PolyfitResiduals.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gseetha04/Transfer-learning/blob/main/PolyfitResiduals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "-u0mVnna-lN0",
        "outputId": "b8619bb0-343b-4c0c-a20f-4ff86cda97a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0,4.95,13.1,0,4.95,8.15,0,8.15,13.1]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38] # 10, not 9, so\n",
        "#the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 15)\n",
        "plt.ylim(0, 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p, res, _, _, _ = np.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "id": "-2zQOgZ6_Aou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2l-Um9u_vBD",
        "outputId": "a717840f-55f8-42c2-bc11-e65cc3fbdabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.52912651]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BLz-7TwxRIq",
        "outputId": "e0fd5ef6-035c-4ba5-d224-9b4e9c6d89c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.03987017  0.78268854]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#KL-divergence"
      ],
      "metadata": {
        "id": "GNM2YG3MBz2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38]  # 10, not 9, so the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "HwuiZGG6B-ta",
        "outputId": "69d37347-6108-4ed9-ccf0-869a36b51633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p, res, _, _, _ = np.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "id": "IiTIphN-CJ8K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clHTWU3LCOqa",
        "outputId": "d21bf38a-a054-485d-c8b3-762386c3e0e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.07462152]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEs7cviSxMfx",
        "outputId": "91e5e355-729f-4ee7-f989-e2dabdc1f31e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.81916225  0.87093901]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OZvcdN7SlKcG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0, 2, 4, 6, 14]\n",
        "y = [0.62,0.69,0.67,0.70, 0.69] # 10, not 9, so\n",
        "#the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 15)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "D3n5z_sHlKgd",
        "outputId": "3ed51dc6-925f-4c08-951e-1429c0383ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FCxe0detWq/b73/++3n77bVVVVammpqbR69bW1iokJESS9OKLLzY5qe/pp5+2wshf//pX/elPf/JZW1VVZd3v2rWrHA6HzwmL9c8pSaNGjVLHjh291oWHh3uEhlmzZmn69OkKDw+X3W5vcgeemZnpsQ2bEhcXp7i4uGbVAoEmUPcZXIE1QLT1EQFf587X75/8PXe+fsLihQsXFBUVZS0vLCzU6dOnvQYBu91uXZtAqjuMsG/fPq8jBx06dNCePXus2r//+7/X+++/77WXiIgIVVZWWo9Hjx6tzZs3++z94sWL1l/v48eP1xtvvNGoJiwsTBEREfr888/VsWNHSVJ2dra2b9/uMwg89dRTVu3u3bt1/Phxn/MUunXr1uiURwCtR2vZZzR3/00YQaty8eLFRkHg3Llq3XdfiMrKhjSo/H+STkuqklSljh2r9Mgj1aqurlJMTIyys7OtynvvvVeHDh3yCAz18wL69u2r/fv3W7WDBw/2CBENxcbGelyYKC0tzefpjpGRkSovL7ceZ2Rk+AwC7du3186dO62//FevXq19+/b5DAL333+/FQSOHTum6urqRhMWG541AQCmEEZwXbjdbh0+fNjnXAKHw6G7777bqn/yySdVUVHh9fDEkCFDPC545HA4VFZW5uOVR0j6uMHjBEnFXiv79++vv/zlL9bjAQMG6LPPPvNaGx8fr+PHj1uPx40bp4MHD3oNAtHR0VqxYoVV+5//+Z86efKk18DQvn17j+sp1I9mMFcAQFvCd9O0AQ2/zKn+MILD4ZBUN1fgD3/4g88zHAYPHmx9h0JNTY0yMzN9Xl3xnnvu8TgU0LdvX/nKsKNGjfIII7/85S919uxZr7X1cxnqtWvn+etYv4N3u8P11VfdL1v7XtWNjERYt7FjwzViRIRiYmI8Kl999VVdvHjR5+GJht58802vvXrzz//8z82uvfy9AQAu4RPyOnC5XDp79qzP6yMkJCRowIABkqSvv/5aq1ev9nklxu9973vWdQ7KysqUlJRk/fzyCYuPPvqo9dXR58+f15gxY3z2+MADD1hhJCQkRNu2bfNZ2/CbJG02m2JiYuRyubyOCAwbNsxj3dmzZ1u1l482dO/uGTAKCgoUGhqq8PBwj2sj7Nwp3Xnn5V290qjPxx/3flXB1NRUn+8NAGBemwkjbrdb58+fV1BQkOx2u6S6Mxz27NnjMwikpaVZO7KjR49q/vz5Xuuqq6v1r//6r/rpT38qSdq/f78VNrzJysrS8uXLrR6auj5CbGysdT8kJERffPGF17rLL2wUERGhb33rWz6vrtjwuyOCgoL0+uuvKywszOvhiYYTPCX57MGbpt7b5S4f0agXyOfOAwCuLKDCSG5ursehifog8Mgjj+gf/uEfJNVdS2Hq1KleA4Mk/eIXv7C+zXHPnj0elzy+3NNPP22FkXPnzmndunU+axvOdaj/QqegoCCv1zxoGDA6deqkxx57zGtdeHi4+vfvb9VGRkaqsLCwUWDwNmGxXbt2ys/Pb85mlSQ9/PDDza5taYF87jwA4MoCKozMnj3b6/KhQ4daYaS2tlZFRUU+n6Ph9RGioqLUu3dvn0Gg4QTEuLg4LV++3GtdRESEx5UYExISdP78+St+mZNUFzBeffXV5rx9BQcHKykpqVm1N5pAPXceAHBlAXU2zdixYxUZGdkoEKSnp1vfz/D1119r165dXgND/Y3rIwSu1nLuPADgyji1FwAAGNXc/TdXRgIAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUQEVRt5/v+46EwAA4MYRUGHk3nulxERp40bTnQAAgGsloMKIVPdlaePGEUgAALhRBFwYqb9e7MyZHLIBAOBGEHBhRKoLJMXFdXNIAABAYAvIMFLvyy9NdwAAAL6pgA4jPXqY7gAAAHxT7Uw3cDVsNqlnz7qvjwcAAIEt4EZGbLa6f3NypOBgo60AAIBr4KrCyKpVq5SYmKiwsDClpKRo165dTdafOXNG06ZNU48ePWS32/V3f/d32rx581U13LOn9Oab0tixV7U6AABoZfw+TLNhwwZlZWUpNzdXKSkpysnJUUZGhg4cOKDu3bs3qq+trdV3v/tdde/eXW+++abi4uJ07NgxderUye9m33lHuvtuRkQAALiR2Nzu+it3NE9KSoqGDx+ulStXSpJcLpfi4+P1+OOPa86cOY3qc3Nz9cILL2j//v0KCQm5qiYrKioUFRWl8vJyRUZGXtVzAACAltXc/bdfh2lqa2tVUFCg9PT0S08QFKT09HTl5+d7Xeftt99Wamqqpk2bJofDoYEDB2rp0qVyNnHFspqaGlVUVHjcAADAjcmvMHLq1Ck5nU45HA6P5Q6HQyUlJV7XOXLkiN588005nU5t3rxZCxYs0PLly/XMM8/4fJ3s7GxFRUVZt/j4eH/aBAAAAeS6n03jcrnUvXt3rVmzRsOGDdP48eM1b9485ebm+lxn7ty5Ki8vt27FxcXXu00AAGCIXxNYo6OjFRwcrNLSUo/lpaWliomJ8bpOjx49FBISouAGs05vvfVWlZSUqLa2VqGhoY3Wsdvtstvt/rQGAAAClF8jI6GhoRo2bJjy8vKsZS6XS3l5eUpNTfW6Tlpamg4fPiyXy2UtO3jwoHr06OE1iAAAgLbF78M0WVlZWrt2rf793/9d+/bt049//GNVVlZqypQpkqRJkyZp7ty5Vv2Pf/xjnT59WjNmzNDBgwf17rvvaunSpZo2bdq1excAACBg+X2dkfHjx+vkyZNauHChSkpKNHToUG3ZssWa1Hr8+HEFBV3KOPHx8dq6datmzZqlwYMHKy4uTjNmzNDs2bOv3bsAAAABy+/rjJjAdUYAAAg81+U6IwAAANcaYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEZdVRhZtWqVEhMTFRYWppSUFO3atatZ661fv142m01jxoy5mpcFAAA3IL/DyIYNG5SVlaVFixapsLBQQ4YMUUZGhsrKyppc7/PPP9dPf/pTjRw58qqbBQAANx6/w8iKFSv0wx/+UFOmTFH//v2Vm5uriIgI/epXv/K5jtPp1IQJE7R48WLdfPPNV3yNmpoaVVRUeNwAAMCNya8wUltbq4KCAqWnp196gqAgpaenKz8/3+d6Tz/9tLp3767HHnusWa+TnZ2tqKgo6xYfH+9PmwAAIID4FUZOnTolp9Mph8PhsdzhcKikpMTrOh988IFee+01rV27ttmvM3fuXJWXl1u34uJif9oEAAABpN31fPKzZ89q4sSJWrt2raKjo5u9nt1ul91uv46dAQCA1sKvMBIdHa3g4GCVlpZ6LC8tLVVMTEyj+r/+9a/6/PPPlZmZaS1zuVx1L9yunQ4cOKDevXtfTd8AAOAG4ddhmtDQUA0bNkx5eXnWMpfLpby8PKWmpjaq79evn/bs2aOioiLrdt999+nOO+9UUVERc0EAAID/h2mysrI0efJkJScna8SIEcrJyVFlZaWmTJkiSZo0aZLi4uKUnZ2tsLAwDRw40GP9Tp06SVKj5QAAoG3yO4yMHz9eJ0+e1MKFC1VSUqKhQ4dqy5Yt1qTW48ePKyiIC7sCAIDmsbndbrfpJq6koqJCUVFRKi8vV2RkpOl2AABAMzR3/80QBgAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAo64qjKxatUqJiYkKCwtTSkqKdu3a5bN27dq1GjlypDp37qzOnTsrPT29yXoAANC2+B1GNmzYoKysLC1atEiFhYUaMmSIMjIyVFZW5rV+586deuihh7Rjxw7l5+crPj5eo0aN0okTJ75x8wAAIPDZ3G63258VUlJSNHz4cK1cuVKS5HK5FB8fr8cff1xz5sy54vpOp1OdO3fWypUrNWnSJK81NTU1qqmpsR5XVFQoPj5e5eXlioyM9KddAABgSEVFhaKioq64//ZrZKS2tlYFBQVKT0+/9ARBQUpPT1d+fn6znqOqqkoXLlxQly5dfNZkZ2crKirKusXHx/vTJgAACCB+hZFTp07J6XTK4XB4LHc4HCopKWnWc8yePVuxsbEegeZyc+fOVXl5uXUrLi72p00AABBA2rXkiz333HNav369du7cqbCwMJ91drtddru9BTsDAACm+BVGoqOjFRwcrNLSUo/lpaWliomJaXLdf/u3f9Nzzz2n7du3a/Dgwf53CgAAbkh+HaYJDQ3VsGHDlJeXZy1zuVzKy8tTamqqz/WWLVumJUuWaMuWLUpOTr76bgEAwA3H78M0WVlZmjx5spKTkzVixAjl5OSosrJSU6ZMkSRNmjRJcXFxys7OliQ9//zzWrhwodatW6fExERrbkmHDh3UoUOHa/hWAABAIPI7jIwfP14nT57UwoULVVJSoqFDh2rLli3WpNbjx48rKOjSgMvq1atVW1urcePGeTzPokWL9POf//ybdQ8AAAKe39cZMaG55ykDAIDW47pcZwQAAOBaI4wAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMCoqwojq1atUmJiosLCwpSSkqJdu3Y1Wf+73/1O/fr1U1hYmAYNGqTNmzdfVbMAAODG43cY2bBhg7KysrRo0SIVFhZqyJAhysjIUFlZmdf6Dz/8UA899JAee+wxffrppxozZozGjBmjvXv3fuPmAQBA4LO53W63PyukpKRo+PDhWrlypSTJ5XIpPj5ejz/+uObMmdOofvz48aqsrNQ777xjLfvWt76loUOHKjc31+tr1NTUqKamxnpcXl6uhIQEFRcXKzIy0p92AQCAIRUVFYqPj9eZM2cUFRXls66dP09aW1urgoICzZ0711oWFBSk9PR05efne10nPz9fWVlZHssyMjL01ltv+Xyd7OxsLV68uNHy+Ph4f9oFAACtwNmzZ69dGDl16pScTqccDofHcofDof3793tdp6SkxGt9SUmJz9eZO3euR4A5c+aMbrrpJh0/frzJN3Ojq0+YbX2EiO1Qh+1Qh+1Qh+1wCduiTmvYDm63W2fPnlVsbGyTdX6FkZZit9tlt9sbLY+KimrTv1j1IiMj2Q5iO9RjO9RhO9RhO1zCtqhjejs0ZxDBrwms0dHRCg4OVmlpqcfy0tJSxcTEeF0nJibGr3oAANC2+BVGQkNDNWzYMOXl5VnLXC6X8vLylJqa6nWd1NRUj3pJ2rZtm896AADQtvh9mCYrK0uTJ09WcnKyRowYoZycHFVWVmrKlCmSpEmTJikuLk7Z2dmSpBkzZujb3/62li9frtGjR2v9+vXavXu31qxZ0+zXtNvtWrRokddDN20J26EO26EO26EO26EO2+EStkWdQNoOfp/aK0krV67UCy+8oJKSEg0dOlQvvfSSUlJSJEnf+c53lJiYqN/85jdW/e9+9zvNnz9fn3/+ufr06aNly5bpnnvuuWZvAgAABK6rCiMAAADXCt9NAwAAjCKMAAAAowgjAADAKMIIAAAwqtWHkVWrVikxMVFhYWFKSUnRrl27TLfUorKzszV8+HB17NhR3bt315gxY3TgwAHTbRn33HPPyWazaebMmaZbMeLEiRN65JFH1LVrV4WHh2vQoEHavXu36bZalNPp1IIFC9SrVy+Fh4erd+/eWrJkiW70Ofl//OMflZmZqdjYWNlstkbf8+V2u7Vw4UL16NFD4eHhSk9P16FDh8w0ex01tR0uXLig2bNna9CgQWrfvr1iY2M1adIkffHFF+Yavk6u9PvQ0NSpU2Wz2ZSTk9Ni/TVXqw4jGzZsUFZWlhYtWqTCwkINGTJEGRkZKisrM91ai3nvvfc0bdo0ffTRR9q2bZsuXLigUaNGqbKy0nRrxnzyySf65S9/qcGDB5tuxYivv/5aaWlpCgkJ0R/+8Ad99tlnWr58uTp37my6tRb1/PPPa/Xq1Vq5cqX27dun559/XsuWLdPLL79surXrqrKyUkOGDNGqVau8/nzZsmV66aWXlJubq48//ljt27dXRkaGzp8/38KdXl9NbYeqqioVFhZqwYIFKiws1MaNG3XgwAHdd999Bjq9vq70+1Bv06ZN+uijj674HTHGuFuxESNGuKdNm2Y9djqd7tjYWHd2drbBrswqKytzS3K/9957plsx4uzZs+4+ffq4t23b5v72t7/tnjFjhumWWtzs2bPdd9xxh+k2jBs9erT70Ucf9Vg2duxY94QJEwx11PIkuTdt2mQ9drlc7piYGPcLL7xgLTtz5ozbbre7f/vb3xrosGVcvh282bVrl1uS+9ixYy3TlAG+tsPf/vY3d1xcnHvv3r3um266yf2LX/yixXu7klY7MlJbW6uCggKlp6dby4KCgpSenq78/HyDnZlVXl4uSerSpYvhTsyYNm2aRo8e7fF70da8/fbbSk5O1v3336/u3bsrKSlJa9euNd1Wi7v99tuVl5engwcPSpL+/Oc/64MPPtD3vvc9w52Zc/ToUZWUlHj8/4iKilJKSkqb/tyU6j47bTabOnXqZLqVFuVyuTRx4kQ9+eSTGjBggOl2fGqV39orSadOnZLT6ZTD4fBY7nA4tH//fkNdmeVyuTRz5kylpaVp4MCBpttpcevXr1dhYaE++eQT060YdeTIEa1evVpZWVn62c9+pk8++URPPPGEQkNDNXnyZNPttZg5c+aooqJC/fr1U3BwsJxOp5599llNmDDBdGvGlJSUSJLXz836n7VF58+f1+zZs/XQQw+1uW/xff7559WuXTs98cQTpltpUqsNI2hs2rRp2rt3rz744APTrbS44uJizZgxQ9u2bVNYWJjpdoxyuVxKTk7W0qVLJUlJSUnau3evcnNz21QYeeONN/T6669r3bp1GjBggIqKijRz5kzFxsa2qe2Apl24cEEPPPCA3G63Vq9ebbqdFlVQUKAXX3xRhYWFstlspttpUqs9TBMdHa3g4GCVlpZ6LC8tLVVMTIyhrsyZPn263nnnHe3YsUM9e/Y03U6LKygoUFlZmW677Ta1a9dO7dq103vvvaeXXnpJ7dq1k9PpNN1ii+nRo4f69+/vsezWW2/V8ePHDXVkxpNPPqk5c+bowQcf1KBBgzRx4kTNmjXL+pLOtqj+s5HPzTr1QeTYsWPatm1bmxsVef/991VWVqaEhATrc/PYsWP6yU9+osTERNPteWi1YSQ0NFTDhg1TXl6etczlcikvL0+pqakGO2tZbrdb06dP16ZNm/Q///M/6tWrl+mWjLjrrru0Z88eFRUVWbfk5GRNmDBBRUVFCg4ONt1ii0lLS2t0evfBgwd10003GerIjKqqKgUFeX6EBQcHy+VyGerIvF69eikmJsbjc7OiokIff/xxm/rclC4FkUOHDmn79u3q2rWr6ZZa3MSJE/W///u/Hp+bsbGxevLJJ7V161bT7Xlo1YdpsrKyNHnyZCUnJ2vEiBHKyclRZWWlpkyZYrq1FjNt2jStW7dOv//979WxY0fruG9UVJTCw8MNd9dyOnbs2GieTPv27dW1a9c2N39m1qxZuv3227V06VI98MAD2rVrl9asWaM1a9aYbq1FZWZm6tlnn1VCQoIGDBigTz/9VCtWrNCjjz5qurXr6ty5czp8+LD1+OjRoyoqKlKXLl2UkJCgmTNn6plnnlGfPn3Uq1cvLViwQLGxsRozZoy5pq+DprZDjx49NG7cOBUWFuqdd96R0+m0Pju7dOmi0NBQU21fc1f6fbg8hIWEhCgmJkZ9+/Zt6VabZvp0nit5+eWX3QkJCe7Q0FD3iBEj3B999JHpllqUJK+3X//616ZbM66tntrrdrvd//Vf/+UeOHCg2263u/v16+des2aN6ZZaXEVFhXvGjBnuhIQEd1hYmPvmm292z5s3z11TU2O6tetqx44dXj8TJk+e7Ha7607vXbBggdvhcLjtdrv7rrvuch84cMBs09dBU9vh6NGjPj87d+zYYbr1a+pKvw+Xa62n9trc7hv8coUAAKBVa7VzRgAAQNtAGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBR/x8RDYNbks5fyQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0, 2, 4, 6, 14]\n",
        "y = [0.62,0.69,0.67,0.70, 0.69] # 10, not 9, so\n",
        "#the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 15)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "id": "qHpPoweG6DTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [1.0, 3.96e-59,1.13e-136, 1.0,0.00010,2.25e-66, 1.0, 1.16e-65, 3.35e-151]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38] # 10, not 9, so\n",
        "#the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0,1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "GjfIabUQcRjA",
        "outputId": "e9f1bf9f-7c69-4099-b62f-881d6aa945bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p, res, _, _, _ = np.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "id": "FV75hyWMgmPx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaMY4tBUgnRQ",
        "outputId": "8d2281cf-92e8-4429-b3af-bbaac14701e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.40025979]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFD8XkNqhANg",
        "outputId": "8cbf234b-d683-4109-a965-c6a6eac80ed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.48917194 0.38749281]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#KL-divergence --- SpanBERT"
      ],
      "metadata": {
        "id": "GWCn1sqQuWp4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [0, 0.942,0.906,0,0,0.279,0.336] # ,0.771,0.286\n",
        "y = [0.94,0.25,0.25,0.96,0.87,0.70,0.37]  # 10, not 9, so the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "DR0P2We8ua3e",
        "outputId": "8f1c55d1-b89b-4743-a5a5-c35d82a42908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3SdaqlPD1EB7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "####KL divergence with medical data#####"
      ],
      "metadata": {
        "id": "G79mSK4N1EFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336,0.32]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38,0.49]  # 10, not 9, so the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "x_3C7gFm1Hay",
        "outputId": "40093b02-c8e7-48a6-a32a-84cb22aea732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly1d_fn"
      ],
      "metadata": {
        "id": "o5U0GLGr4iXU",
        "outputId": "d333d219-c551-4afa-b922-d8c5d61f06fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "poly1d([-0.81284175,  0.85663123])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#p, res, _, _, _ = np.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "id": "_FkixIsS1Hj6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIDYeFCM1Xeh",
        "outputId": "69465fd8-8ff9-4e78-f954-c72c4a983371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.08727707]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336,0.32]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38,0.59]  # 10, not 9, so the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "AiPYfw4uGg1v",
        "outputId": "b21e3a6b-6c39-4715-c721-919bbbdc5c16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###########Spearman correlation #########################################################"
      ],
      "metadata": {
        "id": "z4Prqn_hGg_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "KLd = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336]\n",
        "F1 = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38]\n",
        "\n",
        "res = stats.spearmanr(KLd, F1)\n",
        "\n",
        "print(res.statistic)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkRkU5bAYw9X",
        "outputId": "7cc5e0a3-a510-4ce5-cf36-a9df7a8d9d21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.9831920802501751\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(res.pvalue)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRmilgV0ZOpk",
        "outputId": "2971e9bc-846f-47ce-cf20-5e2e0137d4a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.9939655111787246e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "######Kolgomorov smirnov test#################################################################"
      ],
      "metadata": {
        "id": "CVXnqspgsmDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [1.0, 3.96e-59,1.13e-136, 1.0,0.00010,2.25e-66, 1.0, 1.16e-65, 3.35e-151]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQKWiRjAsmUM",
        "outputId": "dbfe3217-0d40-4cf3-fab0-1bd01bd29d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6272087408492496\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##################Wasserstein distance###########################################################"
      ],
      "metadata": {
        "id": "UD7FX-uEsqZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0,4.95,13.1,0,4.95,8.15,0,8.15,13.1]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzfWhEkKs74Z",
