251 lines
35 KiB
Plaintext
251 lines
35 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "535c30a1-6335-4314-83a1-6e7d23ada40d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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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": 14,
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"id": "7e9842e7-4fb8-404c-b6da-283510f314dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# hint: Pseudo Random and True Random"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b702eeef-b651-4286-9236-2edca6f0353d",
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"metadata": {},
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"source": [
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"### Random Numbers"
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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": 15,
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"id": "ee62a0e7-e2ce-4570-ae65-ef815526d99b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"88\n"
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]
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}
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],
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"source": [
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"x = np.random.randint(100)\n",
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"\n",
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"print(x)"
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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": 16,
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"id": "ad3c430b-dde2-4202-8782-8cfc2cb434f1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.6204661862066633\n"
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]
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}
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],
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"source": [
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"x = np.random.rand()\n",
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"\n",
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"print(x)"
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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": 17,
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"id": "8e033582-3f93-43b6-b254-5dc0d2baad70",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[90 98 7 34 80]\n"
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]
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}
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],
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"source": [
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"x=np.random.randint(100, size=(5))\n",
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"\n",
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"print(x)"
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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": 18,
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"id": "bf4cf008-ec0f-4291-a990-eb3cf2b76f14",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"5\n"
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]
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}
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],
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"source": [
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"x = np.random.choice([3, 5, 7, 9])\n",
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"\n",
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"print(x)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "820e0a69-f14d-4fa9-9abf-ba6259de2abf",
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"metadata": {},
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"source": [
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"### Random Distribution"
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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": 21,
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"id": "d04aefc6-a1c0-4ee5-822a-bca76cc5bbc1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[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",
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" 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",
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" 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"
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]
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}
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],
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"source": [
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"# Probability Density Function: A function that describes a continuous probability. \n",
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"# i.e. probability of all values in an array.\n",
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"x = np.random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
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"\n",
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"print(x)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e5627e9-7b03-45f8-a776-89bcd069fc71",
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"metadata": {},
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"source": [
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"### Normal (Gaussian) Distribution"
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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": 24,
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"id": "b561d19f-041a-4f10-b6b6-ba1827c79abf",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0.17858464 4.11191574 1.13382304]\n",
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" [2.57882466 1.42017738 0.54116511]]\n"
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]
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}
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],
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"source": [
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"# loc - (Mean) where the peak of the bell exists.\n",
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"# scale - (Standard Deviation) how flat the graph distribution should be.\n",
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"# size - The shape of the returned array.\n",
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"x = np.random.normal(loc=1, scale=2, size=(2, 3))\n",
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"\n",
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"print(x)"
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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": 38,
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"id": "1c683c75-bcc2-40ca-8c27-9ed9aa0ccdd4",
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"metadata": {},
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"outputs": [
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{
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"data": {
|
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|
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"image/png": "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
|
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|
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"text/plain": [
|
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|
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"<Figure size 500x500 with 1 Axes>"
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]
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},
|
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|
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"metadata": {},
|
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|
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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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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"sns.displot(np.random.normal(size=1000), kind=\"kde\")\n",
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"\n",
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"plt.show()"
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]
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|
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},
|
||
|
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{
|
||
|
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"cell_type": "markdown",
|
||
|
|
"id": "fbfc0beb-9716-48b6-9b08-d3ef4b867f27",
|
||
|
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"metadata": {},
|
||
|
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"source": [
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|
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"\"Some\" other Distributions\n",
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"- Normal Distribution\n",
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"- Binomial Distribution\n",
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"- Poisson Distribution\n",
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"- Uniform Distribution\n",
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"- Logistic Distribution\n",
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"- Multinomial Distribution\n",
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"- Exponential Distribution\n",
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"- Chi Square Distribution\n",
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"- Rayleigh Distribution\n",
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"- Pareto Distribution\n",
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"- Zipf Distribution\n"
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|
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]
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|
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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": null,
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||
|
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"id": "b7261962-15f5-48db-b2ee-950b2c3c1dc0",
|
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|
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"metadata": {},
|
||
|
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"outputs": [],
|
||
|
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"source": []
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|
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}
|
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|
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],
|
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|
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"metadata": {
|
||
|
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"kernelspec": {
|
||
|
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"display_name": "Python 3 (ipykernel)",
|
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|
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"language": "python",
|
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|
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"name": "python3"
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|
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},
|
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|
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"language_info": {
|
||
|
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"codemirror_mode": {
|
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|
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"name": "ipython",
|
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|
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"version": 3
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|
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},
|
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|
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"file_extension": ".py",
|
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|
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"mimetype": "text/x-python",
|
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|
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"name": "python",
|
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|
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"nbconvert_exporter": "python",
|
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|
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"pygments_lexer": "ipython3",
|
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|
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"version": "3.10.8"
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|
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}
|
||
|
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},
|
||
|
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"nbformat": 4,
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|
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"nbformat_minor": 5
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|
|
}
|