Gaussian_Distribution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOkNzaoQoo5p+r0mqGl9z+C",
      "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/LoPA607/WIDS-NetWeaver/blob/main/Gaussian_Distribution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "6pSLowHwxSrX",
        "outputId": "efc9e81a-b9cb-47fa-fd23-c88005b5c0a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from scipy.stats import skewnorm\n",
        "import matplotlib.pyplot as plt\n",
        "numValues=10000\n",
        "maxValue=100\n",
        "skewness=20\n",
        "random=skewnorm.rvs(a=skewness, loc=maxValue, size=numValues)\n",
        "random=random-min(random)\n",
        "random=random/max(random)\n",
        "random=random*maxValue\n",
        "x=random\n",
        "plt.hist(x);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x**0.5, bins=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "O0Mkrci3yfjN",
        "outputId": "7bce9a32-10de-4b4b-c330-7d9fa1212211"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  1.,   0.,   0.,   1.,   3.,   8.,   9.,  28.,  62., 132., 234.,\n",
              "        248., 348., 359., 410., 412., 405., 419., 495., 478., 443., 502.,\n",
              "        467., 480., 400., 465., 409., 422., 357., 319., 270., 238., 235.,\n",
              "        208., 166., 119., 101.,  95.,  62.,  54.,  48.,  39.,  27.,   6.,\n",
              "          2.,   6.,   5.,   2.,   0.,   1.]),\n",
              " array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ,\n",
              "         2.2,  2.4,  2.6,  2.8,  3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,\n",
              "         4.4,  4.6,  4.8,  5. ,  5.2,  5.4,  5.6,  5.8,  6. ,  6.2,  6.4,\n",
              "         6.6,  6.8,  7. ,  7.2,  7.4,  7.6,  7.8,  8. ,  8.2,  8.4,  8.6,\n",
              "         8.8,  9. ,  9.2,  9.4,  9.6,  9.8, 10. ]),\n",
              " <BarContainer object of 50 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "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": [
        "import numpy as np\n",
        "plt.hist(np.log(x+0.001),bins=50, color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "tf-_EXt0ytK_",
        "outputId": "c81fc4a5-96ca-4c78-aaef-2d9eabde861c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 2.000e+00, 1.000e+00,\n",
              "        5.000e+00, 4.000e+00, 9.000e+00, 2.500e+01, 5.900e+01, 1.340e+02,\n",
              "        2.870e+02, 3.880e+02, 5.600e+02, 6.950e+02, 7.980e+02, 1.042e+03,\n",
              "        1.140e+03, 1.222e+03, 1.296e+03, 1.051e+03, 7.650e+02, 3.700e+02,\n",
              "        1.300e+02, 1.500e+01]),\n",
              " array([-6.90775528e+00, -6.67749657e+00, -6.44723786e+00, -6.21697915e+00,\n",
              "        -5.98672044e+00, -5.75646173e+00, -5.52620302e+00, -5.29594431e+00,\n",
              "        -5.06568560e+00, -4.83542690e+00, -4.60516819e+00, -4.37490948e+00,\n",
              "        -4.14465077e+00, -3.91439206e+00, -3.68413335e+00, -3.45387464e+00,\n",
              "        -3.22361593e+00, -2.99335722e+00, -2.76309851e+00, -2.53283980e+00,\n",
              "        -2.30258109e+00, -2.07232238e+00, -1.84206367e+00, -1.61180497e+00,\n",
              "        -1.38154626e+00, -1.15128755e+00, -9.21028837e-01, -6.90770128e-01,\n",
              "        -4.60511419e-01, -2.30252709e-01,  5.99997000e-06,  2.30264709e-01,\n",
              "         4.60523419e-01,  6.90782128e-01,  9.21040837e-01,  1.15129955e+00,\n",
              "         1.38155826e+00,  1.61181697e+00,  1.84207567e+00,  2.07233438e+00,\n",
              "         2.30259309e+00,  2.53285180e+00,  2.76311051e+00,  2.99336922e+00,\n",
              "         3.22362793e+00,  3.45388664e+00,  3.68414535e+00,  3.91440406e+00,\n",
              "         4.14466277e+00,  4.37492148e+00,  4.60518019e+00]),\n",
              " <BarContainer object of 50 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}