        "outputId": "97286b12-9bff-4ae4-b683-e7a6b912719c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.6268924152476126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ma5vYcwwwnhj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "####################DistilBERT VS KLd results #######################"
      ],
      "metadata": {
        "id": "jdXbdiyNwnwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "KLd = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336]\n",
        "F1 = [0.73, 0.12,0.16, 0.92, 0.31,0.59, 0.78,0.39,0.26]\n",
        "\n",
        "res = stats.spearmanr(KLd, F1)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4CgW235wshT",
        "outputId": "a55fc0fe-859c-4c9d-fbee-83d15ff12ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.9492889050691345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DistilBERT vs wasserstein\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0,4.95,13.1,0,4.95,8.15,0,8.15,13.1]\n",
        "y = [0.73, 0.12,0.16, 0.92, 0.31,0.59, 0.78,0.39,0.26]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHm3cI0uxIZh",
        "outputId": "ab11afaa-59e0-4f57-9b44-0a56952f13cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.6955929539048853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##DistilBER Vs KS test\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [1.0, 3.96e-59,1.13e-136, 1.0,0.00010,2.25e-66, 1.0, 1.16e-65, 3.35e-151]\n",
        "y = [0.73, 0.12,0.16, 0.92, 0.31,0.59, 0.78,0.39,0.26]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO8n4bISxPaA",
        "outputId": "975277ce-49f8-4538-e28b-cd801cecf2e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6950150912113306\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##BERT vs KL div\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "KLd = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336]\n",
        "F1 = [0.79, 0.10, 0.16, 0.92, 0.31, 0.63, 0.29,0.39, 0.27]\n",
        "\n",
        "res = stats.spearmanr(KLd, F1)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCj2vgVQxu62",
        "outputId": "5138c69c-f299-4d54-a7f5-4e2dabe1bcdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.7797730291639321\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#BERT vs wasserstein\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0,4.95,13.1,0,4.95,8.15,0,8.15,13.1]\n",
        "y = [0.79, 0.10, 0.16, 0.92, 0.31, 0.63, 0.29,0.39, 0.27]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-DbnY8LyLr2",
        "outputId": "2012956b-1bd0-4865-ac21-29d453e97caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.49807890526522647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##BERT Vs KS test\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [1.0, 3.96e-59,1.13e-136, 1.0,0.00010,2.25e-66, 1.0, 1.16e-65, 3.35e-151]\n",
        "y = [0.79, 0.10, 0.16, 0.92, 0.31, 0.63, 0.29,0.39, 0.27]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUYdBlyaySYR",
