MeltingEff_WAAM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MeltingEff-WAAM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1AVhNreVvb3V43mwDnostdWyl9cHlYSxt",
      "authorship_tag": "ABX9TyNwxHbXxbQ1nOPYFngn5YEn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GermanOmar/Melting/blob/master/MeltingEff_WAAM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqI9VDT0RdWk"
      },
      "source": [
        "# Melting efficiency evaluation in wire arc additive manufacturing: A machine learning approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OOvQQeg7d0g"
      },
      "source": [
        "<a href=\"https://ibb.co/5Rvr5t7\"><img src=\"https://i.ibb.co/kS04hkd/Fig1-4.png\" WIDTH=\"500\" HEIGHT=\"300\"  border=\"0\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-T6Z2bdRvSm"
      },
      "source": [
        " Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltCb1Iw6Raez"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SM_-phITCP5Q"
      },
      "source": [
        "Randomized search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXtz8RPXCOUJ"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Mbfb5c9yu5p"
      },
      "source": [
        " Upload database:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_V3jfdhPsEKW",
        "outputId": "d56e829f-6519-45a9-be93-e8f7146101b2"
      },
      "source": [
        "!git clone https://github.com/GermanOmar/Melting"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Melting'...\n",
            "remote: Enumerating objects: 40, done.\u001b[K\n",
            "remote: Counting objects: 100% (40/40), done.\u001b[K\n",
            "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
            "remote: Total 40 (delta 15), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (40/40), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "Yx-ovik8xxJx",
        "outputId": "23bed3e6-8b76-4fa9-ef53-417a8fa17250"
      },
      "source": [
        "#Experimental data\n",
        "data = pd.read_table('datas.txt') \n",
        "data.describe()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>WD</th>\n",
              "      <th>WFS</th>\n",
              "      <th>TS</th>\n",
              "      <th>P</th>\n",
              "      <th>ME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.954667</td>\n",
              "      <td>95.451333</td>\n",
              "      <td>8.326533</td>\n",
              "      <td>1819.171733</td>\n",
              "      <td>0.445693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.172653</td>\n",
              "      <td>53.012550</td>\n",
              "      <td>5.220140</td>\n",
              "      <td>757.431591</td>\n",
              "      <td>0.055220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>531.220000</td>\n",
              "      <td>0.307000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>54.585000</td>\n",
              "      <td>4.450000</td>\n",
              "      <td>1171.550000</td>\n",
              "      <td>0.411000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>80.330000</td>\n",
              "      <td>6.670000</td>\n",
              "      <td>1798.200000</td>\n",
              "      <td>0.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.200000</td>\n",
              "      <td>123.250000</td>\n",
              "      <td>10.980000</td>\n",
              "      <td>2435.315000</td>\n",
              "      <td>0.491500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.200000</td>\n",
              "      <td>222.500000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3266.550000</td>\n",
              "      <td>0.553000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              WD         WFS         TS            P         ME\n",
              "count  75.000000   75.000000  75.000000    75.000000  75.000000\n",
              "mean    0.954667   95.451333   8.326533  1819.171733   0.445693\n",
              "std     0.172653   53.012550   5.220140   757.431591   0.055220\n",
              "min     0.800000   28.000000   1.660000   531.220000   0.307000\n",
              "25%     0.800000   54.585000   4.450000  1171.550000   0.411000\n",
              "50%     0.800000   80.330000   6.670000  1798.200000   0.445000\n",
              "75%     1.200000  123.250000  10.980000  2435.315000   0.491500\n",
              "max     1.200000  222.500000  25.000000  3266.550000   0.553000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFVoj9tfSQJJ"
      },
      "source": [
        "\n",
        "Setting inputs - output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "j4RPoTbW_VVs",
        "outputId": "3e2356a4-2f91-43f1-e06f-ee0224681fc3"
      },
      "source": [
        "fields = ['WD', 'WFS','TS','P', 'ME',]\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((15,8))\n",
        "for i, field in enumerate(fields):\n",
        "  plt.subplot(3, 3, i + 1)\n",
        "  plt.plot(data[field], data['ME'], 'b*')\n",
        "  plt.xlabel(field)\n",
        "  plt.ylabel('ME')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "X=data[['WD', 'WFS','TS','P']]\n",
        "Y=data['ME'][:,None]\n",
        "\n",
        "#fig.tight_layout()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "SVvdbKrdZIj5",
        "outputId": "e5458d9b-2f82-4005-bcad-44123289e9f3"
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "#DISPLAY MATRIX - identificar correlacion entre variables\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "\n",
        "#pd.tools.plotting.scatter_matrix(df, alpha=0.2, c='red', hist_kwds={'color':['burlywood']})\n",
        "\n",
        "#scatter_matrix(data, alpha=0.2, marker='o', figsize = (12,12), color = 'blue')\n",
        "scatter_matrix(data, alpha=0.9,marker='s', figsize = (12,12),  c='blue', hist_kwds={'color':['blue']})\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7o8zckSzFI7O"
      },
      "source": [
        "# Melting efficiency prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6pAiesfTDDU"
      },
      "source": [
        " Split data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HI3IlTeHTF_Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00e96980-90c9-47e2-e05d-c974210445c9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1234)\n",
        "print('training samples:', X_train.shape[0],  ' test samples:', X_test.shape[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training samples: 60  test samples: 15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9C38QmsAXm-"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "#y_train = scaler.fit_transform(y_train)\n",
        "#y_test = scaler.transform(y_test)\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Zk7lMoGhdFO"
      },
      "source": [
        "#Results:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVHzaa4EkSPe"
      },
      "source": [
        "# GPs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "abeE5km2aix5",
        "outputId": "3b8cc958-1cc9-4a52-b344-d1757f7db8f6"
      },
      "source": [
        "from sklearn.gaussian_process import GaussianProcessRegressor as GPR\n",
        "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
        "kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n",
        " \n",
        "GP=GPR(alpha=0.001, copy_X_train=True,\n",
        "                         kernel=1**2 * RBF(length_scale=10),\n",
        "                         n_restarts_optimizer=40, normalize_y=True,\n",
        "                         optimizer='fmin_l_bfgs_b', random_state=1234)\n",
        " \n",
        "#Training\n",
        "GP.fit(X_train,y_train)\n",
        " \n",
        "#Testing\n",
        "y_star_train = GP.predict(X_train)\n",
        "y_pred = GP.predict(X_test)\n",
        " \n",
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(y_train, y_star_train, 'bx', label = 'Train')\n",
        "plt.plot([y_star_train.min(), y_star_train.max()], [y_star_train.min(), y_star_train.max()], 'k--')\n",
        "plt.plot(y_test, y_pred, 'ro', label = 'Test')\n",
        "plt.plot([y_pred.min(), y_pred.max()], [y_pred.min(), y_pred.max()], 'k--')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Measured melting efficiency')\n",
        "plt.title(\"Gaussian process regressor\")\n",
        " \n",
        "from sklearn.metrics import mean_squared_error\n",
        "MSE_test = mean_squared_error(y_test, y_pred)\n",
        "MSE_train = mean_squared_error(y_train, y_star_train)\n",
        " \n",
        "print(\"Gaussian Process\")\n",
        "print('MSE train:', MSE_train)\n",
        "print('MSE test:', MSE_test)\n",
        "print(\"MSE train < MSE test?\", MSE_train < MSE_test)\n",
        "print()\n",
        "from sklearn.metrics import r2_score\n",
        "R2_train=r2_score(y_train, y_star_train)\n",
        "print('R2_train: ',R2_train)\n",
        "R2_test=r2_score(y_test, y_pred)\n",
        "print('R2_test: ',R2_test)\n",
        "print()\n",
        "\"\"\" \n",
        "RE=100 * abs(y_test - y_pred) / y_test\n",
        "print('Relative error (%): ',RE)\n",
        "\"\"\"\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_error as MAE \n",
        "#METRICS: R2, RMSE, MAE\n",
        "R2=r2_score(y_test, y_pred)\n",
        "print('R2: ',R2)\n",
        "RMSE = np.sqrt(MSE_test)\n",
        "print(\"RMSE: \", RMSE)\n",
        "MAE = MAE(y_test, y_pred)\n",
        "print(\"MAE: \", MAE)\n",
        "IM=np.sqrt(((1-R2)**2) + (RMSE**2) + (MAE**2) )\n",
        "print(\"IM: \", IM)\n",
        "print()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gaussian Process\n",
            "MSE train: 0.00026153180363424727\n",
            "MSE test: 0.00021366917836543873\n",
            "MSE train < MSE test? False\n",
            "\n",
            "R2_train:  0.9156244609585389\n",
            "R2_test:  0.9190502595880347\n",
            "\n",
            "R2:  0.9190502595880347\n",
            "RMSE:  0.014617427214302753\n",
            "MAE:  0.011378786992505148\n",
            "IM:  0.08304219677098396\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMwDPZgDnsSf"
      },
      "source": [
        "# initialize model and define the space of the hyperparameters to\n",
        "# perform the grid-search over\n",
        "model = GP\n",
        "n_restarts_optimizer = [5, 10, 20, 40]\n",
        "alpha = [0.001, 0.01, 0.1, 1]\n",
        "\n",
