1 files changed, 0 insertions, 126 deletions
diff --git a/sem1/algo/mm9/Untitled.ipynb b/sem1/algo/mm9/Untitled.ipynb
deleted file mode 100644
index a2bd59e..0000000
--- a/sem1/algo/mm9/Untitled.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Bare lige noget preamble"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "from networkx.drawing.nx_agraph import to_agraph\n",
-    "from nxpd import draw, nxpdParams\n",
-    "from numpy.linalg\n",
-    "nxpdParams['show'] = 'ipynb'\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = nx.DiGraph()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G.add_edge(1, 2)\n",
-    "G.add_edge(1, 3)\n",
-    "G.add_node(1)\n",
-    "G.add_edge(1, 4)\n",
-    "G.add_edge(1, 4)\n",
-    "G.add_edge(4, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "draw(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Okay det ser ud til at virke fint. Nu kan vi printe adjecency matrix."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0 1 1 1]\n",
-      " [0 0 0 0]\n",
-      " [0 0 0 0]\n",
-      " [0 0 0 1]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = nx.adjacency_matrix(G)\n",
-    "print(A.todense())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}