|
{
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|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "Y7yjuUwxtdIg"
|
|
},
|
|
"source": [
|
|
"# 基于K-Means的社交媒体病毒性预测\n",
|
|
"\n",
|
|
"## 背景介绍\n",
|
|
"\n",
|
|
"Artificial intelligence is commonly used in various trade circles to automate processes, gather insights on business, and speed up processes. You will use Python to study the usage of artificial intelligence in real-life scenarios - how AI actually impacts industries. \n",
|
|
"\n",
|
|
"Social media is part and parcel of everyone's life nowadays. Artificial intelligence can be effectively used to analyze the trends in social media. \n",
|
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"\n",
|
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"In this notebook, we will focus on how to use a K-Means model to predict the virality of social media posts.\n",
|
|
"\n",
|
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"## Context\n",
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"\n",
|
|
"We will be working with the dataset of articles published by Mashable (a popular social article sharing platform) that is uploaded at [UCI](http://archive.ics.uci.edu/ml/datasets/Online+News+Popularity). We will divide the set of articles into clusters using a K-Means model such that articles within a cluster would have a chance of similar popularity.\n",
|
|
"\n",
|
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"\n",
|
|
"### 知识点:K-Means\n",
|
|
"\n",
|
|
"K-Means is a simple algorithm that divides a dataset into groups such that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).\n",
|
|
"\n",
|
|
"\n",
|
|
"## 打开csv 文件\n",
|
|
"\n",
|
|
"We will use the [scikit-learn](https://scikit-learn.org/stable/) and [pandas](https://pandas.pydata.org/) to work with our dataset. Scikit-learn is a very useful machine learning library that provides efficient tools for predictive data analysis. Pandas is a popular Python library for data science. It offers powerful and flexible data structures to make data manipulation and analysis easier.\n",
|
|
"\n",
|
|
"\n",
|
|
"## 包含模块\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 71
|
|
},
|
|
"colab_type": "code",
|
|
"id": "c54ZY1leww-2",
|
|
"outputId": "ef3ed5c1-e7f5-423b-d197-373d7dcbd3c2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import numpy as np \n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"%matplotlib inline \n",
|
|
"import seaborn as sns\n",
|
|
"sns.set(\"talk\",\"darkgrid\",font_scale=1,font=\"sans-serif\",color_codes=True)\n",
|
|
"from sklearn import metrics\n",
|
|
"from sklearn.decomposition import PCA\n",
|
|
"from sklearn.cluster import KMeans"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "ra_0mvhQxF41"
|
|
},
|
|
"source": [
|
|
"### 导入数据集\n",
|
|
"\n",
|
|
"The dataset contains a set of Mashable articles. Let us visualize the dataset.\n",
|
|
"\n"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 309
|
|
},
|
|
"colab_type": "code",
|
|
"id": "483s82z9xFIw",
|
|
"outputId": "c4c2dfe4-b80f-4f17-e718-71bca7ef0495"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/html": [
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>url</th>\n",
|
|
" <th>timedelta</th>\n",
|
|
" <th>n_tokens_title</th>\n",
|
|
" <th>n_tokens_content</th>\n",
|
|
" <th>n_unique_tokens</th>\n",
|
|
" <th>n_non_stop_words</th>\n",
|
|
" <th>n_non_stop_unique_tokens</th>\n",
|
|
" <th>num_hrefs</th>\n",
|
|
" <th>num_self_hrefs</th>\n",
|
|
" <th>num_imgs</th>\n",
|
|
" <th>...</th>\n",
|
|
" <th>min_positive_polarity</th>\n",
|
|
" <th>max_positive_polarity</th>\n",
|
|
" <th>avg_negative_polarity</th>\n",
|
|
" <th>min_negative_polarity</th>\n",
|
|
" <th>max_negative_polarity</th>\n",
|
|
" <th>title_subjectivity</th>\n",
|
|
" <th>title_sentiment_polarity</th>\n",
|
|
" <th>abs_title_subjectivity</th>\n",
|
|
" <th>abs_title_sentiment_polarity</th>\n",
|
|
" <th>shares</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\n",
|
|
" <td>731.0</td>\n",
|
|
" <td>12.0</td>\n",
|
|
" <td>219.0</td>\n",
|
|
