Cleared outputs, set some init cells master
authorNeil Smith <neil.git@njae.me.uk>
Sat, 24 Feb 2018 22:34:46 +0000 (22:34 +0000)
committerNeil Smith <neil.git@njae.me.uk>
Sat, 24 Feb 2018 22:34:46 +0000 (22:34 +0000)
3dplot.ipynb
section5.1.ipynb
section5.1solutions.ipynb

index 3494384fbae01a4b794311018f9f6c2da88c85f5..6f10a6c8a276f21743feb27d63061a7180d8a66a 100644 (file)
@@ -1,25 +1,29 @@
 {
  "cells": [
   {
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
-      "✔ ggplot2 2.2.1     ✔ purrr   0.2.4\n",
-      "✔ tibble  1.4.2     ✔ dplyr   0.7.4\n",
-      "✔ tidyr   0.8.0     ✔ stringr 1.2.0\n",
-      "✔ readr   1.1.1     ✔ forcats 0.2.0\n",
-      "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
-      "✖ dplyr::filter() masks stats::filter()\n",
-      "✖ dplyr::lag()    masks stats::lag()\n"
-     ]
-    }
-   ],
+   "source": [
+    "# 3d plots of regression surfaces"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "### Initialisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true,
+    "init_cell": true
+   },
+   "outputs": [],
    "source": [
     "library(tidyverse)\n",
     "# library(cowplot)\n",
    "source": [
     "library(tidyverse)\n",
     "# library(cowplot)\n",
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "hidden": true,
+    "init_cell": true
+   },
    "outputs": [],
    "source": [
     "source('plot_extensions.R')"
    "outputs": [],
    "source": [
     "source('plot_extensions.R')"
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "hidden": true,
+    "init_cell": true
+   },
    "outputs": [],
    "source": [
     "library(scatterplot3d)"
    ]
   },
    "outputs": [],
    "source": [
     "library(scatterplot3d)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Show plots"
+   ]
+  },
   {
    "cell_type": "code",
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +76,7 @@
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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cYLll3IgBPQItoNA96VPgsNhnSAuBwKLZW+iAM4nH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\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oWmc5ULUEthRYL/OvuRIlT1OL/EQjgV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\nImii/AWn+cPL2ZUhRgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "s3d <- scatterplot3d(x = rubber$hardness, y = rubber$strength, z = rubber$loss,\n",
     "             xlab = 'Hardness', ylab = \"Strength\", zlab = \"Loss\",\n",
    "source": [
     "s3d <- scatterplot3d(x = rubber$hardness, y = rubber$strength, z = rubber$loss,\n",
     "             xlab = 'Hardness', ylab = \"Strength\", zlab = \"Loss\",\n",
@@ -92,7 +98,7 @@
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead><tr><th scope=col>ventil</th><th scope=col>oxygen</th><th scope=col>oxy2</th></tr></thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>-0.9722499</td><td>-1.607673 </td><td>-1.213737 </td></tr>\n",
-       "\t<tr><td>-1.0549252</td><td>-1.592968 </td><td>-1.210386 </td></tr>\n",
-       "\t<tr><td>-1.0549252</td><td>-1.534147 </td><td>-1.195947 </td></tr>\n",
-       "\t<tr><td>-1.0423986</td><td>-1.531696 </td><td>-1.195309 </td></tr>\n",
-       "\t<tr><td>-1.0398933</td><td>-1.490031 </td><td>-1.184031 </td></tr>\n",
-       "\t<tr><td>-1.0975155</td><td>-1.447549 </td><td>-1.171677 </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "\\begin{tabular}{r|lll}\n",
-       " ventil & oxygen & oxy2\\\\\n",
-       "\\hline\n",
-       "\t -0.9722499 & -1.607673  & -1.213737 \\\\\n",
-       "\t -1.0549252 & -1.592968  & -1.210386 \\\\\n",
-       "\t -1.0549252 & -1.534147  & -1.195947 \\\\\n",
-       "\t -1.0423986 & -1.531696  & -1.195309 \\\\\n",
-       "\t -1.0398933 & -1.490031  & -1.184031 \\\\\n",
-       "\t -1.0975155 & -1.447549  & -1.171677 \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "ventil | oxygen | oxy2 | \n",
-       "|---|---|---|---|---|---|\n",
-       "| -0.9722499 | -1.607673  | -1.213737  | \n",
-       "| -1.0549252 | -1.592968  | -1.210386  | \n",
-       "| -1.0549252 | -1.534147  | -1.195947  | \n",
-       "| -1.0423986 | -1.531696  | -1.195309  | \n",
-       "| -1.0398933 | -1.490031  | -1.184031  | \n",
-       "| -1.0975155 | -1.447549  | -1.171677  | \n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  ventil     oxygen    oxy2     \n",
-       "1 -0.9722499 -1.607673 -1.213737\n",
-       "2 -1.0549252 -1.592968 -1.210386\n",
-       "3 -1.0549252 -1.534147 -1.195947\n",
-       "4 -1.0423986 -1.531696 -1.195309\n",
-       "5 -1.0398933 -1.490031 -1.184031\n",
-       "6 -1.0975155 -1.447549 -1.171677"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "anaerobic.n <- data.frame(scale(anaerobic))\n",
     "head(anaerobic.n)"
    "source": [
     "anaerobic.n <- data.frame(scale(anaerobic))\n",
     "head(anaerobic.n)"
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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NpTacj14J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\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60u1cBwbfbV/dYXqtUeq6juu6wblX/MpvKPkRmdMqk60tCifBQPVX0UKmqG9Os/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",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "s3d <- with(anaerobic.n, scatterplot3d(x = oxygen, y = oxy2, z = ventil,\n",
     "             xlab = 'Oxygen', ylab = \"Oxygen2\", zlab = \"Ventil\",\n",
    "source": [
     "s3d <- with(anaerobic.n, scatterplot3d(x = oxygen, y = oxy2, z = ventil,\n",
     "             xlab = 'Oxygen', ylab = \"Oxygen2\", zlab = \"Ventil\",\n",
index 5a8ef2eb1410334a8b6910d7eb119aa93ba568cb..e24eb064e22c0e837d6d3f08a47ceb6fa581757f 100644 (file)
     "# print(cbind(af,PctExp=afss/sum(afss)*100))"
    ]
   },
     "# print(cbind(af,PctExp=afss/sum(afss)*100))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ggplot(rubber, aes(x=hardness, y=loss)) + \n",
-    "#     geom_point() +\n",
-    "#     stat_smooth(method = \"lm\", col = \"red\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
   {
    "cell_type": "code",
    "execution_count": null,
   {
    "cell_type": "code",
    "execution_count": null,
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "ggplotRegression(fit.s)"
    "outputs": [],
    "source": [
     "ggplotRegression(fit.s)"
index 1858975e99e073e13c51d6de4032e599c4252192..a4bb428b333b277f50ae55b15daa0de33b301bb5 100644 (file)
@@ -38,6 +38,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "hidden": true,
     "init_cell": true
    },
    "outputs": [],
     "init_cell": true
    },
    "outputs": [],