Derivative_Pricing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Derivative_Pricing.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Import Libraries**"
      ],
      "metadata": {
        "id": "MRWDeqvO3dpW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "fukrk-lk19Yr"
      },
      "outputs": [],
      "source": [
        "#Normal distribution functions\n",
        "from scipy.stats import norm\n",
        "\n",
        "# Load libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pandas_datareader.data as web\n",
        "from matplotlib import pyplot\n",
        "from pandas.plotting import scatter_matrix\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "# Error Metrics\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "#Disable the warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Black Scholes Formula**"
      ],
      "metadata": {
        "id": "Ee1QH_QE3c_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_alpha = 0.2\n",
        "true_beta = 0.2\n",
        "true_sigma0 = 0.3\n",
        "interest = 0.1\n",
        "\n",
        "def vol(moneyness, time):\n",
        "    return true_sigma0 + true_alpha * time + true_beta * np.square(moneyness - 1)\n",
        "\n",
        "def call_option_price(moneyness, time, option_vol):\n",
        "    d1=(np.log(1/moneyness)+(interest + np.square(option_vol))*time)/(option_vol*np.sqrt(time))\n",
        "    d2=(np.log(1/moneyness)+(interest - np.square(option_vol))*time)/(option_vol*np.sqrt(time))\n",
        "    N_d1 = norm.cdf(d1)\n",
        "    N_d2 = norm.cdf(d2)\n",
        "    \n",
        "    return N_d1 - moneyness * np.exp(- interest * time) * N_d2"
      ],
      "metadata": {
        "id": "h9QpZDsa3mwm"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Generating Data**"
      ],
      "metadata": {
        "id": "GQnBSyv94n6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N = 10000\n",
        "\n",
        "moneys = 1+0.2*np.random.randn(N)\n",
        "times = np.random.random(N)\n",
        "volatility = np.array([vol(k,t) for k,t in zip(moneys, times)])\n",
        "prices = np.array([call_option_price(k,t,sig) for k,t,sig in zip(moneys, times, volatility)])\n",
        "print('moneys', moneys)\n",
        "print('times', times)\n",
        "print('volatility', volatility)\n",
        "print('prices', prices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXcHFEd74o2u",
        "outputId": "d5c37ce5-8018-4506-f19d-410e25bc5889"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "moneys [1.27026584 0.69639566 1.01101649 ... 1.10583088 1.23534935 0.99448386]\n",
            "times [0.76958228 0.93221025 0.89720878 ... 0.36251987 0.25878299 0.70635322]\n",
            "volatility [0.46852518 0.50487717 0.47946603 ... 0.37474401 0.36283446 0.44127673]\n",
            "prices [0.25926839 0.49587445 0.37490049 ... 0.15126332 0.06502195 0.31534739]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualize**"
      ],
      "metadata": {
        "id": "9gPpQpMS8Tj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y = prices\n",
        "X = np.concatenate([moneys.reshape(-1,1), times.reshape(-1,1), volatility.reshape(-1,1)], axis=1)\n",
        "\n",
        "dataset = pd.DataFrame(np.concatenate([Y.reshape(-1,1), X], axis=1),\n",
        "                       columns=['Price', 'Moneyness', 'Time', 'Volatility'])\n",
        "print(X)\n",
        "print()\n",
        "print(Y)\n",
        "print()\n",
        "print(dataset.head())\n",
        "dataset.hist(bins=50, sharex=False, sharey=False, xlabelsize=1, ylabelsize=1, figsize=(12,12))\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "FQxfDe2h8VHQ",
        "outputId": "d34d9af0-3654-47e6-a33d-1b60fb6abba7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.27026584 0.76958228 0.46852518]\n",
            " [0.69639566 0.93221025 0.50487717]\n",
            " [1.01101649 0.89720878 0.47946603]\n",
            " ...\n",
            " [1.10583088 0.36251987 0.37474401]\n",
            " [1.23534935 0.25878299 0.36283446]\n",
            " [0.99448386 0.70635322 0.44127673]]\n",
            "\n",
            "[0.25926839 0.49587445 0.37490049 ... 0.15126332 0.06502195 0.31534739]\n",
            "\n",
            "      Price  Moneyness      Time  Volatility\n",
            "0  0.259268   1.270266  0.769582    0.468525\n",
            "1  0.495874   0.696396  0.932210    0.504877\n",
            "2  0.374900   1.011016  0.897209    0.479466\n",
            "3  0.314695   0.689199  0.056661    0.330652\n",
            "4  0.296680   0.917449  0.569556    0.415274\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test Split**"
      ],
      "metadata": {
        "id": "45YTe4VZ_klo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#train-test = 80-20\n",
        "validation_size = 0.2\n",
        "train_size = int(len(X) * (1-validation_size))\n",
        "print(train_size)\n",
        "X_train, X_test = X[0:train_size], X[train_size:len(X)]\n",
        "Y_train, Y_test = Y[0:train_size], Y[train_size:len(X)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT0HISuo_l7-",
        "outputId": "0ee58a5c-1f76-4f86-f883-c527e428df84"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Models and K-fold Analysis**"
      ],
      "metadata": {
        "id": "RzzOfEr7Audo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = []\n",
        "\n",
        "#Linear Models and Regression Trees\n",
        "models.append(('LR', LinearRegression()))\n",
        "models.append(('LASSO', Lasso()))\n",
        "models.append(('EN', ElasticNet()))\n",
        "models.append(('KNN', KNeighborsRegressor()))\n",
        "models.append(('DT', DecisionTreeRegressor()))\n",
        "models.append(('SVR', SVR()))\n",
        "\n",
        "#Neural Network Predictor (Multi Layer Perceptron)\n",
        "models.append(('MLP', MLPRegressor()))\n",
        "\n",
        "# Boosting methods\n",
        "models.append(('ABR', AdaBoostRegressor()))\n",
        "models.append(('GBR', GradientBoostingRegressor()))\n",
        "# Bagging methods\n",
        "models.append(('RFR', RandomForestRegressor()))\n",
        "models.append(('ETR', ExtraTreesRegressor()))\n",
        "\n",
        "num_folds = 10\n",
        "# scikit is moving away from mean_squared_error. \n",
        "# In order to avoid confusion, and to allow comparison with other models, we invert the final scores\n",
        "scoring = 'neg_mean_squared_error' \n",
        "names = []\n",
        "kfold_results = []\n",
        "test_results = []\n",
        "train_results = []\n",
        "for name, model in models:\n",
        "    names.append(name)\n",
        "    \n",
        "    ## K Fold analysis:\n",
        "    \n",
        "    kfold = KFold(n_splits=num_folds)\n",
        "    \n",
        "    #converted mean square error to positive. The lower the beter\n",
        "    cv_results = -1* cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
        "    kfold_results.append(cv_results)\n",
        "    \n",
        "\n",
        "    # Full Training period\n",
        "    res = model.fit(X_train, Y_train)\n",
        "    train_result = mean_squared_error(res.predict(X_train), Y_train)\n",
        "    train_results.append(train_result)\n",
        "    \n",
        "    # Test results\n",
        "    test_result = mean_squared_error(res.predict(X_test), Y_test)\n",
        "    test_results.append(test_result)\n",
        "    \n",
        "    msg = \"%s: %f (%f) %f %f\" % (name, cv_results.mean(), cv_results.std(), train_result, test_result)\n",
        "    print(msg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1syKGb2wAvdP",
        "outputId": "d29df7af-556a-4b75-9e60-5a6a2196a9d1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.000358 (0.000030) 0.000357 0.000334\n",
            "LASSO: 0.017056 (0.000512) 0.017048 0.016445\n",
            "EN: 0.017056 (0.000512) 0.017048 0.016445\n",
            "KNN: 0.000015 (0.000018) 0.000009 0.000016\n",
            "DT: 0.000020 (0.000011) 0.000000 0.000016\n",
            "SVR: 0.001636 (0.000114) 0.001698 0.001675\n",
            "MLP: 0.000063 (0.000035) 0.000061 0.000058\n",
            "ABR: 0.001070 (0.000047) 0.001068 0.001128\n",
            "GBR: 0.000038 (0.000011) 0.000028 0.000036\n",
            "RFR: 0.000007 (0.000011) 0.000001 0.000004\n",
            "ETR: 0.000005 (0.000013) 0.000000 0.000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Results**"
      ],
      "metadata": {
        "id": "mu3wgepDCq3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = pyplot.figure()\n",
        "fig.suptitle('Model Comparison: K-fold results')\n",
        "ax = fig.add_subplot(111)\n",
        "pyplot.boxplot(kfold_results)\n",
        "ax.set_xticklabels(names)\n",
        "fig.set_size_inches(15,8)\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "UnYdZhljCsfv",
        "outputId": "938c5c4e-fd50-4266-ab4f-867393ff54d5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Remove LASSO and ElasticNet**"
      ],
      "metadata": {
        "id": "3cidmRUADxXk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compare algorithms\n",
        "fig = pyplot.figure()\n",
        "train = train_results[0:1] + train_results[3:]\n",
        "test = test_results[0:1] + test_results[3:]\n",
        "if 'LASSO' in names:\n",
        "  names.remove('LASSO')\n",
        "if 'EN' in names:\n",
        "  names.remove('EN')\n",
        "\n",
        "ind = np.arange(len(names))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "fig.suptitle('Model Comparison')\n",
        "ax = fig.add_subplot(111)\n",
        "pyplot.bar(ind - width/2, train,  width=width, label='Train Error')\n",
        "pyplot.bar(ind + width/2, test, width=width, label='Test Error')\n",
        "fig.set_size_inches(15,8)\n",
        "pyplot.legend()\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(names)\n",
        "for i in range(len(train)):\n",
        "  p = \"%s: %10f %f\" % (names[i], train[i], test[i])\n",
        "  print(p)\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "4B0fhrh3Dxz6",
        "outputId": "c88a31b5-685f-47bd-97fb-1090708dd206"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR:   0.000357 0.000334\n",
            "KNN:   0.000009 0.000016\n",
            "DT:   0.000000 0.000016\n",
            "SVR:   0.001698 0.001675\n",
            "MLP:   0.000061 0.000058\n",
            "ABR:   0.001068 0.001128\n",
            "GBR:   0.000028 0.000036\n",
            "RFR:   0.000001 0.000004\n",
            "ETR:   0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Improving MLP using GridSearchCV**"
      ],
      "metadata": {
        "id": "URfqMEvkJ4no"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid={'hidden_layer_sizes': [(20,), (50,), (20,20), (20, 30, 20)]}\n",
        "kfold = KFold(n_splits=num_folds)\n",
