From 9bcdb4fcbccdf5649d441acfe4942c6471d0f309 Mon Sep 17 00:00:00 2001
From: Nils Liebreich <nilsl99@zedat.fu-berlin.de>
Date: Wed, 2 Nov 2022 14:21:47 +0100
Subject: [PATCH] Added a jupyter notebook for timing

---
 Aufgabe02Timing.ipynb | 111 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 111 insertions(+)
 create mode 100644 Aufgabe02Timing.ipynb

diff --git a/Aufgabe02Timing.ipynb b/Aufgabe02Timing.ipynb
new file mode 100644
index 0000000..68482b3
--- /dev/null
+++ b/Aufgabe02Timing.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Aufgabe02 import HeapSort, InsertionSort, CountingSort\n",
+    "from time import perf_counter_ns as time\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Counting Sort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_num = 99\n",
+    "num_list = range(max_num + 1)\n",
+    "\n",
+    "n_list = [100*i+10 for i in range(100)]\n",
+    "t_list = [0]*len(n_list)\n",
+    "for i, n in enumerate(n_list):\n",
+    "    for j in range(10):\n",
+    "        array = random.choices(num_list, k=n)\n",
+    "\n",
+    "        t0 = time()\n",
+    "        sorted_array = CountingSort(array, k=max_num)\n",
+    "        t1 = time()\n",
+    "\n",
+    "        t_list[i] += t1 - t0\n",
+    "\n",
+    "t_list_ms = [t/1e+6 for t in t_list]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "ax.grid(\"both\")\n",
+    "\n",
+    "ax.plot(n_list, t_list_ms)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "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": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab