Technologische_Grundlagen/course/numpy/.ipynb_checkpoints/02_random-checkpoint.ipynb

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2024-09-27 07:00:19 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "535c30a1-6335-4314-83a1-6e7d23ada40d",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7e9842e7-4fb8-404c-b6da-283510f314dc",
"metadata": {},
"outputs": [],
"source": [
"# hint: Pseudo Random and True Random"
]
},
{
"cell_type": "markdown",
"id": "b702eeef-b651-4286-9236-2edca6f0353d",
"metadata": {},
"source": [
"### Random Numbers"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ee62a0e7-e2ce-4570-ae65-ef815526d99b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"88\n"
]
}
],
"source": [
"x = np.random.randint(100)\n",
"\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ad3c430b-dde2-4202-8782-8cfc2cb434f1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6204661862066633\n"
]
}
],
"source": [
"x = np.random.rand()\n",
"\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8e033582-3f93-43b6-b254-5dc0d2baad70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[90 98 7 34 80]\n"
]
}
],
"source": [
"x=np.random.randint(100, size=(5))\n",
"\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "bf4cf008-ec0f-4291-a990-eb3cf2b76f14",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"x = np.random.choice([3, 5, 7, 9])\n",
"\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"id": "820e0a69-f14d-4fa9-9abf-ba6259de2abf",
"metadata": {},
"source": [
"### Random Distribution"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d04aefc6-a1c0-4ee5-822a-bca76cc5bbc1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[7 7 3 7 3 7 7 7 7 5 7 7 7 5 7 7 5 5 7 5 5 7 5 7 7 3 7 3 7 3 7 7 7 7 7 7 5\n",
" 7 7 3 7 3 7 7 7 5 7 7 5 7 5 7 5 7 5 7 7 7 7 7 3 5 3 7 5 7 7 7 5 5 7 5 7 5\n",
" 5 5 7 7 5 7 7 5 7 3 7 5 7 3 5 5 5 7 5 7 7 3 7 5 7 7]\n"
]
}
],
"source": [
"# Probability Density Function: A function that describes a continuous probability. \n",
"# i.e. probability of all values in an array.\n",
"x = np.random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
"\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"id": "2e5627e9-7b03-45f8-a776-89bcd069fc71",
"metadata": {},
"source": [
"### Normal (Gaussian) Distribution"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b561d19f-041a-4f10-b6b6-ba1827c79abf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.17858464 4.11191574 1.13382304]\n",
" [2.57882466 1.42017738 0.54116511]]\n"
]
}
],
"source": [
"# loc - (Mean) where the peak of the bell exists.\n",
"# scale - (Standard Deviation) how flat the graph distribution should be.\n",
"# size - The shape of the returned array.\n",
"x = np.random.normal(loc=1, scale=2, size=(2, 3))\n",
"\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "1c683c75-bcc2-40ca-8c27-9ed9aa0ccdd4",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.displot(np.random.normal(size=1000), kind=\"kde\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "fbfc0beb-9716-48b6-9b08-d3ef4b867f27",
"metadata": {},
"source": [
"\"Some\" other Distributions\n",
"- Normal Distribution\n",
"- Binomial Distribution\n",
"- Poisson Distribution\n",
"- Uniform Distribution\n",
"- Logistic Distribution\n",
"- Multinomial Distribution\n",
"- Exponential Distribution\n",
"- Chi Square Distribution\n",
"- Rayleigh Distribution\n",
"- Pareto Distribution\n",
"- Zipf Distribution\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7261962-15f5-48db-b2ee-950b2c3c1dc0",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}