IR.ipynb

{
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  "metadata": {
    "colab": {
      "name": "IR.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pai-Chai-University/colab/blob/master/IR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z60PpIfIeF4Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCEtur7fajGn",
        "colab_type": "code",
        "outputId": "b7a3264e-3834-4fa9-9c4b-8ceade72a5b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "cat(system('python3 -c \"from google.colab import drive\\ndrive.mount()\"', intern=TRUE), sep='\\n', wait=TRUE)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning message in system(\"python3 -c \\\"from google.colab import drive\\ndrive.mount()\\\"\", :\n",
            "“running command 'python3 -c \"from google.colab import drive\n",
            "drive.mount()\"' had status 1”\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "TRUE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6aH0TahbghZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?system"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o11KxDzyC0g",
        "colab_type": "code",
        "outputId": "ef5192d6-51f7-4a9c-894d-38715847d89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "install.packages(\"rgl\", repos = \"http://cran.rstudio.com/\")\n",
        "install.packages(\"ConsRank\", repos = \"http://cran.rstudio.com/\")\n",
        "library(\"ConsRank\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "also installing the dependencies ‘httpuv’, ‘xtable’, ‘sourcetools’, ‘later’, ‘promises’, ‘miniUI’, ‘webshot’, ‘htmlwidgets’, ‘shiny’, ‘crosstalk’, ‘manipulateWidget’\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPYGePw53fWq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "system(\"add-apt-repository -y ppa:marutter/rrutter\")\n",
        "system(\"add-apt-repository -y ppa:marutter/c2d4u\")\n",
        "system(\"apt-get update\")\n",
        "system(\"apt install -y r-cran-rstan\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IzjHJFxv01f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "install.packages('ggmap')\n",
        "install.packages('codetools')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdyV67dk2vGx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library(\"ggmap\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIbhtDzdwYdB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "options(repr.plot.width  = 3,\n",
        "        repr.plot.height = 3)\n",
        "Sys.setenv(USE_CXX14 = 1)\n",
        "library(\"rstan\") # observe startup messages\n",
        "options(mc.cores = parallel::detectCores())\n",
        "rstan_options(auto_write = TRUE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLlQFtlPxD5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x <- seq(0, 2*pi, length.out=50)\n",
        "plot(x, sin(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxBXXzeIx6aA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stanmodelcode <- \"\n",
        "data {\n",
        "    int<lower=0> N; \n",
        "    real y[N];\n",
        "}\n",
        "\n",
        "parameters {\n",
        "    real mu; \n",
        "\n",
        "}\n",
        "\n",
        "model {\n",
        "    target += normal_lpdf(mu | 0, 10);\n",
        "    target += normal_lpdf(y | mu, 1);\n",
        "}\"\n",
        "\n",
        "y <- rnorm(20)\n",
        "dat <- list(N = 20, y = y);\n",
        "\n",
        "fit <- stan(model_code = stanmodelcode, model_name = \"example\",\n",
        "            data = dat, iter = 2012, chains = 8, sample_file = 'norm.csv',\n",
        "            verbose = FALSE,\n",
        "            refresh = -1)\n",
        "\n",
        "print(fit)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vmo1YXVcx91I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot(fit)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ocs9zTCx-Sw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y <- as.matrix(read.table('https://raw.github.com/wiki/stan-dev/rstan/rats.txt', header = TRUE))\n",
        "x <- c(8, 15, 22, 29, 36)\n",
        "xbar <- mean(x)\n",
        "N <- nrow(y)\n",
        "T <- ncol(y)\n",
        "rats_fit <- stan(file = 'https://raw.githubusercontent.com/stan-dev/example-models/master/bugs_examples/vol1/rats/rats.stan')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrED9oZSx_ph",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rats_fit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4HysAnzyA_p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot(rats_fit)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypXiaqB6jKRh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## load APA data set, full version\n",
        "data(APAFULL)\n",
        "## Emond and Mason Branch-and-Bound algorithm.\n",
        "## If the number of object is higher than 20, EMCons function may work for several minutes.\n",
        "## Use either QuickCons, DECOR, FASTcons or FASTDECOR instead\n",
        "CR=EMCons(APAFULL)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uaE0kZ0vkqms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TR=tabulaterows(APAFULL)\n",
        "CR2=FASTcons(TR$X,TR$Wk,maxiter=5)\n",
        "CR3=QuickCons(TR$X,TR$Wk)\n",
        "CR4=DECOR(TR$X,TR$Wk)\n",
        "#####################################\n",
        "### load sports data set\n",
        "#data(sports)\n",
        "### FAST algorithm\n",
        "#CR=FASTcons(sports,maxiter=10)\n",
        "#####################################\n",
        "#######################################\n",
        "### load Emond and Mason data set\n",
        "#data(EMD)\n",
        "### matrix X contains rankings\n",
        "#X=EMD[,1:15]\n",
        "### vector Wk contains frequencies\n",
        "#Wk=EMD[,16]\n",
        "### QUICK algorithm\n",
        "#CR=QuickCons(X,Wk)\n",
        "#######################################"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmCDzKEpkxgc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data(BU)\n",
        "polyplot(BU[,1:3],Wk=BU[,4])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IS3VN3qamf20",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}