Probability & Distrubution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "1wLN41pgiSG7",
        "outputId": "11d61d96-7338-4f28-9212-d8b61e5569aa"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "# Creating a series of data of in range of 1-50.\n",
        "x = np.linspace(1,50,200)\n",
        " \n",
        "#Creating a Function.\n",
        "def normal_dist(x , mean , sd):\n",
        "    prob_density = (np.pi*sd) * np.exp(-0.5*((x-mean)/sd)**2)\n",
        "    return prob_density\n",
        " \n",
        "#Calculate mean and Standard deviation.\n",
        "mean = np.mean(x)\n",
        "sd = np.std(x)\n",
        " \n",
        "#Apply function to the data.\n",
        "pdf = normal_dist(x,mean,sd)\n",
        " \n",
        "#Plotting the Results\n",
        "plt.plot(x,pdf , color = 'red')\n",
        "plt.xlabel('Data points')\n",
        "plt.ylabel('Probability Density')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Probability Density')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U0PIwgj-JTPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgFRHA_klURK",
        "outputId": "60d995e5-0b23-4e69-c642-567985feccd5"
      },
      "source": [
        "from scipy.stats import norm\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        " \n",
        "# Creating the distribution\n",
        "data = np.arange(1,10,0.01)\n",
        "pdf = norm.pdf(data , loc = 5.3 , scale = 1 )\n",
        " \n",
        "#Probability of height to be under 4.5 ft.\n",
        "prob_1 = norm(loc = 5.3 , scale = 1).cdf(4.5)\n",
        "print(prob_1)\n",
        " \n",
        "#probability that the height of the person will be between 6.5 and 4.5 ft.\n",
        " \n",
        "cdf_upper_limit = norm(loc = 5.3 , scale = 1).cdf(6.5)\n",
        "cdf_lower_limit = norm(loc = 5.3 , scale = 1).cdf(4.5)\n",
        "\n",
        "prob_2 = cdf_upper_limit - cdf_lower_limit\n",
        "print(prob_2)\n",
        "\n",
        "#probability that the height of a person chosen randomly will be above 6.5ft\n",
        "\n",
        "cdf_value = norm(loc = 5.3 , scale = 1).cdf(6.5)\n",
        "prob_3 = 1- cdf_value\n",
        "print(prob_3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.21185539858339675\n",
            "0.673074931194895\n",
            "0.11506967022170822\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4qzQiU403bL",
        "outputId": "9f9b7f3f-fe25-4aec-9042-d9dbdc9d4540"
      },
      "source": [
        "from numpy.random import normal\n",
        "# # define the distribution\n",
        "mu = 28\n",
        "sigma = 8\n",
        "n = 10\n",
        "# generate the sample\n",
        "sample = normal(mu, sigma, n)\n",
        "print(sample)\n",
        "\n",
        "#1\n",
        "from numpy import random\n",
        "\n",
        "x = random.normal(size=(2, 3))\n",
        "\n",
        "print(x)\n",
        "\n",
        "#2\n",
        "from numpy import random\n",
        "\n",
        "y = random.normal(loc=1, scale=2, size=(2, 3))\n",
        "\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[29.03856849 43.73947981 40.07498145 36.11885153 26.78547026 36.37613098\n",
            " 37.42252648 32.53439516 34.72029121 27.39910154]\n",
            "[[-0.39087523  0.35040926 -0.48732695]\n",
            " [ 0.41657058 -0.35838606 -0.24900753]]\n",
            "[[0.6798448  0.75871431 2.03433312]\n",
            " [0.32170151 1.95732433 1.47566673]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMjEiXRWARoo"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uS4BQD4ARw5",
        "outputId": "6fe120dc-83cd-4bda-d362-fdd4974b1f5a"
      },
      "source": [
        "from numpy.random import normal\n",
        "#define the distribution\n",
        "mu=50\n",
        "sigma=5\n",
        "n=10\n",
        "#generate the sample\n",
        "sample=normal(mu, sigma, n)\n",
        "print(sample)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[50.09733777 59.66483929 57.00849659 52.76659486 51.31288725 52.05921876\n",
            " 37.08672571 52.32904627 57.81364617 53.79531045]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "DWMCZpdWAdga",
        "outputId": "5c788a3b-d6b2-4b2a-be45-abd6857d73ba"
      },
      "source": [],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YXqZmUZAgws"
      },
      "source": [
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#generate 2nd classification dataset\n",
        "X, Y= make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)\n",
