251 lines
35 KiB
Plaintext
251 lines
35 KiB
Plaintext
{
|
|
"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": {
|
|
"image/png": "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\n",
|
|
"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
|
|
}
|