        "grid = GridSearchCV(estimator=MLPRegressor(), param_grid=param_grid, scoring=scoring, cv=kfold)\n",
        "mlp = grid.fit(X_train, Y_train)\n",
        "print(\"Best: %f using %s\" % (mlp.best_score_, mlp.best_params_))\n",
        "print()\n",
        "#improved MLP\n",
        "predictions = mlp.predict(X_test)\n",
        "print(mean_squared_error(Y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4w-qhH5J8pP",
        "outputId": "ce74683a-04db-4294-e16d-18a90c85cb12"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: -0.000068 using {'hidden_layer_sizes': (20, 30, 20)}\n",
            "\n",
            "6.56856199531193e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Additional analysis: Removing volatility data**"
      ],
      "metadata": {
        "id": "0AsFPZjEkx09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.concatenate([moneys.reshape(-1,1), times.reshape(-1,1)], axis=1)\n",
        "validation_size = 0.2\n",
        "\n",
        "train_size = int(len(X) * (1-validation_size))\n",
        "X_train, X_test = X[0:train_size], X[train_size:len(X)]\n",
        "Y_train, Y_test = Y[0:train_size], Y[train_size:len(X)]\n",
        "\n",
        "models = []\n",
        "#Linear Models and Regression Trees\n",
        "models.append(('LR', LinearRegression()))\n",
        "models.append(('KNN', KNeighborsRegressor()))\n",
        "models.append(('DT', DecisionTreeRegressor()))\n",
        "models.append(('SVR', SVR()))\n",
        "\n",
        "#Neural Network Predictor (Multi Layer Perceptron)\n",
        "models.append(('MLP', MLPRegressor()))\n",
        "\n",
        "# Boosting methods\n",
        "models.append(('ABR', AdaBoostRegressor()))\n",
        "models.append(('GBR', GradientBoostingRegressor()))\n",
        "# Bagging methods\n",
        "models.append(('RFR', RandomForestRegressor()))\n",
        "models.append(('ETR', ExtraTreesRegressor()))\n",
        "\n",
        "\n",
        "num_folds = 10\n",
        "# scikit is moving away from mean_squared_error. \n",
        "# In order to avoid confusion, and to allow comparison with other models, we invert the final scores\n",
        "scoring = 'neg_mean_squared_error' \n",
        "\n",
        "\n",
        "names = []\n",
        "kfold_results = []\n",
        "test_results = []\n",
        "train_results = []\n",
        "for name, model in models:\n",
        "    names.append(name)\n",
        "    \n",
        "    ## K Fold analysis:\n",
        "    \n",
        "    kfold = KFold(n_splits=num_folds)\n",
        "    #converted mean square error to positive. The lower the better\n",
        "    cv_results = -1* cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
        "    kfold_results.append(cv_results)\n",
        "    \n",
        "\n",
        "    # Full Training period\n",
        "    res = model.fit(X_train, Y_train)\n",
        "    train_result = mean_squared_error(res.predict(X_train), Y_train)\n",
        "    train_results.append(train_result)\n",
        "    \n",
        "    # Test results\n",
        "    test_result = mean_squared_error(res.predict(X_test), Y_test)\n",
        "    test_results.append(test_result)\n",
        "    \n",
        "    msg = \"%s: %f (%f) %f %f\" % (name, cv_results.mean(), cv_results.std(), train_result, test_result)\n",
        "    print(msg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT3WQSKZk2IW",
        "outputId": "a4da9e6d-70bc-4ee7-e08f-351da137347a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.000772 (0.000098) 0.000771 0.000709\n",
            "KNN: 0.000015 (0.000018) 0.000009 0.000016\n",
            "DT: 0.000019 (0.000010) 0.000000 0.000034\n",
            "SVR: 0.001768 (0.000089) 0.001769 0.001786\n",
            "MLP: 0.000098 (0.000049) 0.000022 0.000024\n",
            "ABR: 0.001094 (0.000050) 0.001092 0.001152\n",
            "GBR: 0.000034 (0.000006) 0.000027 0.000035\n",
            "RFR: 0.000007 (0.000010) 0.000001 0.000004\n",
            "ETR: 0.000006 (0.000015) 0.000000 0.000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compare algorithms\n",
        "fig = pyplot.figure()\n",
        "\n",
        "ind = np.arange(len(names))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig.suptitle('Algorithm Comparison')\n",
        "ax = fig.add_subplot(111)\n",
        "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
        "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
        "fig.set_size_inches(15,8)\n",
        "pyplot.legend()\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(names)\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "NsNP8_PbnC44",
        "outputId": "f1f29325-1b56-40cf-e37d-29115adaeccd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Additional analysis: Varying the parameters without volatility data**"
      ],
      "metadata": {
        "id": "DmtbUe6k3cE-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_alpha = 0.2\n",
        "true_beta = 0.2\n",
        "true_sigma0 = 0.3\n",
        "interest = 0.1\n",
        "N = 10000\n",
        "def change_parameters():\n",
        "    moneys = 1+0.2*np.random.randn(N)\n",
        "    times = np.random.random(N)\n",
        "    volatility = np.array([vol(k,t) for k,t in zip(moneys, times)])\n",
        "    prices = np.array([call_option_price(k,t,sig) for k,t,sig in zip(moneys, times, volatility)])\n",
        "    Y = prices\n",
        "    X = np.concatenate([moneys.reshape(-1,1), times.reshape(-1,1)], axis=1)\n",
        "    validation_size = 0.2\n",
        "    print('alpha', true_alpha, 'beta', true_beta, 'sigma0', true_sigma0, 'interest', interest, 'N', N)\n",
        "    train_size = int(len(X) * (1-validation_size))\n",
        "    X_train, X_test = X[0:train_size], X[train_size:len(X)]\n",
        "    Y_train, Y_test = Y[0:train_size], Y[train_size:len(X)]\n",
        "    models = []\n",
        "    #Linear Models and Regression Trees\n",
        "    models.append(('LR', LinearRegression()))\n",
        "    models.append(('KNN', KNeighborsRegressor()))\n",
        "    models.append(('DT', DecisionTreeRegressor()))\n",
        "    models.append(('SVR', SVR()))\n",
        "    #Neural Network Predictor (Multi Layer Perceptron)\n",
        "    models.append(('MLP', MLPRegressor()))\n",
        "    # Boosting methods\n",
        "    models.append(('ABR', AdaBoostRegressor()))\n",
        "    models.append(('GBR', GradientBoostingRegressor()))\n",
        "    # Bagging methods\n",
        "    models.append(('RFR', RandomForestRegressor()))\n",
        "    models.append(('ETR', ExtraTreesRegressor()))\n",
        "    num_folds = 10\n",
        "    # scikit is moving away from mean_squared_error. \n",
        "    # In order to avoid confusion, and to allow comparison with other models, we invert the final scores\n",
        "    scoring = 'neg_mean_squared_error' \n",
        "    names = []\n",
        "    test_results = []\n",
        "    train_results = []\n",
        "    for name, model in models:\n",
        "        names.append(name)\n",
        "        # Full Training period\n",
        "        res = model.fit(X_train, Y_train)\n",
        "        train_result = mean_squared_error(res.predict(X_train), Y_train)\n",
        "        train_results.append(train_result)\n",
        "        # Test results\n",
        "        test_result = mean_squared_error(res.predict(X_test), Y_test)\n",
        "        test_results.append(test_result)\n",
        "        \n",
        "        msg = \"%s: %f %f\" % (name, train_result, test_result)\n",
        "        print(msg)\n",
        "    # compare algorithms\n",
        "    fig = pyplot.figure()\n",
        "\n",
        "    ind = np.arange(len(names))  # the x locations for the groups\n",
        "    width = 0.35  # the width of the bars\n",
        "\n",
        "    fig.suptitle('Algorithm Comparison')\n",
        "    ax = fig.add_subplot(111)\n",
        "    pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
        "    pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
        "    fig.set_size_inches(15,8)\n",
        "    pyplot.legend()\n",
        "    ax.set_xticks(ind)\n",
        "    ax.set_xticklabels(names)\n",
        "    pyplot.show()\n",
        "\n",
        "arr = [0.2, 0.4, 0.6, 0.8, 1.0]\n",
        "for true_alpha in arr:\n",
        "    change_parameters()\n",
        "true_alpha = 0.2\n",
        "for true_beta in arr:\n",
        "    change_parameters()\n",
        "true_beta = 0.2\n",
        "for true_sigma0 in arr:\n",
        "    change_parameters()\n",
        "true_sigma0 = 0.3\n",
        "for interest in arr:\n",
        "    change_parameters()\n",
        "interest = 0.1\n",
        "for N in [20000, 30000, 40000, 50000]:\n",
        "    change_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_nkmWCTp3gw2",
        "outputId": "e0ac62e1-4bf0-4516-e841-667c3d590a4a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.000808 0.000742\n",
            "KNN: 0.000007 0.000027\n",
            "DT: 0.000000 0.000026\n",
            "SVR: 0.002030 0.002068\n",
            "MLP: 0.000035 0.000037\n",
            "ABR: 0.000989 0.001039\n",
            "GBR: 0.000025 0.000036\n",
            "RFR: 0.000001 0.000014\n",
            "ETR: 0.000000 0.000011\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.4 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.000819 0.000903\n",
            "KNN: 0.000006 0.000016\n",
            "DT: 0.000000 0.000021\n",
            "SVR: 0.001391 0.001433\n",
            "MLP: 0.000066 0.000080\n",
            "ABR: 0.001186 0.001194\n",
            "GBR: 0.000031 0.000039\n",
            "RFR: 0.000001 0.000005\n",
