440 lines
127 KiB
Plaintext
440 lines
127 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5fbb6606-ab9a-4481-bfb5-72a958b0d72c",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "slide"
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},
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"tags": []
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},
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"source": [
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"# Lösungen SciPy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8455dadf-ad76-4c6e-b6c4-e72764d41e86",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "skip"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from scipy import stats\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78aed4e3-89b5-4baa-995a-2235e17ddd8b",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "slide"
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},
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"tags": []
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},
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"source": [
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"### Aufgabe\n",
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"\n",
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"*7 Punkte*\n",
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"\n",
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"Bestimme mittels Linearer Regression die *best fit* Funktion für die beiden gegebenen Datensets `x_data` & `y_data`, unter beachtung folgender Punkte:\n",
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"\n",
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"- Plotte das Ergebnis angemessen\n",
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"- Nutze SciPys `linregress` Funktion, speichere den Output vor dem entpacken in der Variablen `l`\n",
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"- Definiere die Funktion `reg_line` mit einem Eingabeparameter\n",
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"- Bestimme die Werte für `-0.3` & `3.4` speichere diese als liste in variablen `future`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a5dadce5-e415-4b39-80a5-0ad3cb5e6352",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "fragment"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"random = np.random.default_rng(420)\n",
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"\n",
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"# 2 scuffed up One-Liners :)\n",
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"x_data: np.array = np.sort(np.round(random.random(40)*np.pi, decimals=2))\n",
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"y_data: np.array = np.flip(np.sort(np.round(random.random(40)*np.sqrt(2), decimals=2)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "7b2b2652-1771-4434-ac6e-bf7d80ba15cf",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "subslide"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"l = stats.linregress(x_data,y_data)\n",
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"slope, intercept, _, _, stderr = l\n",
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"\n",
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"def reg_line(x: float) -> float:\n",
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" return float(np.round(slope*x+intercept,decimals=2))\n",
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"\n",
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"ext: tuple = (-0.3, 3.4)\n",
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"rl: np.array = np.vectorize(reg_line)(ext)\n",
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"\n",
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"future: list = [reg_line(ext[0]), reg_line(ext[1])]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "bf16eea1-0524-46e4-84d5-b5b87308941e",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "skip"