        "\n",
        "#define the model\n",
        "8\n",
        "9\n",
        "model  = GaussianNB()\n",
        "\n",
        "#fit the model\n",
        "model.fit(X,Y)\n",
        "\n",
        "#select a single sample\n",
        "Xsample, Ysample=[X[0]], Y[0]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "CxIb67TSFwK2",
        "outputId": "813db064-de3d-40c9-9a26-c0ab9c13c892"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "#\n",
        "# Create a standard normal distribution with mean as 0 and standard deviation as 1\n",
        "#\n",
        "mu = 0\n",
        "std = 1\n",
        "snd = stats.norm(mu, std)\n",
        "#\n",
        "# Generate 100 random values between -5, 5\n",
        "#\n",
        "x = np.linspace(-5, 5, 100)\n",
        "#\n",
        "# Plot the standard normal distribution for different values of random variable\n",
        "# falling in the range -5, 5\n",
        "#\n",
        "plt.figure(figsize=(7.5,7.5))\n",
        "plt.plot(x, snd.pdf(x))\n",
        "plt.xlim(-5, 5)\n",
        "plt.title('Normal Distribution', fontsize='15')\n",
        "plt.xlabel('Values of Random Variable X', fontsize='15')\n",
        "plt.ylabel('Probability', fontsize='15')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "glx2tp6KE-8t",
        "outputId": "34f6b189-7bf8-42db-dcda-cc89eaf0b55c"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "#\n",
        "# Create a standard normal distribution with mean as 0 and standard deviation as 1\n",
        "#\n",
        "mu = 50\n",
        "std = 3\n",
        "snd = stats.norm(mu, std)\n",
        "#\n",
        "# Generate 100 random values between -5, 5\n",
        "#\n",
        "x = np.linspace(-5, 5, 100)\n",
        "#\n",
        "# Plot the standard normal distribution for different values of random variable\n",
        "# falling in the range -5, 5\n",
        "#\n",
        "plt.figure(figsize=(7.5,7.5))\n",
        "plt.plot(x, snd.pdf(x))\n",
        "plt.xlim(-5, 5)\n",
        "plt.title('Normal Distribution', fontsize='15')\n",
        "plt.xlabel('Values of Random Variable X', fontsize='15')\n",
        "plt.ylabel('Probability', fontsize='15')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJp75oocJQ1N"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf8GA1v0JQ8Q"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7OXy0VqlJRGg",
        "outputId": "d322bb31-bb65-4957-fe79-feaddfbf57c3"
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "def normal(x, n):\n",
        "    u = x.mean()\n",
        "    s = x.std()\n",
        "\n",
        "    # normalization\n",
        "    x = (x - u) / s\n",
        "\n",
        "    # divide [x.min(), x.max()] by n\n",
        "    x = np.linspace(x.min(), x.max(), n)\n",
        "\n",
        "    a = ((x - 0) ** 2) / (2 * (1 ** 2))\n",
        "    y = 1 / (s * np.sqrt(2 * np.pi)) * np.exp(-a)\n",
        "\n",
        "    return x, y, x.mean(), x.std()\n",
        "\n",
        "x = np.arange(-100, 100) # define range of x\n",
        "x, y, u, s = normal(x, 10000)\n",
        "\n",
        "plt.plot(x, y, label=r'$\\mu=%.2f,\\ \\sigma=%.2f$' % (u, s))\n",
        "plt.legend()\n",
        "plt.savefig('graph/normal.png')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-4e077cafec4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$\\mu=%.2f,\\ \\sigma=%.2f$'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'graph/normal.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 723\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    724\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2201\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    533\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m                 _png.write_png(renderer._renderer, fh, self.figure.dpi,\n\u001b[1;32m    537\u001b[0m                                metadata={**default_metadata, **metadata})\n",
            "\u001b[0;32m/usr/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mopen_file_cm\u001b[0;34m(path_or_file, mode, encoding)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;34mr\"\"\"Pass through file objects and context-manage `.PathLike`\\s.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m     \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    401\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'graph/normal.png'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70fVi7pJ1rI1"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}