" <td>0.663594</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.815385</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>0.100000</td>\n",
|
|
" <td>0.7</td>\n",
|
|
" <td>-0.350000</td>\n",
|
|
" <td>-0.600</td>\n",
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|
" <td>-0.200000</td>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>-0.187500</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.187500</td>\n",
|
|
" <td>593</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
|
|
" <td>731.0</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>255.0</td>\n",
|
|
" <td>0.604743</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.791946</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>0.033333</td>\n",
|
|
" <td>0.7</td>\n",
|
|
" <td>-0.118750</td>\n",
|
|
" <td>-0.125</td>\n",
|
|
" <td>-0.100000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>711</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
|
|
" <td>731.0</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>211.0</td>\n",
|
|
" <td>0.575130</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.663866</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>0.100000</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-0.466667</td>\n",
|
|
" <td>-0.800</td>\n",
|
|
" <td>-0.133333</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
|
|
" <td>731.0</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>531.0</td>\n",
|
|
" <td>0.503788</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.665635</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>0.136364</td>\n",
|
|
" <td>0.8</td>\n",
|
|
" <td>-0.369697</td>\n",
|
|
" <td>-0.600</td>\n",
|
|
" <td>-0.166667</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1200</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\n",
|
|
" <td>731.0</td>\n",
|
|
" <td>13.0</td>\n",
|
|
" <td>1072.0</td>\n",
|
|
" <td>0.415646</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.540890</td>\n",
|
|
" <td>19.0</td>\n",
|
|
" <td>19.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>...</td>\n",
|
|
" <td>0.033333</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-0.220192</td>\n",
|
|
" <td>-0.500</td>\n",
|
|
" <td>-0.050000</td>\n",
|
|
" <td>0.454545</td>\n",
|
|
" <td>0.136364</td>\n",
|
|
" <td>0.045455</td>\n",
|
|
" <td>0.136364</td>\n",
|
|
" <td>505</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"<p>5 rows × 61 columns</p>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" url timedelta \\\n",
|
|
"0 http://mashable.com/2013/01/07/amazon-instant-... 731.0 \n",
|
|
"1 http://mashable.com/2013/01/07/ap-samsung-spon... 731.0 \n",
|
|
"2 http://mashable.com/2013/01/07/apple-40-billio... 731.0 \n",
|
|
"3 http://mashable.com/2013/01/07/astronaut-notre... 731.0 \n",
|
|
"4 http://mashable.com/2013/01/07/att-u-verse-apps/ 731.0 \n",
|
|
"\n",
|
|
" n_tokens_title n_tokens_content n_unique_tokens n_non_stop_words \\\n",
|
|
"0 12.0 219.0 0.663594 1.0 \n",
|
|
"1 9.0 255.0 0.604743 1.0 \n",
|
|
"2 9.0 211.0 0.575130 1.0 \n",
|
|
"3 9.0 531.0 0.503788 1.0 \n",
|
|
"4 13.0 1072.0 0.415646 1.0 \n",
|
|
"\n",
|
|
" n_non_stop_unique_tokens num_hrefs num_self_hrefs num_imgs ... \\\n",
|
|
"0 0.815385 4.0 2.0 1.0 ... \n",
|
|
"1 0.791946 3.0 1.0 1.0 ... \n",
|
|
"2 0.663866 3.0 1.0 1.0 ... \n",
|
|
"3 0.665635 9.0 0.0 1.0 ... \n",
|
|
"4 0.540890 19.0 19.0 20.0 ... \n",
|
|
"\n",
|
|
" min_positive_polarity max_positive_polarity avg_negative_polarity \\\n",
|
|
"0 0.100000 0.7 -0.350000 \n",
|
|
"1 0.033333 0.7 -0.118750 \n",
|
|
"2 0.100000 1.0 -0.466667 \n",
|
|
"3 0.136364 0.8 -0.369697 \n",
|
|
"4 0.033333 1.0 -0.220192 \n",
|
|
"\n",
|
|
" min_negative_polarity max_negative_polarity title_subjectivity \\\n",
|
|
"0 -0.600 -0.200000 0.500000 \n",
|
|
"1 -0.125 -0.100000 0.000000 \n",
|
|
"2 -0.800 -0.133333 0.000000 \n",
|
|
"3 -0.600 -0.166667 0.000000 \n",
|
|
"4 -0.500 -0.050000 0.454545 \n",
|
|
"\n",
|
|
" title_sentiment_polarity abs_title_subjectivity \\\n",
|
|
"0 -0.187500 0.000000 \n",
|
|
"1 0.000000 0.500000 \n",
|
|
"2 0.000000 0.500000 \n",
|
|
"3 0.000000 0.500000 \n",
|
|
"4 0.136364 0.045455 \n",
|
|
"\n",
|
|