            "ETR: 0.000000 0.000005\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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QkuSQQw7J05/+9DztaU/L0572tIn6m+nxzsMOOyxLly6ddv8Tn/hEXvSiFyVJHv7wh+ehD33oXaHvqKOOmrXAl0wW+vZJctPY/pokj5mpTmttY1WtT7LXUP7pKW33yWZU1bFJvt5a+/woN85Y77lJnpskD3nIQyaYBgAAsCtqreWggw66647cuA9+8IO57LLLctFFF+W0007LF7/4xbt9nvvc5z6b3Z+03ba2Q31kQ1X9dJJXJ3ntluq21t7WWlveWlu+aSUeAACAqRYsWJC1a9feFfruuOOOXH311fnRj36Um266KUcccUROP/30rF+/Prfeemv22GOPfP/739+mYzj88MPz7ne/O0ly3XXX5Wtf+1oOPPDAbXqOmUxyp+/rSfYd2188lE1XZ01VzU+yZ5J1E7Yd93NJlibZdJdvcZLPVdVhrbWbJxgrAACwI9rMRyzMtnvd61658MIL8+IXvzjr16/Pxo0b85KXvCQHHHBAfvd3fzfr169Pay0vfvGLc9/73je/+Zu/meOOOy4f+MAH8uY3vzmHH374j/U39Z2+s846a4tj+IM/+IM8//nPz8EHH5z58+fnnHPOyYIFC7b5XKdTrbXNVxiFuOuSHJlRYLsiye+01q4eq/OCJAe31p43LOTyn1prv11VByV5T0bv8T04yUeT7D8s5JLhnb7/3Vr7+RnOfWOS5a21b29ujMuXL2+rVq3a8mwB5tiSUz44Z+e+ceHvzNm55/IHPQBz45prrskjHvGIuR5Gl6a7tlX12dba8unqb/HxztbaxiQvTPKhJNckuaC1dnVVvb6qnjpUe3uSvYaFWl6W5JSh7dVJLsho0Zd/SvKCscD33iSfSnJgVa2pqpO3erYAAABs1kSf/NdauzjJxVPKXju2vSHJ8TO0PS3JadOUnzjBeZdMMj4AAACmt0Mt5AIAAPRjS6+SsfXuzjUV+gAAgG1u4cKFWbduneC3DbXWsm7duixcuHCr2k30eCcAAMDWWLx4cdasWZO1a9fO9VC6snDhwixevHir2gh9AADANnfve987S5cunethEI93AgAAdE3oAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAxyYKfVV1TFVdW1Wrq+qUaY4vqKrzh+OfqaolY8deNZRfW1VHj5WfXVXfqqqrpvT1l1X15ar6QlW9v6rue/enBwAAsGvbYuirqnlJzkzypCTLkpxYVcumVDs5yS2ttf2SnJHk9KHtsiQrkhyU5JgkZw39Jck5Q9lU/5zk51trhyS5LsmrtnJOAAAADCa503dYktWttetba7cnOS/JsVPqHJvkncP2hUmOrKoays9rrd3WWrshyeqhv7TWLkvynakna619uLW2cdj9dJLFWzknAAAABpOEvn2S3DS2v2Yom7bOENjWJ9lrwrab8+wk/zjdgap6blWtqqpVa9eu3YouAQAAdh077EIuVfWaJBuTvHu64621t7XWlrfWli9atGj7Dg4AAGAnMX+COl9Psu/Y/uKhbLo6a6pqfpI9k6ybsO1PqKpnJnlKkiNba22CMQIAvTp1zzk89/q5OzfANjLJnb4rkuxfVUurareMFmZZOaXOyiQnDdvHJfnYENZWJlkxrO65NMn+SS7f3Mmq6pgkr0jy1NbaDyafCgAAAFNt8U5fa21jVb0wyYeSzEtydmvt6qp6fZJVrbWVSd6e5NyqWp3R4iwrhrZXV9UFSb6U0aOaL2it3ZkkVfXeJE9IsndVrUnyutba25O8JcmCJP88Wgsmn26tPW9bThoA2DpLTvngnJ37xoVzdmqALkzyeGdaaxcnuXhK2WvHtjckOX6GtqclOW2a8hNnqL/fJGMCAABgy3bYhVwAAAC454Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAxyYKfVV1TFVdW1Wrq+qUaY4vqKrzh+OfqaolY8deNZRfW1VHj5WfXVXfqqqrpvR1/6r656r6yvDf+9396QEAAOzathj6qmpekjOTPCnJsiQnVtWyKdVOTnJLa22/JGckOX1ouyzJiiQHJTkmyVlDf0lyzlA21SlJPtpa2z/JR4d9AAAA7oZJ7vQdlmR1a+361trtSc5LcuyUOscmeeewfWGSI6uqhvLzWmu3tdZuSLJ66C+ttcuSfGea84339c4kT9uK+QAAADBmktC3T5KbxvbXDGXT1mmtbUyyPsleE7ad6oGttW8M2zcneeB0larquVW1qqpWrV27doJpAAAA7Hp26IVcWmstSZvh2Ntaa8tba8sXLVq0nUcGAACwc5gk9H09yb5j+4uHsmnrVNX8JHsmWTdh26m+WVUPGvp6UJJvTTBGAAAApjFJ6Lsiyf5VtbSqdstoYZaVU+qsTHLSsH1cko8Nd+lWJlkxrO65NMn+SS7fwvnG+zopyQcmGCMAAADT2GLoG97Re2GSDyW5JskFrbWrq+r1VfXUodrbk+xVVauTvCzDiputtauTXJDkS0n+KckLWmt3JklVvTfJp5IcWFVrqurkoa83JDmqqr6S5NeGfQAAAO6G+ZNUaq1dnOTiKWWvHdvekOT4GdqeluS0acpPnKH+uiRHTjIuAAAANm+HXsgFAACAe0boAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjs2f6wEwS07dc47Ou35uzgsAAEzLnT4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBj8+d6AD1bcsoH5+zcNy6cs1MDAAA7EHf6AAAAOib0AQAAdGyi0FdVx1TVtVW1uqpOmeb4gqo6fzj+mapaMnbsVUP5tVV19Jb6rKojq+pzVXVlVX2iqva7Z1MEAADYdW0x9FXVvCRnJnlSkmVJTqyqZVOqnZzkltbafknOSHL60HZZkhVJDkpyTJKzqmreFvr8myRPb60dmuQ9Sf74nk0RAABg1zXJnb7DkqxurV3fWrs9yXlJjp1S59gk7xy2L0xyZFXVUH5ea+221toNSVYP/W2uz5bkZ4btPZP8292bGgAAAJOs3rlPkpvG9tckecxMdVprG6tqfZK9hvJPT2m7z7A9U5/PSXJxVf0wyfeS/NJ0g6qq5yZ5bpI85CEPmWAaAAAAu54dcSGXlyb5jdba4iTvSPJX01Vqrb2ttba8tbZ80aJF23WAAAAAO4tJQt/Xk+w7tr94KJu2TlXNz+ixzHWbaTtteVUtSvILrbXPDOXnJ3nsRDMBAADgJ0wS+q5Isn9VLa2q3TJamGXllDork5w0bB+X5GOttTaUrxhW91yaZP8kl2+mz1uS7FlVBwx9HZXkmrs/PQAAgF3bFt/pG97Re2GSDyWZl+Ts1trVVfX6JKtaayuTvD3JuVW1Osl3MgpxGepdkORLSTYmeUFr7c4kma7Pofy/JPn7qvpRRiHw2dt0xgAAALuQSRZySWvt4iQXTyl77dj2hiTHz9D2tCSnTdLnUP7+JO+fZFwAAABs3o64kAsAAADbiNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0bKLQV1XHVNW1VbW6qk6Z5viCqjp/OP6ZqloyduxVQ/m1VXX0lvqskdOq6rqquqaqXnzPpggAALDrmr+lClU1L8mZSY5KsibJFVW1srX2pbFqJye5pbW2X1WtSHJ6khOqalmSFUkOSvLgJB+pqgOGNjP1+cwk+yZ5eGvtR1X1gG0xUQAAgF3RJHf6DkuyurV2fWvt9iTnJTl2Sp1jk7xz2L4wyZFVVUP5ea2121prNyRZPfS3uT6fn+T1rbUfJUlr7Vt3f3oAAAC7tklC3z5JbhrbXzOUTVuntbYxyfoke22m7eb6/LmM7hKuqqp/rKr9pxtUVT13qLNq7dq1E0wDAABg17MjLuSyIMmG1tryJP8zydnTVWqtva21try1tnzRokXbdYAAAAA7i0lC39czesduk8VD2bR1qmp+kj2TrNtM2831uSbJ/xq235/kkAnGCAAAwDQmCX1XJNm/qpZW1W4ZLcyyckqdlUlOGraPS/Kx1lobylcMq3suTbJ/ksu30Oc/JDli2H58kuvu3tQAAADY4uqdrbWNVfXCJB9KMi/J2a21q6vq9UlWtdZWJnl7knOranWS72QU4jLUuyDJl5JsTPKC1tqdSTJdn8Mp35Dk3VX10iS3JnnOtpsuAADArmWLoS9JWmsXJ7l4Stlrx7Y3JDl+hranJTltkj6H8u8mefIk4wIAAGDzdsSFXAAAANhGhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHJgp9VXVMVV1bVaur6pRpji+oqvOH45+pqiVjx141lF9bVUdvRZ9vqqpb7960AAAASCYIfVU1L8mZSZ6UZFmSE6tq2ZRqJye5pbW2X5Izkpw+tF2WZEWSg5Ick+Ssqpq3pT6ranmS+93DuQEAAOzyJrnTd1iS1a2161trtyc5L8mxU+ocm+Sdw/aFSY6sqhrKz2ut3dZauyHJ6qG/GfscAuUbwV0AAA+zSURBVOFfJnnFPZsaAAAAk4S+fZLcNLa/Ziibtk5rbWOS9Un22kzbzfX5wiQrW2vf2Nygquq5VbWqqlatXbt2gmkAAADsenaohVyq6sFJjk/y5i3Vba29rbW2vLW2fNGiRbM/OAAAgJ3QJKHv60n2HdtfPJRNW6eq5ifZM8m6zbSdqfyRSfZLsrqqbkzy01W1esK5AAAAMMUkoe+KJPtX1dKq2i2jhVlWTqmzMslJw/ZxST7WWmtD+Yphdc+lSfZPcvlMfbbWPtha+9nW2pLW2pIkPxgWhwEAAOBumL+lCq21jVX1wiQfSjIvydmttaur6vVJVrXWViZ5e5Jzh7ty38koxGWod0GSLyXZmOQFrbU7k2S6Prf99AAAAHZtWwx9SdJauzjJxVPKXju2vSGjd/Gma3taktMm6XOaOrtPMj4AAACmt0Mt5AIAAMC2JfQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6NlHoq6pjquraqlpdVadMc3xBVZ0/HP9MVS0ZO/aqofzaqjp6S31W1buH8quq6uyquvc9myIAAMCua4uhr6rmJTkzyZOSLEtyYlUtm1Lt5CS3tNb2S3JGktOHtsuSrEhyUJJjkpxVVfO20Oe7kzw8ycFJfirJc+7RDAEAAHZhk9zpOyzJ6tba9a2125Ocl+TYKXWOTfLOYfvCJEdWVQ3l57XWbmut3ZBk9dDfjH221i5ugySXJ1l8z6YIAACw65ok9O2T5Kax/TVD2bR1Wmsbk6xPstdm2m6xz+Gxzt9L8k/TDaqqnltVq6pq1dq1ayeYBgAAwK5nR17I5awkl7XWPj7dwdba21pry1tryxctWrSdhwYAALBzmD9Bna8n2Xdsf/FQNl2dNVU1P8meSdZtoe2MfVbV65IsSvL7E4wPAACAGUxyp++KJPtX1dKq2i2jhVlWTqmzMslJw/ZxST42vJO3MsmKYXXPpUn2z+g9vRn7rKrnJDk6yYmttR/ds+kBAADs2rZ4p6+1trGqXpjkQ0nmJTm7tXZ1Vb0+yarW2sokb09yblWtTvKdjEJchnoXJPlSko1JXtBauzNJputzOOVbk3w1yadGa8Hkf7XWXr/NZgwAALALmeTxzrTWLk5y8ZSy145tb0hy/AxtT0ty2iR9DuUTjQkAAIAt25EXcgEAAOAeEvoAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQsflzPQAA2Omduuccnnv93J0bgJ2CO30AAAAdE/oAAAA6JvQBAAB0zDt9AHRjySkfnJPz3rhwTk4LABNxpw8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0zEc2AADsqk7dcw7PvX7uzg27GHf6AAAAOib0AQAAdMzjnQAAc2zJKR+ck/PeuHBOTgtsZ+70AQAAdEzoAwAA6JjHO4Edy1ytJGcVOQCgU0IfbC3LWwMAsBPxeCcAAEDHJgp9VXVMVV1bVaur6pRpji+oqvOH45+pqiVjx141lF9bVUdvqc+qWjr0sXroc7d7NkUAAIBd1xYf76yqeUnOTHJUkjVJrqiqla21L41VOznJLa21/apqRZLTk5xQVcuSrEhyUJIHJ/lIVR0wtJmpz9OTnNFaO6+q3jr0/TfbYrL0Y66Wtk4sbw0ATMh76rPH6zZbZZI7fYclWd1au761dnuS85IcO6XOsUneOWxfmOTIqqqh/LzW2m2ttRuSrB76m7bPoc0Thz4y9Pm0uz89AACAXdskC7nsk+Smsf01SR4zU53W2saqWp9kr6H801Pa7jNsT9fnXkm+21rbOE39H1NVz03y3GH31qq6doK57DIq2TvJt7f7if+0tvspt7c5u7bJLnF942t31vjanT2u7exyfWePazvr/EybPb52f9JDZzqw067e2Vp7W5K3zfU4dlRVtaq1tnyux9Ej13Z2ub6zx7WdPa7t7HJ9Z49rO7tc39nj2m6dSR7v/HqSfcf2Fw9l09apqvlJ9kyybjNtZypfl+S+Qx8znQsAAIAJTRL6rkiy/7Cq5m4ZLcyyckqdlUlOGraPS/Kx1lobylcMq3suTbJ/kstn6nNoc8nQR4Y+P3D3pwcAALBr2+LjncM7ei9M8qEk85Kc3Vq7uqpen2RVa21lkrcnObeqVif5TkYhLkO9C5J8KcnGJC9ord2ZJNP1OZzylUnOq6o/S/J/h77Zeh59nT2u7exyfWePazt7XNvZ5frOHtd2drm+s8e13Qo1urkGAABAjyb6cHYAAAB2TkIfAABAx4S+nVxV3TpN2alV9fWqurKqvlRVJ87F2HY249eyqn6jqq6rqocO1/MHVfWAGeq2qnrj2P7Lq+rU7TbwnVRV3Tl8jV5dVZ+vqj+sqntV1dFD+ZVVdWtVXTtsv2uux7yjq6rXDNfzC8M1e11V/cWUOodW1TXD9o1V9cWh/r9U1Yyf77MrG/6O/93Y/vyqWltV/3vYf2ZVvWWaduPX98NV9bPbc9w7k6p62nCdHz7sL6mqHw5fx5+vqk9W1YHDsSdU1frh2Jer6r/P7eh3XFX1wKp6T1VdX1WfrapPVdVvTbmGX6iqj2z6GTd8Pa8du74vnet57KjGfo5dVVUXVdV9h/Lxr99Nf3ZzbbfO2PXd9OeUqnr/sL167Gv4yqp6bFVdOvzO8PmquqKqDp3rOexIhL5+ndFaOzTJsUn+/6q691wPaGdRVUcmeVOSJ7XWvjoUfzvJH87Q5LYk/6mq9t4e4+vID1trh7bWDkpyVJInJXlda+1DQ/mhSVYlefqw/4w5He0Orqp+OclTkjyqtXZIkl/LaDXkE6ZUXZHkvWP7Rwz1L03yx9thqDujf0/y81X1U8P+UZn844Q2Xd9VSV49G4PrxIlJPjH8d5N/Hf7u/0KSd+bHr9/Hh+8Rj0zylKr6le031J1DVVWSf0hyWWvtYa21R2f093/xUOXjw/U9JKNV1V8w1vz84fr+SpLXVNX4x2zxHzb9HPv5jBYyHL+Gm75+N/25fSh3bSf3wynX8A2ttd8art9z8h9fw4e21j45tHn68D3jrCR/OWcj3wEJfZ1rrX0lyQ+S3G+ux7IzqKrHJfmfSZ7SWvvXsUNnJzmhqu4/TbONGa0g5V/s7qbW2reSPDfJC4dfVNh6D0ry7dbabUnSWvt2a+2yJLdU1WPG6v12fjz0bfKpJPvM/jB3WhcnefKwfWKmv4abc1mS/bbpiDpRVbsn+dUkJ2dY/XsaP5PklqmFrbUfJrkyvnan88Qkt7fW3rqpoLX21dbam8crDd9z98j013ddktUZfX9h87bqe6hrO+v8TJtC6OtcVT0qyVeGX6rZvAUZ/avo01prX55y7NaMgt9/naHtmUmeXlV7zuL4utZauz6jj3B5wJbqMq0PJ9m3Ro8ln1VVjx/K35vhF+mq+qUk3xn+MWiqYzL6+md652X0ubMLkxyS5DNb2f4pSb64zUfVh2OT/FNr7bok66rq0UP5zw2Pbf1rkpcl+aupDavqfhl9BvBl2220O4+DknxuM8cPr6ork3wtoycDzp5aoaoekmRhki/Mygg7UVXzkhyZH/8c601fv1dW1ZnTtHFtt+ynpjzeOfXJlc3xM20Koa9fL62qqzP6xeS0uR7MTuKOJJ/M6F+bp/OmJCdV1R5TD7TWvpfkXUlePHvDg5m11m5N8uiM7piuTXJ+VT0zyflJjquqe+UnH+1Mkkuq6usZPV67tXevdhmttS8kWZLRXb6Lt6LpJcMv1j+T5C+2VHkXdWJGoTrDfzc94rnp8bifS/KS/Phnch1eVZ/P6DHbD7XWbt5uo91JVdWZm951Goo2PRq3b5J3JPlvY9VPqKovZHQn6qzW2obtPd6dxE8Nf79vTvLAJP88dmz88c7xxz5d28lNfbzz/AnavLuqbkjymoz+QZ6B0NevM4Z3pf5zkrcP/zrN5v0oo0ffDquqn3j3prX23STvyY8/sz/uf2QUGO8zayPsWFU9LMmdSdyVvptaa3e21i5trb0uyQuT/OfW2k1Jbkjy+Iy+H0z9oXlEkodm9Ijcn27P8e6EVib579m6cHzEpndSh+8hjBkemX9ikr+tqhuT/FFG34enPua9MsnjxvY/Pry3c1CSky3YMK2rkzxq084QPI5MsmiaulOv7/nDu36PTfIGixDN6IfD+2UPzehrdqbfD8a5trPr6UkeltF7wG/eQt1ditDXudbayowWEDhprseyM2it/SCj93aeXlXT3fH7qyS/n2T+NG2/k+SCzHynkBlU1aIkb03yltZam+vx7Iyq6sCq2n+s6NAkmxYiem+SM5Jc31pbM7Vta21jRndSnjHDe6uMnJ3kT1trHtPcdo5Lcm5r7aGttSXDXacbkkxd3OJXk/zr1MattRuSvCHJK2d9pDufjyVZWFXPHyv76RnqznR9VyU5NzO/2kDu+t3hxUn+sKp+4veDGdq4trNk+D3iT5L8Ug0rAiP09eCnq2rN2J+XTVPn9UleNjzexRYM4e2YJH9cVU+dcuzbSd6f0ft/03ljEqt4TmbTs/pXJ/lIRu+kudN09+2e5J01+piWLyRZluTU4dj7MrojMuMdqtbaN4bjk/xL9S6ptbamtfamGQ4/c8r34sUz1OPHnZjR99Rxf5/kVfmPd6I+n+TPM1qtbzpvTfK4qloyW4PcGQ2/+D4tyeOr6oaqujyjux+bAvLhY9f39zLzCtWnJ3nWdK828B9aa/83o/fztuZjslzbzZv6Tt8bJm04LPL0xoyeHiBJ+Ud1AACAfrnzAwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHTs/wFoYvZ6EDilmwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.6 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.000938 0.000911\n",
            "KNN: 0.000010 0.000015\n",
            "DT: 0.000000 0.000030\n",
            "SVR: 0.001630 0.001610\n",
            "MLP: 0.000041 0.000029\n",
            "ABR: 0.001333 0.001373\n",
            "GBR: 0.000033 0.000042\n",
            "RFR: 0.000001 0.000016\n",
            "ETR: 0.000000 0.000012\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.8 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.001100 0.000911\n",
            "KNN: 0.000008 0.000009\n",
            "DT: 0.000000 0.000021\n",
            "SVR: 0.001190 0.001126\n",
            "MLP: 0.000084 0.000064\n",
            "ABR: 0.001377 0.001362\n",
            "GBR: 0.000032 0.000039\n",
            "RFR: 0.000001 0.000004\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 1.0 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.001104 0.001109\n",
            "KNN: 0.000008 0.000010\n",
            "DT: 0.000000 0.000022\n",
            "SVR: 0.001130 0.001114\n",
            "MLP: 0.000086 0.000084\n",
            "ABR: 0.001369 0.001368\n",
            "GBR: 0.000027 0.000034\n",
            "RFR: 0.000001 0.000004\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.000784 0.000789\n",
            "KNN: 0.000010 0.000010\n",
            "DT: 0.000000 0.000021\n",
            "SVR: 0.002594 0.002567\n",
            "MLP: 0.000036 0.000036\n",
            "ABR: 0.001046 0.001069\n",
            "GBR: 0.000025 0.000031\n",
            "RFR: 0.000001 0.000004\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.4 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.000925 0.000952\n",
            "KNN: 0.000007 0.000014\n",
            "DT: 0.000000 0.000017\n",
            "SVR: 0.002061 0.002080\n",
            "MLP: 0.000025 0.000027\n",
            "ABR: 0.000977 0.001025\n",
            "GBR: 0.000027 0.000036\n",
            "RFR: 0.000001 0.000006\n",
            "ETR: 0.000000 0.000003\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.6 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.001198 0.001249\n",
            "KNN: 0.000005 0.000048\n",
            "DT: 0.000000 0.000059\n",
            "SVR: 0.002535 0.002573\n",
            "MLP: 0.000037 0.000043\n",
            "ABR: 0.000922 0.001016\n",
            "GBR: 0.000027 0.000071\n",
            "RFR: 0.000001 0.000053\n",
            "ETR: 0.000000 0.000043\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.8 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.001275 0.001487\n",