        "\n",
        "grid = dict(n_restarts_optimizer=n_restarts_optimizer, alpha=alpha )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6R3o1gzqEQZ",
        "outputId": "a8df6d61-d078-4520-cf8a-36b20acbd5c1"
      },
      "source": [
        "# initialize a cross-validation fold and perform a randomized-search\n",
        "# to tune the hyperparameters\n",
        "print(\"[INFO] grid searching over the hyperparameters...\")\n",
        "cvFold = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1234)\n",
        "randomSearch = RandomizedSearchCV(estimator=model, n_jobs=-1,\n",
        "\tcv=cvFold, param_distributions=grid,\n",
        "\tscoring=\"neg_mean_squared_error\")\n",
        "searchResults = randomSearch.fit(X_train, y_train)\n",
        "# extract the best model and evaluate it\n",
        "print(\"[INFO] evaluating...\")\n",
        "bestModel = searchResults.best_estimator_\n",
        "print(\"R2: {:.2f}\".format(bestModel.score(X_test, y_test)))\n",
        "print(bestModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] grid searching over the hyperparameters...\n",
            "[INFO] evaluating...\n",
            "R2: 0.90\n",
            "GaussianProcessRegressor(alpha=0.001, copy_X_train=True,\n",
            "                         kernel=1**2 * RBF(length_scale=10),\n",
            "                         n_restarts_optimizer=40, normalize_y=True,\n",
            "                         optimizer='fmin_l_bfgs_b', random_state=1234)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkMQ_W07zri6"
      },
      "source": [
        "# XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLTTuAFyzJeP"
      },
      "source": [
        "import pickle\n",
        "import xgboost as xgb\n",
        "\n",
        "xgbr = xgb.XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
        "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
        "             max_depth=10, min_child_weight=1, missing=None, n_estimators=1000,\n",
        "             n_jobs=1, nthread=None, objective='reg:linear', random_state=1234,\n",
        "             reg_alpha=0, reg_lambda=0.1, scale_pos_weight=1, seed=None,\n",
        "             silent=None, subsample=1, verbosity=0)\n",
        "xgbr.fit(X_train, y_train)\n",
        "y_pred = xgbr.predict(X_test)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saBkb2to1tVK",
        "outputId": "71bf6b7c-7999-4907-f361-92fd4e6ea584"
      },
      "source": [
        "score = xgbr.score(X_train, y_train)  \n",
        "print(\"Training score: \", score)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training score:  0.9998938184447697\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "P-auJlXw2D8o",
        "outputId": "6f61802a-db3d-4942-f0bb-f3b322d90174"
      },
      "source": [
        "print(\"-XGBRegressor -\")\n",
        "print(\"------------------\")\n",
        "\n",
        "#EVAL\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "MSE_test = MSE(y_test, y_pred)\n",
        "\n",
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(y_train, y_star_train, 'bx', label = 'Train')\n",
        "plt.plot([y_star_train.min(), y_star_train.max()], [y_star_train.min(), y_star_train.max()], 'k--')\n",
        "plt.plot(y_test, y_pred, 'ro', label = 'Test')\n",
        "plt.plot([y_pred.min(), y_pred.max()], [y_pred.min(), y_pred.max()], 'k--')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Measured melting efficiency')\n",
        "plt.title(\"Extreme Gradient boosting regressor\")\n",
        "\n",
        "print('MSE test:', MSE_test)\n",
        "\n",
        "print()\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_error as MAE \n",
        "\n",
        "#METRICS: R2, RMSE, MAE\n",
        "R2=r2_score(y_test, y_pred)\n",
        "print('R2: ',R2)\n",
        "RMSE = np.sqrt(MSE_test)\n",
        "print(\"RMSE: \", RMSE)\n",
        "MAE = MAE(y_test, y_pred)\n",
        "print(\"MAE: \", MAE)\n",
        "IM=np.sqrt(((1-R2)**2) + (RMSE**2) + (MAE**2) )\n",
        "print(\"IM: \", IM)\n",
        "print()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-XGBRegressor -\n",
            "------------------\n",
            "MSE test: 0.00035191618918450034\n",
            "\n",
            "R2:  0.86667462111671\n",
            "RMSE:  0.01875942934058764\n",
            "MAE:  0.015450480699539174\n",
            "IM:  0.13552228671847358\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hY5StbfZukbo"
      },
      "source": [
        "# initialize model and define the space of the hyperparameters to\n",
        "# perform the grid-search over\n",
        "model = xgbr\n",
        "n_estimators = [10, 100, 1000, 10000]\n",
        "learning_rate = [0.1, 0.01, 1e-3]\n",
        "max_depth = [5, 10, 20, 40]\n",
        "reg_lambda = [0.01, 0.1, 1]\n",
        "\n",
        "\n",
        "grid = dict(n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bhkrvScvA2q",
        "outputId": "e50471f8-f89f-4177-9048-1c201486dd52"
      },
      "source": [
        "# initialize a cross-validation fold and perform a randomized-search\n",
        "# to tune the hyperparameters\n",
        "print(\"[INFO] grid searching over the hyperparameters...\")\n",
        "cvFold = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "randomSearch = RandomizedSearchCV(estimator=model, n_jobs=-1,\n",
        "\tcv=cvFold, param_distributions=grid,\n",
        "\tscoring=\"neg_mean_squared_error\")\n",
        "searchResults = randomSearch.fit(X_train, y_train)\n",
        "# extract the best model and evaluate it\n",
        "print(\"[INFO] evaluating...\")\n",
        "bestModel = searchResults.best_estimator_\n",
        "print(\"R2: {:.2f}\".format(bestModel.score(X_test, y_test)))\n",
        "print(bestModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] grid searching over the hyperparameters...\n",
            "[INFO] evaluating...\n",
            "R2: 0.87\n",
            "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
            "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
            "             max_depth=10, min_child_weight=1, missing=None, n_estimators=100,\n",
            "             n_jobs=1, nthread=None, objective='reg:linear', random_state=1234,\n",
            "             reg_alpha=0, reg_lambda=0.1, scale_pos_weight=1, seed=None,\n",
            "             silent=None, subsample=1, verbosity=0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0z6iA4Tv5y6c",
        "outputId": "7da234aa-7980-4285-f390-d1915aa36bd9"
      },
      "source": [
        "# Feature importance analysis\n",
        "\n",
        "importance = xgbr.feature_importances_\n",
        "# summarize feature importance\n",
        "\n",
        "for i,v in enumerate(importance):\n",
        "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
        "# plot feature importance\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature: 0, Score: 0.05216\n",
            "Feature: 1, Score: 0.10476\n",
            "Feature: 2, Score: 0.11565\n",
            "Feature: 3, Score: 0.72744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "f5jXaBcveTeJ",
        "outputId": "cfe55db7-7e32-478c-fd1d-6901fead8511"
      },
      "source": [
        "# Create lists for the plot\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "#plt.rcParams[\"axes.grid\"] = False\n",
        "\n",
        "labels = ['WD', 'WFS', 'TS', 'P']\n",
        "x_pos = np.arange(len(labels))\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches((9,6))\n",
        "\n",
        "ax.bar(x_pos, importance, align='center', alpha=0.95, color='blue')\n",
        "ax.set_ylabel('Importance')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_title('Features Importance Analysis')\n",
        "ax.yaxis.grid(True)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZ2wVx6IxyZb"
      },
      "source": [
        "#MLP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "p4DpcAABxvuI",
        "outputId": "204a737e-86b5-4907-ec72-567f59f81bbe"
      },
      "source": [
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "MLP =MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
        "             beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
        "             hidden_layer_sizes=5, learning_rate='adaptive',\n",
        "             learning_rate_init=0.001, max_fun=15000, max_iter=3000,\n",
        "             momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
        "             power_t=0.5, random_state=1234, shuffle=True, solver='lbfgs',\n",
        "             tol=0.0001, validation_fraction=0.1, verbose=False,\n",
        "             warm_start=False)\n",
        "#MLP = MLPRegressor(hidden_layer_sizes=(1000,1000,1000), activation='relu', solver='adam', max_iter=800)\n",
        "\n",
        "#START TRAINING\n",
        "MLP.fit(X_train,y_train)\n",
        "#PREDICTION\n",
        "y_pred=MLP.predict(X_test)\n",
        "\n",
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(y_train, y_star_train, 'bx', label = 'Train')\n",
        "plt.plot([y_star_train.min(), y_star_train.max()], [y_star_train.min(), y_star_train.max()], 'k--')\n",
        "plt.plot(y_test, y_pred, 'ro', label = 'Test')\n",
        "plt.plot([y_pred.min(), y_pred.max()], [y_pred.min(), y_pred.max()], 'k--')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Measured melting efficiency')\n",
        "plt.title(\"Multi-layer perceptron regressor\")\n",
        "print()\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_error as MAE \n",
        "R2_train=r2_score(y_train, y_star_train)\n",
        "print('R2_train: ',R2_train)\n",
        "\n",
        "#METRICS: R2, RMSE, MAE\n",
        "R2=r2_score(y_test, y_pred)\n",
        "print('R2: ',R2)\n",
        "RMSE = np.sqrt(MSE_test)\n",
        "print(\"RMSE: \", RMSE)\n",
        "MAE = MAE(y_test, y_pred)\n",
        "print(\"MAE: \", MAE)\n",
        "IM=np.sqrt(((1-R2)**2) + (RMSE**2) + (MAE**2) )\n",
        "print(\"IM: \", IM)\n",
        "print()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "R2_train:  0.9156244609585389\n",
            "R2:  0.886392265874318\n",
            "RMSE:  0.01875942934058764\n",
            "MAE:  0.014355931797153047\n",
