owstron · March 4, 2019 15:17
diff --git a/cs166_8-1.ipynb b/cs166_8-1.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CS166_8-1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/nik1997/1ef1da14914de160490727736f1de78c/cs166_8-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "_zm6u_fjKBcF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EyZxUD6AKoDP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def calculate_q(k):\n",
        "    '''\n",
        "    Use a numerical root finder to determine q from the equation\n",
        "    q = exp(k*(q*1)).\n",
        "    '''\n",
        "    from scipy.optimize import root\n",
        "    return root(lambda q: q - np.exp(k * (q - 1)), 0).x[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TcazuYhdKlTz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Use the code below to compute q for different values of ⟨k⟩ in the range [1, 10]. Note that ⟨k⟩ will not necessarily be\n",
        "an integer since it is an average degree."
      ]
    },
    {
      "metadata": {
        "id": "-M2dSrJzKeuQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ks = np.linspace(1, 10, 100)\n",
        "\n",
        "qs = [calculate_q(k) for k in ks]\n",
        "\n",
        "num_nodes = 100\n",
        "LCC = (1 - np.array(qs))*num_nodes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z-fOGTYpLekI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "6addc3b9-e859-46cb-b02e-d39eb7152e33"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(ks, LCC)\n",
        "plt.xlabel('Ks')\n",
        "plt.ylabel('Probability of being in LCC')\n",
        "plt.title('Size of LCC vs <k> with network size 100')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Probability of being in LCC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
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SbrzxRqlK0bTWThd27K5BQkwEbpuWpXQ5RESkAoJ49sVoFQvXUz5//vgQvjpwCsvmXYfc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\niDTm/wMJYbIvgUEvfAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
 }
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "CS166_8-1.ipynb",
	"version": "0.3.2",
	"provenance": [],
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/nik1997/1ef1da14914de160490727736f1de78c/cs166_8-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"metadata": {
	"id": "_zm6u_fjKBcF",
	"colab_type": "code",
	"colab": {}
	},
	"cell_type": "code",
	"source": [
	"import matplotlib.pyplot as plt\n",
	"import numpy as np"
	],
	"execution_count": 0,
	"outputs": []
	},
	{
	"metadata": {
	"id": "EyZxUD6AKoDP",
	"colab_type": "code",
	"colab": {}
	},
	"cell_type": "code",
	"source": [
	"def calculate_q(k):\n",
	" '''\n",
	" Use a numerical root finder to determine q from the equation\n",
	" q = exp(k(q1)).\n",
	" '''\n",
	" from scipy.optimize import root\n",
	" return root(lambda q: q - np.exp(k * (q - 1)), 0).x[0]"
	],
	"execution_count": 0,
	"outputs": []
	},
	{
	"metadata": {
	"id": "TcazuYhdKlTz",
	"colab_type": "text"
	},
	"cell_type": "markdown",
	"source": [
	"Use the code below to compute q for different values of ⟨k⟩ in the range [1, 10]. Note that ⟨k⟩ will not necessarily be\n",
	"an integer since it is an average degree."
	]
	},
	{
	"metadata": {
	"id": "-M2dSrJzKeuQ",
	"colab_type": "code",
	"colab": {}
	},
	"cell_type": "code",
	"source": [
	"ks = np.linspace(1, 10, 100)\n",
	"\n",
	"qs = [calculate_q(k) for k in ks]\n",
	"\n",
	"num_nodes = 100\n",
	"LCC = (1 - np.array(qs))*num_nodes"
	],
	"execution_count": 0,
	"outputs": []
	},
	{
	"metadata": {
	"id": "Z-fOGTYpLekI",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 378
	},
	"outputId": "6addc3b9-e859-46cb-b02e-d39eb7152e33"
	},
	"cell_type": "code",
	"source": [
	"plt.plot(ks, LCC)\n",
	"plt.xlabel('Ks')\n",
	"plt.ylabel('Probability of being in LCC')\n",
	"plt.title('Size of LCC vs <k> with network size 100')"
	],
	"execution_count": 43,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"Text(0, 0.5, 'Probability of being in LCC')"
	]
	},
	"metadata": {
	"tags": []
	},
	"execution_count": 43
	},
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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7N1xuLwerERGRoi65+3j69GnU1NTA4XAgNjYWTzzxBLZs2SJlbarTP1gt08Lr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\niDTm/wMJYbIvgUEvfAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 576x396 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": []
	}
	}
	]
	}
	]
	}
No results found