            "KNN: 0.000007 0.000018\n",
            "DT: 0.000000 0.000024\n",
            "SVR: 0.002920 0.002892\n",
            "MLP: 0.000091 0.000103\n",
            "ABR: 0.001194 0.001177\n",
            "GBR: 0.000025 0.000035\n",
            "RFR: 0.000001 0.000008\n",
            "ETR: 0.000000 0.000003\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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cxSYh9AEAAF3ZaaedcvXVVydJTjzxxGy33XZ54xvfOO32l156abbbbrtJQ1+SHHnkkXn/+98/ZftVq1Zl9uzZU+5Pt92G4vFOAACge1dddVWe+cxn5qlPfWqe85zn5Pbbb0+SvPe9783ChQuzzz775Kijjsott9ySD37wgzn11FOz77775otf/OK0+r/00ktzwAEH5AUveEEWLlz4E/v33ntvXvayl+XJT35y9ttvv1xyySVJRncOX/CCF+TZz372g3cnNzR3+gAAgK611vLa1742n/70pzNv3rycd955edvb3pYzzjgjJ598cm6++ebMmTMn3//+97Pjjjvmla985VrvDp533nm5/PLLH9z/8pe/nCT56le/mmuvvTYLFizIpZde+pD9d7/73amqfP3rX883vvGN/OZv/ma++c1vPtjummuuyWMf+9iNcv1CHwAA0LX77rsv1157bQ4++OAkyerVq7PLLrskSfbZZ5+86EUvymGHHZbDDjtsWv1N9Xjn/vvvnwULFky6f/nll+e1r31tkuQXfuEX8vjHP/7B0HfwwQdvtMCXCH0AAEDnWmvZe++9H7wjN+4zn/lMLrvsslx44YU56aST8vWvf/1hn+fRj370Wven225D804fAADQtTlz5mT58uUPhr4HHngg1113XX784x/n1ltvzYEHHphTTjklK1euzD333JPtt98+d9999wYdwwEHHJCPfvSjSZJvfvOb+fd///fstddeG/QcU3GnDwAA2Phm8ON7HvWoR+WCCy7Icccdl5UrV2bVqlU5/vjjs+eee+b3fu/3snLlyrTWctxxx2XHHXfMb//2b+fwww/Ppz/96bzvfe/LAQcc8JD+Jr7Td/rpp69zDH/4h3+YV73qVXnyk5+c2bNn58wzz8ycOXM2+LVOplprm+REG9OiRYvakiVLZnoYAOs0/4TPzNi5b5n7uzN2bp/TB7D1ueGGG/KkJz1ppofRpcnmtqquaq0tmqy+xzsBAAA6JvQBAAB0TOgDAAA2ih5eJdvcPJw5FfoAAIANbu7cuVmxYoXgtwG11rJixYrMnTt3vdpZvRMAANjgdttttyxbtizLly+f6aF0Ze7cudltt93Wq43QBwAAbHDbbLNNFixYMNPDIB7vBAAA6JrQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOTSv0VdUhVXVjVS2tqhMmOT6nqs4bjn+lquaPHXvLUH5jVT1nKNu9qi6pquur6rqq+qOx+idW1W1VdfXw9VuP/DIBAAC2TrPXVaGqZiU5LcnBSZYlubKqFrfWrh+rdmySu1prT6yqo5KckuTIqlqY5Kgkeyf5uSSfq6o9k6xK8obW2leravskV1XVZ8f6PLW19lcb6iIBAAC2VtO507d/kqWttZtaa/cnOTfJoRPqHJrkrGH7giQHVVUN5ee21u5rrd2cZGmS/Vtrt7fWvpokrbW7k9yQZNdHfjkAAACMm07o2zXJrWP7y/KTAe3BOq21VUlWJtlpOm2HR0H3S/KVseLXVNU1VXVGVT1mskFV1SuqaklVLVm+fPk0LgMAAGDrM6MLuVTVdkk+meT41toPhuIPJPn5JPsmuT3Juydr21r7UGttUWtt0bx58zbJeAEAALY00wl9tyXZfWx/t6Fs0jpVNTvJDklWrK1tVW2TUeD7aGvtf62p0Fr7TmttdWvtx0n+JqPHSwEAAHgYphP6rkyyR1UtqKptM1qYZfGEOouTHDNsH57kC621NpQfNazuuSDJHkmuGN73+3CSG1prfz3eUVXtMrb7wiTXru9FAQAAMLLO1Ttba6uq6jVJLk4yK8kZrbXrquqdSZa01hZnFODOqaqlSe7MKBhmqHd+kuszWrHz1a211VX160lenOTrVXX1cKq3ttYuSvKXVbVvkpbkliR/sAGvFwAAYKuyztCXJEMYu2hC2dvHtu9NcsQUbU9KctKEssuT1BT1XzydMQEAALBuM7qQCwAAABuX0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHZtW6KuqQ6rqxqpaWlUnTHJ8TlWdNxz/SlXNHzv2lqH8xqp6zlC2e1VdUlXXV9V1VfVHY/UfW1WfrapvDX8+5pFfJgAAwNZpnaGvqmYlOS3Jc5MsTHJ0VS2cUO3YJHe11p6Y5NQkpwxtFyY5KsneSQ5JcvrQ36okb2itLUzyK0lePdbnCUk+31rbI8nnh30AAAAehunc6ds/ydLW2k2ttfuTnJvk0Al1Dk1y1rB9QZKDqqqG8nNba/e11m5OsjTJ/q2121trX02S1trdSW5IsuskfZ2V5LCHd2kAAABMJ/TtmuTWsf1l+a+A9hN1WmurkqxMstN02g6Pgu6X5CtD0eNaa7cP23ckedxkg6qqV1TVkqpasnz58mlcBgAAwNZnRhdyqartknwyyfGttR9MPN5aa0naZG1bax9qrS1qrS2aN2/eRh4pAADAlmk6oe+2JLuP7e82lE1ap6pmJ9khyYq1ta2qbTIKfB9trf2vsTrfqapdhjq7JPnudC8GAACAh5pO6LsyyR5VtaCqts1oYZbFE+osTnLMsH14ki8Md+kWJzlqWN1zQZI9klwxvO/34SQ3tNb+ei19HZPk0+t7UQAAAIzMXleF1tqqqnpNkouTzEpyRmvtuqp6Z5IlrbXFGQW4c6pqaZI7MwqGGeqdn+T6jFbsfHVrbXVV/XqSFyf5elVdPZzqra21i5KcnOT8qjo2ybeT/M6GvGAAAICtyTpDX5IMYeyiCWVvH9u+N8kRU7Q9KclJE8ouT1JT1F+R5KDpjAsAAIC1m9GFXAAAANi4hD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6Ni0Ql9VHVJVN1bV0qo6YZLjc6rqvOH4V6pq/tixtwzlN1bVc8bKz6iq71bVtRP6OrGqbquqq4ev33r4lwcAALB1W2foq6pZSU5L8twkC5McXVULJ1Q7NsldrbUnJjk1ySlD24VJjkqyd5JDkpw+9JckZw5lkzm1tbbv8HXR+l0SAAAAa0znTt/+SZa21m5qrd2f5Nwkh06oc2iSs4btC5IcVFU1lJ/bWruvtXZzkqVDf2mtXZbkzg1wDQAAAExhOqFv1yS3ju0vG8omrdNaW5VkZZKdptl2Mq+pqmuGR0AfM1mFqnpFVS2pqiXLly+fRpcAAABbn81xIZcPJPn5JPsmuT3Juyer1Fr7UGttUWtt0bx58zbl+AAAALYYs6dR57Yku4/t7zaUTVZnWVXNTrJDkhXTbPsQrbXvrNmuqr9J8g/TGCMTnbjDDJ135cycFwAAmNR07vRdmWSPqlpQVdtmtDDL4gl1Fic5Ztg+PMkXWmttKD9qWN1zQZI9klyxtpNV1S5juy9Mcu1UdQEAAFi7dd7pa62tqqrXJLk4yawkZ7TWrquqdyZZ0lpbnOTDSc6pqqUZLc5y1ND2uqo6P8n1SVYleXVrbXWSVNXHkzwryc5VtSzJO1prH07yl1W1b5KW5JYkf7AhLxgAAGBrMp3HOzN8bMJFE8rePrZ9b5Ijpmh7UpKTJik/eor6L57OmAAAAFi3zXEhFwAAADYQoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjs2d6AD2bf8JnZuzct8ydsVMDAACbEXf6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRs9kwPAADY/M0/4TMzdu5bTn7ejJ0boAfu9AEAAHRM6AMAAOiYxzsBgM3biTvM4LlXzty5ATYQd/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBj0wp9VXVIVd1YVUur6oRJjs+pqvOG41+pqvljx94ylN9YVc8ZKz+jqr5bVddO6OuxVfXZqvrW8OdjHv7lAQAAbN3WGfqqalaS05I8N8nCJEdX1cIJ1Y5Ncldr7YlJTk1yytB2YZKjkuyd5JAkpw/9JcmZQ9lEJyT5fGttjySfH/YBAAB4GKZzp2//JEtbaze11u5Pcm6SQyfUOTTJWcP2BUkOqqoays9trd3XWrs5ydKhv7TWLkty5yTnG+/rrCSHrcf1AAAAMGY6oW/XJLeO7S8byiat01pblWRlkp2m2Xaix7XWbh+270jyuMkqVdUrqmpJVS1Zvnz5NC4DAABg67NZL+TSWmtJ2hTHPtRaW9RaWzRv3rxNPDIAAIAtw3RC321Jdh/b320om7ROVc1OskOSFdNsO9F3qmqXoa9dknx3GmMEAABgEtMJfVcm2aOqFlTVthktzLJ4Qp3FSY4Ztg9P8oXhLt3iJEcNq3suSLJHkivWcb7xvo5J8ulpjBEAAIBJrDP0De/ovSbJxUluSHJ+a+26qnpnVb1gqPbhJDtV1dIkr8+w4mZr7bok5ye5Psk/Jnl1a211klTVx5N8OcleVbWsqo4d+jo5ycFV9a0kvzHsAwAA8DDMnk6l1tpFSS6aUPb2se17kxwxRduTkpw0SfnRU9RfkeSg6YwLAACAtdusF3IBAADgkRH6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAFgc+O4AAA65SURBVKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBj0wp9VXVIVd1YVUur6oRJjs+pqvOG41+pqvljx94ylN9YVc9ZV59VdWZV3VxVVw9f+z6ySwQAANh6zV5XhaqaleS0JAcnWZbkyqpa3Fq7fqzasUnuaq09saqOSnJKkiOramGSo5LsneTnknyuqvYc2qytzz9urV2wAa4PAABgqzadO337J1naWruptXZ/knOTHDqhzqFJzhq2L0hyUFXVUH5ua+2+1trNSZYO/U2nTwAAAB6h6YS+XZPcOra/bCibtE5rbVWSlUl2WkvbdfV5UlVdU1WnVtWcyQZVVa+oqiVVtWT58uXTuAwAAICtz+a4kMtbkvxCkl9O8tgkb56sUmvtQ621Ra21RfPmzduU4wMAANhiTCf03ZZk97H93YaySetU1ewkOyRZsZa2U/bZWru9jdyX5CMZPQoKAADAwzCd0Hdlkj2qakFVbZvRwiyLJ9RZnOSYYfvwJF9orbWh/Khhdc8FSfZIcsXa+qyqXYY/K8lhSa59JBcIAACwNVvn6p2ttVVV9ZokFyeZleSM1tp1VfXOJEtaa4uTfDjJOVW1NMmdGYW4DPXOT3J9klVJXt1aW50kk/U5nPKjVTUvSSW5OskrN9zlAgAAbF3WGfqSpLV2UZKLJpS9fWz73iRHTNH2pCQnTafPofzZ0xkTAAAA67Y5LuQCAADABiL0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdGz2TA8AALZ4J+4wg+deOXPnBmCL4E4fAABAx9zpA6Ab80/4zIyc95a5M3JaAJgWd/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjvmcPgCArdWJO8zguVfO3LlhKyP0AZuXmfoFxC8fwAyaf8JnZuS8t8ydkdMCm9i0Ql9VHZLkPUlmJfnb1trJE47PSXJ2kqcmWZHkyNbaLcOxtyQ5NsnqJMe11i5eW59VtSDJuUl2SnJVkhe31u5/ZJcJG5B/FQUAYAuyznf6qmpWktOSPDfJwiRHV9XCCdWOTXJXa+2JSU5NcsrQdmGSo5LsneSQJKdX1ax19HlKklOHvu4a+gYAAOBhmM6dvv2TLG2t3ZQkVXVukkOTXD9W59AkJw7bFyR5f1XVUH5ua+2+JDdX1dKhv0zWZ1XdkOTZSX53qHPW0O8HHtbV0a2Zegwm8SgMADBNXlnYeDx5tV6mE/p2TXLr2P6yJE+bqk5rbVVVrczo8cxdk/zLhLa7DtuT9blTku+31lZNUv8hquoVSV4x7N5TVTdO41q2GpXsnOR7m/zEf1ab/JSb2ozNbbJVzG987240vnc3HnO7cZnfjcfcbnT+Ttt4fO/+pMdPdWCLXciltfahJB+a6XFsrqpqSWtt0UyPo0fmduMyvxuPud14zO3GZX43HnO7cZnfjcfcrp/pfE7fbUl2H9vfbSibtE5VzU6yQ0YLukzVdqryFUl2HPqY6lwAAABM03RC35VJ9qiqBVW1bUYLsyyeUGdxkmOG7cOTfKG11obyo6pqzrAq5x5Jrpiqz6HNJUMfGfr89MO/PAAAgK3bOh/vHN7Re02SizP6eIUzWmvXVdU7kyxprS1O8uEk5wwLtdyZUYjLUO/8jBZ9WZXk1a211UkyWZ/DKd+c5NyqeleSfx36Zv159HXjMbcbl/ndeMztxmNuNy7zu/GY243L/G485nY91OjmGgAAAD2azuOdAAAAbKGEPgAAgI4JfVu4qrpnkrITq+q2qrq6qq6vqqNnYmxbmvG5rKrfqqpvVtXjh/n8YVX9zBR1W1W9e2z/jVV14iYb+BaqqlYP36PXVdXXquoNVfWoqnrOUH51Vd1TVTcO22fP9Jg3d1X1tmE+rxnm7B1V9RcT6uxbVTcM27dU1deH+v9cVVN+vs/WbPh//O/G9mdX1fKq+odh/6VV9f5J2o3P7z9V1c9uynFvSarqsGGef2HYn19VPxq+j79WVV+qqr2GY8+qqpXDsW9U1V/N7Og3X1X1uKr6WFXdVFVXVdWXq+qFE+bwmqr63Jq/44bv5+Vj8/u6mb6OzdXY32PXVtWFVbXjUD7+/bvma1tzu37G5nfN1wlV9alhe+nY9/DVVfX0qrp0+J3ha1V1ZVXtO9PXsDkR+vp1amtt3ySHJvn/qmqbmR7QlqKqDkry3iTPba19eyj+XpI3TNHkviT/V1XtvCnG15Eftdb2ba3tneTgJM9N8o7W2sVD+b5JliR50bD/khkd7Wauqn41yfOTPKW1tk+S38hoNeQjJ1Q9KsnHx/YPHOpfmuRPNsFQt0T/meQXq+qnhv2DM/2PE1ozv0uSvHVjDK4TRye5fPhzjX8b/t//pSRn5aHz98XhZ8R+SZ5fVb+26Ya6ZaiqSvL3SS5rrT2htfbUjP7/322o8sVhfvfJaFX1V481P2+Y319L8raqGv+YLf7Lmr/HfjGjhQzH53DN9++ar/uHcnM7fT+aMIcnt9ZeOMzf7+e/vof3ba19aWjzouFnxulJ/t8ZG/lmSOjrXGvtW0l+mOQxMz2WLUFVPSPJ3yR5fmvt38YOnZHkyKp67CTNVmW0gpR/sXuYWmvfTfKKJK8ZflFh/e2S5HuttfuSpLX2vdbaZUnuqqqnjdX7nTw09K3x5SS7bvxhbrEuSvK8YfvoTD6Ha3NZkidu0BF1oqq2S/LrSY7NsPr3JP5bkrsmFrbWfpTk6vjencyzk9zfWvvgmoLW2rdba+8brzT8zN0+k8/viiRLM/r5wtqt189Qc7vR+TttAqGvc1X1lCTfGn6pZu3mZPSvooe11r4x4dg9GQW/P5qi7WlJXlRVO2zE8XWttXZTRh/h8jPrqsuk/inJ7jV6LPn0qnrmUP7xDL9IV9WvJLlz+MegiQ7J6PufyZ2b0efOzk2yT5KvrGf75yf5+gYfVR8OTfKPrbVvJllRVU8dyn9+eGzr35K8PslfT2xYVY/J6DOAL9tko91y7J3kq2s5fkBVXZ3k3zN6MuCMiRWq6r8nmZvkmo0ywk5U1awkB+Whn2O95vv36qo6bZI25nbdfmrC450Tn1xZG3+nTSD09et1VXVdRr+YnDTTg9lCPJDkSxn9a/Nk3pvkmKrafuKB1toPkpyd5LiNNzyYWmvtniRPzeiO6fIk51XVS5Ocl+TwqnpUfvLRziS5pKpuy+jx2vW9e7XVaK1dk2R+Rnf5LlqPppcMv1j/tyR/sa7KW6mjMwrVGf5c84jnmsfjfj7J8XnoZ3IdUFVfy+gx24tba3dsstFuoarqtDXvOg1Fax6N2z3JR5L85Vj1I6vqmozuRJ3eWrt3U493C/FTw//fdyR5XJLPjh0bf7xz/LFPczt9Ex/vPG8abT5aVTcneVtG/yDPQOjr16nDu1L/I8mHh3+dZu1+nNGjb/tX1U+8e9Na+36Sj+Whz+yP+58ZBcZHb7QRdqyqnpBkdRJ3pR+m1trq1tqlrbV3JHlNkv/RWrs1yc1JnpnRz4OJf2kemOTxGT0i92ebcrxboMVJ/irrF44PXPNO6vAzhDHDI/PPTvK3VXVLkj/O6OfwxMe8Fyd5xtj+F4f3dvZOcqwFGyZ1XZKnrNkZgsdBSeZNUnfi/J43vOv39CQnW4RoSj8a3i97fEbfs1P9fjDO3G5cL0ryhIzeA37fOupuVYS+zrXWFme0gMAxMz2WLUFr7YcZvbfzoqqa7I7fXyf5gySzJ2l7Z5LzM/WdQqZQVfOSfDDJ+1trbabHsyWqqr2qao+xon2TrFmI6ONJTk1yU2tt2cS2rbVVGd1JeckU760yckaSP2uteUxzwzk8yTmttce31uYPd51uTjJxcYtfT/JvExu31m5OcnKSN2/0kW55vpBkblW9aqzsp6eoO9X8LklyTqZ+tYE8+LvDcUneUFU/8fvBFG3M7UYy/B7xp0l+pYYVgRH6evDTVbVs7Ov1k9R5Z5LXD493sQ5DeDskyZ9U1QsmHPtekk9l9P7fZN6dxCqe07PmWf3rknwuo3fS3Gl6+LZLclaNPqblmiQLk5w4HPtERndEprxD1Vq7fTg+nX+p3iq11pa11t47xeGXTvhZvNsU9XioozP6mTruk0nekv96J+prSf48o9X6JvPBJM+oqvkba5BbouEX38OSPLOqbq6qKzK6+7EmIB8wNr8vztQrVJ+S5GWTvdrAf2mt/WtG7+etz8dkmdu1m/hO38nTbTgs8vTujJ4eIEn5R3UAAIB+ufMDAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdOz/B7g6fcj4AcjjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 1.0 sigma0 0.3 interest 0.1 N 10000\n",
            "LR: 0.001678 0.001612\n",
            "KNN: 0.000009 0.000012\n",
            "DT: 0.000000 0.000019\n",
            "SVR: 0.002828 0.002814\n",
            "MLP: 0.000129 0.000115\n",
            "ABR: 0.001211 0.001232\n",
            "GBR: 0.000030 0.000041\n",
            "RFR: 0.000001 0.000008\n",
            "ETR: 0.000000 0.000004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.2 interest 0.1 N 10000\n",
            "LR: 0.001046 0.001075\n",
            "KNN: 0.000007 0.000007\n",
            "DT: 0.000000 0.000014\n",
            "SVR: 0.004862 0.004938\n",
            "MLP: 0.000035 0.000030\n",
            "ABR: 0.000863 0.000861\n",
            "GBR: 0.000020 0.000026\n",
            "RFR: 0.000000 0.000003\n",