            "IM:  0.1160376069217246\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BvJ57NH78eISEhCicLywshIGB8v+d77zzTrWeT0REVBcw8VEjOxtTBAbFYMm7PeHUzgK37qXJX2uCm5sbRo4cid9//x329vbw8/PDxx9/jJycHEgkEri4uOCzzz4DAGzduhW5ublYsmQJjh8/jrCwMDRp0gTx8fFo3Lgxtm7dCgsLiwqeSERENZ0m/wivCziBQ42c2llgybs9ERgUgwOnbiskQZqSnZ2Nn3/+GevXr0eTJk2wY8cOHD9+HMHBwYiNjUVkZGSZ9/39999YsmQJwsPD0a5dOxw4cEBjMRIRkfYU/RF+614aAMj/CLezMdVxZDUDe3zUzKmdBdz72+LImbvwHmqv0aQHALy8vORfS6VSbNiwATdu3IAgCEhPT8edO3fg7Oxc6r7u3bvD2toaANClSxdERUVpNE4iItKO4n+Eu/e3RURUQpl/hP/1118wMDBAp06dVG67LvQmscdHzW7dS0NEVAK8h9ojIipBnnFrSsOGDeVf79mzBy9evMDRo0cRGhqKIUOGQCKRlHlf8Q1Z9fX1622RRiKiuqj4H+Hu/W1LJT0hISEYN24cVq5cWal260JvEhMfNSo+p2faCAd5xq3p5KfIy5cvYWFhAbFYjJSUFPz2229aeS4REdUsyv4Il8lk2LBhA+bPn48uXbpg165dlWpXF1M61I2JjxrFJ2Yp/AAU/YDEJ2Zp5fk+Pj64fv06PDw8sGzZMvTr108rzyUiopqjoj/C79+/j3feeQeHDx+GmZlZpduvqDepphMJgiDoOghdkkgkiI2NhaOjo8Lwz+3bt+Hg4KDDyGoOfi+IiGqPsubhnLn8Fx4+fo7Zk51RUFAAAwODKu9KUJRYlTd/SJeUfa4X4eRmIiKiOqTkJOM//vgDn8yfgbfeeguzvAfB0NCwym0X701yamcBp3bmtW64i0NdREREddSRI0cwceJENG7cGP/5z3+qvfekrqd0qAMTHyIiojpGKpVizZo1+Pjjj9GnTx+EhYXhr0Sh1GKbW/fScOxcvMrtjnezK9Wz49TOotYsZQeY+BAREdU5+fn5uHz5Mt5//30cOHAAzZo1qxNL0dWBc3yIiIjqiISEBJibm8PExAQnTpxAo0aN5OdULWxY17HHh4iIqA64fPkyRo0ahRUrVgCAQtJTpLYvRVcHJj5ERES13N69ezFlyhRYWVnBz89P6XXa3l2gJuJQVy0xceJE5Ofno6CgAAkJCbCzez2RrGPHjggICFCpjejoaBQUFGDgwIGaDJWIiLSkoKAAK1euRFBQEIYMGYJvv/0WjRs3LvNaZUvR/TpJgTOhkKRnQGxuBhufqbB0Kb3HY13BxEfNUi9GInH/QbX/AB09ehQA8PjxY4wfPx4hISGVbuPatWvIzc1l4kNEVEekp6fj5MmTmD9/Pvz9/aGvr6/02rKWovt1kuLVTwegV1gAAJCkpeP+th0AUGeTHyY+apR6MRL3t+2A7P9tDKrpH6CLFy/iu+++Q35+PgwNDbF06VJ07doVDx48wNKlS/Hq1SvIZDKMHTsWAwcOxOHDhyGTyRAVFYVRo0Zh9uzZao+JiIg078GDB7C1tYW1tTXOnz+P5s2bV3hPmUvOz4TKk54iMokEifsPMvGprocPH8Lf3x9ZWVkwNTVFYGAgbG1tFa7ZunUrfvzxR1haWgIAunfvjlWrVgEAvvjiC1y9ehVGRkZo2LAhli9fjs6dOwN4vUfV06dPYWJiAgB49913MX78eG29NbnE/QflSU8RTf0AJSYmYvv27di9ezdMTEwQHx+PWbNm4cKFC/jxxx/h5uaGOXPmAACeP3+Opk2bYvLkycjNzcWSJUvUGgsREWnPpk2bsGnTJnzwwQdYs2aNSkmPMpL0jEodrwu0lvisWrUKU6ZMgaenJ0JCQuRjkiV5eXmV+cHs7OyMZcuWwdDQEOfPn4efnx/Onj0rP79ixQq4urpq9D1URJs/QJcuXUJiYiKmTp0qP1ZYWIj09HT06tULGzduxKtXr9CnTx/07dtX7c8nIiLtkslkeP/993H27FkYGxvD09Oz2m2Kzc0gSUsvdbzQpClu3UtTWPV1614a4hOzalWxwrJoZVVXRkYG4uLi4OHhAQDw8PBAXFwcMjMzVW7D1dVVvr9I165dkZycDJlMppF4q0psXvYut8qOV9egQYMQEhIi/+/y5cswNzfH8OHDcfDgQdjY2OD777/Hp59+qpHnExGRdrx48QKDBg3C2bNn0aJFC0RHR6NHjx7VbtfGZyr0SmzkqScWo9HosXW22KFWEp+kpCRYWVnJJ13p6+vD0tISSUlJpa4NDw/H6NGj8cEHH+DGjRtltnfw4EEMHjwYenr/C3/Dhg0YPXo0Fi9ejJSUFM28kQoo+wGy8Zmq5I6qGzBgAC5duoT4+P+VGr916xYA4NGjR7CwsMC4cePw4Ycf4u+//wYAmJiY4OXLl2qPhYiINGvDhg1ISEhA3759ER0dDXNzc7W0a+nijLYfzoXYwhwQiSC2MEfbD+eip7eHvNjhgVO3a91GpOWpUZObJ0+ejLlz58LQ0BBXrlzB/PnzcfLkSTRr1kx+TXh4OEJDQ3Hw4EH5sQ0bNsDa2hpSqRQ7d+7EokWLcOjQIa3HXzSPRxOrukqytbXFxo0bsXz5cuTl5aGgoADdu3eHk5MTIiIiEBoaCkNDQ4hEIixbtgwAMGTIEAQHB8PT05OTm4mIaoGUlBRYWVlh5cqV6Natm0bmr1q6OJf5OVW82KH3UPs6kfQAAAQtSE9PF3r06CEUFhYKgiAIhYWFQo8ePYSMjIxy7xs7dqwQHR0tf/3rr78Kb7/9tvDvv/8qvefly5eCg4ODIJVKVYotLy9PiImJEfLy8hSOx8XFqXR/fcDvBRGR9q1du1Zo1aqVcOTIEZ08/6/4VGHK5yeF/RFxwpTPTwp/xafqJI7KUva5XkQrQ11mZmZwcHBAWFgYACAsLAwODg6lZqIXH6K6ffs2njx5gjfffBMAcP78eQQEBGD37t1o3bq1/LqiCb1FwsPDYW9vrzAMRkREVFvIZDJ4e3vju+++Q4MGDdCpUyetx1C82OG0EQ7yYa+6UOlZa0Ndq1evhr+/P7Zv344mTZogMDAQADBr1iwsWLAAnTt3xqZNm/DPP/9AT08PhoaG2LBhAywsXnetLV26FIaGhliwYIG8zb1790IsFmP27NkoKHhdh8DS0hKbNm3S1tsiIiJSm8zMTIwYMQJPnjxBmzZtcPr0aTRt2lTrcZRV7HDJuz0Rn5hV64e8RIIgCLoOQpckEgliY2Ph6OgIcbGJybdv34aDg4MOI6s5+L0gItKOKVOm4OLFi3B1dUVQUBBHL6pA2ed6kRo1ubmmkclk9f6HrqaVDCAiqotevnyJxo0bY8uWLQgPD8d7772n65DqrPr9qV6ORo0a4cmTJ8jPz0d97BQTBAH5+fl48uQJGjVqpOtwiIjqrGXLlsHR0RE3b96Eubk5kx4NY4+PEq1bt0Z6ejoePXqEwsJCXYejEwYGBmjatKna6kUQEdH/FBYWYty4cfjzzz/RuHFjmJrW/uKAFTl2Lh52NqY6rQjNxEcJPT09WFpayvcNIyIiUpfk5GS4u7sjNTUVbdu2xcmTJ+X7TdZldjamCsUQi68e0xYmPkRERFo2adIkpKamYuTIkdi5c2e9mU9atDosMCgG7v1tERGVoPWK0Ex8iIiItCQ/Px9GRkbYsmULYmJiMHPmTF2HpHW6rghdP1JMIiIiHfPz80OXLl2QlpaGrl271sukB3g9pyciKgHeQ+0REZWg9aKI7PEhIiLSoLy8PHh6eiI2NhbNmjWrlyuFixSf0+PUzgJO7cy1vgEqe3yIiIg05N9//0XPnj0RGxuLTp06ISYmpl4vmimvIrS2sMeHiIhIA2QyGdzd3fHs2TNMmDAB33zzja5D0rmylqy/7vnh5GYiIqJaq6jy/6pVq5CdnY33339f1yHR/8OhLiIiqreOnYsvNbn21r00HDsXX6X2ZDIZ5syZAxcXF0gkEkycOJFJTw3DxIeIiOqtooJ6RclP0eRbO5vyqyiXlTBdi01Ezz4DERYWhuzsbBQUFGgsbqo6Jj5ERFRvFS+od+DUbZVXGJVMmMLP/YEJHm5IefoI3bt3x7Vr1+pFJebaiIkPERHVCVUdtipeUM+9v61KE22LJ0y7g69j7vSJkBa8go+PD0JDQ2FoaFit90Kaw8SHiIjqhLKGrdbsjoa+vkjhupLJUFUL6jm1s8CIfm8g+NK/GDhkLDZu3IivvvpKfW+INIKJDxER1QllDVtNG9EBR3+LVzqHp3hBvWkjHOT3V5T8yGQyjB0/GRu/WIxJQ+yg13oYHHsP1fh7pOrjcnYiIqozSu4D5eXSDm+1aqp0U8zyCuopG/J6/vw53IYMQ/LTx7Bu2RqTh9ihi52F1isQU9Wwx4eIiOqMsoatypvDM97NrlSi4tTOosxCewAQFxeHXr16IfnpYzh164Vr0VdhaGiokwrEVDXs8SEiojpB2T5QE9+2U0iGnNqZV6lX5vHjx3B3d0dhYSHmzJmDlStXKpzXdgViqhomPkREVCeUNWw18W07HIi4g5Uz+1R7U8yWLVti8ODBGDt2LLy8vDTxFkgLREJ93iYWgEQiQWxsLBwdHSEWi3UdDhERqdGxc/GwszFVSHJu3UtDfGKW0uGs4qRSKaZNm4bevXvDz89Pk6GSmlT0uc4eHyIiqrOqsylmZmYmhg0bhqSkJDx79gyLFi2CSCSq8D6q2Ti5mYiIqITr16+jV69eSEpKwpAhQ3Dy5MlqJT3q3hOMqo6JDxER1VtlJSRHQn7DmDFjkJeXBz8/P+zbtw96etX7uKzqnmCkfkx8iIiozlPW45KUkVMqITl+9QVs37LD7t27sXjxYrU8v6p7gpH6cY4PERHVeUU9LkXJRvGl787dWiFg7+9Iit4FcauB2LjiAzh9NVbtMZQsrsikRzeY+BARUZ1XvMelZAXn5ORkXD+2FNkvnsFeTx9O7ZZoJIaSxRWrWk+IqodDXUREVC+UVcH56tWr6Nu3H7JfPEPnni5o0WuWypuUVkZV9wQj9dNa4vPw4UN4e3tj+PDh8Pb2RkJCQqlrtm7din79+sHT0xOenp744osv5OdevXqFRYsWYejQoRgxYgTOnz+v0jkiIiKgdI/Ld7sPYeLEiSgoyMcHcxfhVMiPWDq9t0YSkvL2BCPt0tpQ16pVqzBlyhR4enoiJCQEK1euRFBQUKnrvLy8sGRJ6W7G3bt3w8TEBGfOnEFCQgKmTp2KX3/9FY0aNSr3HBERUVnbWazaehImTczgv3Itpk8eA0C1TUqrojr1hEi9tNLjk5GRgbi4OHh4eAAAPDw8EBcXh8zMTJXbiIiIgLe3NwDA1tYWjo6OiIyMrPAcERFRUY+LfevGePfdd9FYPxtffDQSX249Kk96ipS3SSnVflpJfJKSkmBlZQV9fX0AgL6+PiwtLZGUlFTq2vDwcIwePRoffPABbty4IT/+9OlTtGrVSv7a2toaycnJFZ4jIqLaTR3F/8a72aGpYS569OiB3377Ddu3b4dTOwtMeNte3eFSDVejJjdPnjwZv/32G0JDQzFjxgzMnz8fz54903VYRES1Uk2qFpx6MRIxM+fgitcExMycg9SLqvfKq6P434ULF+Di4oKsrCxMmjQJGzdurPR7oLpBK4mPtbU1UlJSIJVKAbze9C01NRXW1tYK11lYWMDQ0BAAMGDAAFhbWyM+/vUvaMuWLfHkyRP5tUlJSWjRokWF54iI6quaUi049WIk7m/bAUlaOiAIkKSl4/62HSonP9Ut/rdv3z5MnToVhYWFWLduHTZv3lydt0O1nFYSHzMzMzg4OCAsLAwAEBYWBgcHBzRv3lzhupSUFPnXt2/fxpMnT/Dmm28CAEaMGIEjR44AABISEvD3339j0KBBFZ4jIqqvakq14MT9ByGTSBSOySQSJO4/qHIbZS1FV1WbNm3QrFkznDhxAtOnT1f5PqqbtLaqa/Xq1fD398f27dvRpEkTBAYGAgBmzZqFBQsWoHPnzti0aRP++ecf6OnpwdDQEBs2bICFxesf7hkzZsDf3x9Dhw6Fnp4e1qxZAxMTkwrPERHVZ+Uig0sAACAASURBVDWhWrAkPaNSx8tS2eJ/2dnZ8Pf3R0BAANzc3HDr1q1q77dFdYNIEARB10HokkQiQWxsLBwdHSEWi3UdDhGRWhUNb5WsVqxNMTPnvB7mKkFsYY6e/7ezwvtLLkUv+bqke/fuwcPDAy9fvsSyZcvw4YcfquV9UO1Q0ec6018iojqqJlQLTr0YCWmepNRxPbEYNj5TVWqjMsX/wsPD4ebmhpcvX+K9995j0kOlsMeHPT5EVEcdOxcPOxtThV6RW/fSEJ+YpZU6NUWTmkvO79FvbIK3Zs2ApYuzWp/3zTffYMOGDdDT08PGjRsxefJktbZPtUNFn+vcpJSIqI7SdbXgsiY1A4CBsbHak54ijRs3xpEjR9ClS5dK36vrRJG0g0NdRESkEeqY1FyR58+fY9WqVZBKpVi4cCFu3bpVpaQHqDnL/0mzVOrxefbsGZo1a6bpWIiIqA4Rm5uVPanZ3Ewt7cfGxmLs2LHIzc1F37594e7uDiMjoyq3V3z5vy4ng5NmqdTj4+rqinnz5uHUqVPIz8/XdExERFQH2PhMhV6JORaVmdRcnmPHjsHd3R25ubmYP38+3N3dq90mUL16QVQ7qNTjc+7cOYSFheH777/HypUrMXz4cHh6eqJnz56ajo+IiGqg1IuRSNx/EJL0DIjNzWDjM7XUvJ2i1xVdV1lr1qzBzp07oa+vj++++w5jxoyp+CYVVbZeENU+lV7V9eDBA4SEhCA0NBQikQhjxozBhAkTFDYJrU24qouIqHLKWq2lJxaj7YdzNTZpubgPPvgAUVFRCAkJQfv27dXWbmXrBVHNpPY6Punp6UhPT0dOTg5sbGyQkpKCsWPHYteuXWoJmIiIajZ1bEFRWenp6fi///s/AMDOnTtx8+ZNtSY9QOXqBVHtpdJQV3x8PH755ReEhYWhQYMG8PLyQkhIiHwj0Pnz52PMmDGYPXu2RoMlIiLd08ZqreL+/PNPTJo0CXl5eRgwYAAcHBzkG1qrk66X/5N2qJT4TJs2DaNGjcI333wDJyenUudbt26N9957T+3BERFRzaPp1VrF7d+/H0uXLoUgCBg57n0UGJornGedHaoslRKfy5cvV5hdL1y4UC0BERFRzWbjM7XMOT7qWK1V3GeffYaDBw/CwMAAu3fvhqVtF6VzcIhUpdIcn8DAQFy/fl3h2PXr17Fu3TqNBEVERDWXpYsz2n44F2ILc0AkgtjCXO0Tm2UyGa5evYqmTZviwoULGDJkiEKdnQOnbnPiMVWJSqu6+vbti8jISIXCUPn5+XBxccHVq1c1GqCmcVUXEVHNkZSUhFu3bmH48OF4/vw5jI2NS/3bfODUbRw5cxfeQ+0xbYSDjiKlmkote3WJRCKUzI+kUilkMpl6oiQionrvypUrmDZtGqRSKf76668ydwxgnR2qLpWGunr27Imvv/5anujIZDJs3bqVBQyJiEgtvv/+e3h7e6OgoADLli1TmvQUDW9NG+EgH/Yq2luLSBUq9fgsX74cc+bMwcCBA9GyZUskJSXBwsICO3bs0HR8RERUx/n6+uLEiRMwMjJCUFAQBg0aVOZ15dXZUWevD3dpr9tUrtwsk8nw119/ITk5GdbW1nBycoKeXu3f3J1zfIiIdCcnJwfdu3eHWCxGREREjdgFgBWcaze1zPEBAD09PXTr1k1hXo9MJqsTyQ8REZVP3b0gCQkJyMnJQadOnXDmzBm0aNGiWjurqxN3aa/bVMpa/vnnH3h7e6Nr167o1KkTOnXqhI4dO6JTp06ajo+IiGoAOxtThfk0Rb0gdjamlW7r7NmzcHFxwfjx41FQUAAbG5sak/QU4S7tdZdKPT7+/v5wdXXF+vXrYWxsrOmYiIiohlFXL8jXX3+NjRs3QiQSYfny5RrZekIduHqs7lIp8Xny5An8/PwgEok0HQ8REdVQxXtBvIfaVyoRkMlkmDlzJk6fPg2xWIyjR4+iR48eGoy26krO6XFqZ845PnWISkNdQ4cOxeXLlzUdCxER1WAle0Eqs4z833//xZkzZ2BlZYXff/+9xiY9AHdpr+tU6vGRSCTw9fVFjx49YG6uuEHchg0bNBIYERHVHFXtBUlMTESDBg3wxhtvIDQ0FI6OjjAwUHldjU4UTdYuPqG76D8ua6/9VOrxadeuHWbNmoXu3bvDxsZG4T8iIqr9jp2LL9WDc+teGo6diwdQtV6QsLAwDBw4EOPHj4cgCOjatWuNT3qKU+eEbqo5VPoJ9PX11XQcRESkQ0Uf8sp2Pi+rh6OoF6Qs69evx7Zt26Cnp4ePPvqoVs4R5bL2uknl1PvKlSsIDw9HZmYmduzYgb///hvZ2dno16+fJuMjIiItUNeHvEwmw9SpUxEZGYmGDRvi+PHj6Ny5s4ai1rzqTOimmkmloa79+/dj9erVsLW1xR9//AEAMDY2xjfffKPR4IiISHvUUbsmKioKkZGRaNOmDa5du1arkx6gehO6qWZSKfHZt28f9uzZg9mzZ8srNb/11lt4+PChRoMjIiLtqc6H/JMnTyCRSDBw4EDs3bsXUVFRZW40WptwU9S6SaXEJycnB9bW1gAgH6ctLCysVOGphw8fwtvbG8OHD4e3tzcSEhKUXvvgwQN06dIFgYGB8mPTp0+Hp6cnPD094eHhgfbt2+POnTsAXhdYdHZ2lp//7rvvVI6LiIiq9yF/9OhR9OvXD3PnzgXwugRKXdjOiMva6yaV5vj06tULu3btwrx58+THgoKC0KdPH5UftGrVKkyZMgWenp4ICQnBypUrERQUVOo6qVSKVatWYciQIQrH9+7dK//67Nmz+Prrr9GhQwf5sdmzZ2PatGkqx0NERP9T1Z3PP//8c/zwww/Q19fH+PHjtRWuVlR2QjfVDiolPitWrMDcuXNx9OhR5OTkYPjw4WjUqBF27typ0kMyMjIQFxeHPXv2AAA8PDywdu1aZGZmonnz5grX7tq1C4MHD0Zubi5yc3PLbO/nn3+uc79gRES6VNkP+cLCQkyYMAF//PEHGjdujNDQUNjZsbYN1XwqJT6WlpY4duwYbt26hadPn8La2hpOTk4qd2UmJSXBysoK+vr6AAB9fX1YWloiKSlJIfG5c+cOLl++jKCgIGzfvr3MttLS0nD16lWsX79e4fiePXtw5MgRtGnTBp988gnatm2rUmxERPVd6sVIJO4/CEl6BsTmZrDxmQpLF+dy79m3bx/++OMPtG3bFidPnoSJiYmWolWNuneTp7pD5eXsIpEIXbp0QZcuXTQSSEFBAT7//HMEBATIE6SyBAcHY9CgQQoJk5+fHywsLKCnp4fg4GDMnDkTZ8+eLbcdIqL6qGSSY9qzB9LOXYBMIgEASNLScX/bDgAoM/lJT0+HmZkZ3n//fRgbG+Odd96pkfN5KqpLRPWX0sTH3d0dERERAAAXFxelxacuXLhQ4UOsra2RkpICqVQKfX19SKVSpKamyidMA697chITEzF79mwAwIsXLyAIArKzs7F27Vr5dcePH8dnn32m0L6VlZX8ay8vLwQEBCA5ORmtWrWqMDYiovoi9WIk7m/boZDkpEScLnWdTCJB4v6DpRKfoKAgrFixAosWLcLHH3+MqVOnaiXuqmDxQVJGaeJTPNnYuHFjtR5iZmYGBwcHhIWFwdPTE2FhYXBwcFDotWnZsiWio6Plr7du3Yrc3FwsWbJEfuz69et4+fIlnJ0VfxlTUlLkyc+lS5egp6enkAwRERGQuP+gPOmpiCQ9Q+H1J598gsOHD8PAwADdunXTRHhqx+KDVBaliU/Pnv/rDuzdu3e1H7R69Wr4+/tj+/btaNKkiXyp+qxZs7BgwQKVilwdP34cXl5epYawlixZgoyMDIhEIpiYmOC7776rVfvBEBFpQ8lkpjxiczMAQH5+Pjw9PXHr1i2YmpoiPDwctra2GopQvUrWJXJqZ87khyASBEGo6CJfX19Mnz5dIRmKiYlBUFAQtmzZotEANU0ikSA2NhaOjo4Qi8W6DoeISGNiZs6BJC29wuv0xGK0/XAuLF2c4e/vj/3796Njx44IDQ2FsbGxFiKtvpK7yZd8TXVXRZ/rKs1I++OPP0p1bXbt2lVhaIqIiGo2G5+p0CvxQaAnFsPKfTjEFuaASASxhTnafjgXDXt0B/C6Ts/SpUtx5syZWpP0ACw+SMqpNB5kZGSEV69eKSxXzM3N5XASEVEtUjRZuaKl69u3b0fguz74/vvvMWzYMPj6+uoi3Gph8UFSRqXMZeDAgVi5ciXWrFkDExMTZGdnY82aNRg0aJCm4yMiIjWydHEut0bP3LlzERoaCiMjI5iammoxMiLtUGmoy9/fH9nZ2ejduzf69euH3r17Izs7G8uWLdN0fEREpAW5ublwdXVFaGgozM3NERUVpZaFLUQ1jUo9Pk2bNsWuXbuQmpqK5ORkWFtbw8KC3YVERHXFBx98gLt376Jbt244fvw4jIyMdB0SkUYoTXwEQZAXLZTJZAAAc3NzmJubKxyriRU7iYhINQUFBTA0NMTGjRtx6NChUgViieoapYlP9+7dcePGDQBAx44dS1VuLkqMbt++rdkIiYhIIzZt2oQdO3bg5MmTaNeuHZMeqheUJj4nT56Uf/3bb79pJRgiItI8mUyG999/H2fPnoWxsTHy8/N1HRKR1ihNfCZOnIjLly8DAL799lsEBARoLSgiItKMFy9ewN3dHQkJCWjRogVOnz4tn8JAVB8onaBTWFiIZ8+eAQBOny69iR0REdU+o0ePRkJCAvr27Yvo6GgmPVTvKO3x8fb2xuDBg9GsWTPk5eVh8ODBZV6nyu7sRESkWzKZDHp6elizZg2uXr0Kf39/XYdEpBPl7tWVlJSEJ0+eYMaMGfj+++/LvKa213ngXl1EVNd9+eWXCA4Oxrlz59CkSRNdh0OkURV9rivt8Vm0aBG+/vprWFtbY9myZbU+wSEiqm9kMhkmT56MK1euoGHDhsjOzmbiQ/We0jk+ly9fRlFnUGBgoNYCIiKi6svMzETfvn1x5coV2NjYICYmBi1bttR1WHXesXPxuHUvTeHYrXtpOHYuXkcRUUlKe3x69uwJb29v2NraIj8/X2l9hw0bNmgsOCIiqjypVApXV1ekp6fDzc0N+/btY7FZLbGzMUVgUIx8Z/hb99Lkr6lmUJr4fPPNNzh16hSePn0KALCxsdFaUEREVDWCIEBfXx/z5s1DdnY2Fi9erOuQ6hWndhZY8m5PBAbFwL2/LSKiEuRJENUMShMfsVgMT09PAK+Xtvv6+motKCIiqrxly5YhNjYWJ06cwNy5c3UdTr3l1M4C7v1tceTMXXgPtWfSU8OotEnpRx99hPv37+PUqVPIyMjAypUr8eDBA+Tn56NDhw6ajpGIiMpRWFiIcePG4c8//4SpqSlycnI4iVmHbt1LQ0RUAryH2iMiKgFO7cyZ/NQgKg36RkREYNq0aUhJSUFwcDAAICcnB1999ZVGgyMiovIlJyejV69e+PPPP9GuXTtER0cz6dGh4nN6po1wkA97lZzwTLqjUuKzZcsW7NmzB2vWrIG+vj4AoEOHDrhz545GgyMiIuWys7Ph7OyM1NRUjBo1CufPn4eJiYmuw6rX4hOzFOb0FM35iU/M0nFkVESloa7MzEy0b98eAOS7tItEolI7thMRkfaYmJhgxIgRsLe35zzMGmK8m12pY07tLDjUVYOolPh06tQJISEh8PLykh8LDw+Hk5OTxgIjIqKy+fn5wdDQEBs2bMCWLVt0HQ5RraJS4rN8+XLMmDEDP//8M3JzczFjxgw8fPgQP/zwg6bjIyKqUY6di4edjanCX/C37qUhPjGrzL/21fmMf+6lYM+mj/HPP/+gRYsWKCgogKGhoVqeSVRfqDTHp23btoiIiMCUKVOwaNEijBs3DqGhobC1tdVweERElaPpyrlFBeqKnlE0mdXOxlQt7St7xuptp7DGzxv//PMPOnXqhCtXrtSqpCf1YiRiZs7BFa8JiJk5B6kXI3UdEtVT+qtXr16tyoWGhoaws7ND9+7dYW9vDyMjIw2Hph1SqRSpqamwtLSEgYFKHWBEVIMVSmWvE5E2prBq3kiemIxxfgtWzRtVu32r5o1g1+Z1YvJKUoi9YXFqL1BX8hk7Dl3C9ROf49WrXEyYMAE//vhjrfr3KvViJO5v24HCFy8BANLcXGRdvwmxpQUa2b6h4+iorqnoc732/OYQEalAG5VztVGgrvgzJrzdBfn3OsPLywszZsxQ+7M0LXH/QcgkEoVjMokEifsPwtLFWUdRUX3FzVuIqM4pnjS497dVe2JSskBdWTVaqjvkdvNuCv6/9cvxhuF/8eu1x1i3+YdamfQAgCQ9o1LHiTSJiQ8R1TmqJCbVaVuVAnXVmQsU/XciJnqNQur9aPwTHVHri+CJzc0qdZxIk1Se41NXcY4PUd1SPDEZ2vsN+VyZojk/1XX55lOMcX5L3otUNB8nPjELHd/83wd5VecC3b9/H14ew/EqOxM9evRAeHg4rM0bl/mM2sKgaVNkXb8JQSqVH9MTi/HmzA84x4fUrqLPdZEgCEJFjbi4uJRZrNDIyAhWVlYYNmwY3nnnnVqZOEgkEsTGxsLR0RFisVjX4RBRNWljuXllHDh1Wz4XaNoIh3Kv/euvvzB69GhIpVL4+PjUqW2BUi9GInH/QUjSMyA2N4ONz1TO7yGNqOhzXaVMxcfHB7/88gt8fHxgbW2NpKQkHDx4ECNGjEDTpk2xZ88eJCUl4bPPPlPaxsOHD+Hv74+srCyYmpoiMDBQ6XL4Bw8eYOzYsZgyZQqWLFkCAPD390dUVBSaNWsGABgxYgTmzZsHAEhPT8dnn32GJ0+eQCwWY+3atejSpYsqb42I6piaVDm3sptVtmnTBi1atICfnx/eeecdjcWli+TQ0sWZiQ7VDIIKRo4cKSQnJyscS05OFkaOHCkIgiDcv39fcHZ2LrcNHx8fITg4WBAEQQgODhZ8fHzKvK6wsFCYNm2a8PHHHwtfffWV/PiSJUuE/fv3l3mPv7+/sG3bNkEQBOGPP/4Qhg4dKshkMlXempCXlyfExMQIeXl5Kl1PRKSKv+JThSmfnxT+ik8t83URqVQqzJ8/X7h27ZogCILK/3ZpIzai2qiiz3WVJjenpaWhUSPFsfEGDRogNTUVAPDmm2/ixYsXSu/PyMhAXFwcPDw8AAAeHh6Ii4tDZmZmqWt37dqFwYMHV6o44qlTpzB58mQAQM+ePWFkZIS///5b5fuJiNRNlc0qnz9/jgEDBiA4OBgbN24EAK3sgVh8yf+BU7flc6K4nxTVByolPq6urpg3bx6ioqJw//59REVF4aOPPoKrqysA4MaNG2jdurXS+5OSkmBlZSXf2V1fXx+WlpZISkpSuO7OnTu4fPkypk+fXmY7e/bswejRozF//nzcv38fAPDs2TMIgoDmzZvLr7O2tkZycrIqb42ISCPGu9mVSiSc2lnIh5Li4uLQq1cvJCYmYsCAATh8+LBW49P0kn+imkqlOT5r1qzB1q1bsXLlSqSmpsLCwgLu7u748MMPAbwel965c2e1AikoKMDnn3+OgIAAeYJUnJ+fHywsLKCnp4fg4GDMnDkTZ8+erdYziYh04dy5c3jvvfcgk8kwZ84crFy5UusxVHb+kTZxIjRpkkqJj1gsxuLFi7F48eIyz1tYlP/LYm1tjZSUFEilUujr68uXmllbW8uvSUtLQ2JiImbPng0AePHiBQRBQHZ2NtauXQsrKyv5tV5eXggICEBycjJatWoFAMjMzJT3+iQlJaFFixaqvDUiIq2zsLCAiYkJAgIC4OXlpfXnF1/y/3rit3mNGe4q2t6iqNKzJC0d97ftAAAmP6QWKq8/f/DgAe7cuYPc3FyF4xMmTKjwXjMzMzg4OCAsLAyenp4ICwuDg4ODwvBUy5YtER0dLX+9detW5Obmyld1paSkyJOfS5cuQU9PT/56xIgROHz4MObPn4+YmBjk5eXB0dFR1bdGRKRxhYWFWLJkCT777DN07twZsbGxZfZua0N58490nfhwewvSNJUSnx07dmDbtm3o0KEDjI2N5cdFIpFKiQ8ArF69Gv7+/ti+fTuaNGmCwMBAAMCsWbOwYMECdO7cudz7lyxZgoyMDIhEIpiYmOC7776T1w365JNP8OmnnyI4OBhisRgbNmyAnh6LUhNRzZCZmYlhw4YhKSkJgiBg06ZNOkt6gJq15L8kbm9BmqZSAcN+/fphz5496NChgzZi0ioWMCSiyqhsDZzr169j4sSJyMvLw5AhQ7Bnzx7+YVaOmJlzIElLL3VcbGGOnv9XvbmkVD9U9Lmu0m+fsbEx3nrrLbUHR0RUGdXd+FMdKrMH17Fjx+Dp6Ym8vDz4+flh3759THoqYOMzFXolPqz0xGLY+EzVUURU16j0G7hw4UJ8+eWXSE1NhUwmU/iPiEhbqrPxp7pUpgaOIAgQi8X44YcflC4OIUWWLs5o++FciC3MAZEIYgtztP1wLuf3kNqoNNRVNMRVvLCWIAgQiUS4ffu25qLTAg51EdUuRcmOe39bREQl6GwlkrI9uAoKChAQEIDPPvsMxsbGkEgk/LeFSIvUslfXb7/9pvbAiIiqonjhPe+h9jVqD67k5GQMHz4c6enpaN68OXx9fZn0ENUwKiU+RbVyiIh0TdeF95TVwBnRGVj52Xzk5+dj9OjR8PX11VpMRKQ6pYnP559/jrVr1wIAPv30U6X7x2zYsEEzkRERlVATCu+VVQOnY+N78F8UCJEIWL58OebPn1+ltnWxazpRfaM08Sm+99Ybb7yhlWCIiMpTEwrvlZWA5L1IhVhshD179sDFxaXKbRdN3i56j8UTPSJSD5UmN6elpZW5LYWy47UJJzcTUVXk5eVh7969mDNnDgAgJycHJiYm1W63pkzeJqqt1FLHZ/jw4WUeHzVqVPWiIyKqhR49eoQePXpg7dq1+PXXX+UV5dWBu6YTaZZKiU9ZnULZ2dlK5/0QEdVV58+fh4uLC7KysjBp0iSlfxhWVcnJ2yULNhJR9ZS7qsvFxQUikQgSiQSDBw9WOJeVlcUeHyKqV7799lsEBARAJBJh3bp1mD59ulrbrwmTt4nqunITn40bN0IQBMyePVth9ZZIJIKZmRm3sSCieuXcuXMwNjbGoUOH0Lt3b7W3XxMmbxPVdSpNbn716hUaNGigjXi0jpObiag82dnZOHfuHMaMGYPc3FwUFhaiSZMmug6LiJSocuXmb775RqUHLFy4sOrRERHVYPHx8Rg9ejRevnwJBwcH2Nmxlg5Rbac08UlOTtZmHERESumisF94eDjmzZsHqVSK6dOnM+khqiOUJj4BAQHajIOISCltF/YLDAzEli1bIBKJ4LfkCyxeMFN+riZXUk69GInE/QchSc+A2NwMNj5Tuas5UQkqLWcHgPv372Pbtm1Ys2YNAODBgwe4c+eOxgIjIipSNMk3MCgGB07d1uhKJ6lUip9++gkNGjTA1zsO4EZ6K/mS8qKEy87GVO3Pra7Ui5G4v20HJGnpgCBAkpaO+9t2IPVipK5DI6pRVEp8IiIiMHXqVKSkpCA4OBjA6yqlX331lUaDIyICXg91AVAo7Ff8uDo8f/4cf/zxB/T19REeHo6YmBhM8BistYSruhL3H4RMIlE4JpNIkLj/oI4iIqqZVNqdfcuWLdi7dy86dOiAiIgIAECHDh3Y40NEWmFnY4p1e64BALyH2iP00gOEXnqA5e+rZ0l5bGwsxo4di4KCAty8eRMtWrSQnyteSdl7qL1KSY8uhpwk6RmVOk5UX6nU45OZmYn27dsDgLxas0gkYuVmIqr1jh07Bnd3d+Tm5mLWrFkwNVUcxqpsJWVdDTmJzc0qdZyovlIp8enUqRNCQkIUjoWHh8PJyUkjQRERFRefmIXl7/fG6EFv4ciZuxg96C0sf7834hOzqtXuypUrsWDBAohEInz33XdYvny5wvnik6injXCQD3uVl/zoasjJxmcq9ErULNETi2HjM1WjzyWqbVQa6lq+fDlmzJiBn3/+Gbm5uZgxYwYePnyIH374QdPxERFhvJtdqZ4Xp3bm1VpZ9fLlSxw4cAAmJib45Zdf5L3axVWlkrKuhpyKhtK4qouofCpVbgZeV28+f/48nj59CmtrawwePBiNGjXSdHwax8rNRDVfyT2sSr6ujIyMDOTk5MDGxgaxsbGwsbFRayXmmJlzXg9zlSC2MIeR/5c1dik8UV1R0ee6yolPXcXEh6jmU1cBwz///BOTJk1Cw4YNcePGDRgYqNTpXSlFc3yKD3fpicUwHD8Fm//Rr7GrwojqiipvWVHc06dP8e233+L27dvIzc1VOHf69Gn1REpEpERZyc3r3ctVTyD279+PpUuXQhAE+Pr6aiTpAUoMOaWl44WhCfIGjsDPTHqIagSVfvMXLlyIt956CwsWLICxsbGmYyIiUqvFixfj0KFDMDAwwO7duzFkyBCNPs/SxVmeAB04dfv/LYW3ZdJDVAOolPg8ePAAR44cgZ6eyoWeiYhqhIcPH+Lw4cNo2rQpTp48CVtbW609u6wJ2Ux+iHRLpcTH1dUV165dQ9++fTUdDxGRWmRmZsLAwABvvvkm9u3bh379+qFhw4Zae/63P93Epb+eYPn7vf/fsJw51u25hkFdWsF3UletxUFEilRKfFasWIHJkyfDxsYGZmaKxbC4mSkR1TSXL1+Gj4+PvNr822+/rf0glNV3Zd1XIp1SKfFZunQp9PX10bZt2yqvfHr48CH8/f2RlZUFU1NTBAYGKu1yfvDgAcaOHYspU6ZgyZIlAIAvvvgCV69ehZGRERo2bIjly5ejc+fOAAAfHx88ffoUJiYmAIB3330X48ePr1KcRFS77dy5E2vXrgUAeHp66iwO34ld4dytFQKDYuDe3xYRUQny3h8i0h2VEp/ff/8dly5dkicWVbFqKvDiGAAAIABJREFU1SpMmTIFnp6eCAkJwcqVKxEUFFTqOqlUilWrVpWafOjs7Ixly5bB0NAQ58+fh5+fH86ePSs/v2LFCri6ulY5PiKqPnUtO68qX19fnDhxAkZGRti/fz8GDhyo8WeWpyr7fBGRZqk0W7l9+/bIyqp6afiMjAzExcXBw8MDAODh4YG4uDhkZmaWunbXrl0YPHhwqd4gV1dXGBoaAgC6du2K5ORkyGSyKsdEROpnZ2OqsKVDUaFBOxtTHDsXX2qrh1v30srcYb0y1xa5cOECTpw4ATMzM1y5ckXnSQ9Q+X2+iEjzVEp8+vbtixkzZmDnzp34+eefFf5TRVJSEqysrKCvrw8A0NfXh6WlJZKSkhSuu3PnDi5fvozp06eX297BgwcxePBghVVmGzZswOjRo7F48WKkpKSoFBcRqVfRlg6BQTE4cOq2QnXl8pKikipz7bNnzyCVSjF48GAEBgYiJiYGLVu21OwbVUFV9vkiIs1Taajrzz//hKWlJS5fvqxwXCQSYcKECWoJpKCgAJ9//jkCAgLkCVJZwsPDERoaioMH/7fh34YNG2BtbQ2pVIqdO3di0aJFOHTokFriIqLKUTa8UzwpKprzoqygn6rXnj17FjNnzsTYsWOxefNmTJs2TSvvURVV2eeLiDRPpcRn//791XqItbU1UlJSIJVKoa+vD6lUitTUVFhbW8uvSUtLQ2JiImbPng0AePHiBQRBQHZ2tnyi4pkzZ7B582bs3bsX5ubmCu0Dr3uS3n33XXz77beQyWSsO0SkA+XVrqnMnJeKrv3666+xceNGiEQi9OjRQ6PvqSrUUW2aiNRPMzXbSzAzM4ODgwPCwsLg6emJsLAwODg4oHnz5vJrWrZsiejoaPnrrVu3Ijc3V76q6/z58wgICMCePXvQunVr+XWFhYXIysqSJ0Lh4eGwt7dn0kOkAyU3D3VqZ15qc1FVC/opu1Ymk2HmzJk4ffo0xGIxjh49WiMTHyKqmbSS+ADA6tWr4e/vj+3bt6NJkyYIDAwEAMyaNQsLFiyQL01XZunSpTA0NMSCBQvkx/bu3QuxWIzZs2ejoKAAAGBpaYlNmzZp7o0QkVLlDe8AKDcpKq68BOr3cydw+vRpWFlZ4fTp07CwYA8KEamOu7Nzd3YirQjf/hOaXTsDIesZxOZmsPGZiuRWDmUudS9rWfy12ET8myrBmEG22LJlCxYuXFitjUZ1vfSeiDSjos91jgcRkcalXoxE8wvBEJ5lAoIASVo67m/bgRZPbpeZZIx3s1NISMLCwuA92gUvHl2GoaEhPvnkk2rvrl6ZlWNEVHco/Zfj33//VamBNm3aqC0YIqqbEvcfhEwiUTgmk0iQuP+gfBdzZdavX49t27ZBT09PYV5gdVVmlRkR1R1KE5+hQ4dCJBJBEASIRP/bXKbk69u3b2s2QiKqtYqGkyTpGWWeV3YcAGQyGaZMmYJLly6hYcOGOHHiBBwdHdUaHysrE9U/ShOfO3fuyL8+duwYoqKi8NFHH6Fly5Z4+vQptm3bhn79+mklSCKqnYqGkz40bfZ6mKsEsblZGXe9tnTpUly6dAlt2rRBREQEmjVrpvb4KrPKjIjqBpUmNzs7O+PXX3+FsbGx/NirV68wfPhwREZGajRATePkZiLNunUvDce+PowhSVegV1ggP64nFqPth3NLDXUVFBTA0NAQmZmZ2Lx5M7744guNlKcouXKs5Gsiqp3UMrlZJpPhyZMnCseePn3KvbKI6qHK7qPl1M4Cdh5DENq8DwpMmgIiEcQW5mUmPUePHkXHjh1x7do1NG/eHGvXrtVYTa6Klt4TUd2k0rKI6dOn47333sO4cePQokULJCcn4/jx43jvvfc0HR8R1TBFw1dl9ZSUpWg4yX38CHwf1UFpj8qKFSuwZ88e6OvrIycnR9Nvg5WVieoplev4REZG4tSpU0hNTYWFhQXc3d3h7Fz+aozagENdRJVXlOxUtBpKleGkwsL/v707D2vqyv8H/iZsimhRBAxYSqtgaXEBwa8bKogCI4hWUUZBOy4VbUvVLtBal+qoZcZWR8altda6Va1FseCOGwodq+hUBFRQKcouaF1YE+7vD39kCGsQkhDyfj1Pnye59yT3c69p8uGcc89HgokTJ+LSpUvo2LEjoqOjYWtb9zo6XHuHiBrT2O+6wgthDBs2rE0kOkTUfIreDaVIoc7AwEBcunQJPXr0wJEjR2BsbFzvcZva20REVJNCg+fl5eVYu3YtRo4cKauJc+HCBezatUupwRFR61Tzbqiac36q1FyIEHie/Exwt0VVZ/OSJUswZcoUnD17tsGkp+q1VWvv7DqWysnIRNRkCiU+q1atwq1bt7BmzRrZGj62trbYs2ePUoMjotanei9LoJe9LBGpL/mpy/bt29GvXz9kZ2fjjTfewD//+U+FJzFX723yHmzDpIeImkShoa7Y2FicOHECRkZGsi8nCwsL5OXlKTU4Imp9FBm+asiHH36IvXv3Qk9PD/n5+bC0tGzS8bn2DhE1h0KJj76+PqRSqdy2oqIimJiwpg2RtnnRu6HKy8sxduxYJCUlwcTEBIcPH4aNjU2Tjt1Q1XYmP0SkCIX6lr28vBAaGiqr35Wfn4/ly5djzJgxSg2OiNoOLy8vJCUl4Y033kBiYmKTkx6Aa+8QUfMplPgsWLAA3bt3x9ixY/H48WN4enrC3Nwc7777rrLjI6I2YuDISRjl7YeTJ0/KVoFvaOHDujQ0WZqISBEKJT4GBgb47LPPcPXqVSQkJODKlSv47LPPYGBgoOz4iEiDbdq0Ce7u7igpKUHA5AmosPSVTYKuGrayteaQORGpjkJzfAYMGIDffvsNANClSxfZ9kGDBuHXX39VTmREpNGCg4MRHR0NQ0NDPHjwAH16viy7A6yxhQ+JiJRFoR6fioqKOrexVhcR1VRcXAw3NzdER0eja9euiI+Px29ppbiWXiB3K7rT6+acm0NEKtdgj8+UKVOgo6OD8vJyTJ06VW5fbm4uHB0dlRocEWkWqVSKoUOHIi8vD05OToiMjISBgQFsnz0f1vIfaYujCRkY0b87ziXeR4+xb6o7ZCLSMg0mPv7+/hAEAUlJSZg4caJsu46ODkxNTTFw4EClB0hEmkNXVxdvvfUWnjx5gvDwcNn2Pj3N4D/SFt//kowR/bsj8UY+Zox9E/tPpeE1q5c43EVEKtNg4jN+/HgAQN++fdGjRw+VBEREmufrr79GUlISvv/+e3z++ed1tpFKBYzo3x1nEu9j8ig7jBveE69ZvaTwwodERC1BoTk+e/bswZUrV+S2XblyBStXrlRKUESkGSorKzF9+nR89dVXiI+Px+PHj+tta2ttgsQb+XL1vXgrOhGpmkKJT0xMDBwcHOS2OTg4ICYmRilBEVHr9/jxY7i6uiI2NhbdunVDQkICXnrppTrbtkR9LyKilqBQ4qOjoyOrpFxFKpXyri4iLfX06VMMHDgQGRkZGDhwIC5evIiuXbvW254rLhNRa6HQOj7Ozs5Yt24dPv74Y4hEIlRWViIiIgLOzs7Kjo+IWqEOHTrA0dERtra2WLZsWaPtq4az8s/FIXPnbpQ9KIRhV1O4Bk0FwKEuIlIdHaFmV04dcnNzMWfOHBQUFMDS0hI5OTkwMzPD5s2b0a1bN1XEqTRlZWW4fv06HBwcYGhoqO5wiJQi8nQabK1N5CYRX0svQFrmoybNsVm5ciUMDAzw8ccfNzmG/HNxuL1hMyrLymTbRIaG6PFuMMyHD1NKvESkfRr7XVdoqKtbt244ePAgNmzYgJkzZ2LDhg04cOCAxic9RNrC1tpEbk5NU8tFVFZWYtKkSdi4cSN27twJiUTS5Bgyd+6WS3oAoLKsDJk7dyscb86DZ7XmBTW13hcRaTeFhroAQCQSccFCIg1VNadGkXIRNXtbioqK4D5yFAryc2FtbY1jx45BT0/hrw6ZsgeFCm+vL14AsknSfXqayU2aJiJSRL3fXt7e3jh69CgAYPjw4dDR0amz3dmzZ5USGBG1rOrlIiaPsqt37Zyq3pbQac4w6yDB0KGuKC0tgfP/DcHBn/dCJFKoo7gWw66mKCt4UOf2psTLel9E1Bz1Jj4rVqyQPf7nP//Z7APdvXsXYWFhePToEUxMTBAeHg4bG5s62965cwfjx4/HlClTEBoaCgAoKSnBp59+iuTkZOjq6iI0NBRubm6N7iOi566lF+BoQoZsHZ0+PbvWmTBU720Z2b8bdAw64a+TgrBm9dJmHd86aGqdc3ysg6bW2b6+eBVN4IiI6lJv4lP9jq0BAwY0+0BLly7FlClT4Ofnh0OHDmHJkiXYsWNHrXZSqRRLly6Fh4eH3PatW7fC2NgYJ0+eREZGBqZOnYoTJ06gQ4cODe4jIvl1dJ4nD13lntd0YNcGdDe0xMG4ciz9eheCvN9odgxVE5ir39VlHTS11sTmxuIFoFACR0RUl3oTn3/9618KvcEHH3zQaJvCwkKkpKRg27ZtAAAfHx+sWLECRUVF6NKli1zbb7/9FiNGjEBxcTGKi4tl248ePYovv/wSAGBjYwMHBwfExcXJhuTq20dEDa+jUz1pkEgkGD9+PK5cuYL2nSywbO1uHE3IQF9bsxZJLsyHD6sz0VE03rirWfg1KUfhBI6IqKZ6E5/c3FzZ47KyMpw4cQIODg6wsrJCdnY2kpKSMHr0aIUOkpOTAwsLC+jq6gJ4XsjQ3NwcOTk5conPjRs3cOHCBezYsQMbN26Ue4/s7GxYWVnJnovFYlmMDe0jItR5C3jVsFGV3NxceHl5oaCgAO1f6oZdew9gYJ9X1JJc1BevogkcEVF96k18Vq9eLXu8YMECfPXVV/D09JRtO3HiBI4dO9ZigVRUVGDx4sVYvXq1LEEi0kSauAZNSkoKfHx8UFZWhr4uw7HqH2vRz84CQOtKLhRJ4IiIGqLQ7RlxcXG15ty4u7vj3LlzCh1ELBYjLy8PUqkUwPN5PPn5+RCLxbI2BQUFyMzMxDvvvAN3d3ds374dP/30ExYvXgwAsLS0RFZWlqx9Tk6ObB2hhvYRqVpz18xRh06dOqFdu3b49NNPcSTqR1nSU4XFRImorVAo8XnllVewe7f8ImN79uyBtbW1QgcxNTWFvb29rKhpTEwM7O3t5Ya5LC0tcfHiRZw+fRqnT5/G9OnTMWnSJNndZV5eXti3bx8AICMjA0lJSXB1dW10H5GqVb8ratex1FY9B+WLL77ArVu30L17dyQlJeG9995Td0hEREql0Cpkf//73/Hee+/hu+++g4WFBfLy8qCnp4eIiAiFD7Rs2TKEhYVh48aN6NSpE8LDwwEAs2fPRkhICHr37t3g62fOnImwsDCMGjUKIpEIy5cvh7GxcaP7iFqaIkNZrf2W69LSUowdOxbJycm4du0aIiMjOcRMRFpBoVpdwPM5OL///jvy8/NhZmaGfv36QV9fX9nxKR1rdVFT1bzVuubz6m1UvcieIknZvXv34O3tjYcPH8LBwQGHDh1Cu3btlB4bEZEqtEitrppcXFxQUVEhd7s5kbZobCireiIU6GUva1uzxpQyNDa/KCEhAa6urnj48CH8/f1x/PhxJj1EpFUUGuq6efMm5s6dCwMDA+Tl5eEvf/kLLl26hIMHD2LdunXKjpGo1WloKEudt1xXHWv51osY3FuMxBv5cj1T5xLvyYaDZ86cqdRYiIhaI4V6fJYtW4aQkBC54oQuLi5ITExUanBErVXNcgrVe3MmuNvWSnBUeVdUn55mGNxbjDOJ99H/dXM4vGaKjz5dhr9vOYcx3u5ISUlh0kNEWkuhxCc9PR1+fn4AICtWamRkhLJqNXeItIU6h7IUcS29AIk38uHWvzti/5OOvv0HYc+OLehQdAZ9eppxaIuItJpCiY+VlRWuX78ut+3atWsK385O1JY0NJSlbtWTMr//M0Hq4c9RlH8f4ld6YcsmxcrQEBG1ZQrN8fnggw8wZ84cBAQEoKKiAt988w327t0rV8GdSFu05tWDq5Ky3DtXMWvWLEilUvQZ6IUOtuORkvFQKTHmn4tTqPAoEVFroFCPj5ubG7777jsUFRXBxcUFWVlZiIiIwNChQ5UdHxE1QdX8ol8vX0dlJTA/dCmORm7Fkln/p5ThuPxzcbi9YTPKCh4AgoCygge4vWEz8s/FtehxiIhaSqPr+EilUnh6euLIkSMwMDBQVVwqw3V8qK2orKzEli1bMHPmTByKuwtxZxEGO/aQ7VdGvbDLs+Y8T3pqMDTrCufvvmmx4xARKaqx3/VGh7p0dXWhq6uLsrKyNpn4ELUFf/75J7y8vJCZmQmpVIp58+bVaqOM4biyB4VN2k5EpG4KDXVNmzYN8+fPx2+//YbMzEzcu3dP9h8RqVdKSgpcXFyQmZmJIUOGIDg4WGXHNuxq2qTtRETqptDk5qpJzPHx8XLbdXR0kJqa2vJREZFCoqKi8P7776OyshJz5szBkiVLVHp866CpuL1hMyqrLW0hMjSEddBUlcZBRKQohRKfGzduKDsOInoBsbGxAHQQumQ1QuZMk21XxnyeulTdvcW7uohIUzSY+JSUlGDTpk24desW3nzzTcyZM4fzfIjUTCKRIDo6GuPHj8e6deswMXAethz5A9fSC2oVTVUF8+HDmOgQkcZoMPFZvnw5rl+/DldXVxw/fhyPHj3C4sWLVRUbEdVQWFgIT09P5OTkoGPHjvDw8MCIgW+gS1cztVSDJyLSNA1Obj5//jy2bt2KTz75BFu2bMGZM2dUFRcR1XDlyhUMGDAAOTk58PDwgLu7u2xf9aKp3oNtmPQQEdWjwcSnuLgY5ubmAACxWIynT5+qJCgikrd7926MHTsWpaWlWLhwIbZv3w6R6H//+zZUNJWIiP6nwaEuqVSK//znP6ha41Aikcg9B4BBgwYpN0Iiwo4dO6Crq4stW7Zg9OjRcvuqz+l5vlZPV7nnRET0Pw2u3Fy9K73OF+vo4NSpUy0elCpx5WZqrcrLy5GYmIhBgwbh0aNHKC4uhqWlZa12kafTYGttIpfkqOquLiKi1qax3/VGS1a0dUx8qDXKzc2Fp6cnCgsLcfbsWfTs2VPdIRERaYTGftcVWrmZiFQnISEBgwYNwoMHD+Dj48Okh4ioBTHxIWpFvvvuO0yaNAkVFRVYtGgRNm/erO6QiIjaFIVWbiYi5ZNKpVi3bh309PSwY8cODBvGRQGJiFoaEx8iNSstLcW9e/dga2uLQ4cOoX379nVOYiYioubjUBeRGn2zNw79+jnBy8sLjx8/Ro8ePfCgWB+Rp9PUHRoRUZvExIdITU6fPo2VoUF48uRPuLqNRqdOnWRr8tham6g7PCKiNolDXURqEBERgS+//BI6OjqYN/9TpDzpgV3HUllni4hIyZj4EKnYkydPsGbNGhgaGmLfvn1wcXHBrmOp2HfyFiaPsmPSQ0SkREx8iFSkuLgYxcXF6Nq1K3bv3g07OzuYm5vXqrPVp2dXJj9ERErCxIdIBdLS0uDr64uOHTvi119/xdChQwGwzhYRkapxcjORkh0+fBgjR47EkydP4OnpCT29//29kZb5SC7J6dPTDKHTnJGW+Uhd4RIRtWkq6/G5e/cuwsLC8OjRI5iYmCA8PBw2NjZybSIjI/HDDz9AJBKhsrIS/v7+mDZtGgDgk08+wc2bN2Vtb968iQ0bNmDkyJGIiIjAjz/+CHNzcwCAk5MTli5dqqpTI6pXeHg41q9fD5FIhLVr12LSpEly++sqIvq858eMxUeJiJRAZUVKp02bhgkTJsDPzw+HDh1CZGQkduzYIdfm6dOn6NChA3R0dPD06VP4+vpi06ZNeP311+Xa3bhxA9OnT8f58+dhYGCAiIgIFBcXIzQ0tMlxsUgpKcudO3fg6uqK9u3bIzIyEn379m3S62sOg9V8TkREtbWKIqWFhYVISUmBj48PAMDHxwcpKSkoKiqSa2dsbAwdHR0Az1ezraiokD2v7ueff4avry8MDAyUHzy1OZGn03AtvUBu27X0ghZbNLC4uBglJSV47bXX8NVXX+HSpUtNTnqA/w17he+4jF3HUpn0EBG1AJUkPjk5ObCwsICuri4AQFdXF+bm5sjJyanV9tSpUxgzZgzc3Nwwa9Ys9OrVS25/eXk5oqOjMWHCBLnthw8fhq+vL2bMmIGrV68q72RI49lamyB8x2VZ8tOSiwZev34d/fr1w/Tp0wEAAQEB6Ny58wu/X5+eZvAebIN9J2/Be7ANkx4iomZqdZObR44cicOHD+P48eM4dOgQ7ty5I7c/NjYWlpaWsLe3l20LCAjAqVOnEB0djZkzZ2LevHl4+PChqkMnDaGsnpTIyEh4e3vj2bNncHJyapFYa97qXrOnioiImkYliY9YLEZeXh6kUimA51Wo8/PzIRaL632NpaUlevfujbNnz8ptj4yMrNXbY2ZmBn19fQDAkCFDIBaLkZbGWkdUv5buSVmyZAlCQkKgo6ODTZs2ISwsrNkxVp/TE+hlL0vWmPwQEb04lSQ+pqamsLe3R0xMDAAgJiYG9vb26NKli1y727dvyx4XFRXh4sWLsLOzk23Lzc1FYmIifH195V6Xl5cne5yamoqsrCy8+uqryjgVaiNasifl1KlT2Lp1K4yNjXHy5EmMHTu2RWLkre5ERC1PZbezL1u2DGFhYdi4cSM6deqE8PBwAMDs2bMREhKC3r17Y9++fYiPj4eenh4EQUBgYKBsoTcAOHjwINzc3PDSSy/JvffXX3+N5ORkiEQi6Ovr4x//+AfMzDgXgurWUosGlpSUoF27dnB3d8eCBQvwzjvvoFOnTi0WZ0O3uhMR0YtR2e3srRVvZ9c+LbE+TmJiIv76178iKCgIixcvVlaoRETURI39rrNkBWmd5vak7Ny5E59++ikEQYCJSfPvBCMiItVh4kPUBB999BH27NkDPT09bN26FR4eHuoOiYiImoCJD5GCvv32W+zZswcvvfQSjhw5UqvkChERtX6tbh0fotZGIpEAAIKCghAYGIjLly8z6SEi0lBMfIgacOHCBTg4OOCXX35B+/btER4eDiMjI3WHRUREL4iJD1E9vvnmGwQEBODp06f4888/1R0OERG1AM7xIarDu+++i6ioKBgYGGDnzp1y60kREZHmYo8PtTrKrp7emLCwMERFRcHU1BQJCQlMeoiI2hAmPtTqKLN6ekOq1vJ8//334ePjg8uXLzdYT46IiDQPEx9qdZRVPb0hsbGx6Nu3L1JTU2FlZYVvvvkGBgYGLX4cdfdmERFpOyY+1Cq1dPX0hqxduxbTp09HUVER7t69q7TjAOrrzSIiouc4uZlapZrV0/v07NriyU9lZSVmzpyJEydOwNDQEPv370f//v1b9Bg1Ve/N8h5sg6MJGUrvzSIiov9h4kOtTktVT2/MtGnTcObMGVhYWOD48eMwM1NN8lG9N2vyKDsmPUREKsShLmp10jIfySU5Vb0kaZmPWvQ47733Hjw8PPDbb7+pLOkBavdm1ZzzQ0REyqMjVN3KoqUaK19PbUt0dDTCw8MRHR2Nzp07q/z4NXuzaj4nIqLmaex3nT0+pDVWrVqF4OBg/PHHH7hz545aYlBVbxYREdWNc3yozausrMTUqVMRFxcHIyMjHDx4EA4ODmqJZYK7ba1tz+cxsbeHiEgVmPhQm+ft7Y3r16/j5ZdfxtGjR9UyxEVERK0DEx9q8/z9/dGtWzds27YNIhFHd4mItBl/BahN2r9/PyZOnAiJRIJZs2Zh+/btTHqIiIg9PtT2fP7559i2bRv09PSQlZWFV155Rd0hERFRK8E/ganVaG4dK4lEgnHjxmHbtm3o2LEjYmNjmfQQEZEcJj7UarxoHav8c3G4OGM2Jvbth0uXLqHPy9a4fPkybG1r30FFRETajUNd1Gq8SB2r/HNxuL1hMyrLyiA2bIe3X34FY162RnHiFRgPH6bC6ImISBOwx4dalaZWZY9e8zXSiwoBAIEvv4K/WIghlFcgc+duVYRLREQahokPtSpNqWO1cOFCfHH1Mnbez6y1r+xBoTLDJCIiDcWhLmo1FK3KXl5ejrFjxyIpKQn2nV7Ch6/Vnstj2NVUlaETEZGGYI8PtRqK1LF69uwZBgwYgKSkJLzxxhv4Yf16dOrQQe59RIaGsA6aqtLYiYhIM7DHh1oNRepYGRoawsLCAq6uroiIiAAAGBgYIHPnbpQ9KIRhV1NYB02FOSc2ExFRHVSW+Ny9exdhYWF49OgRTExMEB4eDhsbG7k2kZGR+OGHHyASiVBZWQl/f39MmzYNABAREYEff/wR5ubmAAAnJycsXboUAFBSUoJPP/0UycnJ0NXVRWhoKNzc3FR1aqQCmzdvhqGhIf72t7/h2LFj0NHRke0zHz6MiQ4RESlEZYnP0qVLMWXKFPj5+eHQoUNYsmQJduzYIdfG09MTb731FnR0dPD06VP4+vpiwIABeP311wEA48aNQ2hoaK333rp1K4yNjXHy5ElkZGRg6tSpOHHiBDrUGAIhzRQcHIzo6GiYmJhg2rRp0NXVVXdIRESkoVQyx6ewsBApKSnw8fEBAPj4+CAlJQVFRUVy7YyNjWV/yZeWlqKiokLuL/v6HD16FJMnTwYA2NjYwMHBAXFxcS18FqRqxcXFcHNzQ3R0NLp27YrY2FgmPURE1CwqSXxycnJgYWEh+9HS1dWFubk5cnJyarU9deoUxowZAzc3N8yaNQu9evWS7Tt8+DB8fX0xY8YMXL16VbY9OzsbVlZWsudisRi5ublKPCNStoKCAjg7O+PWrVtwcnLCpUuXIBaL1R0WERFpuFZ3V9fIkSNx+PBhHD9+HIcOHcKdO3cAAAEBATh16hSio6Mxc+ZMzJs3Dw8fPlRztKQs+vr60NfXR2BgIKKjo2FgYKDukIiIqA1QSeIjFouRl5cHqVQKAJBKpcjPz2/wL3hLS0tWdkoaAAATv0lEQVT07t0bZ8+eBQCYmZlBX18fADBkyBCIxWKkpaXJ2mZlZclem5OTg27duinpbEiZNmzYgF9//RUmJiZITExEeHi4ukMiIqI2RCWJj6mpKezt7RETEwMAiImJgb29Pbp06SLX7vbt27LHRUVFuHjxIuzs7AAAeXl5sn2pqanIysrCq6++CgDw8vLCvn37AAAZGRlISkqCq6urUs+JWlZlZSWmT5+OVatW4aOPPgIA6OlxtQUiImpZKvtlWbZsGcLCwrBx40Z06tRJ9pf87NmzERISgt69e2Pfvn2Ij4+Hnp4eBEFAYGAghg4dCgD4+uuvkZycDJFIBH19ffzjH/+Amdnz9V1mzpyJsLAwjBo1CiKRCMuXL4exsbGqTk3r5J+La9F1cx4/fgwvLy/88ccfEIvF+OWXX1owWiIiov/REQRBUHcQ6lRWVobr16/DwcEBhoaG6g6n1ateDb2KyNAQPd4NfqHk586dO/Dy8sKzZ88wePBg7N27l3duERHRC2vsd73VTW6m1i1z5265pAcAKsvKXrgaukQigUQiwezZs7F//34mPUREpFScREFNUl/V86ZWQz9+/DgcHBxgZ2eH5ORktG/fviXCIyIiahB7fJQg8nQarqUXyG27ll6AyNNpaoqo5dRX9VzRauiCIGDt2rWYMWMG1q1bBwBMeoiISGWY+CiBrbUJwndcliU/19ILEL7jMmytTdQcWfNZB02FqMaYqaLV0EtKSjBv3jysWbMGEyZMwIoVK5QVJhERUZ041KUEfXqaIXSaM8J3XIb3YBscTchA6DRnuSrjmqpqAnNT7+rKy8vD22+/jaSkJCxatAhz585VqBwJERFRS2LioyR9eprBe7AN9p28hcmj7NpE0lPlRaqhGxkZQV9fH9u2bcOoUaOUFBkREVHDmPgoybX0AhxNyMDkUXY4mpCBPj27tqnkp6k6duyIQ4cOsZeHiIjUinN8lKBqTk/oNGcEetnLhr1qTnjWNkx6iIhI3Zj4KEFa5iO5OT1Vc37SMh+pOTIiIiLtxqEuJZjgbltrW5+eZlo91EVERNQasMeHiIiItAYTHyIiItIaTHyIiIhIazDxISIiIq3BxIeIiIi0BhMfIiIi0hpMfIiIiEhrMPEhIiIircHEh4iIiLSG1q/cLAgCAKC8vFzNkRAREVFzVf2eV/2+16T1iU9FRQUA4NatW2qOhIiIiFpKRUUF2rVrV2u7jlBfSqQlKisr8ezZM+jr67N6OBERkYYTBAEVFRXo0KEDRKLaM3q0PvEhIiIi7cHJzURERKQ1mPgQERGR1mDiQ0RERFqDiQ8RERFpDSY+REREpDWY+BAREZHWYOJDREREWkPrE5+7d+9i8uTJ8PT0xOTJk5GRkVGrTWRkJHx9feHn5wdfX1/s2LFDtk8qleKLL76Ah4cHRo0ahf3796swes3U3GseERGBQYMGwc/PD35+fvjiiy9UGL1mUuSaV7lz5w769u2L8PBw2baSkhLMnz8fo0aNgpeXF86cOaOCqDVbc695WFgYhg0bJvucb9q0SQVRazZFrnlD3x/8nDdNc6+32j7jgpYLCgoSoqKiBEEQhKioKCEoKKhWmydPngiVlZWyxyNGjBBSU1MFQRCEgwcPCjNmzBCkUqlQWFgouLq6Cvfu3VPdCWig5l7z9evXC19++aXqAm4DFLnmgiAIEolECAwMFBYuXCh3jSMiIoRFixYJgiAId+/eFQYPHiw8ffpU+YFrsOZe89DQUGHnzp0qibWtUOSaN/T9wc950zT3eqvrM67VPT6FhYVISUmBj48PAMDHxwcpKSkoKiqSa2dsbCwrZ1FaWoqKigrZ8yNHjsDf3x8ikQhdunSBh4cHjh07ptoT0SAtcc2paRS95gDw7bffYsSIEbCxsZHbfvToUUyePBkAYGNjAwcHB8TFxSk9dk3VEtecmqYp17w+/JwrriWut7podeKTk5MDCwsL6OrqAgB0dXVhbm6OnJycWm1PnTqFMWPGwM3NDbNmzUKvXr1k72FpaSlrJxaLkZubq5oT0EAtcc0B4PDhw/D19cWMGTNw9epVlcWviRS95jdu3MCFCxfw9ttv13qP7OxsWFlZyZ7zc96wlrjmALBt2zb4+vpi3rx5uH37trLD1mhN+W6p7/uDn3PFtcT1BtTzGdf66uyKGjlyJEaOHIns7Gy8++67GDZsGF577TV1h9Wm1XfNAwICEBwcDH19fcTHx2PevHk4cuQIOnfurO6QNVZFRQUWL16M1atXy77ISLkau+YLFiyAmZkZRCIRoqKiMGvWLMTGxvLfp5n4/aFaDV1vdX3GtbrHRywWIy8vD1KpFMDzicr5+fkQi8X1vsbS0hK9e/fG2bNnZe+RnZ0t25+Tk4Nu3bopNW5N1hLX3MzMDPr6+gCAIUOGQCwWIy0tTemxaypFrnlBQQEyMzPxzjvvwN3dHdu3b8dPP/2ExYsXA3j+b5CVlSVrz895w1rimltYWMgqS48bNw7FxcXsfWiAot8tDX1/8HOuuJa43ur6jGt14mNqagp7e3vExMQAAGJiYmBvb48uXbrItave/VZUVISLFy/Czs4OAODl5YX9+/ejsrISRUVFiI2Nhaenp+pOQsO0xDXPy8uT7UtNTUVWVhZeffVVFUSvmRS55paWlrh48SJOnz6N06dPY/r06Zg0aRJWrFgB4PnnfN++fQCAjIwMJCUlwdXVVfUnoyFa4ppX/5yfP38eIpEIFhYWqj0RDaLod0tD3x/8nCuuJa63uj7jOoIgCEo/Sit2+/ZthIWF4fHjx+jUqRPCw8Px2muvYfbs2QgJCUHv3r2xatUqxMfHQ09PD4IgwN/fH0FBQQCeZ7nLly9HfHw8AGD27NmyyXFUt+Ze89DQUCQnJ0MkEkFfXx8hISEYPny4ms+qdVPkmlcXERGB4uJihIaGAgCKi4sRFhaG1NRUiEQifPzxx/Dw8FDHqWiM5l7zt99+G4WFhdDR0YGxsTE++eQT9OvXTx2nojEUueYNfX/wc940zb3e6vqMa33iQ0RERNpDq4e6iIiISLsw8SEiIiKtwcSHiIiItAYTHyIiItIaTHyIiIhIazDxISK1OHDgAP7617+q5Fi9evXCH3/8Ue/+MWPG4OLFiyqJpbrExESMHj0ajo6OiI2NxYMHDzB16lQ4Ojriyy+/xObNm7Fo0aJG32fJkiXYsGGDCiIm0nwsWUHUirm7uyM/Px9xcXFyC4ONGzcOqampOHXqFLp3767GCDVPWFgYLCwssGDBAtm2w4cPqyWW9evXY+rUqZg+fToAYMOGDejcuTOuXLnSpKK8y5cvV1aIRG0Oe3yIWjkrKyu5H+abN2+ipKREjRHVJggCKisr1R2GxsnOzoatra3c8x49ejQp6SGipmHiQ9TK+fn5ISoqSvY8KioK48aNk2tTXl6O8PBwjBgxAoMHD8aSJUtQWloKAPjzzz8xZ84cDBw4EC4uLpgzZ45cPZwDBw5g5MiRcHR0hLu7O3755RcAz1cS/uijj2Tt7t+/j169ekEikQAAgoKCsHbtWgQEBKBv3764d+8ebt++jb/97W8YMGAAPD09ceTIEdnrHz58iODgYDg5OWHixInIzMys95yrjhUZGYnhw4fDxcUFe/bswbVr1+Dr6wtnZ+davRw///wzvL294eLigpkzZ8rVXKqyb98+REdHY+vWrXB0dERwcDCA5z1rCQkJsvP+4IMP8Mknn8DR0RFjxoxBUlKS7D2Sk5Mxbtw4ODo6IiQkBPPnz8fatWvrPZf64vLw8MC9e/cQHBwMR0dHLFy4EFFRUbLYEhISav0bXL58GQEBAXB2dsbw4cNx4MABAM97sarHcObMGfj5+cHZ2RkBAQG4ceOGbJ+7uzu2bt0KX19f9O/fH/Pnz0dZWZlsf2xsLPz8/ODk5AQPDw/ExcXh6NGjeOutt+TOa9u2bZg7d269503UaglE1Gq5ubkJ8fHxwujRo4X09HRBIpEIrq6uwv379wU7Ozvh3r17giAIwsqVK4U5c+YIDx8+FJ48eSLMmTNHWLNmjSAIglBUVCQcO3ZMKC4uFp48eSK8//77wty5cwVBEIRnz54Jjo6Owu3btwVBEIS8vDzh1q1bgiAIwvr164UPP/xQFsu9e/cEOzs7oaKiQhAEQQgMDBSGDx8u3Lp1S6ioqBAeP34sDBs2TPj555+FiooKITk5WRgwYICQlpYmCIIgzJ8/XwgJCRGePXsm3Lx5Uxg6dKgQEBBQ53lXHWvx4sVCaWmpcP78ecHBwUGYO3eu8ODBAyE3N1cYOHCgcPHiRUEQBOHkyZOCh4eHkJ6eLlRUVAgbNmwQJk+eLHs/Ozs7ISMjQxAEQQgNDRW+/vrrOq9z1Xk7ODgIZ8+eFSQSibBmzRrB399fEARBKCsrE0aMGCH88MMPQnl5uXD8+HHhzTffrPV+VRqLq/px64qt+r/B/fv3hX79+gnR0dFCeXm5UFRUJKSkpNR6XXJysjBw4EDhv//9ryCRSIQDBw4Ibm5uQllZmeyYEyZMEHJzc4WHDx8KXl5ewo8//igIgiD8/vvvgpOTk3DhwgVBKpUKubm5Qnp6ulBWVia4uLgI6enpstj8/PyEY8eO1XneRK0Ze3yINEBVr098fDx69OghV8hPEAT89NNP+Oyzz2BiYgJjY2PMmTNHNjzWuXNneHp6on379jA2NsbcuXNx6dIl2etFIhHS0tJQWloKc3NzuaGXxowfPx62trbQ09PD+fPnYWVlhQkTJkBPTw9vvPEGPD09cezYMUilUpw4cQIhISEwMjKCnZ0dxo8f3+j7v/vuuzA0NMTQoUNhZGQEHx8fmJqawsLCAs7OzkhJSQEA7N27F++88w569OgBPT09BAcHywoivoj+/ftj+PDh0NXVhZ+fn6zH5Pfff4dEIsG0adOgr6+P0aNH16q5VV1LxhUTE4PBgwfDx8cH+vr66Ny5M+zt7Wu127dvHyZPnoy+fftCV1cX48ePh76+Pv773//K2gQFBcHCwgImJiZwc3NDamoqgOe9UxMmTMCQIUNkBSN79OgBAwMDeHt7y3oD09LSkJWVBTc3tyafB5G6cXIzkQbw8/NDYGAg7t+/Dz8/P7l9RUVFKCkpkRuKEKrNuSkpKcHq1atx/vx5/PnnnwCAZ8+eQSqVwsjICGvXrsX333+PRYsWwcnJCaGhoejRo4dCcYnFYtnjrKwsXLt2Dc7OzrJtUqkUY8eORVFRESQSiVx7S0vLRt/f1NRU9tjQ0LDW8+LiYgDP58asWrUK4eHhctcgLy8PVlZWCp1LdV27dpU9bteuHcrKyiCRSJCfnw8LCwu5OTjVz6mmlowrJycH1tbWjbbLzs5GVFQUdu3aJdtWUVGB/Px82XMzMzPZ4/bt28v25eTk1Fvwd/z48Vi4cCHmz5+PQ4cOwdvbGwYGBk06B6LWgIkPkQawsrJC9+7dce7cOaxcuVJuX+fOndGuXTscPnxYrieoyvfff4+7d+/ip59+gpmZGVJTUzFu3DgI/78+saurK1xdXVFaWop169Zh8eLF+PHHH9G+fXvZPCEAePDgQa33rpkAuLi4YNu2bbXaSaVS6OnpIScnR5ZU5eTkvNjFqINYLEZwcDDGjh3baNvmTBw2MzNDXl4eBEGQvU9OTg5efvnlZsfVGLFYjGvXrinULjg4+IXm34jF4nrnXvXr1w/6+vq4fPkyYmJisGbNmia/P1FrwKEuIg2xcuVKbN++HUZGRnLbRSIR/P39sWrVKhQWFgIA8vLycP78eQDPe3cMDQ3RqVMnPHr0CP/+979lr33w4AFiY2NRXFwMAwMDGBkZQSR6/rVgb2+PS5cuITs7G0+ePME333zTYHwjRoxARkYGoqKiUFFRgYqKCly7dg23b9+Grq4uRo0ahX//+98oKSlBeno6Dh482GLXJiAgAN9++y3S0tIAAE+ePMHRo0frbGtqaor79++/0HH69esHXV1d7Nq1CxKJBLGxsXITn5sTV2N8fX2RkJCAI0eOQCKR4OHDh7Ihqur8/f2xd+9e/P777xAEAcXFxTh79iyePn3a6DEmTpyIAwcO4Ndff0VlZSXy8vJw+/Zt2f5x48Zh+fLl0NPTk+vZI9IkTHyINIS1tXW980k+/vhjvPLKK5g0aRKcnJzw9ttv4+7duwCA6dOno6ysDAMHDsTkyZPh6uoqe11lZSV++OEHuLq6YsCAAbh06RKWLVsGABgyZAj+8pe/YOzYsXjrrbcanc9hbGyMrVu34siRI3B1dcXQoUOxZs0alJeXA3i+yF5xcTGGDBmCsLCwWncJNceoUaMwa9YsLFy4EE5OTvDx8UFcXFydbSdOnIj09HQ4Oztj3rx5TTqOgYEBIiIi8PPPP8PFxQW//PILRowYUe+QT1PiaoylpSW2bNmCbdu2YcCAARg3bpzc3VpVevfujRUrVmD58uVwcXHB6NGjZXd/NaZPnz5YvXo1Vq1ahf79+yMwMBDZ2dmy/X5+fkhLS2uRHiwiddERqvq7iYioyfz9/REQEIAJEyaoOxSlKy0txaBBg3Dw4EHY2NioOxyiF8IeHyKiJvjtt99QUFAAiUSCgwcP4ubNm3K9aG3Znj170Lt3byY9pNE4uZmIqAnu3r2L+fPno6SkBN27d8f69ethbm6u7rCUzt3dHYIgsCYYaTwOdREREZHW4FAXERERaQ0mPkRERKQ1mPgQERGR1mDiQ0RERFqDiQ8RERFpDSY+REREpDX+H2ZQWGzK4HhsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TD-gtnbJ2NIx"
      },
      "source": [
        "# initialize model and define the space of the hyperparameters to\n",
        "# perform the grid-search over\n",
        "model = MLP\n",
        "hidden_layer_sizes = [5, 8, 10]\n",
        "activation = ['relu','tanh']\n",
        "solver = [\"lbfgs\", \"sgd\", \"adam\"]\n",
        "\n",
        "grid = dict(hidden_layer_sizes=hidden_layer_sizes, activation=activation, solver=solver )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13DxrgWo3mGg",
        "outputId": "d2e68fbf-d58c-4e4a-ba71-8173ccc7606d"
      },
      "source": [
        "# initialize a cross-validation fold and perform a randomized-search\n",
        "# to tune the hyperparameters\n",
        "print(\"[INFO] grid searching over the hyperparameters...\")\n",
        "cvFold = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1234)\n",
        "randomSearch = RandomizedSearchCV(estimator=model, n_jobs=-1,\n",
        "\tcv=cvFold, param_distributions=grid,\n",
        "\tscoring=\"neg_mean_squared_error\")\n",
        "searchResults = randomSearch.fit(X_train, y_train)\n",
        "# extract the best model and evaluate it\n",
        "print(\"[INFO] evaluating...\")\n",
        "bestModel = searchResults.best_estimator_\n",
        "print(\"R2: {:.2f}\".format(bestModel.score(X_test, y_test)))\n",
        "print(bestModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] grid searching over the hyperparameters...\n",
            "[INFO] evaluating...\n",
            "R2: 0.89\n",
            "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
            "             beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
            "             hidden_layer_sizes=5, learning_rate='adaptive',\n",
            "             learning_rate_init=0.001, max_fun=15000, max_iter=3000,\n",
            "             momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
            "             power_t=0.5, random_state=1234, shuffle=True, solver='lbfgs',\n",
            "             tol=0.0001, validation_fraction=0.1, verbose=False,\n",
            "             warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOYI1QkauabG"
      },
      "source": [
        "## Comparisson"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "DH0SyeJQ04d1",
        "outputId": "dce6d873-9c78-4a61-cfa7-97c83d05197f"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "y1=GP.predict(X_test)\n",
        "y2=xgbr.predict(X_test)\n",
        "y3=MLP.predict(X_test)\n",
        "\n",
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((7,5))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(y_train, y_star_train, 'k.', label = 'Train')\n",
        "plt.plot([y_star_train.min(), y_star_train.max()], [y_star_train.min(), y_star_train.max()], 'k--')\n",
        "plt.plot(y_test, y1, 'bs', label = 'GPR')\n",
        "plt.plot(y_test, y2, 'r^', label = 'XGBR')\n",
        "plt.plot(y_test, y3, 'go', label = 'MLP')\n",
        "\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Measured melting efficiency')\n",
        "plt.title(\"Machine learning regressors\")\n",
        "print()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPy9q6RU3ZF2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "cb7e3a14-dfb8-4ac8-f0fd-af53858d2d4d"
      },
      "source": [
        "models = [ GP, xgbr, MLP]\n",
        "labels = ['GPR', 'XGBR', 'MLP']\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "\n",
        "for mod, label in zip( models, labels): \n",
        "  y_pred = mod.predict(X_test)\n",
        "  plt.plot(y_test, y_pred, 's' ,label = label)\n",
        "\n",
        "plt.plot([y_pred.min(), y_pred.max()], [y_pred.min(), y_pred.max()], 'k--')\n",
        "plt.ylabel('$y^*_{test}$')\n",
        "plt.xlabel('$y_{test}$')\n",
        "plt.legend()  \n",
        "  "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbfe0abdf90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNDmSIe0TMW4"
      },
      "source": [
        "CROSS-VALIDATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIEmJUZF69lo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2c26541-c296-4d31-8037-683877d7d8ec"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from sklearn.metrics import mean_absolute_error \n",
        "from sklearn.model_selection import cross_val_score as CV\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "models = [ xgbr,  GP, MLP, ]\n",
        "labels = ['XGBR', 'GPs', 'MLP', '']\n",
        "IMS = []\n",
        "\n",
        "print(\"--------------------------------------------------------------\")\n",
        "print(\"%5s | %5s | %5s | %5s | %5s \" % (\"ML algorithm\",  \"CV-R2\", \"CV-MSE\", \"CV-MAE\", \"IM\"))\n",
        "print(\"--------------------------------------------------------------\")\n",
        "\n",
        "for mod, label in zip( models, labels): \n",
        "  y_star_train = mod.predict(X_train)\n",
        " \n",
        "  scoreR2 = CV(mod, X_train, y_train, cv=5, scoring='r2')\n",
        "  #print(-1*scoreR2)\n",
        "  scoreMSE = CV(mod, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "  #print(-1*scoreMSE)\n",
        "  scoreMAE = CV(mod, X_train, y_train, cv=5, scoring='neg_mean_absolute_error')\n",
        "  #print(-1*scoreMAE)\n",
        "  IM=np.sqrt(((1-scoreR2)**2) + (scoreMSE) + (scoreMAE**2) )\n",
        "  #print(\"IM: \", IM)\n",
        "  \n",
        " \n",
        "  print(\"%15s | %.4f | %.4f | %.4f | %.4f \" % (label,  scoreR2.mean(), -1*scoreMSE.mean(), -1*scoreMAE.mean() , np.sqrt(((1-scoreR2.mean())**2) + (-1*scoreMSE.mean()) + (scoreMAE.mean()**2) )))\n",
        "print(\"--------------------------------------------------------------\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------\n",
            "ML algorithm | CV-R2 | CV-MSE | CV-MAE |    IM \n",
            "--------------------------------------------------------------\n",
            "           XGBR | 0.8453 | 0.0005 | 0.0153 | 0.1570 \n",
            "            GPs | 0.8678 | 0.0004 | 0.0153 | 0.1345 \n",
            "            MLP | 0.8172 | 0.0005 | 0.0173 | 0.1851 \n",
            "--------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAsAcApfOYtL"
      },
      "source": [
        "Results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZBfy5s4PC5s"
      },
      "source": [
        "R_GP=np.array(GP.predict(X_test))\n",
        "R_XGB=np.array(xgbr.predict(X_test))\n",
        "R_MLP=np.array(MLP.predict(X_test))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MoRV_bpXVuYf",
        "outputId": "a90d4b65-c07e-4434-fc7b-6b9c1e717489"
      },
      "source": [
        "R_GP.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zakleEb3Oq-Q"
      },
      "source": [
        "R1_m=np.mean(R_GP*100)\n",
        "R2_m=np.mean(R_XGB*100)\n",
        "R3_m=np.mean(R_MLP*100)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEJ6NHQGSITf",