" abs_title_sentiment_polarity shares \n",
|
|
"0 0.187500 593 \n",
|
|
"1 0.000000 711 \n",
|
|
"2 0.000000 1500 \n",
|
|
"3 0.000000 1200 \n",
|
|
"4 0.136364 505 \n",
|
|
"\n",
|
|
"[5 rows x 61 columns]"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#Importing the dataset\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "33cobD6nyZaW"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#We are dropping the url column since it has only strings and won't help us in clustering\n",
|
|
"X = df.drop('url',axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "wEIpEMcay4dB"
|
|
},
|
|
"source": [
|
|
"## 任务 1: Drop the timedelta column from dataset X and store the result in X"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "pL7dN_L4zBv4"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X= X.drop(' timedelta',axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 279
|
|
},
|
|
"colab_type": "code",
|
|
"id": "_qrAWP0rxQid",
|
|
"outputId": "a46612b8-3fe3-4843-bd21-473119d16780"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#K-Means algorithm can be called by KMeans function. We pass the number of clusters as an argument\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"#let us fit the kmeans model by kmeans.fit function\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "rguobzIb0Cy_"
|
|
},
|
|
"source": [
|
|
"\n",
|
|
"The above clustering is definitely not good as we can clearly see that the clusters are not separated from each other.\n",
|
|
"\n",
|
|
"We will try a smaller dataset now.\n",
|
|
"\n",
|
|
"### Smaller dataset\n",
|
|
"\n",
|
|
"The smaller dataset is created by taking the first 2 columns of X: n_tokens_title and n_tokens_content.\t"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 279
|
|
},
|
|
"colab_type": "code",
|
|
"id": "7k0n_lP-yPrg",
|
|
"outputId": "02d3e566-1aeb-4808-cd5b-f5f60d25028b"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "PKvgkK5m1EJ8"
|
|
},
|
|
"source": [
|
|
"Here we can see that the clusters are clearly separated. The output of the kmeans algorithm is really good here. The articles in a single cluster would have similar potential of being viral.\n",
|
|
"\n",
|
|
"\n",
|
|
"## 任务 2: Select the first ten columns of X and store the result in variable X2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "OB74H6Bl07H4"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X2 = X.iloc[:, 0:10]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 279
|
|
},
|
|
"colab_type": "code",
|
|
"id": "tgHrk7aD1hyj",
|
|
"outputId": "c301e843-3e54-434a-ae1c-bee62f7b521a"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#Now, let us apply K-Means algorithm on X2 with 6 clusters\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "d5P_zapa2YyE"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Let us take a subset of dataset X, called X3\n",
|
|
"X3 = X.iloc[:, 5:13]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "YT27EPSF4GjN"
|
|
},
|
|
"source": [
|
|
"## 任务 3: Apply K-Means on dataset X3 with 10 clusters and visualise the data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 279
|
|
},
|
|
"colab_type": "code",
|
|
"id": "ajERfFcU4BXR",
|
|
"outputId": "913b18bd-25c0-4083-9800-c7422fd40d74"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "Untitled2.ipynb",
|
|
"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
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|
"codemirror_mode": {
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|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
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|
"version": "3.6.5"
|
|
},
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|
"toc": {
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|
"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
|
|
"sideBar": true,
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"skip_h1_title": false,
|
|
"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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|
"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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