            "ETR: 0.000000 0.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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e7LnnnjnvvPPyuMc9Lm9/+9vzrne9K/Pnz8+yZcty5pln5l3velfmzZuX97///XnHO96Rgw8+eLPHv/LKK/OGN7whj3nMY3LLLbfk3e9+9w9t33DDDTnppJOycuXKzJ8/P295y1tyyCGH5Lzzzsvf/M3f5N57782GDRvyqU99aqtet2AHAADsEFprecUrXpEPf/jDWbRoUS6++OK8/vWvz7nnnpszzzwzt99+exYsWJBvfetb2X333XPiiSdu8i7fxRdfnKuvvvrB7c9+9rNJks9//vO58cYbs3Tp0lx55ZU/tP3mN785VZUvfvGLueWWW/JLv/RL+fKXv/xguxtuuCGPfexjt/q1C3YAAMAO4f7778+NN96Yww47LEmyYcOGPO5xj0uSPOUpT8lxxx2XF73oRXnRi140reNNNRXzoIMOytKlSyfdvvrqq/OKV7wiSfJTP/VT2WeffR4MdocddtiMhLpEsAMAAHYQrbUccMABD95ZG/eRj3wkV111VS677LKcccYZ+eIXv/iwz/PoRz96k9vTbbc1+bgDAABgh7BgwYKsWbPmwWD3wAMP5Kabbsr3v//93HnnnTnkkENy1llnZd26dbn33nuz66675p577tmqfTj44IPzgQ98IEny5S9/Of/8z/+c/ffff6ueYzLu2AEAAFvHZj6eYKY96lGPyqWXXppTTjkl69aty/r163Pqqadmv/32y2/8xm9k3bp1aa3llFNOye67755f/uVfzpFHHpkPf/jDky6eMvEZu3POOWezffid3/mdnHTSSXnyk5+c+fPn57zzzsuCBQu2+rVOVK21GT/J1rJ8+fK2cuXK2e4GwGbN7qqYvz47J57lH+YAbHs333xznvSkJ812N3ZYk41vVV3XWls+sa6pmAAAAJ0T7AAAADon2AEAAA9bT4929WRLx1WwAwAAHpaFCxdm7dq1wt1W1lrL2rVrs3Dhwmm3sSomAADwsCxevDirV6/OmjVrZrsrO5yFCxdm8eLF064v2AEAAA/LTjvtlKVLl852N4ipmAAAAN0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOTSvYVdXhVXVrVa2qqtMm2b+gqi4e9n+uqpaM7XvtUH5rVT1vQrt5VfV/q+rvHumFAAAAzFWbDXZVNS/J2Umen2RZkmOratmEaick+WZr7YlJ3prkrKHtsiTHJDkgyeFJzhmOt9HvJrn5kV4EAADAXDadO3YHJVnVWruttfa9JBclOWJCnSOSvG94fWmSQ6uqhvKLWmv3t9ZuT7JqOF6qanGSFyT5y0d+GQAAAHPXdILdXknuHNtePZRNWqe1tj7JuiR7bKbtnyd5TZLvb+rkVfWyqlpZVSvXrFkzje4CAADMLbOyeEpVvTDJ11tr122ubmvt3a215a215YsWLdoGvQMAAOjLdILd15LsPba9eCibtE5VzU+yW5K1m2j7C0l+paruyGhq5y9W1fsfRv8BAADmvOkEu2uT7FtVS6tq54wWQ1kxoc6KJMcPr49M8snWWhvKjxlWzVyaZN8k17TWXttaW9xaWzIc75Ottd/YCtcDAAAw58zfXIXW2vqqenmSjyaZl+Tc1tpNVfWmJCtbayuSvCfJBVW1KsndGYW1DPUuSfKlJOuTnNxa2zBD1wIAADAnbTbYJUlr7fIkl08oe+PY6/uSHDVF2zOSnLGJY1+Z5Mrp9AMAAICHmpXFUwAAANh6BDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdm1awq6rDq+rWqlpVVadNsn9BVV087P9cVS0Z2/faofzWqnreULawqq6pqi9U1U1V9cdb64IAAADmms0Gu6qal+TsJM9PsizJsVW1bEK1E5J8s7X2xCRvTXLW0HZZkmOSHJDk8CTnDMe7P8kvttaemuTAJIdX1c9tnUsCAACYW6Zzx+6gJKtaa7e11r6X5KIkR0yoc0SS9w2vL01yaFXVUH5Ra+3+1trtSVYlOaiN3DvU32n4ao/wWgAAAOak6QS7vZLcOba9eiibtE5rbX2SdUn22FTbqppXVdcn+XqSf2itfW6yk1fVy6pqZVWtXLNmzTS6CwAAMLfM2uIprbUNrbUDkyxOclBV/fQU9d7dWlveWlu+aNGibdtJAACADkwn2H0tyd5j24uHsknrVNX8JLslWTudtq21byW5IqNn8AAAANhC0wl21ybZt6qWVtXOGS2GsmJCnRVJjh9eH5nkk621NpQfM6yauTTJvkmuqapFVbV7klTVjyQ5LMktj/xyAAAA5p75m6vQWltfVS9P8tEk85Kc21q7qarelGRla21FkvckuaCqViW5O6Pwl6HeJUm+lGR9kpNbaxuq6nFJ3jeskPmoJJe01v5uJi4QAABgR7fZYJckrbXLk1w+oeyNY6/vS3LUFG3PSHLGhLIbkjxtSzsLAADAQ83a4ikAAABsHYIdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzk0r2FXV4VV1a1WtqqrTJtm/oKouHvZ/rqqWjO177VB+a1U9byjbu6quqKovVdVNVfW7W+uCAAAA5prNBruqmpfk7CTPT7IsybFVtWxCtROSfLO19sQkb01y1tB2WZJjkhyQ5PAk5wzHW5/k91pry5L8XJKTJzkmAAAA0zCdO3YHJVnVWruttfa9JBclOWJCnSOSvG94fWmSQ6uqhvKLWmv3t9ZuT7IqyUGttX9trX0+SVpr9yS5Oclej/xyAAAA5p7pBLu9ktw5tr06Dw1hD9Zpra1Psi7JHtNpO0zbfFqSz0128qp6WVWtrKqVa9asmUZ3AQAA5pZZXTylqnZJ8tdJTm2tfXuyOq21d7fWlrfWli9atGjbdhAAAKAD0wl2X0uy99j24qFs0jpVNT/JbknWbqptVe2UUaj7QGvtbx5O5wEAAJhesLs2yb5VtbSqds5oMZQVE+qsSHL88PrIJJ9srbWh/Jhh1cylSfZNcs3w/N17ktzcWnvL1rgQAACAuWr+5iq01tZX1cuTfDTJvCTnttZuqqo3JVnZWluRUUi7oKpWJbk7o/CXod4lSb6U0UqYJ7fWNlTVs5L8ZpIvVtX1w6le11q7fGtfIAAAwI5us8EuSYbAdfmEsjeOvb4vyVFTtD0jyRkTyq5OUlvaWQAAAB5qVhdPAQAA4JET7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM7Nn+0O8Aicvtssnnvd7J0bAAD4Ie7YAQAAdM4du61gyWkfmZXz3rFwVk4LAABsZ6Z1x66qDq+qW6tqVVWdNsn+BVV18bD/c1W1ZGzfa4fyW6vqeWPl51bV16vqxq1xIQAAAHPVZoNdVc1LcnaS5ydZluTYqlo2odoJSb7ZWntikrcmOWtouyzJMUkOSHJ4knOG4yXJeUMZAAAAj8B07tgdlGRVa+221tr3klyU5IgJdY5I8r7h9aVJDq2qGsovaq3d31q7Pcmq4XhprV2V5O6tcA0AAABz2nSC3V5J7hzbXj2UTVqntbY+yboke0yz7SZV1cuqamVVrVyzZs2WNAUAAJgTtvtVMVtr726tLW+tLV+0aNFsdwcAAGC7M51g97Uke49tLx7KJq1TVfOT7JZk7TTbAgAA8AhMJ9hdm2TfqlpaVTtntBjKigl1ViQ5fnh9ZJJPttbaUH7MsGrm0iT7Jrlm63QdAACAZBrBbnhm7uVJPprk5iSXtNZuqqo3VdWvDNXek2SPqlqV5FVJThva3pTkkiRfSvL3SU5urW1Ikqr6qySfTbJ/Va2uqhO27qUBAADMDdP6gPLW2uVJLp9Q9sax1/clOWqKtmckOWOS8mO3qKcAAABMartfPAUAAIBNE+wAAAA6J9gBAAB0TrADAADonGAHAADQuWmtigkAzA1LTvvIrJ37jjNfMGvnBuidYAcAbB9O322Wzrtuds4LsBWZigkAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AJ8wRf4AAArxSURBVAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBzgh0AAEDnBDsAAIDOCXYAAACdE+wAAAA6J9gBAAB0TrADAADonGAHAADQOcEOAACgc4IdAABA5wQ7AACAzgl2AAAAnRPsAAAAOifYAQAAdE6wAwAA6JxgBwAA0DnBDgAAoHOCHQAAQOcEOwAAgM7Nn06lqjo8yduSzEvyl621MyfsX5Dk/CQ/k2RtkqNba3cM+16b5IQkG5Kc0lr76HSOCQCTWXLaR2bt3Hcs/PVZO3dOXzd75wZgu7fZYFdV85KcneSwJKuTXFtVK1prXxqrdkKSb7bWnlhVxyQ5K8nRVbUsyTFJDkjy+CQfr6r9hjabOyYAwA7DHyWAmTSdO3YHJVnVWrstSarqoiRHJBkPYUckOX14fWmSd1ZVDeUXtdbuT3J7Va0ajpdpHBPm5g/BufAD8PTdZvHcc2B8AYA5ZzrBbq8kd45tr07yjKnqtNbWV9W6JHsM5f84oe1ew+vNHTNJUlUvS/KyYfPeqrp1Gn2eEyrZM8k3ZuXkf1yzctptadbGdw6Mbbx3Z5T37szxfXdmee/OHO/dGTV7Yzs3GN+H2meywmk9YzebWmvvTvLu2e7H9qiqVrbWls92P3ZUxnfmGNuZZXxnjrGdWcZ35hjbmWNsZ5bxnb7prIr5tSR7j20vHsomrVNV85PsltEiKlO1nc4xAQAAmIbpBLtrk+xbVUuraueMFkNZMaHOiiTHD6+PTPLJ1lobyo+pqgVVtTTJvkmumeYxAQAAmIbNTsUcnpl7eZKPZvTRBOe21m6qqjclWdlaW5HkPUkuGBZHuTujoJah3iUZLYqyPsnJrbUNSTLZMbf+5e3wTFGdWcZ35hjbmWV8Z46xnVnGd+YY25ljbGeW8Z2mGt1YAwAAoFfTmYoJAADAdkywAwAA6Jxg14mquneSstOr6mtVdX1Vfamqjp2NvvVmfCyr6j9X1Zerap9hPL9TVT8+Rd1WVW8e2351VZ2+zTreoaraMLw/b6qqL1TV71XVo6rqeUP59VV1b1XdOrw+f7b7vL2rqtcP43nDMGZ/VFV/NqHOgVV18/D6jqr64lD/U1U16Wff8OC/8fePbc+vqjVV9XfD9kuq6p2TtBsf449V1U9uy373oqpeNIzxTw3bS6rqu8P7+AtV9Zmq2n/Y99yqWjfsu6Wq/ufs9n77VlU/UVUXVtVtVXVdVX22qn51wjjeUFUf3/gzbng/rxkb41fO9nVsr8Z+lt1YVZdV1e5D+fh7eOPXzsZ2+sbGduPXaVX1oeH1qrH37/VV9cyqunL4neELVXVtVR0429ewPRHs+vfW1tqBSY5I8v9V1U6z3aFeVNWhSd6e5Pmtta8Oxd9I8ntTNLk/yX+pqj23Rf92EN9trR3YWjsgyWFJnp/kj1prHx3KD0yyMslxw/aLZ7W327mq+vkkL0zy9NbaU5L8pyRXJDl6QtVjkvzV2PYhQ/0rk/zhNuhqr/49yU9X1Y8M24dl+h/Fs3GMVyZ53Ux0bgdwbJKrh/9u9E/Dv/2nJnlffnjsPj18j3hakhdW1S9su672o6oqyd8muaq19oTW2s9k9D1g8VDl08MYPyWjVclPHmt+8TDGv5Dk9VU1/lFU/MDGn2U/ndEigeNjuPE9vPHre0O5sZ2e704YvzNba786jN1v5wfv3wNba58Z2hw3fM84J8n/O2s93w4JdjuI1tpXknwnyWNmuy89qKpnJ/lfSV7YWvunsV3nJjm6qh47SbP1Ga3M5C9vD0Nr7etJXpbk5cMvImy5xyX5Rmvt/iRprX2jtXZVkm9W1TPG6v1afjjYbfTZJHvNfDe7dnmSFwyvj83k47gpVyV54lbt0Q6gqnZJ8qwkJ2RYOXsSP5bkmxMLW2vfTXJ9vHen8otJvtdae9fGgtbaV1tr7xivNHzf3TWTj/HaJKsy+h7Dpm3R91FjO6P8TJtAsNtBVNXTk3xl+OWZTVuQ0V83X9Rau2XCvnszCne/O0Xbs5McV1W7zWD/dlittdsy+oiTH99cXSb1sSR712j68DlV9Zyh/K8y/LJcVT+X5O7hjz0THZ7Re5+pXZTR568uTPKUJJ/bwvYvTPLFrd6r/h2R5O9ba19OsraqfmYo/4/DFKt/SvKqJG+Z2LCqHpPR5+Betc1625cDknx+E/sPrqrrk/xzRnf5z51Yoar+Q5KFSW6YkR7uIKpqXpJD88OfvbzxPXx9VZ09SRtju2k/MmEq5sQZKJviZ9oEgl3/XllVN2X0y8cZs92ZTjyQ5DMZ/eV4Mm9PcnxV7TpxR2vt20nOT3LKzHUPJtdauzfJz2R053NNkour6iVJLk5yZFU9Kg+dhpkkV1TV1zKaCruld6DmlNbaDUmWZHS37vItaHrF8MvzjyX5s81VnoOOzSg0Z/jvxumYG6ex/cckp+aHP6/q4Kr6QkbTYT/aWrtrm/W2Y1V19sbnj4aijVPZ9k7y3iT/Y6z60VV1Q0Z3lM5prd23rfvbiR8Z/n3fleQnkvzD2L7xqZjjUzSN7fRMnIp58TTafKCqbk/y+oz+4M5AsOvfW4fnl/5rkvcMf2Vm076f0VS1g6rqIc/CtNa+leTC/PAc+nF/nlEofPSM9XAHVVVPSLIhiTvLD1NrbUNr7crW2h8leXmS/9pauzPJ7Umek9H3gok/GA9Jsk9G09n+eFv2t1MrkvzPbFkIPmTjc6LD9xAGw9T2X0zyl1V1R5Lfz+h78MQp2SuSPHts+9PDczQHJDnBIglTuinJ0zduDOHi0CSLJqk7cYwvHp69e2aSMy38M6XvDs987ZPR+3aq3w/GGduZc1ySJ2T0XO47NlN3ThHsdhCttRUZPbR//Gz3pQette9k9BzNcVU12Z27tyT570nmT9L27iSXZOo7fkyiqhYleVeSd7bW2mz3p0dVtX9V7TtWdGCSjQv//FWStya5rbW2emLb1tr6jO6IvHiKZ0j5gXOT/HFrzZTKrePIJBe01vZprS0Z7hzdnmTiYhLPSvJPExu31m5PcmaSP5jxnvbpk0kWVtVJY2U/OkXdqcZ4ZZILMvVjCOTB3x1OSfJ7VfWQ3w+maGNsZ8Dwe8QbkvxcDSvtItj15EeravXY16smqfOmJK8apmOxGUNAOzzJH1bVr0zY940kH8roebzJvDmJ1TE3b+Pc+ZuSfDyjZ8TcMXr4dknyvhp9vMkNSZYlOX3Y98GM7mxMeZeptfavw/7p/LV5zmqtrW6tvX2K3S+Z8L148RT1+IFjM/p+Ou6vk7w2P3g+6QtJ/jSjVfAm864kz66qJTPVyV4Nv+C+KMlzqur2qromozsZG4PwwWNj/JuZeuXns5L81mSPIfADrbX/m9HzclvyEVPGdmoTn7E7c7oNh4WV3pzRLACSlD+cAwAA9M2dHQAAgM4JdgAAAJ0T7AAAADon2AEAAHROsAMAAOicYAcAANA5wQ4AAKBz/z8WBw4vAp+38AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.4 interest 0.1 N 10000\n",
            "LR: 0.000733 0.000658\n",
            "KNN: 0.000010 0.000017\n",
            "DT: 0.000000 0.000020\n",
            "SVR: 0.000983 0.001020\n",
            "MLP: 0.000054 0.000051\n",
            "ABR: 0.001196 0.001278\n",
            "GBR: 0.000024 0.000035\n",
            "RFR: 0.000001 0.000008\n",
            "ETR: 0.000000 0.000004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.6 interest 0.1 N 10000\n",
            "LR: 0.000826 0.000984\n",
            "KNN: 0.000008 0.000021\n",
            "DT: 0.000000 0.000030\n",
            "SVR: 0.002710 0.002732\n",
            "MLP: 0.000092 0.000140\n",
            "ABR: 0.001090 0.001119\n",
            "GBR: 0.000023 0.000034\n",
            "RFR: 0.000001 0.000013\n",
            "ETR: 0.000000 0.000010\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.8 interest 0.1 N 10000\n",
            "LR: 0.001310 0.001267\n",
            "KNN: 0.000008 0.000013\n",
            "DT: 0.000000 0.000023\n",
            "SVR: 0.003858 0.003883\n",
            "MLP: 0.000141 0.000130\n",
            "ABR: 0.001025 0.001049\n",
            "GBR: 0.000022 0.000029\n",
            "RFR: 0.000001 0.000008\n",
            "ETR: 0.000000 0.000004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 1.0 interest 0.1 N 10000\n",
            "LR: 0.002103 0.002168\n",
            "KNN: 0.000011 0.000013\n",
            "DT: 0.000000 0.000024\n",
            "SVR: 0.003258 0.003427\n",
            "MLP: 0.000184 0.000200\n",
            "ABR: 0.001054 0.001037\n",
            "GBR: 0.000021 0.000027\n",
            "RFR: 0.000002 0.000005\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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7hNAHAAB0Zb/99st1112XJDnzzDOz11575TWvec3E7a+88srstdde04a+JDnllFPyjne8Y8b2GzZsyPz582fcn7Td9uLxTgAAoHvXXnttnvGMZ+SpT31qnv3sZ+euu+5KkrztbW/LsmXLcvjhh2fFihW5/fbb8653vSvnnntujjjiiHzqU5+a6PxXXnlljj766Dzvec/LsmXLvm//vvvuy4tf/OI85SlPyZFHHpkrrrgiyWjm8HnPe16e9axnPTQ7ub2Z6QMAALrWWssrX/nKfPCDH8yiRYtyySWX5I1vfGPOP//8nH322bntttuyYMGCfPOb38y+++6bl770pZudHbzkkkty1VVXPbT/mc98Jknyuc99LjfccEOWLl2aK6+88mH7b33rW1NV+cIXvpAvfvGL+bmf+7l86Utfeqjd9ddfn8c+9rGzMn6hDwAA6Nr999+fG264Iccdd1ySZOPGjdl///2TJIcffnie//zn58QTT8yJJ5440flmerzzqKOOytKlS6fdv+qqq/LKV74ySfIjP/IjeeITn/hQ6DvuuONmLfAlQh8AANC51loOO+ywh2bkxn34wx/OJz/5yXzoQx/KWWedlS984QvbfJ1HP/rRm92ftN325p0+AACgawsWLMjatWsfCn0PPvhgbrzxxnzve9/LHXfckWOOOSbnnHNO1q9fn3vvvTd77713vv3tb2/XPhx99NF5//vfnyT50pe+lH/+53/OoYceul2vMRMzfQAAwOzbzEcszLZHPepRueyyy/KqV70q69evz4YNG/LqV786hxxySH75l38569evT2str3rVq7LvvvvmP/2n/5STTjopH/zgB/P2t789Rx999MPON/Wdvne+851b7MOv/dqv5WUve1me8pSnZP78+bnggguyYMGC7T7W6VRrbYdcaDYtX768rVq1aq67AbBz296fj7RV1567v+gBmBs333xznvzkJ891N7o03b2tqmtba8unq+/xTgAAgI4JfQAAAB0T+gAAgFnRw6tkO5ttuadCHwAAsN0tXLgw69atE/y2o9Za1q1bl4ULF25VO6t3AgAA293ixYuzZs2arF27dq670pWFCxdm8eLFW9VG6AMAALa7PfbYI0uXLp3rbhCPdwIAAHTNTB/ADrTkjA/P2bVv37rH/wGATpjpAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGMThb6qOr6qbqmq1VV1xjTHF1TVJcPxz1bVkrFjrx/Kb6mqZw9lC6vq6qr6fFXdWFW/N1Z/6XCO1cM593zkwwQAANg9bTH0VdW8JOcl+fkky5KcWlXLplQ7Pck3WmsHJTk3yTlD22VJViQ5LMnxSd45nO/+JM9qrf1YkiOSHF9VPzWc65wk5w7n+sZwbgAAALbBJDN9RyVZ3Vq7tbX2QJKLk5wwpc4JSS4cti9LcmxV1VB+cWvt/tbabUlWJzmqjdw71N9j+GpDm2cN58hwzhO3cWwAAAC7vUlC3wFJ7hjbXzOUTVuntbYhyfok+22ubVXNq6rrknwtyUdba58d2nxzOMdM18rQ/iVVtaqqVq1du3aCYQAAAOx+5mwhl9baxtbaEUkWJzmqqn50K9u/u7W2vLW2fNGiRbPTSQAAgF3cJKHvziQHju0vHsqmrVNV85Psk2TdJG1ba99MckVG7/ytS7LvcI6ZrgUAAMCEJgl91yQ5eFhVc8+MFmZZOaXOyiSnDdsnJflEa60N5SuG1T2XJjk4ydVVtaiq9k2SqvqBJMcl+eLQ5orhHBnO+cFtHx4AAMDubf6WKrTWNlTVK5J8JMm8JOe31m6sqrckWdVaW5nkPUkuqqrVSe7JKBhmqHdpkpuSbEjy8tbaxqraP8mFw0qej0pyaWvtfw2XfF2Si6vq95P843BuAAAAtsEWQ1+StNYuT3L5lLI3jW3fl+TkGdqeleSsKWXXJzlyhvq3ZrRiKAAAAI/QnC3kAgAAwOwT+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOjYRKGvqo6vqluqanVVnTHN8QVVdclw/LNVtWTs2OuH8luq6tlD2YFVdUVV3VRVN1bVr4/VP7Oq7qyq64avX3jkwwQAANg9zd9Shaqal+S8JMclWZPkmqpa2Vq7aaza6Um+0Vo7qKpWJDknySlVtSzJiiSHJXlCko9V1SFJNiT5rdba56pq7yTXVtVHx855bmvtj7bXIAEAAHZXk8z0HZVkdWvt1tbaA0kuTnLClDonJLlw2L4sybFVVUP5xa21+1trtyVZneSo1tpdrbXPJUlr7dtJbk5ywCMfDgAAAOMmCX0HJLljbH9Nvj+gPVSntbYhyfok+03SdngU9Mgknx0rfkVVXV9V51fVY6brVFW9pKpWVdWqtWvXTjAMAACA3c+cLuRSVXsl+eskr26tfWso/tMkP5zkiCR3JXnrdG1ba+9urS1vrS1ftGjRDukvAADArmaS0HdnkgPH9hcPZdPWqar5SfZJsm5zbatqj4wC3/tba/9jU4XW2ldbaxtba99L8mcZPV4KAADANpgk9F2T5OCqWlpVe2a0MMvKKXVWJjlt2D4pySdaa20oXzGs7rk0ycFJrh7e93tPkptba388fqKq2n9s9xeT3LC1gwIAAGBki6t3ttY2VNUrknwkybwk57fWbqyqtyRZ1VpbmVGAu6iqVie5J6NgmKHepUluymjFzpe31jZW1c8meUGSL1TVdcOl3tBauzzJH1bVEUlaktuT/Op2HC8AAMBuZYuhL0mGMHb5lLI3jW3fl+TkGdqeleSsKWVXJakZ6r9gkj4BAACwZXO6kAsAAACzS+gDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6Nj8ue4As+TMfebouuvn5roAAMC0zPQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHfM5fbNoyRkfnrNr375wzi4NAADsRMz0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADo2UeirquOr6paqWl1VZ0xzfEFVXTIc/2xVLRk79vqh/JaqevZQdmBVXVFVN1XVjVX162P1H1tVH62qLw9/PuaRDxMAAGD3tMXQV1XzkpyX5OeTLEtyalUtm1Lt9CTfaK0dlOTcJOcMbZclWZHksCTHJ3nncL4NSX6rtbYsyU8lefnYOc9I8vHW2sFJPj7sAwAAsA0mmek7Ksnq1tqtrbUHklyc5IQpdU5IcuGwfVmSY6uqhvKLW2v3t9ZuS7I6yVGttbtaa59Lktbat5PcnOSAac51YZITt21oAAAATBL6Dkhyx9j+mvxbQPu+Oq21DUnWJ9lvkrbDo6BHJvnsUPT41tpdw/bdSR4/Xaeq6iVVtaqqVq1du3aCYQAAAOx+5nQhl6raK8lfJ3l1a+1bU4+31lqSNl3b1tq7W2vLW2vLFy1aNMs9BQAA2DVNEvruTHLg2P7ioWzaOlU1P8k+SdZtrm1V7ZFR4Ht/a+1/jNX5alXtP9TZP8nXJh0MAAAADzdJ6LsmycFVtbSq9sxoYZaVU+qsTHLasH1Skk8Ms3Qrk6wYVvdcmuTgJFcP7/u9J8nNrbU/3sy5Tkvywa0dFAAAACPzt1Shtbahql6R5CNJ5iU5v7V2Y1W9Jcmq1trKjALcRVW1Osk9GQXDDPUuTXJTRit2vry1trGqfjbJC5J8oaquGy71htba5UnOTnJpVZ2e5CtJfml7DhgAAGB3ssXQlyRDGLt8StmbxrbvS3LyDG3PSnLWlLKrktQM9dclOXaSfgEAALB5c7qQCwAAALNL6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANCxiUJfVR1fVbdU1eqqOmOa4wuq6pLh+GerasnYsdcP5bdU1bPHys+vqq9V1Q1TznVmVd1ZVdcNX7+w7cMDAADYvW0x9FXVvCTnJfn5JMuSnFpVy6ZUOz3JN1prByU5N8k5Q9tlSVYkOSzJ8UneOZwvSS4YyqZzbmvtiOHr8q0bEgAAAJtMMtN3VJLVrbVbW2sPJLk4yQlT6pyQ5MJh+7Ikx1ZVDeUXt9bub63dlmT1cL601j6Z5J7tMAYAAABmMEnoOyDJHWP7a4ayaeu01jYkWZ9kvwnbTucVVXX98AjoYyaoDwAAwDR2xoVc/jTJDyc5IsldSd46XaWqeklVraqqVWvXrt2R/QMAANhlTBL67kxy4Nj+4qFs2jpVNT/JPknWTdj2YVprX22tbWytfS/Jn2V4HHSaeu9urS1vrS1ftGjRBMMAAADY/UwS+q5JcnBVLa2qPTNamGXllDork5w2bJ+U5BOttTaUrxhW91ya5OAkV2/uYlW1/9juLya5Yaa6AAAAbN78LVVorW2oqlck+UiSeUnOb63dWFVvSbKqtbYyyXuSXFRVqzNanGXF0PbGqro0yU1JNiR5eWttY5JU1V8meWaSx1XVmiRvbq29J8kfVtURSVqS25P86vYcMAAAwO5ki6EvSYaPTbh8StmbxrbvS3LyDG3PSnLWNOWnzlD/BZP0CQAAgC3bGRdyAQAAYDsR+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI7Nn+sOABhDSpUAABDGSURBVAA7vyVnfHjOrn372c+Zs2sD9MBMHwAAQMfM9AEAO7cz95nDa6+fu2sDbCdm+gAAADo2UeirquOr6paqWl1VZ0xzfEFVXTIc/2xVLRk79vqh/JaqevZY+flV9bWqumHKuR5bVR+tqi8Pfz5m24cHAACwe9ti6KuqeUnOS/LzSZYlObWqlk2pdnqSb7TWDkpybpJzhrbLkqxIcliS45O8czhfklwwlE11RpKPt9YOTvLxYR8AAIBtMMlM31FJVrfWbm2tPZDk4iQnTKlzQpILh+3LkhxbVTWUX9xau7+1dluS1cP50lr7ZJJ7prne+LkuTHLiVowHAACAMZOEvgOS3DG2v2Yom7ZOa21DkvVJ9puw7VSPb63dNWzfneTx01WqqpdU1aqqWrV27doJhgEAALD72akXcmmttSRthmPvbq0tb60tX7Ro0Q7uGQAAwK5hktB3Z5IDx/YXD2XT1qmq+Un2SbJuwrZTfbWq9h/OtX+Sr03QRwAAAKYxSei7JsnBVbW0qvbMaGGWlVPqrExy2rB9UpJPDLN0K5OsGFb3XJrk4CRXb+F64+c6LckHJ+gjAAAA09hi6Bve0XtFko8kuTnJpa21G6vqLVX1vKHae5LsV1Wrk/xmhhU3W2s3Jrk0yU1J/i7Jy1trG5Okqv4yyWeSHFpVa6rq9OFcZyc5rqq+nOQ/DvsAAABsg/mTVGqtXZ7k8illbxrbvi/JyTO0PSvJWdOUnzpD/XVJjp2kXwAAAGzeTr2QCwAAAI+M0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjE4W+qjq+qm6pqtVVdcY0xxdU1SXD8c9W1ZKxY68fym+pqmdv6ZxVdUFV3VZV1w1fRzyyIQIAAOy+5m+pQlXNS3JekuOSrElyTVWtbK3dNFbt9CTfaK0dVFUrkpyT5JSqWpZkRZLDkjwhyceq6pChzebO+duttcu2w/gAAAB2a5PM9B2VZHVr7dbW2gNJLk5ywpQ6JyS5cNi+LMmxVVVD+cWttftba7clWT2cb5JzAgAA8AhNEvoOSHLH2P6aoWzaOq21DUnWJ9lvM223dM6zqur6qjq3qhZM16mqeklVraqqVWvXrp1gGAAAALufnXEhl9cn+ZEkP5HksUleN12l1tq7W2vLW2vLFy1atCP7BwAAsMuYJPTdmeTAsf3FQ9m0dapqfpJ9kqzbTNsZz9lau6uN3J/kvRk9CgoAAMA2mCT0XZPk4KpaWlV7ZrQwy8opdVYmOW3YPinJJ1prbShfMazuuTTJwUmu3tw5q2r/4c9KcmKSGx7JAAEAAHZnW1y9s7W2oapekeQjSeYlOb+1dmNVvSXJqtbayiTvSXJRVa1Ock9GIS5DvUuT3JRkQ5KXt9Y2Jsl05xwu+f6qWpSkklyX5KXbb7gAAAC7ly2GviRprV2e5PIpZW8a274vyckztD0ryVmTnHMof9YkfQIAAGDLdsaFXAAAANhOhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQsflz3QEA2OWduc8cXnv93F0bgF2CmT4AAICOmekDoBtLzvjwnFz39oVzclkAmIiZPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHZs/SaWqOj7JnySZl+TPW2tnTzm+IMn7kjw1ybokp7TWbh+OvT7J6Uk2JnlVa+0jmztnVS1NcnGS/ZJcm+QFrbUHHtkwgV3GmfvM0XXXz811AZIsOePDc3Ld2xf+lzm5bhI/d2EH2mLoq6p5Sc5LclySNUmuqaqVrbWbxqqdnuQbrbWDqmpFknOSnFJVy5KsSHJYkick+VhVHTK0memc5yQ5t7V2cVW9azj3n26PwdKPufrLMfEXJAAAu5ZJZvqOSrK6tXZrklTVxUlOSDIe+k5IcuawfVmSd1RVDeUXt9buT3JbVa0ezpfpzllVNyd5VpJNv1VfOJxX6AMAYNfi6RV2EpOEvgOS3DG2vybJT85Up7W2oarWZ/R45gFJ/mFK2wOG7enOuV+Sb7bWNkxT/2Gq6iVJXjLs3ltVt0wwlt1GJY9L8vUdfuHfqx1+yR1tzu5tslvc3/jenTW+d2ePezu73N/Z497OOn+nzZ65+97deT1xpgMTvdO3M2qtvTvJu+e6HzurqlrVWls+1/3okXs7u9zf2ePezh73dna5v7PHvZ1d7u/scW+3ziSrd96Z5MCx/cVD2bR1qmp+kn0yWtBlprYzla9Lsu9wjpmuBQAAwIQmCX3XJDm4qpZW1Z4ZLcyyckqdlUlOG7ZPSvKJ1lobyldU1YJhVc6Dk1w90zmHNlcM58hwzg9u+/AAAAB2b1t8vHN4R+8VST6S0ccrnN9au7Gq3pJkVWttZZL3JLloWKjlnoxCXIZ6l2a06MuGJC9vrW1MkunOOVzydUkurqrfT/KPw7nZeh59nT3u7exyf2ePezt73NvZ5f7OHvd2drm/s8e93Qo1mlwDAACgR5M83gkAAMAuSugDAADomNC3i6uqe6cpO7Oq7qyq66rqpqo6dS76tqsZv5dV9QtV9aWqeuJwP79TVT84Q91WVW8d239NVZ25wzq+i6qqjcP36I1V9fmq+q2qelRVPXsov66q7q2qW4bt9811n3d2VfXG4X5eP9yzN1fVf5tS54iqunnYvr2qvjDU/99VNePn++zOhv/H/2Jsf35Vra2q/zXsv6iq3jFNu/H7+/dV9UM7st+7kqo6cbjPPzLsL6mq7w7fx5+vqk9X1aHDsWdW1frh2Ber6o/mtvc7r6p6fFV9oKpuraprq+ozVfWLU+7h9VX1sU1/xw3fz2vH7u9vzPU4dlZjf4/dUFUfqqp9h/Lx799NX3u6t1tn7P5u+jqjqv5m2F499j18XVU9raquHH5n+HxVXVNVR8z1GHYmQl+/zm2tHZHkhCT/X1XtMdcd2lVU1bFJ3pbk51trXxmKv57kt2Zocn+S/6uqHrcj+teR77bWjmitHZbkuCQ/n+TNrbWPDOVHJFmV5PnD/gvntLc7uar66STPTfLjrbXDk/zHjFZDPmVK1RVJ/nJs/5ih/pVJfmcHdHVX9K9JfrSqfmDYPy6Tf5zQpvu7KskbZqNznTg1yVXDn5v80/D//o8luTAPv3+fGn5GHJnkuVX1Mzuuq7uGqqok/zPJJ1trT2qtPTWj//8XD1U+NdzfwzNaVf3lY80vGe7vzyR5Y1WNf8wW/2bT32M/mtFChuP3cNP376avB4Zy93Zy351yD89urf3icP/+a/7te/iI1tqnhzbPH35mvDPJ/ztnPd8JCX2da619Ocl3kjxmrvuyK6iqpyf5syTPba3909ih85OcUlWPnabZhoxWkPIvdtuotfa1JC9J8orhFxW23v5Jvt5auz9JWmtfb619Msk3quonx+r9Uh4e+jb5TJIDZr+bu6zLkzxn2D4109/DzflkkoO2a486UVV7JfnZJKdnWP17Gv8+yTemFrbWvpvkuvjenc6zkjzQWnvXpoLW2ldaa28frzT8zN0709/fdUlWZ/Tzhc3bqp+h7u2s83faFEJf56rqx5N8efilms1bkNG/ip7YWvvilGP3ZhT8fn2GtucleX5V7TOL/etaa+3WjD7C5Qe3VJdp/X2SA2v0WPI7q+oZQ/lfZvhFuqp+Ksk9wz8GTXV8Rt//TO/ijD53dmGSw5N8divbPzfJF7Z7r/pwQpK/a619Kcm6qnrqUP7Dw2Nb/5TkN5P88dSGVfWYjD4D+JM7rLe7jsOSfG4zx4+uquuS/HNGTwacP7VCVf2HJAuTXD8rPexEVc1Lcmwe/jnWm75/r6uq86Zp495u2Q9Mebxz6pMrm+PvtCmEvn79RlXdmNEvJmfNdWd2EQ8m+XRG/9o8nbclOa2q9p56oLX2rSTvS/Kq2esezKy1dm+Sp2Y0Y7o2ySVV9aIklyQ5qaoele9/tDNJrqiqOzN6vHZrZ692G62165MsyWiW7/KtaHrF8Iv1v0/y37ZUeTd1akahOsOfmx7x3PR43A8neXUe/plcR1fV5zN6zPYjrbW7d1hvd1FVdd6md52Gok2Pxh2Y5L1J/nCs+ilVdX1GM1HvbK3dt6P7u4v4geH/77uTPD7JR8eOjT/eOf7Yp3s7uamPd14yQZv3V9VtSd6Y0T/IMxD6+nXu8K7Uf07ynuFfp9m872X06NtRVfV979601r6Z5AN5+DP74/57RoHx0bPWw45V1ZOSbExiVnobtdY2ttaubK29Ockrkvzn1todSW5L8oyMfh5M/UvzmCRPzOgRud/bkf3dBa1M8kfZunB8zKZ3UoefIYwZHpl/VpI/r6rbk/x2Rj+Hpz7mvTLJ08f2PzW8t3NYktMt2DCtG5P8+KadIXgcm2TRNHWn3t9Lhnf9npbkbIsQzei7w/tlT8zoe3am3w/Gubez6/lJnpTRe8Bv30Ld3YrQ17nW2sqMFhA4ba77sitorX0no/d2nl9V0834/XGSX00yf5q29yS5NDPPFDKDqlqU5F1J3tFaa3Pdn11RVR1aVQePFR2RZNNCRH+Z5Nwkt7bW1kxt21rbkNFMygtneG+VkfOT/F5rzWOa289JSS5qrT2xtbZkmHW6LcnUxS1+Nsk/TW3cWrstydlJXjfrPd31fCLJwqp62VjZv5uh7kz3d1WSizLzqw3kod8dXpXkt6rq+34/mKGNeztLht8jfjfJT9WwIjBCXw/+XVWtGfv6zWnqvCXJbw6Pd7EFQ3g7PsnvVNXzphz7epK/yej9v+m8NYlVPCez6Vn9G5N8LKN30sw0bbu9klxYo49puT7JsiRnDsf+KqMZkRlnqFprdw3HJ/mX6t1Sa21Na+1tMxx+0ZSfxYtnqMfDnZrRz9Rxf53k9fm3d6I+n+QPMlqtbzrvSvL0qloyW53cFQ2/+J6Y5BlVdVtVXZ3R7MemgHz02P19QWZeofqcJC+e7tUG/k1r7R8zej9vaz4my73dvKnv9J09acNhkae3ZvT0AEnKP6oDAAD0y8wPAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0LH/H6PhQcVttczOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.2 N 10000\n",
            "LR: 0.000816 0.000827\n",
            "KNN: 0.000008 0.000007\n",
            "DT: 0.000000 0.000016\n",
            "SVR: 0.001249 0.001213\n",
            "MLP: 0.000065 0.000064\n",
            "ABR: 0.001109 0.001135\n",
            "GBR: 0.000027 0.000035\n",
            "RFR: 0.000001 0.000003\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.4 N 10000\n",
            "LR: 0.000960 0.000983\n",
            "KNN: 0.000008 0.000012\n",
            "DT: 0.000000 0.000019\n",
            "SVR: 0.000686 0.000674\n",
            "MLP: 0.000046 0.000050\n",
            "ABR: 0.001204 0.001240\n",
            "GBR: 0.000028 0.000037\n",
            "RFR: 0.000001 0.000005\n",
            "ETR: 0.000000 0.000002\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.6 N 10000\n",
            "LR: 0.001078 0.001046\n",
            "KNN: 0.000008 0.000009\n",
            "DT: 0.000000 0.000023\n",
            "SVR: 0.001265 0.001138\n",
            "MLP: 0.000080 0.000083\n",
            "ABR: 0.001481 0.001474\n",
            "GBR: 0.000031 0.000038\n",
            "RFR: 0.000001 0.000005\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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55JC8973vTZJcf/31+frXv579999/q55jJlb6AACA2beJj1iYbfe5z31y4YUX5uSTT866deuyYcOGvPSlL81+++2X3//938+6devSWsvJJ5+cPffcM7/927+do48+Oh/60Ifylre8JYcccshP9Df1mb6zzz57s2P4oz/6o7zwhS/MgQcemPnz5+fcc8/NggULtvpcp1OttW1yotm0fPnytnLlyrkeBgB0a8kpH5mzc9+06+/N2bnn8odU2NFde+21edSjHjXXw+jSdNe2qq5srS2frr7bOwEAADom9AEAAHRM6AMAAGZFD4+SbW/uyTUV+gAAgK1u1113zdq1awW/rai1lrVr12bXXXfdonbe3gkAAGx1ixcvzurVq7NmzZq5HkpXdt111yxevHiL2gh9AADAVnff+943S5cunethkAlv76yqI6rquqpaVVWnTHN8QVWdPxz/XFUtGTv26qH8uqo6fKz8nKr6dlVdPcM5/7iqWlU9cMunBQAAQDJB6KuqeUnOSvK0JMuSHF9Vy6ZUOynJd1tr+yQ5M8kZQ9tlSY5LckCSI5KcPfSXJOcOZdOdc+8kv5nk61s4HwAAAMZMstL3uCSrWms3tNbuSHJekqOm1DkqybuG7QuTHFZVNZSf11q7vbV2Y5JVQ39prV2W5NYZznlmklcm8dQnAADAvTBJ6Nsryc1j+6uHsmnrtNY2JFmXZOGEbX9CVR2V5ButtS9spt7zq2plVa30cCgAAMD0tquPbKiqn03ymiSv21zd1trbW2vLW2vLFy1aNPuDAwAA2AFNEvq+kWTvsf3FQ9m0dapqfpI9kqydsO24X0iyNMkXquqmof7nq+ohE4wTAACAKSYJfVck2beqllbVLhm9mGXFlDorkpw4bB+d5JNt9CmMK5IcN7zdc2mSfZNcPtOJWmtfaq09qLW2pLW2JKPbQR/TWrtli2YFAABAkglC3/CM3ouTXJzk2iQXtNauqao3VNWRQ7V3JFlYVauSvDzJKUPba5JckOTLST6a5EWttbuSpKren+SzSfavqtVVddLWnRoAAAATfTh7a+2iJBdNKXvd2Pb6JMfM0Pa0JKdNU378BOddMsn4AAAAmN529SIXAAAAti6hDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOjY/LkeAMDOZMkpH5mzc990+tPn7NwAwNyx0gcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOjZR6KuqI6rquqpaVVWnTHN8QVWdPxz/XFUtGTv26qH8uqo6fKz8nKr6dlVdPaWvv66qr1TVF6vqg1W15z2fHgAAwM5ts6GvquYlOSvJ05IsS3J8VS2bUu2kJN9tre2T5MwkZwxtlyU5LskBSY5IcvbQX5KcO5RN9c9JfrG1dlCS65O8egvnBAAAwGCSlb7HJVnVWruhtXZHkvOSHDWlzlFJ3jVsX5jksKqqofy81trtrbUbk6wa+ktr7bIkt049WWvtY621DcPuvyZZvIVzAgAAYDBJ6Nsryc1j+6uHsmnrDIFtXZKFE7bdlOcm+afpDlTV86tqZVWtXLNmzRZ0CQAAsPPYbl/kUlWvTbIhyXunO95ae3trbXlrbfmiRYu27eAAAAB2EJOEvm8k2Xtsf/FQNm2dqpqfZI8kayds+1Oq6tlJnpHkhNZam2CMAAAATGOS0HdFkn2ramlV7ZLRi1lWTKmzIsmJw/bRST45hLUVSY4b3u65NMm+SS7f1Mmq6ogkr0xyZGvth5NPBQAAgKk2G/qGZ/RenOTiJNcmuaC1dk1VvaGqjhyqvSPJwqpaleTlSU4Z2l6T5IIkX07y0SQvaq3dlSRV9f4kn02yf1WtrqqThr7emmT3JP9cVVdV1du20lwBAAB2OvMnqdRauyjJRVPKXje2vT7JMTO0PS3JadOUHz9D/X0mGRMAAACbt92+yAUAAIB7T+gDