of size individuals'''\n",
        "      ############################################################################\n",
        "      # TODO: Generate a population of size individuals with random coefficients and intercepts\n",
        "      # Coefficients and intercept should be in the range [-1, 1] since all data has been normalized with the maximum value\n",
        "      raise NotImplementedError(\"Please complete the Generate population exercise\")\n",
        "      ############################################################################\n",
        "      population = self.population\n",
        "      solutions = ...\n",
        "      return solutions\n",
        "\n",
        "  def selectBest(self, solutions):\n",
        "      fitnesses = np.apply_along_axis(self.fitness, 1, solutions)\n",
        "      return solutions[np.argsort(fitnesses, axis=0)[:20], :]\n",
        "\n",
        "  def crossover(sol1, sol2):\n",
        "      '''Perform crossover between two solutions by exchanging the intercepts and maintaining the coefficients'''\n",
        "      ############################################################################\n",
        "      # TODO: Perform crossover between sol1 and sol2 by exchanging the intercepts and maintaining the coefficients\n",
        "      # Return the offspring solutions\n",
        "      raise NotImplementedError(\"Please complete the crossover exercise\")\n",
        "      ############################################################################\n",
        "      offsprings = ...\n",
        "      return offsprings\n",
        "\n",
        "  def mutation(sol):\n",
        "      '''Perform mutation on the solution'''\n",
        "      ############################################################################\n",
        "      # TODO: Perform mutation on the solution with a certain probability (mutationProb)\n",
        "      raise NotImplementedError(\"Please complete the mutation exercise\")\n",
        "      ############################################################################\n",
        "      sol = ...\n",
        "      return sol\n",
        "\n",
        "  # Perform generationas while executing the genetic algorithm\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfLNQd8BADVt"
      },
      "source": [
        "## Run the algorithm and track the results with plot and compare with best linear regression model prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6oulI7VuADVt"
      },
      "outputs": [],
      "source": [
        "genetic_instance = Genetic(generations=100, population=400)\n",
        "\n",
        "solutions = genetic_instance.genPopulation()\n",
        "generations = genetic_instance.get_generations()\n",
        "\n",
        "best = []\n",
        "inter_sol = []\n",
        "fig = plt.figure(figsize=(8, 6))\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ion()\n",
        "fig.show()\n",
        "fig.canvas.draw()\n",
        "\n",
        "for i in range(generations):\n",
        "    bestSols = genetic_instance.selectBest(solutions)\n",
        "    best = bestSols[0]\n",
        "    predict = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1).dot(best.transpose())\n",
        "    inter_sol.append(predict)\n",
        "    ax.clear()\n",
        "    plt.scatter(X, y, marker='o', color='blue', s=3)\n",
        "    plt.plot(X, line, color='red', label='Regression')\n",
        "    plt.plot(X, predict, color='green', label='GA')\n",
        "    plt.xlabel(inp_feature)\n",
        "    plt.ylabel(out_feature)\n",
        "    plt.legend(loc='best')\n",
        "    fig.canvas.draw()\n",
        "    # Elitism\n",
        "    newPopulation = np.array(best)\n",
        "    for j in range(len(bestSols) - 1):\n",
        "        newPopulation = np.vstack((newPopulation, genetic_instance.crossover(bestSols[j], bestSols[j + 1])))\n",
        "    ############################################################################\n",
        "    # TODO: mutate your new population\n",
        "    raise NotImplementedError(\"Please complete this mutation part\")\n",
        "    ############################################################################\n",
        "    newPopulation = ...\n",
        "    solutions = newPopulation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WKmuQT7ADVu"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# Use the best candidate solution to predict the data and plot the GA found line\n",
        "predict = np.concatenate((X, np.ones((X.shape[0],1))), axis=1).dot(best.transpose())\n",
        "plt.scatter(X, y, marker='o', color='blue', s=3)\n",
        "plt.plot(X, line, color='red', label='Regression')\n",
        "plt.plot(X, predict, color='green', label='GA')\n",
        "plt.xlabel(inp_feature)\n",
        "plt.ylabel(out_feature)\n",
        "plt.legend(loc='best')"
      ]
    }
  ],
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