        "outputId": "08da6512-3a9d-4770-a5ac-bd5b3e82d2bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5085476277156078\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "######SpanBERT VS KL div#################\n",
        "##BERT vs KL div\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "KLd = [0, 0.942,0.906,0,0.771,0.286,0,0.279,0.336]\n",
        "F1 = [0.92, 0.20, 0.20, 0.94, 0.32, 0.64, 0.83,0.55, 0.26]\n",
        "\n",
        "res = stats.spearmanr(KLd, F1)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTIXFT3iyalA",
        "outputId": "363f3c2c-56ec-42a7-98b1-85afd5975e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.9447578376368713\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SpanBERT vs wasserstein\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [0,4.95,13.1,0,4.95,8.15,0,8.15,13.1]\n",
        "y = [0.92, 0.20, 0.20, 0.94, 0.32, 0.64, 0.83,0.55, 0.26]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yf1VHB92ywjp",
        "outputId": "51cbb81d-db1a-491a-e6cd-ca117442cda6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.7330037952447698\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##SpanBERT Vs KS test\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x= [1.0, 3.96e-59,1.13e-136, 1.0,0.00010,2.25e-66, 1.0, 1.16e-65, 3.35e-151]\n",
        "y = [0.92, 0.20, 0.20, 0.94, 0.32, 0.64, 0.83,0.55, 0.26]\n",
        "\n",
        "res = stats.spearmanr(x, y)\n",
        "\n",
        "print(res.statistic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oGFRBnNy6aD",
        "outputId": "ee61772c-fdfd-4c37-a819-b644922a6a0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7234632090012078\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FxyKEYFN9FqE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "######MMD VS F1############"
      ],
      "metadata": {
        "id": "oWqSBqfg9F0t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [0, 0.78, 0.80, 0, 0.11, 0.15, 0, 0.15, 0.36]\n",
        "y = [0.86,0.075,0.11,0.94,0.33,0.69,0.83,0.74,0.38]  # 10, not 9, so the fit isn't perfect\n",
        "\n",
        "coef = np.polyfit(x,y,1)\n",
        "poly1d_fn = np.poly1d(coef)\n",
        "# poly1d_fn is now a function which takes in x and returns an estimate for y\n",
        "\n",
        "plt.plot(x,y, 'bo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "dowd1otT9QvG",
        "outputId": "a5bb8cb7-93ef-435d-c108-86189890b748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p, res, _, _, _ = np.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "id": "4_5SfYqFAm3i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6AZu9b5ArHe",
        "outputId": "e4c14a6a-775c-481a-a159-96f6566f7104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.17625334]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EFHGpPWAs-x",
        "outputId": "53842577-2798-4cff-e5b0-82b76ddcfc17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.92479103  0.79202877]\n"
          ]
        }
      ]
    }
  ]
}