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGPz53oAAGwjp+4xh+deN3fnBoCdnJU+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBj8+d6AD1bcspH5uzcN53+9Dk7NwAAsP2w0gcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomA9n79Wpe8zRedfNzXkBAIBpWekDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6NhEoa+qjqiq66pqVVWdMs3xBVV1/nD8c1W1ZOzYq4fy66rq8LHyc6rq21V19ZS+HlBV/1xVXx3+e/97Pj0AAICd22ZDX1XNS3JWkqclWZbk+KpaNqXaSUm+21rbJ8mZSc4Y2i5LclySA5IckeTsob8kOXcom+qUJJ9ore2b5BPDPgAAAPfAJCt9j0uyqrV2Q2vtjiTnJTlqSp2jkrxr2L4wyWFVVUP5ea2121trNyZZNfSX1tplSW6d5nzjfb0ryTO3YD4AAACMmST07ZXk5rH91UPZtHVaaxuSrEuycMK2Uz24tfbNYfuWJA+erlJVPb+qVlbVyjVr1kwwDQAAgJ3Pdv0il9ZaS9JmOPb21try1tryRYsWbeORAQAA7BgmCX3fSLL32P7ioWzaOlU1P8keSdZO2Haqb1XVQ4e+Hprk2xOMEQAAgGlMEvquSLJvVS2tql0yejHLiil1ViQ5cdg+Osknh1W6FUmOG97uuTTJvkku38z5xvs6McmHJhgjAAAA09hs6Bue0XtxkouTXJvkgtbaNVX1hqo6cqj2jiQLq2pVkpdneONma+2aJBck+XKSjyZ5UWvtriSpqvcn+WyS/atqdVWdNPR1epKnVtVXk/zGsA8AAMA9MH+SSq21i5JcNKXsdWPb65McM0Pb05KcNk358TPUX5vksEnGBQAAwKZt1y9yAQAA4N4R+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMcmCn1VdURVXVdVq6rqlGmOL6iq84fjn6uqJWPHXj2UX1dVh2+uz6o6rKo+X1VXVdWnq2qfezdFAACAnddmQ19VzUtyVpKnJVmW5PiqWjal2klJvtta2yfJmUnOGNouS3JckgOSHJHk7Kqat5k+/zbJCa21g5O8L8mf3rspAgAA7LwmWel7XJJVrbUbWmt3JDkvyVFT6hyV5F3D9oVJDquqGsrPa63d3lq7Mcmqob9N9dmS/NywvUeSf79nUwMAAGD+BHX2SnLz2P7qJI+fqU5rbUNVrUuycCj/1ylt9xq2Z+rzeUkuqqofJfl+kl+ZblBV9fwkz0+Sn//5n59gGgAAADuf7fFFLi9L8luttcVJ3pnkb6ar1Fp7e2tteWtt+aJFi7bpAAEAAHYUk4S+byTZe2x/8VA2bZ2qmp/RbZlrN9F22vKqWpTkl1prnxvKz0/yhIlmAgAAwE+ZJPRdkWTfqlpaVbtk9GKWFVPqrEhy4rB9dJJPttbaUH7c8HbPpUn2TXL5Jvr8bpI9qmq/oa+nJrn2nk8PAABg57bZZ/qGZ/RenOTiJPOSnNNau6aq3pBkZWttRZJ3JHlPVa1KcmtGIS5DvQuSfDnJhiQvaq3dlSTT9TmU/+ck/1BVP84oBD53q84YAABgJzLJi1zSWrsoyUVTyl43tr0+yTEztD0tyWmT9DmUfzDJBycZFwAAAJu2Pb7IBQAAgK1E6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB2bKPRV1RFVdV1VraqqU6Y5vqCqzh+Of66qlowde/VQfl1VHb65PmvktKq6vqquraqT790UAQAAdl7zN1ehquYlOSvJU5OsTnJFVa1orX15rNpJSb7bWtunqo5LckaSY6tqWZLjkhyQ5GFJPl5V+w1tZurz2Un2TvLI1tqPq+pBW2OiAAAAO6NJVvoel2RVa+2G1todSc5LctSUOkcledewfWGSw6qqhvLzWmu3t9ZuTLJq6G9Tfb4wyRtaaz9Oktbat+/59AAAAHZuk4S+vZLcPLa/eiibtk5rbUOSdUkWbqLtpvr8hYxWCVdW1T9V1b7TDaqqnj/UWblmzZoJpgEAALDz2R5f5LIgyfrW2vIk/yPJOdNVaq29vbW2vLW2fNGiRdt0gAAAADuKSULfNzJ6xm6jxUPZtHWqan6SPZKs3UTbTfW5Osn/HLY/mOSgCcYIAADANCYJfVck2beqllbVLhm9mGXFlDorkpw4bB+d5JOttTaUHze83XNpkn2TXL6ZPv8xyaHD9pOSXH/PpgYAAMBm397ZWttQVS9OcnGSeUnOaa1dU1VvSLKytbYiyTuSvKeqViW5NaMQl6HeBUm+nGRDkhe11u5Kkun6HE55epL3VtXLktyW5Hlbb7oAAAA7l82GviRprV2U5KIpZa8b216f5JgZ2p6W5LRJ+hzKv5fk6ZOMCwAAgE3bHl/kAgAAwFYi9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI5NFPqq6oiquq6qVlXVKdMcX1BV5w/HP1dVS8aOvXoov66qDt+CPt9cVbfds2kBAACQTBD6qmpekrOSPC3JsiTHV9WyKdVOSvLd1to+Sc5McsbQdlmS45IckOSIJGdX1bzN9VlVy5Pc/17ODQAAYKc3yUrf45Ksaq3d0Fq7I8l5SY6aUueoJO8ati9MclhV1VB+Xmvt9tbajUEImCgAAA+7SURBVElWDf3N2OcQCP86ySvv3dQAAACYJPTtleTmsf3VQ9m0dVprG5KsS7JwE2031eeLk6xorX1zU4OqqudX1cqqWrlmzZoJpgEAALDz2a5e5FJVD0tyTJK3bK5ua+3trbXlrbXlixYtmv3BAQAA7IAmCX3fSLL32P7ioWzaOlU1P8keSdZuou1M5Y9Osk+SVVV1U5KfrapVE84FAACAKSYJfVck2beqllbVLhm9mGXFlDorkpw4bB+d5JOttTaUHze83XNpkn2TXD5Tn621j7TWHtJaW9JaW5Lkh8PLYQAAALgH5m+uQmttQ1W9OMnFSeYlOae1dk1VvSHJytbaiiTvSPKeYVXu1oxCXIZ6FyT5cpINSV7UWrsrSabrc+tPDwAAYOe22dCXJK21i5JcNKXsdWPb6zN6Fm+6tqclOW2SPqeps9sk4wMAAGB629WLXAAAANi6hD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANCxiUJfVR1RVddV1aqqOmWa4wuq6vzh+OeqasnYsVcP5ddV1eGb67Oq3juUX11V51TVfe/dFAEAAHZemw19VTUvyVlJnpZkWZLjq2rZlGonJflua22fJGcmOWNouyzJcUkOSHJEkrOrat5m+nxvkkcmOTDJzyR53r2aIQAAwE5skpW+xyVZ1Vq7obV2R5Lzkhw1pc5RSd41bF+Y5LCqqqH8vNba7a21G5OsGvqbsc/W2kVtkOTyJIvv3RQBAAB2XpOEvr2S3Dy2v3oom7ZOa21DknVJFm6i7Wb7HG7r/IMkH51uUFX1/KpaWVUr16xZM8E0AAAAdj7b84tczk5yWWvtU9MdbK29vbW2vLW2fNGiRdt4aAAAADuG+RPU+UaSvcf2Fw9l09VZXVXzk+yRZO1m2s7YZ1W9PsmiJH84wfgAAACYwSQrfVck2beqllbVLhm9mGXFlDorkpw4bB+d5JPDM3krkhw3vN1zaZJ9M3pOb8Y+q+p5SQ5Pcnxr7cf3bnoAAAA7t82u9LXWNlTVi5NcnGReknNaa9dU1RuSrGytrUjyjiTvqapVSW7NKMRlqHdBki8n2ZDkRa21u5Jkuj6HU74tydeSfHb0Lpj8z9baG7bajAEAAHYik9zemdbaRUkumlL2urHt9UmOmaHtaUlOm6TPoXyiMQEAALB52/OLXAAAALiXhD4AAICOCX0AAAAdE/oAAAA6JvQBAAB0TOgDAADomNAHAADQMaEPAACgY0IfAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB0T+gAAADom9AEAAHRM6AMAAOiY0AcAANAxoQ8AAKBjQh8AAEDHhD4AAICOzZ/rAQDADu/UPebw3Ovm7twA7BCEPgC6seSUj8zJeW/adU5OCwATcXsnAABAx4Q+AACAjgl9AAAAHRP6AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI4JfQAAAB2bP9cDAABgjpy6xxyee93cnRt2Mlb6AAAAOmalDwBgji055SNzct6bdp2T0wLbmJU+AACAjlnpA7Yvc/V8iWdLAIBOWekDAADomNAHAADQMaEPAACgYxM901dVRyR5U5J5Sf6utXb6lOMLkrw7yWOTrE1ybGvtpuHYq5OclOSuJCe31i7eVJ9VtTTJeUkWJrkyyR+01u64d9OErchnGgEAsAPZbOirqnlJzkry1CSrk1xRVStaa18eq3ZSku+21vapquOSnJHk2KpaluS4JAckeViSj1fVfkObmfo8I8mZrbXzquptQ99/uzUmSz/m6tXWiddbAwAT8nIythOTrPQ9Lsmq1toNSVJV5yU5Ksl46DsqyanD9oVJ3lpVNZSf11q7PcmNVbVq6C/T9VlV1yZ5SpLfG+q8a+hX6AMAYIv5RTFMFvr2SnLz2P7qJI+fqU5rbUNVrcvo9sy9kvzrlLZ7DdvT9bkwyfdaaxumqf8Tqur5SZ4/7N5WVddNMJedRiUPTPKdbX7iP69tfsptbc6ubbJTXN/42p01vnZnj2s7u1zf2ePazi4/j82qufva3X49fKYDO+zn9LXW3p7k7XM9ju1VVa1srS2f63H0yLWdXa7v7HFtZ49rO7tc39nj2s4u13f2uLZbZpK3d34jyd5j+4uHsmnrVNX8JHtk9EKXmdrOVL42yZ5DHzOdCwAAgAlNEvquSLJvVS2tql0yejHLiil1ViQ5cdg+OsknW2ttKD+uqhYMb+XcN8nlM/U5tLlk6CNDnx+659MDAADYuW329s7hGb0XJ7k4o49XOKe1dk1VvSHJytbaiiTvSPKe4UUtt2YU4jLUuyCjl75sSPKi1tpdSTJdn8MpX5XkvKr6iyT/e+ibLefW19nj2s4u13f2uLazx7WdXa7v7HFtZ5frO3tc2y1Qo8U1AAAAejTJ7Z0AAADsoIQ+AACAjgl9O7iqum2aslOr6htVdVVVfbmqjp+Lse1oxq9lVf1WVV1fVQ8frucPq+pBM9RtVfXGsf1XVNWp22zgO6iqumv4Gr2mqr5QVX9cVfepqsOH8quq6raqum7Yfvdcj3l7V1WvHa7nF4dr9vqq+qspdQ6uqmuH7Zuq6ktD/X+pqhk/32dnNvwd//ux/flVtaaq/v9h/9lV9dZp2o1f349V1UO25bh3JFX1zOE6P3LYX1JVPxq+jr9QVZ+pqv2HY0+uqnXDsa9U1X+b29Fvv6rqwVX1vqq6oaqurKrPVtXvTLmGX6yqj2/8N274el4zdn1fNtfz2F6N/Tt2dVV9uKr2HMrHv343/tnFtd0yY9d3459TquqDw/aqsa/hq6rqCVV16fAzwxeq6oqqOniu57A9Efr6dWZr7eAkRyX5/6rqvnM9oB1FVR2W5M1JntZa+9pQ/J0kfzxDk9uT/D9V9cBtMb6O/Ki1dnBr7YAkT03ytCSvb61dPJQfnGRlkhOG/WfN6Wi3c1X1q0mekeQxrbWDkvxGRm9DPnZK1eOSvH9s/9Ch/qVJ/nQbDHVH9H+S/GJV/cyw/9RM/nFCG6/vyiSvmY3BdeL4JJ8e/rvRvw1/938pybvyk9fvU8P3iEcneUZV/dq2G+qOoaoqyT8muay19ojW2mMz+vu/eKjyqeH6HpTRW9VfNNb8/OH6/lqS11bV+Mds8R82/jv2ixm9yHD8Gm78+t34546h3LWd3I+mXMPTW2u/M1y/5+U/voYPbq19ZmhzwvA94+wkfz1nI98OCX2da619NckPk9x/rseyI6iqJyb5H0me0Vr7t7FD5yQ5tqoeME2zDRm9Qcpv7O6h1tq3kzw/yYuHH1TYcg9N8p3W2u1J0lr7TmvtsiTfrarHj9X73fxk6Nvos0n2mv1h7rAuSvL0Yfv4TH8NN+WyJPts1RF1oqp2S/LrSU7K8Pbvafxcku9OLWyt/SjJVfG1O52nJLmjtfa2jQWtta+11t4yXmn4nrt7pr++a5Osyuj7C5u2Rd9DXdtZ59+0KYS+zlXVY5J8dfihmk1bkNFvRZ/ZWvvKlGO3ZRT8/ssMbc9KckJV7TGL4+taa+2GjD7C5UGbq8u0PpZk7xrdlnx2VT1pKH9/hh+kq+pXktw6/DJoqiMy+vpneudl9LmzuyY5KMnntrD9M5J8aauPqg9HJfloa+36JGur6rFD+S8Mt239W5KXJ/mbqQ2r6v4ZfQbwZdtstDuOA5J8fhPHD6mqq5J8PaM7A86ZWqGqfj7Jrkm+OCsj7ERVzUtyWH7yc6w3fv1eVVVnTdPGtd28n5lye+fUO1c2xb9pUwh9/XpZVV2T0Q8mp831YHYQdyb5TEa/bZ7Om5OcWFW7Tz3QWvt+kncnOXn2hgcza63dluSxGa2YrklyflU9O8n5SY6uqvvkp2/tTJJLquobGd1eu6WrVzuN1toXkyzJaJXvoi1oesnwg/XPJfmrzVXeSR2fUajO8N+Nt3huvD3uF5K8ND/5mVyHVNUXMrrN9uLW2i3bbLQ7qKo6a+OzTkPRxlvj9k7yziT/daz6sVX1xYxWos5ura3f1uPdQfzM8Pf7liQPTvLPY8fGb+8cv+3TtZ3c1Ns7z5+gzXur6sYkr83oF/IMhL5+nTk8K/Wfkrxj+O00m/bjjG59e1xV/dSzN6217yV5X37ynv1x/z2jwHi/WRthx6rqEUnuSmJV+h5qrd3VWru0tfb6JC9O8p9aazcnuTHJkzL6fjD1H81Dkzw8o1vk/nxbjncHtCLJf8uWheNDNz6TOnwPYcxwy/xTkvxdVd2U5E8y+j489TbvFUmeOLb/qeG5nQOSnOSFDdO6JsljNu4MweOwJIumqTv1+p4/POv3hCSnewnRjH40PF/28Iy+Zmf6+WCcazu7TkjyiIyeA37LZuruVIS+zrXWVmT0AoET53osO4LW2g8zem7nhKqabsXvb5L8YZL507S9NckFmXmlkBlU1aIkb0vy1tZam+vx7Iiqav+q2nes6OAkG19E9P4kZya5obW2emrb1tqGjFZSnjXDc6uMnJPkz1trbtPceo5O8p7W2sNba0uGVacbk0x9ucWvJ/m3qY1bazcmOT3Jq2Z9pDueTybZtapeOFb2szPUnen6rkzynsz8aAO5+2eHk5P8cVX91M8HM7RxbWfJ8HPEnyX5lRreCIzQ14OfrarVY39ePk2dNyR5+XB7F5sxhLcjkvxpVR055dh3knwwo+f/pvPGJN7iOZmN9+pfk+TjGT2TZqXpntstybtq9DEtX0yyLMmpw7EPZLQiMuMKVWvtm8PxSX5TvVNqra1urb15hsPPnvK9ePEM9fhJx2f0PXXcPyR5df7jmagvJPnLjN7WN523JXliVS2ZrUHuiIYffJ+Z5ElVdWNVXZ7R6sfGgHzI2PX9g8z8huozkjxnukcb+A+ttf+d0fN5W/IxWa7tpk19pu/0SRsOL3l6Y0Z3D5Ck/FIdAACgX1Z+AAAAOib0AQAAdEzoAwAA6JjQBwAA0DGhDwAAoGNCHwAAQMeEPgAAgI79X5dM7/y26pQ4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.8 N 10000\n",
            "LR: 0.001120 0.001187\n",
            "KNN: 0.000009 0.000010\n",
            "DT: 0.000000 0.000026\n",
            "SVR: 0.002021 0.002049\n",
            "MLP: 0.000698 0.000723\n",
            "ABR: 0.001490 0.001598\n",
            "GBR: 0.000034 0.000041\n",
            "RFR: 0.000002 0.000007\n",
            "ETR: 0.000000 0.000002\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 1.0 N 10000\n",
            "LR: 0.001210 0.001238\n",
            "KNN: 0.000007 0.000022\n",
            "DT: 0.000000 0.000034\n",
            "SVR: 0.002058 0.002174\n",
            "MLP: 0.000172 0.000195\n",
            "ABR: 0.001642 0.001786\n",
            "GBR: 0.000033 0.000048\n",
            "RFR: 0.000001 0.000012\n",
            "ETR: 0.000000 0.000009\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 20000\n",
            "LR: 0.000767 0.000843\n",
            "KNN: 0.000005 0.000004\n",
            "DT: 0.000000 0.000008\n",
            "SVR: 0.002605 0.002611\n",
            "MLP: 0.000029 0.000030\n",
            "ABR: 0.001058 0.001040\n",
            "GBR: 0.000023 0.000026\n",
            "RFR: 0.000001 0.000002\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 30000\n",
            "LR: 0.000799 0.000809\n",
            "KNN: 0.000002 0.000003\n",
            "DT: 0.000000 0.000005\n",
            "SVR: 0.002467 0.002465\n",
            "MLP: 0.000009 0.000009\n",
            "ABR: 0.001038 0.001060\n",
            "GBR: 0.000026 0.000028\n",
            "RFR: 0.000000 0.000001\n",
            "ETR: 0.000000 0.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 40000\n",
            "LR: 0.000816 0.000774\n",
            "KNN: 0.000002 0.000002\n",
            "DT: 0.000000 0.000004\n",
            "SVR: 0.001935 0.001874\n",
            "MLP: 0.000011 0.000010\n",
            "ABR: 0.001027 0.001016\n",
            "GBR: 0.000024 0.000027\n",
            "RFR: 0.000000 0.000001\n",