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},
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0, 0.5, 'Y')"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.title(\"Scattered Random Values\")\n",
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"plt.grid()\n",
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"plt.xlabel(\"X\")\n",
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"plt.ylabel(\"Y\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "0a27e646-5b06-4f83-abf4-7925350a0ea7",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": "subslide"
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},
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.scatter(x_data,y_data, color='g', label=\"Original Data\")\n",
|
||
|
|
"plt.plot(ext, rl, color='m', label=\"Fitted Line\")\n",
|
||
|
|
"plt.scatter(ext, future, color='r', label=\"Predicted Data\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "9ca0bc7d-6fcb-475f-bdfc-c0b63579cc93",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Aufgabe \n",
|
||
|
|
"\n",
|
||
|
|
"*6 Punkte*\n",
|
||
|
|
"\n",
|
||
|
|
"Gegeben sind die nach Altersgruppe aufgeschlüsselten Durschnittskörpergrößen (in cm) von Frauen in Deutschland. (Zu finden beim [Statistischen Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html))\n",
|
||
|
|
"\n",
|
||
|
|
"Gehe wie folgt vor:\n",
|
||
|
|
"\n",
|
||
|
|
"- Berechne das arithmetische Mittel nutze dafür NumPy. und speichere das Ergebnis mit einer Genauigkeit von 1 Dezimalstelle nach dem Komma in der Variablen `avg_height`.\n",
|
||
|
|
"- Gegeben ist auch die Standardabweichung von 15cm, stelle die Normalverteilung mittels `norm.pdf` auf. Speichere den Wert in `norm_height` und finde einen geeigneten linespace zum plotten.\n",
|
||
|
|
"- Berechne folgend die Körpergröße unter die 80% aller Frauen (nach Datenset) fallen. Speichere den Wert in der Variablen `avg_percentile`.\n",
|
||
|
|
"- Plotte das Ergebnis. Orientiere dich gerne an dem Bienenbeispiel. Finde eine geeignete Darstellung. *Tipp: Da die Y-Achse in diesem Beispiel keinen Sinn ergibt kannst du sie einfach austellen mit `plt.yticks([])`*"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"id": "2b3711d0-aed5-4be2-a6a5-18df1767a97f",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Given\n",
|
||
|
|
"avg_height_per_woman = {\n",
|
||
|
|
" \"18 - 20\": 167.6,\n",
|
||
|
|
" \"20 - 25\": 167.7,\n",
|
||
|
|
" \"25 - 30\": 167.3,\n",
|
||
|
|
" \"30 - 35\": 167.2,\n",
|
||
|
|
" \"35 - 40\": 167.3,\n",
|
||
|
|
" \"40 - 45\": 167.5,\n",
|
||
|
|
" \"45 - 50\": 167.1,\n",
|
||
|
|
" \"50 - 55\": 167.1,\n",
|
||
|
|
" \"55 - 60\": 166.9,\n",
|
||
|
|
" \"60 - 65\": 165.4,\n",
|
||
|
|
" \"65 - 70\": 164.5,\n",
|
||
|
|
" \"70 - 75\": 163.9,\n",
|
||
|
|
" \"75+\": 162.8\n",
|
||
|
|
"}\n",
|
||
|
|
"\n",
|
||
|
|
"avg_height = None\n",
|
||
|
|
"norm_height = None\n",
|
||
|
|
"avg_percentile = None"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "9e931834-455b-4596-b1d6-4166a1e06852",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"avg_height = np.round(\n",
|
||
|
|
" np.mean(list(avg_height_per_woman.values()))\n",
|
||
|
|
" , decimals=1)\n",
|
||
|
|
"std_sigma = 15\n",
|
||
|
|
"\n",
|
||
|
|
"norm_x = np.linspace(120, 220, 1000)\n",
|
||
|
|
"norm_height = stats.norm(avg_height, std_sigma).pdf(norm_x)\n",
|
||
|
|
"\n",
|
||
|
|
"# Height of 80th percentile of woman heights\n",
|
||
|
|
"avg_percentile = stats.norm(avg_height, std_sigma).ppf(0.8)\n",
|
||
|
|
"\n",
|
||
|
|
"x_percentile = np.arange(norm_x[0], avg_percentile, 0.01)\n",
|
||
|
|