        "outputId": "ec2fe377-f278-422a-d113-4643842c6ccf"
      },
      "source": [
        "R3_m\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44.12108971511211"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgV4E_KkPBSn"
      },
      "source": [
        "R1_sd=np.std(R_GP*100)\n",
        "R2_sd=np.std(R_XGB*100)\n",
        "R3_sd=np.std(R_MLP*100)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11klV8sRWMBJ",
        "outputId": "6f74512a-3bfa-48aa-9d48-baa6bab658ae"
      },
      "source": [
        "R3_sd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.8037585405376815"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHzpyRMaPHmT"
      },
      "source": [
        "# Create lists for the plot\n",
        "labels = ['GPR', 'XGBR', 'MLP']\n",
        "x_pos = np.arange(len(labels))\n",
        "CTEs = [R1_m, R2_m, R3_m]\n",
        "error = [R1_sd, R2_sd, R3_sd ]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "vLISX-IgQbGo",
        "outputId": "bed6506a-02a8-452c-e7e8-13e2f05ea4de"
      },
      "source": [
        "# Build the plot\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "#fig.set_size_inches((8,5))\n",
        "width = [0.4,0.4, 0.4]\n",
        "\n",
        "ax.bar(x_pos, CTEs, yerr=error, align='center', alpha=0.95, ecolor='black', capsize=10, width=width, color='blue')\n",
        "ax.set_ylabel('Melting efficiency (%)')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_title('Melting efficiency predictions')\n",
        "ax.yaxis.grid(True)\n",
        "\n",
        "# Save the figure and show\n",
        "plt.tight_layout()\n",
        "plt.savefig('bar_plot_with_error_bars.png')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6rpZ3V6-LuH"
      },
      "source": [
        "# Travel speed assessment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "oPdNDh7Su4Uu",
        "outputId": "0bdb9925-0d3f-41f5-f781-884449103761"
      },
      "source": [
        "#TS data\n",
        "data3 = pd.read_table('TS2.txt') \n",
        "data3.describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>WD</th>\n",
              "      <th>WFS</th>\n",
              "      <th>TS</th>\n",
              "      <th>P</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.500000e+01</td>\n",
              "      <td>75.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>8.326533</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.229469e-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.220140</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>4.450000</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>6.670000</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>10.980000</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.000000e-01</td>\n",
              "      <td>95.0</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2245.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 WD   WFS         TS       P\n",
              "count  7.500000e+01  75.0  75.000000    75.0\n",
              "mean   8.000000e-01  95.0   8.326533  2245.0\n",
              "std    1.229469e-15   0.0   5.220140     0.0\n",
              "min    8.000000e-01  95.0   1.660000  2245.0\n",
              "25%    8.000000e-01  95.0   4.450000  2245.0\n",
              "50%    8.000000e-01  95.0   6.670000  2245.0\n",
              "75%    8.000000e-01  95.0  10.980000  2245.0\n",
              "max    8.000000e-01  95.0  25.000000  2245.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mpaEqrDw_ny"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_TS = scaler.fit_transform(data3)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpYh3ys2xIUG"
      },
      "source": [
        "TSXGB=np.array(xgbr.predict(X_TS))\n",
        "TSGP=np.array(GP.predict(X_TS))\n",
        "TSMLP=np.array(MLP.predict(X_TS))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxeWF5A2xzAa"
      },
      "source": [
        "YTS=data3['TS'][:,None]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "-W6X3ZT0xUuz",
        "outputId": "c71cdb5a-dcf2-4643-b5f8-b3653e3eb134"
      },
      "source": [
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(YTS, TSXGB, 'bo', label = 'XGBR')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Travel speed')\n",
        "plt.title(\"XGBR\")\n",
        "print()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "rs_qVaKjyH4p",
        "outputId": "0ffee355-772c-443c-c27c-17bfa4c0a25c"
      },
      "source": [
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(YTS, TSGP, 'ko', label = 'GPR')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Travel speed')\n",
        "plt.title(\"GPR\")\n",
        "print()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "tsY4f4WKybSF",
        "outputId": "fcae5106-68e2-4caa-c27f-d7857879aec9"
      },
      "source": [
        "#Plots\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((9,6))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 30})\n",
        "\n",
        "plt.plot(YTS, TSMLP, 'ro', label = 'MLP')\n",
        "plt.legend()\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Travel speed')\n",
        "plt.title(\"MLP\")\n",
        "print()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "1UqtbdBcylrD",
        "outputId": "4aa96874-9bb7-4602-b432-a7015ef04572"
      },
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"axes.grid\"] = False\n",
        "\n",
        "models = [ GP, xgbr, MLP]\n",
        "labels = ['GPR', 'XGBR', 'MLP']\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches((7,5))\n",
        "\n",
        "for mod, label in zip( models, labels): \n",
        "  y_pred = mod.predict(X_TS)\n",
        "  plt.plot(YTS, y_pred, 's' ,label = label)\n",
        "\n",
        "plt.ylabel('Predicted melting efficiency')\n",
        "plt.xlabel('Travel speed [mm/s]')\n",
        "plt.legend() "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbfe154d690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//QYvLy+4uLggLS0NDg4OAJ5djXbt2lXo9yQiE6CLdRKLf/bTGIQiEYBR1SyMSI2gcJw/fz4mT56MXr16wcXFBRkZGWjcuDE2bNgg6CSOjo7w8PCATCZDQEAAZDIZPDw8lEH3XFZWFpydnQEA169fR1paGpo3bw4A8PPzw+7du9G2bVvcvXsXV65cwSeffFKV70pEtcD3qwKMXQKZAUHh2KRJE+zfvx+//vorMjMzIZVK0a5duypN2B0ZGYnQ0FCsX78ednZ2iIl5dgtl4sSJCAkJQdu2bfHpp5/i2rVrsLCwgKWlJVasWKG8mgwODkZoaCgGDBgACwsLLF68GLa2ttX4ykRERBUTKaq4BIP6c8aasKJFcXExrl69Ck9PT1hZcYYLIn15e9dMoI6GFRKeWuHbd1fj7d1TtH7228B/6rEyovIqygZBV47Xrl3D4sWLcfPmTRQXP/vDVygUEIlEuH79uu4rJqKaSVMwVtROZKIEhWNoaCh8fHywbNkyLmVERNVmiHUSiXRBUDimpaVh1qxZXO+QyIzpYhiG9Z2BnNCbagRB4ThgwAAkJSWhd+/e+q6HiEyULoZhcEJvqikEhWNxcTGmTZuGN954A40aNVLZtmLFCr0URkREZCyCwvHVV1/Fq6++qu9aiKime2qltbcqUU0iKBynTZum7zqIqBb49t3Vxi6BSCcEhSMA/Pjjj4iPj0dubi42bNiAK1euoKCgAN27d9dnfURERAYnKBy//PJL7NixAyNHjkRCQgIAwNraGkuXLmU4EtUAuhh8z2EYZE4EheO//vUvbN++Ha6urti0aRMAoEWLFkhNTdVrcURkOjgMg8yJoHAsLCxUrqDxfKzj06dPYWlpqb/KiMikcBgGmRNBE6N26dIFGzduVGnbsWMHl4wiIqJaSdCV44IFC/DBBx9gz549KCwshK+vL2xsbPDFF1/ouz4iIiKDExSOTk5O+O6773D58mWkp6dXa8kqItIdXUzlRkTaCR7KIRKJ0L59e7Rv316f9RCRBtrCUJ22fRQKQNPUyFVbsI7IfGgNx4EDB+Lw4cMAgL59+2qddPzkyZN6KYyI/qcq85dq8uRCBZ1pRr3UoYlqJa3hGBUVpXy9cuVKgxRDRERkCrSGY+fOnZWvvby8DFIMERGRKRA8t+r48eNVAvPnn3/Gjh07sHbtWr0VR2QOdDF7DRHplqBwvHDhAtasWaPS1qFDB3z44Yd6KYqIqknL6hcHPwkwcCFENZugcJRIJHj8+DFsbW2VbUVFRahTR3BnVyJ6CQ2s7TR2ylGUSPDkP/1UG981UFFEtZigdOvVqxfCw8OxePFi2NraoqCgAIsXL0bv3r31XR9RjaeLMYnq+w3+KE4ntRGRZoLCMTQ0FB9//DG8vLzQoEEDPHr0CH369MGKFSv0XR9RjSJ0PCJQteEZYyKPaJz0m4j0Q1A4NmjQABs3bkR2djYyMzMhlUrRuHFjfddGVOO87HhEbRiMRIalNRwVCoVy4H9ZWRkAoFGjRmjUqJFKG6eQIyKi2kZrOHbq1Am//PILAKBNmzblZsh5Hp7Xr1/Xb4VEtZwuh2twbUUi3dAajocOHVK+/uGHHwxSDBFVHYdpEOme1nAcOXIkkpKSAACff/45oqOjX+pEqampCA0NRV5eHuzt7RETEwM3NzeN+6akpGDYsGEICgrC3LlzlZ8PDw9Hfn4+SkpKMGjQIEyfPv2laiKqjD4H6DewtgNQcc9TBh+RcWgNx6dPn+Lhw4do2LAhEhISXjocIyIiEBQUhICAAMTFxSE8PBw7duwot59cLkdERAT69++v0r5y5Ur4+vpi9OjRKCwshL+/P/r27Yt27dq9VF1EuqRtPOLLLiVlX99KY6cc3kYl0g+t4RgYGAhvb280bNgQT548gbe3t8b9hKzKkZOTg+TkZGzbtg0A4O/vj6ioKOTm5sLBwUFl340bN8Lb2xtFRUUoKipStotEIvz1118AgCdPnkAkEpX7LJGx6WstxS8jK1hVg4h0Tms4zpo1C6NGjUJaWhqCg4NfakxjRkYGnJ2dIRaLAQBisRhOTk7IyMhQCbgbN24gKSkJO3bswPr161WOMW/ePHzwwQf46quvkJ+fjzlz5sDV1bXaNREREWmjNRxnzpyJ1atXQyqVYt68eXpfmaO0tBQLFy5EdHS0MkRftHv3bgQEBGDChAnIzs7GmDFj4OnpycWXiYhI57SGY1JSknK4RkxMDAIDA6t9EqlUiqysLMjlcojFYsjlcmRnZ0MqlSr3uX//Pu7du4dJkyYBAPLz86FQKFBQUICoqCh8+eWXOH78OADAyckJ3bp1w4ULFxiOJJgupnEjIvNQ4XqOgYGBcHNzQ0lJCebMmaNxPyG3Wx0dHeHh4QGZTIaAgADIZDJ4eHio3FJ1cXHB+fPnle9jY2NRVFSk7K3q6uqKM2fOYOjQoSgoKMDFixfRr1+/cuci0kbb7DX6mtXmOW1Tv9nXt+KzRCITpTUc16xZgyNHjiA9PR0A8Le//e2lThQZGYnQ0FCsX78ednZ2iIl59i/1iRMnIiQkBG3btq3w89HR0ViyZAm2bt2Kp0+fYtCgQejbt+9L1US1W0XDMITSxQB9bVO/PW/ncA0i06M1HK2srBAQ8Ow/2qdPn2LatGkvdSJ3d3fs2bOnXPumTZs07q8+htHT0xPffPPNS9VAREQkhKCJx6dPn447d+7gyJEjyMnJQXh4OFJSUlBSUoLWrVvru0aiGoOrZxDVDoJmDT98+DBGjx6NrKwsHDhwAABQWFiI5cuX67U4opqGwUhUOwi6cly7di22bduG1q1b4/DhwwCA1q1b48aNG3otjsgQnk/jRkT0nKBwzM3NRatWrQBAuTqHSCQqt1IHkaG87LAMXa6EUV2c+o3IdAkKx9dffx1xcXEYOnSosi0+Pp7zmpLRGGtYxstgr1SimkNQOM6fPx/BwcHYu3cvioqKEBwcjNTUVGzdulXf9RFVmylcHRJRzSQoHN3d3XH48GEkJibC29sbUqkU3t7esLGx0Xd9RDUKV88gqh0EhSMA1K1bF4MGDdJnLUQ1Hme8IaodBA3lICIiMieCrxyJzNHgj+K0bmMHG6Lai1eOVCOJoHkYkbZ2IqKq4JUj1Ui7A9dXvhMRUTUJCse+fftqHPAvkUjg7OyMN998E++88w7q1GHWEhFRzScozcaMGYPvv/8eY8aMgVQqRUZGBnbt2gU/Pz80aNAA27ZtQ0ZGhtY1H4mIiGoSQeG4f/9+bN26Fc7Ozsq2Pn364P3330d8fDy6du2K9957j+FIOlPRWowc3E9E+iaoQ879+/fLDfivW7cusrOzAQDNmzdHfr7pTttFRERUFYKuHH18fDBlyhRMmTIFzs7OyMrKwhdffAEfHx8AwC+//AJXV1e9FkpkDByuQWSeBIXj4sWLERsbi/DwcGRnZ6Nx48YYOHAgPvzwQwDAK6+8gi+++EKvhRIRERmKoHC0srLC7NmzMXv2bI3bGzdurNOiiPRhTOQRrfOecto3InqR4LEXKSkpuHHjBoqKilTaR4wYofOiyPwE7p4KBRR6PYemYKyonYjMl6Bw3LBhA9atW4fWrVvD2tpa2S4SiRiOpBP6DkYioqoQFI7/+te/sGfPHrRu3Vrf9RCVU92hGxXNi0pEVBFBQzmsra3RokULfddCRERkEgSF44wZM7BkyRJkZ2ejrKxM5X9ERES1jaDbqqGhoQCAPXv2KNsUCgVEIhGuX7+un8qIDMS+vpWxSyAiEyMoHH/44Qd910FmTgSRxk45+lqCioP7iagigsKxadOm+q6DzByXoCIiU6I1HBcuXIioqCgAwMcff6xxySoAWLFihX4qIyIiMhKt4fjiXKnNmjV76ROlpqYiNDQUeXl5sLe3R0xMDNzc3DTum5KSgmHDhiEoKAhz585Vtn/55ZfYtWsXLC0tYWFhgbg4dtU3VxUN03h+y5S3TomourSG4+TJk5WvAwMDNU4Rd//+fcEnioiIQFBQEAICAhAXF4fw8HDs2LGj3H5yuRwRERHo37+/SvvRo0dx5MgR7N27F7a2tnjw4IHgcxMREVWFoKEcvr6+GtvfeustQSfJyclBcnIy/P39AQD+/v5ITk5Gbm5uuX03btwIb2/vcleVW7duxbRp02BrawsAaNSokaBzExERVZWgcFQoyvciLCgo0PocUl1GRgacnZ0hFosBAGKxGE5OTsjIyFDZ78aNG0hKSsL48ePLHePOnTv49ddfMWrUKAwfPhzffvutoHMTERFVVYW9Vfv27QuRSITi4mJ4e3urbMvLyxN85ShEaWkpFi5ciOjoaGWIvkgulyMjIwNfffUVHj58iHfeeQfNmzdHly5ddFYDERERUEk4rly5EgqFApMmTVLplSoSieDo6Ch4SjmpVIqsrCzI5XKIxWLI5XJkZ2dDKpUq97l//z7u3buHSZMmAQDy8/OhUChQUFCAqKgouLi4wN/fHxYWFnB0dESPHj1w+fJlhiMREelcheHo5eUFADh37hzq1q1b7ZM4OjrCw8MDMpkMAQEBkMlk8PDwgIODg3IfFxcXnD9/Xvk+NjYWRUVFyt6q/v7+OHPmDLp06YKioiJcvHgRAwYMqHZNpD9v756idVt1JxEnIjIkreG4Zs0aQQeYMWOGoP0iIyMRGhqK9evXw87ODjExMQCAiRMnIiQkBG3btq3w8+PHj8fChQuVt3IDAgLQs2dPQeemmqeyoRocpkFE+qQ1HDMzM3V6Ind3d5W5WZ/btGmTxv2nT5+u8t7a2horV67UaU1ERESaaA3H6OhoQ9ZBRERkMgTNrQo8G0px5MgR5OTkIDw8HCkpKSgpKeECyKQTQ2bHQcOIISIioxA0zvHw4cN49913kZWVhQMHDgAACgsLsXz5cr0WR+aDwUhEpkTQlePatWuxfft2tG7dGocPHwYAtG7dGjdu3NBrcURERMYgKBxzc3PRqlUrAFDOiiMSiQTPkEPmhcM1iKimExSOr7/+OuLi4jB06FBlW3x8PNq1a6e3woie47ANIjI0QeE4f/58BAcHY+/evSgqKkJwcDBSU1OxdetWfddHRERkcILC0d3dHYcPH0ZiYiK8vb0hlUrh7e0NGxsbfddHZkIk0twph3fuicgYBA/lqFu3LgYNGqTPWsiMfb+Kt06JyHQICsf09HR8/vnnuH79OoqKilS2JSQk6KUwopro99XBkBfmCdpXbGOPZjO3IGXpP7Tu02L+d1U67vNjqhNyjspoq+HFc1a2j5BjVCZl6QgAmsb+iNBi/l5Bx6iMLuo0hXMY6jy16bs8JygcZ8yYgRYtWiAkJATW1tY6LYCoNhEajPratyrHrCptx36xvbJ9hByjctoGxepusKxu6jT+OQx1ntr0XZ4TFI4pKSnYvXs3LCwEzRlAZq6yScOJiEydoHD08fHBv//9b3Tr1k3f9ZCJ4fJTRGSOBIXjggULMGrUKPztb3+Do6OjyjZOUE5ERLWNoHAMCwuDWCyGu7s7rKys9F0TERGRUQkKx3PnzuHMmTOwtbXVdz1ENZrYxr5KvVV1fdyqHLOqtNXw4jkr20fIMSongrbeqrqimzqNfw5Dnac2fZfnRApF5eshjBo1CqtWrYKrq6vOCzCE4uJiXL16FZ6enrzyraLqPHNkhxwiqgkqygZBV47dunVDcHAwhg8fXu6Z44gRI3RXKRldRWFIRGQuBIXjxYsX4eTkhKSkJJV2kUjEcKRy7OtbIe+vYo3tREQ1gaBw/PLLL/VdB9UAQodufBnpp+dKiIj0i6P6iYiI1DAciYiI1AhelYNIvRcqe54SUW3FK0cSpPIBP0REtYfWK8c//vhD0AFeeeUVnRVDxqfe6aaiMYvmQBdLPRFRzaM1HAcMGACRSASFQgHRC8uxq7+/fv26fiskgzL3MCQiAioIxxs3bihff/fdd/jpp58wffp0uLi4ID09HevWrUP37t0NUiQREZEhCeqQs2bNGhw9elS50LGbmxsWL14MX19fDB8+XNCJUlNTERoairy8PNjb2yMmJgZubm4a901JScGwYcMQFBSEuXPnqmw7f/48xo8fj/nz52P06NGCzk3/E7h7KhQa5qUUQYTdgeuNUBERkQVrdD8AABkjSURBVOkR1CGnrKwMaWlpKm3p6ekoKysTfKKIiAgEBQUhISEBQUFBCA8P17ifXC5HREQE+vfvX25bQUEBVq1ahT59+gg+L6nSFIwVtRMRmSNBV47jx4/HuHHjMHz4cDRp0gSZmZnYt28fxo0bJ+gkOTk5SE5OxrZt2wAA/v7+iIqKQm5uLhwcHFT23bhxI7y9vVFUVISioiKVbcuXL0dwcDBOnjwp6LykWzV96Mbvq4O1zujfbOYWI1RERKZK0JXjhAkTsGzZMjx48AAnTpzA/fv3sWzZMkycOFHQSTIyMuDs7AyxWAwAEIvFcHJyQkZGhsp+N27cQFJSEsaPH1/uGKdOncJff/0FPz9OTUbVo23JJ6FLTBGR+RA8CUCfPn30ejuztLQUCxcuRHR0tDJEn8vPz8cnn3yivPIkMhQO1yAyT4LCsaSkBOvWrYNMJkNeXh4uXryIpKQk3L17V1CnGKlUiqysLMjlcojFYsjlcmRnZ0MqlSr3uX//Pu7du4dJkyYBeBaICoUCBQUFCAgIwP379zFy5EgAwMOHD5GYmIi8vDxMmzatOt+btKjpt06JiHRBUDguW7YMWVlZWLVqlfJW6muvvYbo6GhB4ejo6AgPDw/IZDIEBARAJpPBw8ND5Xmji4sLzp8/r3wfGxuLoqIiZW/Vs2fPKreFhobC09OTvVWpQhUN4CciqoigZ47Hjx/HJ598go4dO8LC4tlHnJ2dkZWVJfhEkZGR2LlzJ3x9fbFz504sWrQIADBx4kRcuXKlGqVTdWibBo7TwxER/Y+gK0dLS0vI5XKVttzcXNjb2ws+kbu7O/bs2VOufdOmTRr3nz59utZjLV++XPB5SdWTCxV0aBpluDpMidhG+N8xEZkHQeHo5+eHuXPnIiwsDACQnZ2NZcuW4a233tJrcUT6wo42RFQRQbdVZ82aBVdXVwwZMgT5+fnw9fWFk5MTPvzwQ33XR0REZHCCrhwlEgnmzZuHefPmITc3Fw0bNlSZfJxMw9u7p2jdpr7aBhERaScoHL28vPDvf/8bAFR6mHbv3l2lFymZPg7VICKqnKBwLC0t1dhWlblViQyNzxWJqLoqDMegoCCIRCKUlJTg3XffVdmWmZmJjh076rU4IiIiY6gwHEeOHAmFQoErV65gxIgRynaRSARHR0d069ZN7wWSduoLE9f1MlIhRES1TIXhOGzYMABA+/bt4e7ubpCCSDjrLkfAflFERLonaCjH119/jUuXLqm0Xbp0CUuXLtVLUSSM0GDk7DdERFUjKBxlMhk8PT1V2jw9PSGTyfRSFL28x//2U/6vwllxiIioHEG9VUUiERRqlx9yuZy9VU0Yh2wQEVWfoCvHzp07Y/Xq1cowLCsrQ2xsLDp37qzX4oiIiIxB0JXj/PnzMXnyZPTq1QsuLi7IyMhA48aNsWHDBn3XR0REZHCCwrFJkybYv38/fv31V2RmZkIqlaJdu3bK5auIiIhqE0HhCAAWFhYc9G9iOF8qEZF+aA3HgQMH4vDhwwCAvn37ap1o/OTJk3opjIiIyFi0hmNUVJTy9cqVKw1SjDkbE3kEeX8Vl2u3r2+FLyM5FIOIyJC0huOLPVG9vDgvmb5pCsaK2omISH+0huOaNWsEHWDGjBk6K4aIiMgUaA3HzMxM5evi4mIcPXoUnp6eaNq0KdLT03HlyhW8+eabBimSar+UpSMAaJrnToQW8/cauhwiMnNawzE6Olr5etasWfjkk0/g6+urbDt69CiOHDmi3+rIjGibAFaBlKX/UGlpMf+7cm3q24mIXoaggYqnT59G//79Vdr69euHU6dO6aUoIiIiYxI0zrFZs2bYtWsXxo4dq2z7+uuv8be//U1vhZkbbctPPZvSlvOkEhEZkqBwXLJkCaZNm4bNmzfD2dkZWVlZqFOnDmJjY/Vdn9nQtvwU12skIjI8QeHYpk0bJCQk4Ndff0V2djYaN26MDh06wNLSUt/1ERERGVy1Jkft0qULSktLUVRUpOt6yGzxEpmITIegK8ebN29iypQpkEgkyMrKwqBBg3DhwgXs378fq1ev1neNNc7gj+K0buM6i5qpD9eoqDcqEZG+CQrHyMhIhISEYOjQoejSpQuAZ1ePCxYs0GtxZL4qG47B4RpEpE+CwvH27dsICHh2xfN8AvJ69eqhuFj41GapqakIDQ1FXl4e7O3tERMTAzc3N437pqSkYNiwYQgKCsLcuXMBAIsWLcLZs2chkUhQr149zJ8/H23bthV8fjIcjkEkoppOUDg2bdoUV69eVQmjy5cvV2koR0REBIKCghAQEIC4uDiEh4djx44d5faTy+WIiIgoN66yT58+mDdvHiwtLZGYmIhZs2bh+PHjgs+vT0Nmx/13yEX1cfkpIiLTISgcZ8yYgcmTJ2PUqFEoLS3FF198gW+++UZl5Y6K5OTkIDk5Gdu2bQMA+Pv7IyoqCrm5uXBwcFDZd+PGjfD29kZRUZFKhx8fHx/l6w4dOiAzMxNlZWUmseDyywZjdVXludzzK7bKruqqc0wiotpGULL4+Phg8+bNyM3NRZcuXZCWlobY2Fj06tVL0EkyMjLg7OwMsVgMABCLxXByckJGRobKfjdu3EBSUhLGjx9f4fF27doFb29vkwhGIiKqfSq9cpTL5fD19cWhQ4cQGRmpt0JKS0uxcOFCREdHK0NUk/j4eBw8eBC7du3SWy1ERGTeKg1HsVgMsViM4uJiSCSSap1EKpUiKysLcrkcYrEYcrkc2dnZkEqlyn3u37+Pe/fuYdKkSQCA/Px8KBQKFBQUKG/fHjt2DJ999hm2b9+ORo0aVasWQ+PQDSKimkfQM8exY8di5syZmDx5Mpo0aaLssQoAr7zySqWfd3R0hIeHB2QyGQICAiCTyeDh4aHyvNHFxQXnz59Xvo+NjUVRUZGyt2piYiKio6Oxbds2uLq6Cv6CREREVSUoHJ9fuf34448q7SKRCNevXxd0osjISISGhmL9+vWws7NDTEwMAGDixIkICQmpdFhGWFgYLC0tERISomzbvn07GjZsKOj8L0Nbb1SRCPh+VQBEIs2dcsx1XlR21CGimk5QON64ceOlT+Tu7o49e/aUa9+0aZPG/adPn67y/ty5cy9dQ3Vp6436vP37Vbx1SkRUm1QYjo8fP8Y///lP3Lp1C6+//jomT55c7eeO5uTt3VO0btPleMbqXKFx5hkiospVOBZi8eLFSExMRIsWLZCQkKC8FUpERFSbVRiOZ86cwZYtWzBnzhxs2rQJiYmJhqqLiIjIaCoMx6KiIjg5OQF4NhyjoKDAIEUREREZU4XPHOVyOc6dOwfFf3uePH36VOU9AHTv3l2/FZoA9kYlIjIvFYajo6Mj5s2bp3xvb2+v8l4kEuGHH37QX3Umgr1RiYjMS4XheOLECUPVUaMF7p4KBYw0+zgREemcoHGOVLGKgpFLURER1Txc1oKIiEgNw5GIiEgNw5GIiEgNw5GIiEgNw1EHRNA84FFbOxERmTb2VtWB3YHrjV0CERHpEK8ciYiI1DAciYiI1PC2qgDaZsARQcRbqkREtRCvHAXQNgMOp4wjIqqdGI5ERERqGI5ERERqGI5ERERqGI5ERERqGI4CcAYcIiLzwqEcAnC4BhGReeGVIxERkRqGIxERkRqGIxERkRqDhWNqaioCAwPh6+uLwMBA3L17V+u+KSkpaN++PWJiYpRtjx8/xsyZMzFgwAD4+fkhMTHRAFUTEZE5Mlg4RkREICgoCAkJCQgKCkJ4eLjG/eRyOSIiItC/f3+V9i1btsDW1hbHjh3Dhg0bsGDBAhQWFhqidCIiMjMGCcecnBwkJyfD398fAODv74/k5GTk5uaW23fjxo3w9vaGm5ubSvvhw4cRGBgIAHBzc4OnpydOnz6t99qJiMj8GCQcMzIy4OzsDLFYDAAQi8VwcnJCRkaGyn43btxAUlISxo8fX+4Y6enpaNq0qfK9VCpFZmamXusmIiLzZDLjHEtLS7Fw4UJER0crQ5SIiMgYDBKOUqkUWVlZkMvlEIvFkMvlyM7OhlQqVe5z//593Lt3D5MmTQIA5OfnQ6FQoKCgAFFRUXBxcUFaWhocHBwAPLsa7dq1qyHKJyIiM2OQcHR0dISHhwdkMhkCAgIgk8ng4eGhDDoAcHFxwfnz55XvY2NjUVRUhLlz5wIA/Pz8sHv3brRt2xZ3797FlStX8MknnxiifCIiMjMG660aGRmJnTt3wtfXFzt37sSiRYsAABMnTsSVK1cq/XxwcDDy8/MxYMAATJ48GYsXL4atra2+yyYiIjMkUigUtX45++LiYly9ehWenp6wsrIydjlERGQCKsoGzpBDRESkhuFIRESkxmSGcpi7lKX/0LqtxfzvDFgJERHxypGIiEgNw5GIiEgNw5GIiEgNnzkKkLJ0BABNI15EaDF/bwXby+PzQyIi08crR0G0BZ+iku1ERFQTMRyJiIjU8LaqieDtViIi08ErRyIiIjUMRyIiIjUMR0FElbRr205ERDURnzkK0GL+3pfaTkRENQuvHImIiNQwHImIiNQwHImIiNQwHImIiNQwHImIiNQwHImIiNSYxVAOheLZxOAlJSVGroSIiEzF80x4nhEvMotwLC0tBQDcunXLyJUQEZGpKS0thbW1tUqbSKEpMmuZsrIyFBYWwtLSEiIRZ7MhIqJnV4ylpaWwsbGBhYXqU0azCEciIqKqYIccIiIiNQxHIiIiNQxHIiIiNQxHIiIiNQxHIiIiNQxHIiIiNQxHIiIiNWYxQw6p6tevHyQSCaysrAAAs2fPRu/evY1clemIiYlBQkIC0tLScPDgQbRs2RIAkJqaitDQUOTl5cHe3h4xMTFwc3MzbrEmQNvvxb8zzR4+fIg5c+bg3r17kEgkaNasGRYvXgwHBwf85z//QXh4OIqLi9G0aVOsXLkSjo6Oxi7ZqCr6vVq1aoWWLVsqB/CvWLECrVq10s2JFWR2fHx8FDdv3jR2GSbrwoULivT09HK/05gxYxQHDhxQKBQKxYEDBxRjxowxVokmRdvvxb8zzR4+fKg4d+6c8v3y5csVYWFhCrlcrujfv7/iwoULCoVCoVi3bp0iNDTUWGWaDG2/l0KhULRs2VJRUFCgl/PytiqRms6dO0Mqlaq05eTkIDk5Gf7+/gAAf39/JCcnIzc31xglmhRNvxdpZ29vj65duyrfd+jQAenp6bh69SqsrKzQuXNnAMCoUaNw5MgRY5VpMrT9XvrG26pmavbs2VAoFHjjjTfwf//3f7CzszN2SSYtIyMDzs7OEIvFAACxWAwnJydkZGTAwcHByNWZLv6dVaysrAxff/01+vXrh4yMDLi4uCi3OTg4oKysTHkbn1R/r+fGjBkDuVyOPn36YPr06ZBIJDo5F68czdCuXbvw/fff47vvvoNCocDixYuNXRLVQvw7q1xUVBTq1auH0aNHG7uUGkH99zp58iT27duHXbt24fbt21i3bp3OzsVwNEPPb4FJJBIEBQXh0qVLRq7I9EmlUmRlZUEulwMA5HI5srOzeTuxAvw7q1hMTAx+//13rF69GhYWFpBKpSq3C3Nzc2FhYcGrxv9S/72A//2N2draYuTIkTr9G2M4mpmioiL89ddfAJ4t13Lo0CF4eHgYuSrT5+joCA8PD8hkMgCATCaDh4cHb6lqwb+zin366ae4evUq1q1bp7wN6OnpiSdPnuDnn38GAHzzzTfw8/MzZpkmQ9Pv9ejRIzx58gQA8PTpUyQkJOj0b4xLVpmZP/74A9OnT4dcLkdZWRnc3d2xYMECODk5Gbs0k7FkyRIcPXoUDx48QMOGDWFvb4/4+HjcuXMHoaGhyM/Ph52dHWJiYtCiRQtjl2t0mn6vDRs28O9Mi99++w3+/v5wc3NTLrDr6uqKdevW4dKlS4iIiFAZytGoUSMjV2xc2n6vCRMmIDw8HCKRCE+fPkXHjh0xb9482NjY6OS8DEciIiI1vK1KRESkhuFIRESkhuFIRESkhuFIRESkhuFIRESkhuFIRESkhuFIZGTnz59Hnz59jF1GhVq1aoXff/9d47Z+/fqhXbt2+Pjjjw1cVcVKSkrQsWNHvP766/jss8+MXQ7VMJx4nMxax44dla8fP34MiUSinFx80aJFGDJkiLFKq1E2bNiAHj16GPScwcHBeO+999CrVy+N2yUSCX755ReEhoYatC6qHRiOZNZ++eUX5et+/fphyZIlGv9P/unTp6hTh/+5mIqioiJcvXoVXl5exi6FaineViXS4Pmtzo0bN6Jnz54ICwvDo0ePMHnyZHTr1g1dunTB5MmTkZmZCQA4dOgQhg8frnKM7du344MPPgDw7BZfTEwMvL290aNHD4SHhyvnhayIQqHAsmXL0L17d3Tq1AmDBw/GrVu3AAChoaEIDw/He++9h44dO2L06NFIS0tTfvbOnTt477334OXlBV9fXxw6dEi5rbJ6Nm/ejF69eqFXr17Yu3dvlX+/Vq1aYdeuXXjzzTfRsWNHrF69Gvfu3cOoUaPQqVMnzJgxAyUlJSq/9aZNm9C9e3f06tULx48fx6lTp+Dr6wsvLy9s2LBB5fhnz55Fx44dIZFIcPnyZQwfPhydOnVCjx49EB0dXeV6idQxHIm0ePDgAR49eoTExERERUWhrKwMw4cPR2JiIhITE2FlZaVchsnHxwepqam4e/eu8vMHDx7E4MGDAQCrVq1CamoqDhw4gKNHjyI7O1vQ8jpJSUn4+eefkZCQgIsXL2L16tUqqzQcPHgQU6dOxfnz59G6dWvMnj0bwLMrq/fffx/+/v746aef8Nlnn2HRokW4fft2pfWcPn0aW7duxdatW3H06FGcPXu2Wr9fUlIS9u3bh2+//RabN2/GwoULsXLlSpw6dQq//fYb4uPjVX7r4uJinD59GiEhIViwYIFyuatdu3Zh/fr1+OOPP5T7nzp1Ct7e3gCApUuXYuzYsbh06RKOHTuGgQMHVqteohcxHIm0sLCwQEhICCQSCaytrdGwYUP4+vqibt26sLW1xZQpU3DhwgUAQN26dfH3v/9duWrH3bt3kZKSgn79+kGhUODbb7/FvHnzYG9vD1tbW0yePFklHLSpU6cOCgsLkZKSAoVCAXd3d5XJu729vdGlSxdIJBLMmjUL//nPf5CRkYGTJ0+iadOm+Mc//oE6deqgTZs28PX1xZEjRyqt5/Dhwxg+fDhatmyJevXqYdq0adX6/SZMmABbW1u89tpraNmyJXr27IlXXnkF9evXR58+fZCcnKzyPadMmQJLS0sMGjQIDx8+xNixY5Wff/XVV3Hz5k3l/qdPn0bfvn2Vn7137x5yc3NhY2ODDh06VKteohfxIQqRFg0bNoSVlZXy/ePHjxEdHY0zZ87g0aNHAIDCwkLI5XKIxWIMHjwYy5cvx7Rp0yCTydC/f3/UrVsXOTk5ePz4scptV4VCgbKyskpr6N69O959910sXrwYaWlpePPNNzF37lzY2toCAJo0aaLc18bGBg0aNEB2djbS0tJw+fJldO7cWbldLpdjyJAhyM3NrbCe7OxseHp6Krc1bdq0qj8dAKisJmFlZVXu/YMHD5Tv7e3tlR2hnq+84OjoqLJ/YWEhAODmzZuoX7++ci2/pUuXYu3atRg4cCBcXV0xbdo0+Pj4VKtmoucYjkRaiEQilfdbt25Famoqvv32WzRu3BjXr1/H0KFD8Xxhmx49eiA3NxfXr1+HTCZDWFgYgGcha21tjfj4eDg7O1e5jrFjx2Ls2LHIycnBzJkzsXnzZsycORMAlM88gWdB/ejRIzg5OUEqlaJLly7Ytm1bueOVlZVVWI+TkxMyMjKU719cgNcUnDp1SmXoi5ubGz799FOUlZXh6NGjCAkJwfnz51GvXj0jVkk1HW+rEglUWFgIKysr2NnZIS8vD59//rnKdktLS/j5+WHFihV49OgRevbsCeDZ7dmRI0di2bJlyMnJAQBkZWXhzJkzlZ7z8uXL+PXXX1FaWoq6detCIpEoV0EHngXFzz//jJKSEqxZswbt27eHVCqFt7c37t69iwMHDqC0tBSlpaW4fPky7ty5U2k9fn5+2L9/P27fvo3Hjx+X+57Gdvr0aeXzRgCIi4tDbm4uLCwsYGdnBwAqvxFRdfAviEigcePGobi4GN26dUNgYCB69+5dbp/Bgwfjp59+gp+fn8rQj48//hjNmjXD22+/jU6dOmH8+PFITU2t9JyFhYVYsGABvLy84OPjA3t7ewQHByu3+/v7Y926dejatSuuXbuGlStXAgBsbW2xZcsWHDp0CL1790avXr2watUqZQ/Riurp27cvxo0bh3HjxmHAgAHo1q3bS/1uupSfn4/bt2+rjE89c+YM3nrrLXTs2BFLly7FZ599prw1S1RdXOyYqIYKDQ2Fs7MzZs2aZdQ6fH19cf/+fQwYMAAxMTF6PdehQ4eQkJCANWvWVLpvSUkJevTogadPn2LChAnV7lhE5onPHInopSQkJBjsXHZ2dhg/frygfSUSCX7++Wf9FkS1FsORiGoMbVPFEekab6sSERGpYYccIiIiNQxHIiIiNQxHIiIiNQxHIiIiNQxHIiIiNf8Pxu6fjmCstmwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xfctx83i-RKl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}