            "ETR: 0.000000 0.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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QdwYBAICFc9pe93F/m7Z5eOPGjTnqqKOSJF/5yleyaNGiLFu2LEly1VVXZY899thm+8svvzx77LFHfumXfulHjp177rn5gz/4g7u/sD5J3vWud2XlypU7ehb3C2EQAAAYjX322SfXXnttkuS0007LQx/60Lz85S+fuf3ll1+ehz70ofOGwSQ5/vjjc9ZZZ221/ebNm7N48eKt7s/a7r7gMVEAAGDUrrnmmjzxiU/ME57whBx99NG59dZbkyRveMMbsnLlyhx66KFZvXp1br755rzlLW/J61//+hx22GH58Ic/PFP/l19+eY444og84xnPyMqVK39k/7bbbssLXvCCPO5xj8vjH//4fOhDH0oyudP4jGc8I09+8pPvvpt5X3JnEAAAGK3uzktf+tK85z3vybJly3LhhRfmVa96Vc4555ycccYZuemmm7JkyZJ861vfyt57750XvehF27ybeOGFF+bKK6+8e/+jH/1okuQTn/hErrvuuqxYsSKXX375D+2/7nWvS1XlU5/6VD772c/m137t1/K5z33u7naf/OQn8/CHP/w+P3dhEAAAGK3bb7891113XZ7ylKckSe6666488pGPTJIceuihee5zn5tnPvOZeeYznzlTf1t7TPTwww/PihUr5t2/8sor89KXvjRJ8jM/8zN59KMffXcYfMpTnrJTgmAiDAIAACPW3TnkkEPuvoM37dJLL80VV1yRSy65JKeffno+9alP3eNxfuzHfmyb+7O2uy95ZxAAABitJUuWZMOGDXeHwTvvvDPXX399fvCDH+SWW27Jk570pJx55pnZtGlTvvvd72bPPffMd77znft0DkcccUTe+c53Jkk+97nP5Utf+lIOPvjg+3SM+bgzCAAALJztfBXEzvagBz0oF198cU455ZRs2rQpmzdvzste9rIcdNBB+c3f/M1s2rQp3Z1TTjkle++9d/7Vv/pXOe644/Ke97wnb3zjG3PEEUf8UH9z3xl805vetN05/Nt/+2/z4he/OI973OOyePHinHvuuVmyZMl9fq5zVXfv9EEWyqpVq3rt2rULPQ2AmSw/9dIFGffmM562IOMCME6f+cxn8tjHPnahp/GANN+1raprunvVfPXdGQQYu/v6y353aOyF/ddgABgz7wwCAACMkDAIAADcrx7Ir6otlHtyTYVBAADgfrN06dJs3LhRILwPdXc2btyYpUuX7lA77wwCAAD3m/333z/r16/Phg0bFnoqDyhLly7N/vvvv0NthEEAAOB+8+AHPzgrVqxY6GkQj4kCAACMkjAIAAAwQsIgAADACAmDAAAAIyQMAgAAjJAwCAAAMELCIAAAwAgJgwAAACMkDAIAAIyQMAgAADBCwiAAAMAICYMAAAAjJAwCAACMkDAIAAAwQsIgAADACAmDAAAAIyQMAgAAjNBMYbCqjqmqG6pqXVWdOs/xJVV14XD841W1fOrYK4fyG6rq6Knyc6rqa1V13Zy+Lqyqa4fPzVV17VC+vKq+P3XsLff0pAEAAMZu8fYqVNWiJGcneUqS9Umurqo13f3pqWonJflmdz+mqlYnOTPJ8VW1MsnqJIckeVSS91fVQd19V5Jzk5yV5Lzp8br7+KmxX5dk09ThL3T3YTt+mgAAAEyb5c7g4UnWdfeN3X1HkguSHDunzrFJ3jFsX5zkqKqqofyC7r69u29Ksm7oL919RZJvbG3Qof2zk7x7B84HAACAGcwSBvdLcsvU/vqhbN463b05k7t5+8zYdmuOSPLV7v78VNmKqvqHqvr7qjpivkZV9cKqWltVazds2DDjUAAAAOOyKy8gc0J++K7grUl+qrsfn+T3kryrqn58bqPufmt3r+ruVcuWLbufpgoAALB7mSUMfjnJAVP7+w9l89apqsVJ9kqycca2P2Lo418nuXBL2fCo6cZh+5okX0hy0AzzBwAAYI5ZwuDVSQ6sqhVVtUcmC8KsmVNnTZITh+3jknywu3soXz2sNroiyYFJrpphzH+Z5LPdvX5LQVUtGxazSVX99NDXjTP0BQAAwBzbXU20uzdX1UuSvDfJoiTndPf1VfXaJGu7e02StyU5v6rWZbIozOqh7fVVdVGSTyfZnOTkYSXRVNW7kxyZZN+qWp/kNd39tmHY1fnRhWN+Nclrq+rOJD9I8qLu3uoCNAAAAGxdTW7gPTCtWrWq165du9DTAJjJ8lMvXZBxb176nAUZN0ly2qbt1wEA7rGquqa7V813bFdeQAYAAICdRBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZopjBYVcdU1Q1Vta6qTp3n+JKqunA4/vGqWj517JVD+Q1VdfRU+TlV9bWqum5OX6dV1Zer6trh8+vb6wsAAIAds90wWFWLkpyd5KlJViY5oapWzql2UpJvdvdjkrw+yZlD25VJVic5JMkxSd409Jck5w5l83l9dx82fC6boS8AAAB2wCx3Bg9Psq67b+zuO5JckOTYOXWOTfKOYfviJEdVVQ3lF3T37d19U5J1Q3/p7iuSfGMH5rrVvgAAANgxs4TB/ZLcMrW/fiibt053b06yKck+M7adz0uq6pPDo6QP24F5pKpeWFVrq2rthg0bZhgKAABgfHbFBWTenOSfJTksya1JXrcjjbv7rd29qrtXLVu2bGfMDwAAYLc3Sxj8cpIDpvb3H8rmrVNVi5PslWTjjG1/SHd/tbvv6u4fJPkv+T+Pgu5wXwAAAMxvljB4dZIDq2pFVe2RySIua+bUWZPkxGH7uCQf7O4eylcPq42uSHJgkqu2NVhVPXJq9zeSbFltdIf7AgAAYH6Lt1ehuzdX1UuSvDfJoiTndPf1VfXaJGu7e02StyU5v6rWZbIozOqh7fVVdVGSTyfZnOTk7r4rSarq3UmOTLJvVa1P8prufluS/1hVhyXpJDcn+Tfb6wsAAIAdU5MbeA9Mq1at6rVr1y70NABmsvzUSxdk3JuXPmdBxk2SnLZp4cYGgBGoqmu6e9V8x3bFBWQAAADYyYRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABihmcJgVR1TVTdU1bqqOnWe40uq6sLh+MeravnUsVcO5TdU1dFT5edU1deq6ro5ff2nqvpsVX2yqv6qqvYeypdX1fer6trh85Z7etIAAABjt90wWFWLkpyd5KlJViY5oapWzql2UpJvdvdjkrw+yZlD25VJVic5JMkxSd409Jck5w5lc70vyT/v7kOTfC7JK6eOfaG7Dxs+L5rtFAEAAJhrljuDhydZ1903dvcdSS5IcuycOscmecewfXGSo6qqhvILuvv27r4pybqhv3T3FUm+MXew7v677t487H4syf47eE4AAABsxyxhcL8kt0ztrx/K5q0zBLlNSfaZse22/N9J/nZqf0VV/UNV/X1VHTFfg6p6YVWtraq1GzZs2IGhAAAAxmOXXUCmql6VZHOSdw5Ftyb5qe5+fJLfS/Kuqvrxue26+63dvaq7Vy1btuz+mzAAAMBuZJYw+OUkB0zt7z+UzVunqhYn2SvJxhnb/oiqen6Spyd5bnd3kgyPmm4ctq9J8oUkB80wfwAAAOaYJQxeneTAqlpRVXtksiDMmjl11iQ5cdg+LskHhxC3JsnqYbXRFUkOTHLVtgarqmOSvCLJM7r7e1Ply7YsPlNVPz30deMM8wcAAGCOxdur0N2bq+olSd6bZFGSc7r7+qp6bZK13b0myduSnF9V6zJZFGb10Pb6qrooyaczeeTz5O6+K0mq6t1Jjkyyb1WtT/Ka7n5bkrOSLEnyvskaNPnYsHLoryZ5bVXdmeQHSV7U3T+yAA0AAADbV8NTmA9Iq1at6rVr1y70NABmsvzUSxdk3JuXPmdBxk2SnLZp4cYGgBGoqmu6e9V8x3bZBWQAAADYeYRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBFavNATAAB2b8tPvXRBxr35jKctyLgADxTuDAIAAIyQO4MAwO7ptL0WcOxNCzc2wH1kpjuDVXVMVd1QVeuq6tR5ji+pqguH4x+vquVTx145lN9QVUdPlZ9TVV+rquvm9PXwqnpfVX1++PVhQ3lV1RuGvj5ZVT93T08aAABg7LYbBqtqUZKzkzw1ycokJ1TVyjnVTkryze5+TJLXJzlzaLsyyeokhyQ5Jsmbhv6S5NyhbK5Tk3yguw9M8oFhP8P4Bw6fFyZ582ynCAAAwFyz3Bk8PMm67r6xu+9IckGSY+fUOTbJO4bti5McVVU1lF/Q3bd3901J1g39pbuvSPKNecab7usdSZ45VX5eT3wsyd5V9chZThIAAIAfNksY3C/JLVP764eyeet09+Ykm5LsM2PbuR7R3bcO219J8ogdmEeq6oVVtbaq1m7YsGE7QwEAAIzTLr2aaHd3kt7BNm/t7lXdvWrZsmU7aWYAAAC7t1nC4JeTHDC1v/9QNm+dqlqcZK8kG2dsO9dXtzz+Ofz6tR2YBwAAADOYJQxeneTAqlpRVXtksiDMmjl11iQ5cdg+LskHh7t6a5KsHlYbXZHJ4i9XbWe86b5OTPKeqfLnDauK/oskm6YeJwUAAGAHbPd7Brt7c1W9JMl7kyxKck53X19Vr02ytrvXJHlbkvOral0mi8KsHtpeX1UXJfl0ks1JTu7uu5Kkqt6d5Mgk+1bV+iSv6e63JTkjyUVVdVKSLyZ59jCVy5L8eiaL0HwvyQvuiwsAAAAwRjN96Xx3X5ZJGJsue/XU9m1JnrWVtqcnOX2e8hO2Un9jkqPmKe8kJ88yXwAAALZtl15ABgAAgJ1DGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARmjxQk9gjJafeumCjX3zGU9bsLEBAIBdhzuDAAAAIyQMAgAAjJAwCAAAMELCIAAAwAgJgwAAACMkDAIAAIyQMAgAADBCwiAAAMAI+dL5sTltrwUce9PCjQ0AAF772LIAABROSURBVPwQdwYBAABGaKYwWFXHVNUNVbWuqk6d5/iSqrpwOP7xqlo+deyVQ/kNVXX09vqsqg9X1bXD55+q6q+H8iOratPUsVffmxMHAAAYs+0+JlpVi5KcneQpSdYnubqq1nT3p6eqnZTkm939mKpaneTMJMdX1cokq5MckuRRSd5fVQcNbebts7uPmBr7L5O8Z2qcD3f30+/pyQIAADAxy53Bw5Os6+4bu/uOJBckOXZOnWOTvGPYvjjJUVVVQ/kF3X17d9+UZN3Q33b7rKofT/LkJH99z04NAACArZklDO6X5Jap/fVD2bx1untzkk1J9tlG21n6fGaSD3T3t6fKfrGq/rGq/raqDplvslX1wqpaW1VrN2zYMMPpAQAAjM+uvIDMCUnePbX/iSSP7u6fTfLGbOWOYXe/tbtXdfeqZcuW3Q/TBAAA2P3MEga/nOSAqf39h7J561TV4iR7Jdm4jbbb7LOq9s3kUdJLt5R197e7+7vD9mVJHjzUAwAAYAfNEgavTnJgVa2oqj0yWRBmzZw6a5KcOGwfl+SD3d1D+ephtdEVSQ5MctUMfR6X5G+6+7YtBVX1k8N7iKmqw4e5b9yx0wUAACCZYTXR7t5cVS9J8t4ki5Kc093XV9Vrk6zt7jVJ3pbk/Kpal+QbmYS7DPUuSvLpJJuTnNzddyXJfH1ODbs6yRlzpnJckhdX1eYk30+yegicAAAA7KDthsHk7scyL5tT9uqp7duSPGsrbU9PcvosfU4dO3KesrOSnDXLfAEAANi2XXkBGQAAAHYSYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGKGZwmBVHVNVN1TVuqo6dZ7jS6rqwuH4x6tq+dSxVw7lN1TV0dvrs6rOraqbqura4XPYUF5V9Yah/ier6ufuzYkDAACM2XbDYFUtSnJ2kqcmWZnkhKpaOafaSUm+2d2PSfL6JGcObVcmWZ3kkCTHJHlTVS2aoc8/6O7Dhs+1Q9lTkxw4fF6Y5M335IQBAACY7c7g4UnWdfeN3X1HkguSHDunzrFJ3jFsX5zkqKqqofyC7r69u29Ksm7ob5Y+5zo2yXk98bEke1fVI2eYPwAAAHPMEgb3S3LL1P76oWzeOt29OcmmJPtso+32+jx9eBT09VW1ZAfmkap6YVWtraq1GzZsmOH0AAAAxmdXXEDmlUl+JsnPJ3l4kj/ckcbd/dbuXtXdq5YtW7Yz5gcAALDbmyUMfjnJAVP7+w9l89apqsVJ9kqycRttt9pnd986PAp6e5K3Z/JI6azzAAAAYAazhMGrkxxYVSuqao9MFoRZM6fOmiQnDtvHJflgd/dQvnpYbXRFJou/XLWtPre8Bzi8c/jMJNdNjfG8YVXRf5FkU3ffeo/OGgAAYOQWb69Cd2+uqpckeW+SRUnO6e7rq+q1SdZ295okb0tyflWtS/KNTMJdhnoXJfl0ks1JTu7uu5Jkvj6HId9ZVcuSVJJrk7xoKL8sya9nsgjN95K84F6fPQAAwEhtNwwmSXdflkkYmy579dT2bUmetZW2pyc5fZY+h/Inb6WfTnLyLPMFAABg23bFBWQAAADYyYRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABghYRAAAGCEhEEAAIAREgYBAABGSBgEAAAYIWEQAABghIRBAACAERIGAQAARkgYBAAAGCFhEAAAYISEQQAAgBESBgEAAEZIGAQAABihmcJgVR1TVTdU1bqqOnWe40uq6sLh+MeravnUsVcO5TdU1dHb67Oq3jmUX1dV51TVg4fyI6tqU1VdO3xefW9OHAAAYMy2GwaralGSs5M8NcnKJCdU1co51U5K8s3ufkyS1yc5c2i7MsnqJIckOSbJm6pq0Xb6fGeSn0nyuCQPSfLbU+N8uLsPGz6vvScnDAAAwGx3Bg9Psq67b+zuO5JckOTYOXWOTfKOYfviJEdVVQ3lF3T37d19U5J1Q39b7bO7L+tBkquS7H/vThEAAIC5ZgmD+yW5ZWp//VA2b53u3pxkU5J9ttF2u30Oj4f+VpL/MVX8i1X1j1X1t1V1yHyTraoXVtXaqlq7YcOGGU4PAABgfHblBWTelOSK7v7wsP+JJI/u7p9N8sYkfz1fo+5+a3ev6u5Vy5Ytu5+mCgAAsHuZJQx+OckBU/v7D2Xz1qmqxUn2SrJxG2232WdVvSbJsiS/t6Wsu7/d3d8dti9L8uCq2neG+QMAADDHLGHw6iQHVtWKqtojkwVh1sypsybJicP2cUk+OLzztybJ6mG10RVJDszkPcCt9llVv53k6CQndPcPtgxQVT85vIeYqjp8mPvGe3LSAAAAY7d4exW6e3NVvSTJe5MsSnJOd19fVa9Nsra71yR5W5Lzq2pdkm9kEu4y1LsoyaeTbE5ycnfflSTz9TkM+ZYkX0zy0SH7/fdh5dDjkry4qjYn+X6S1UPgBAAAYAdtNwwmdz+WedmcsldPbd+W5FlbaXt6ktNn6XMon3dO3X1WkrNmmS8AAADbtisvIAMAAMBOIgwCAACMkDAIAAAwQsIgAADACAmDAAAAIyQMAgAAjJAwCAAAMELCIAAAwAgJgwAAACMkDAIAAIyQMAgAADBCwiAAAMAICYMAAAAjJAwCAACMkDAIAAAwQsIgAADACAmDAAAAIyQMAgAAjJAwCAAAMELCIAAAwAgJgwAAACMkDAIAAIyQMAgAADBCwiAAAMAICYMAAAAjJAwCAACMkDAIAAAwQsIgAADACAmDAAAAIyQMAgAAjJAwCAAAMELCIAAAwAgJgwAAACMkDAIAAIyQMAgAADBCwiAAAMAICYMAAAAjJAwCAACMkDAIAAAwQsIgAADACAmDAAAAIyQMAgAAjNDihZ4AAAC7oNP2WqBxNy3MuDBCwiAAwC5q+amXLtjYNy9dsKGB+4nHRAEAAEZopjuDVXVMkv+cZFGS/9rdZ8w5viTJeUmekGRjkuO7++bh2CuTnJTkriSndPd7t9VnVa1IckGSfZJck+S3uvuObY0BANuyoHdXznjago0NANuy3TBYVYuSnJ3kKUnWJ7m6qtZ096enqp2U5Jvd/ZiqWp3kzCTHV9XKJKuTHJLkUUneX1UHDW221ueZSV7f3RdU1VuGvt+8tTHu7QUAgJ3Ke1cA7KJmuTN4eJJ13X1jklTVBUmOTTIdBo9NctqwfXGSs6qqhvILuvv2JDdV1bqhv8zXZ1V9JsmTkzxnqPOOod83b22M7u4dOWEe+BbqDsDNS5+z/Uo7yxj+0rdQf6FOxnF9AYDRmSUM7pfklqn99Ul+YWt1untzVW3K5DHP/ZJ8bE7b/Ybt+frcJ8m3unvzPPW3NsbXpydSVS9M8sJh97tVdcMM5zgaleybOdfsfvPvakGGvb+4tjud67uT+NnduRbs+rq2O5fru/OM4NpmIX92x8H1/WGP3tqBB9xqot391iRvXeh57Kqqam13r1roeTwQubY7l+u787i2O5fru/O4tjuX67vzuLY7l+s7u1lWE/1ykgOm9vcfyuatU1WLk+yVySIvW2u7tfKNSfYe+pg71tbGAAAAYAfNEgavTnJgVa2oqj0yWRBmzZw6a5KcOGwfl+SDw7t8a5KsrqolwyqhBya5amt9Dm0+NPSRoc/3bGcMAAAAdtB2HxMd3s97SZL3ZvI1EOd09/VV9doka7t7TZK3JTl/WCDmG5mEuwz1LspksZnNSU7u7ruSZL4+hyH/MMkFVfWnSf5h6DtbG4Md5hHance13blc353Htd25XN+dx7XduVzfnce13blc3xmVm2sAAADjM8tjogAAADzACIMAAAAjJAw+gFXVd+cpO62qvlxV11bVp6vqhIWY2+5m+lpW1a9X1eeq6tHD9fxeVf3EVup2Vb1uav/lVXXa/Tbx3VBV3TX8fF5fVf9YVb9fVQ+qqqOH8mur6rtVdcOwfd5Cz3lXV1WvGq7nJ4dr9pqq+g9z6hxWVZ8Ztm+uqk8N9f++qrb6/URjN/w//hdT+4urakNV/c2w//yqOmuedtPX+O+q6ifvz3nvLqrqmcM1/plhf3lVfX/4Of7HqvpIVR08HDuyqjYNxz5bVX+2sLPfdVXVI6rqXVV1Y1VdU1UfrarfmHMNP1lV79/y59vws7xh6vr+7kKfx65q6s+x66rqkqraeyif/vnd8tnDtd0xU9d3y+fUqvqrYXvd1M/wtVX1S1V1+fB3hn+sqqur6rCFPoddiTA4Tq/v7sOSHJvk/6+qBy/0hHYXVXVUkjckeWp3f3Eo/nqS399Kk9uT/Ouq2vf+mN8DxPe7+7DuPiTJU5I8Nclruvu9Q/lhSdYmee6w/7wFne0urqp+McnTk/xcdx+a5F9msmrz8XOqrk7y7qn9Jw31L0/yx/fDVHdX/yvJP6+qhwz7T8mPfv3S1my5xmuT/NHOmNwDwAlJrhx+3eILw//7P5vkHfnha/fh4feIxyd5elX98v031d1DVVWSv05yRXf/dHc/IZP///cfqnx4uL6HZrL6+8lTzS8cru8vJ3lVVU1/TRj/x5Y/x/55JoseTl/DLT+/Wz53DOWu7ey+P+cantHdvzFcv9/O//kZPqy7PzK0ee7we8abkvynBZv5LkgYHLHu/nyS7yV52ELPZXdQVb+a5L8keXp3f2Hq0DlJjq+qh8/TbHMmK1r5V757oLu/luSFSV4y/AWGHffIJF/v7tuTpLu/3t1XJPlmVf3CVL1n54fD4BYfTbLfzp/mbu2yJE8btk/I/NdxW65I8pj7dEYPAFX10CS/kuSkbH0F8R9P8s25hd39/STXxs/ufJ6c5I7ufsuWgu7+Yne/cbrS8Hvunpn/+m5Msi6T31/Yth36PdS13en8mTaHMDhiVfVzST4//IWbbVuSyb+kPrO7Pzvn2HczCYS/s5W2Zyd5blXttRPn94DV3Tdm8hU0P7G9uszr75IcUJNHm99UVU8cyt+d4S/YVfUvknxj+AeiuY7J5Gefrbsgk+/UXZrk0CQf38H2T0/yqft8Vru/Y5P8j+7+XJKNVfWEofyfDY9/fSHJ7yX587kNq+phmXy38RX322x3H4ck+cQ2jh9RVdcm+VImTxKcM7dCVf1UkqVJPrlTZvgAUVWLkhyVH/5+7i0/v9dW1dnztHFtt+8hcx4Tnfuky7b4M20OYXCcfreqrs/kLyynL/RkdhN3JvlIJv9CPZ83JDmxqvace6C7v53kvCSn7Lzpwfy6+7tJnpDJHdYNSS6squcnuTDJcVX1oPzoI6JJ8qGq+nImj+nu6J2uUenuT+Z/t3c/ITpFYRzHv09ZMGQ32dBMIyk2spBkRthYSY3SNPmXhZQkkoT8WTDkX/41C3/ShIakZqFEKDspUZa8yhQLyQoLPBbnXO+d617zjrwzve7vU9P03ntOnU7v3LnPPc9zLrQSVgXvjKDrw3jTPRk4PFzjEuoiBNrE30mqaJJmNx3YytD3ibWb2XNCqu5dd38/aqNtUGZ2LqmlioeSFLtpwGXgaKr5KjN7QVi5Ou/uX0d7vA1iQvzbfg9MAe6lzqXTRNPpo5rb2mXTRPtr6HPVzCrAbsJDeokUDJbTyViP1QlcjE+z5c9+ENLo5pnZb7U97v4JuMbQuoC0U4RAcmLdRvifMrM24DugFey/5O7f3f2Ru+8DNgOd7v4WqACLCNeC7D/TxUALIdXuwGiOt0ENAMcYWeC8OKl7jdcQiWLa/RLggpm9AXYQrsHZdPEBoCP1+XGsC5oNbNBGEbleAnOTDzEgWQo057TNzm9/rCVcAPRo46NCX2L9WgvhO1t0b5Cmua2vbqCNUGd8Zpi2paJgsMTcfYCwccHasR5LI3D3z4S6oG4zy1shPAFsBMbl9P0I3KB4ZVFymFkz0AucdXcf6/E0IjObaWYzUofmAMnmR9eBk8Brdx/M9nX3b4SVlzUFNbFSdQk44O5K9/w3VgJ97t7i7q1xlaoCZDfVWAi8ynZ29wrQA+ys+0gbzwNgvJltSh1rKmhbNL9PgT6KyyOEX/cNW4DtZvbbvUFBH81tncT7iL3AfIs7FIuCwf9dk5kNpn625bQ5CGyLqWIyjBjULQP2mNnyzLkPwG1CfWGe44B2FR1eUgvwErhPqHnTytTfmwRcsfAqmRfALGB/PHeTsIJSuJrl7u/i+VqebJeWuw+6++mC0+sy1+KpBe2kqotwPU27BeyiWnP1HDhE2D0wTy/QYWat9RpkI4o3xCuARWZWMbMnhNWSJHBuT83vaop3yz4CrM8rj5Aqd39GqP8byau8NLd/lq0Z7Km1Y9xc6jgh20AA08N2ERERERGR8tFqkIiIiIiISAkpGBQRERERESkhBYMiIiIiIiIlpGBQRERERESkhBQMioiIiIiIlJCCQRERERERkRJSMCgiIiIiIlJCPwGEGwf1XJ3a3AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0.2 beta 0.2 sigma0 0.3 interest 0.1 N 50000\n",
            "LR: 0.000794 0.000827\n",
            "KNN: 0.000001 0.000004\n",
            "DT: 0.000000 0.000003\n",
            "SVR: 0.002298 0.002302\n",
            "MLP: 0.000010 0.000009\n",
            "ABR: 0.001056 0.001079\n",
            "GBR: 0.000024 0.000027\n",
            "RFR: 0.000000 0.000001\n",
            "ETR: 0.000000 0.000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}