"y_percentile = stats.norm(avg_height, std_sigma).pdf(x_percentile)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"id": "e36af98b-3fa7-4c3b-9418-cb2792da5235",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 900x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(figsize=(9,3))\n",
|
||
|
|
"ax.plot(norm_x, norm_height, color='r')\n",
|
||
|
|
"ax.fill_between(x_percentile, y_percentile, 0, alpha=.5, color='#fa0000')\n",
|
||
|
|
"ax.text(0.4,0.18,\n",
|
||
|
|
"f\"80th percentile of Womens heigth\\n fall under {avg_percentile:.1f}cm\",\n",
|
||
|
|
"ha='center', va='center', transform=ax.transAxes,\n",
|
||
|
|
"bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n",
|
||
|
|
"color='#de2e0b'\n",
|
||
|
|
")\n",
|
||
|
|
"plt.title(\"Woman Height Normal Distribution\")\n",
|
||
|
|
"plt.xlabel(\"Height\")\n",
|
||
|
|
"plt.yticks([]) # hide y\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "7335367c-46e3-4396-bc41-d9d2e6621194",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "slide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Aufgabe\n",
|
||
|
|
"\n",
|
||
|
|
"*7 Punkte*\n",
|
||
|
|
"\n",
|
||
|
|
"Gegeben sind zwei Würfel aus dem Spiel **Super Mario Party**. Es wird im folgenden angenommen das die Charaktere ihre Würfel würfeln und deren Augenzahl addiert wird.\n",
|
||
|
|
"\n",
|
||
|
|
"- Stelle alle Kombinationen der Gegebenen Daten auf, speichere diese als Dictionary in der Variablen `dist_mp`.\n",
|
||
|
|
"- Exthrahiere den am dritt häufigsten Aufkommenden Wert und speicher diesen in der Variablen `best3` als tuple in der Form `(<Augenzahl>, <Auftritte>)`.\n",
|
||
|
|
"- Plotte eine geeignete binomiale Verteilung. *Tipp: schaue dir an wie du den Plot mittels matplotlib vergrößerst*"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "27720a28-4a96-4567-b2f0-52961bdd387f",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Einige der Würfel gebem dem Charakter Münzen. Finde eine logische Schlussfolgerung diese mit anderen Werten zu ersetzen. Überlege dabei welche Auswirkungen es auf die Augenzahl hat das der Charakter Münzen bekomment.\n",
|
||
|
|
"\n",
|
||
|
|
"|Charakter|Dice|\n",
|
||
|
|
"|-|-|\n",
|
||
|
|
"|Mario|1, 3, 3, 3, 5, 6|\n",
|
||
|
|
"|Peach|0, 2, 4, 4, 4, 6|\n",
|
||
|
|
"|Boo|-2 Coins, -2 Coins, 5, 5, 7, 7|\n",
|
||
|
|
"|Donkey Kong|+5 Coins, 0, 0, 0, 10, 10|"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "e6b079fa-95b0-4a6c-90d2-fc39b8a045b7",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# define dices\n",
|
||
|
|
"mario = [1, 3, 3, 3, 5, 6]\n",
|
||
|
|
"peach = [0, 2, 4, 4, 4, 6]\n",
|
||
|
|
"boo = [0, 0, 5, 5, 7, 7]\n",
|
||
|
|
"dk = [0, 0, 0, 0, 10, 10]\n",
|
||
|
|
"\n",
|
||
|
|
"# Create Keys\n",
|
||
|
|
"keys: set = {\n",
|
||
|
|
" m+p+b+d\n",
|
||
|
|
" for m in mario for p in peach\n",
|
||
|
|
" for b in boo for d in dk\n",
|
||
|
|
"}\n",
|
||
|
|
"\n",
|
||
|
|
"# Creating a dict with all keys\n",
|
||
|
|
"dist_mp: dict = {k: 0 for k in keys}\n",
|
||
|
|
"\n",
|
||
|
|
"# Summing all possible combinations and store them inside the dict\n",
|
||
|
|
"for m in mario:\n",
|
||
|
|
" for p in peach:\n",
|
||
|
|
" for b in boo:\n",
|
||
|
|
" for d in dk:\n",
|
||
|
|
" dice_roll: int = m+p+b+d\n",
|
||
|
|
" dist_mp[dice_roll] += 1\n",
|
||
|
|
"\n",
|
||
|
|
"best3: tuple = (10, dist_mp[10])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "ecbad816-1cf1-4cb8-ad95-e4da62fd6164",
|
||
|
|
"metadata": {
|
||
|
|
"editable": true,
|
||
|
|
"slideshow": {
|
||
|
|
"slide_type": "subslide"
|
||
|
|
},
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Plot\n",
|
||
|
|
"plt.bar(dist_mp.keys(), dist_mp.values(), color='#F00a00')\n",
|
||
|
|
"plt.xticks(list(dist_mp.keys()))\n",
|
||
|
|
"plt.title(\"Mario Party Dice role Distribution\")\n",
|
||
|
|
"plt.xlabel(\"Points rolled\")\n",
|
||
|
|
"plt.ylabel(\"Number of occurrences\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.12.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|