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master-thesis/EvolutionaryAlgorithm/InitializationPlots.ipynb

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Better Initialization
2019-12-01 14:04:07 +01:00
{
"cells": [
{
"cell_type": "code",
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"execution_count": 1,
Better Initialization
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"metadata": {},
"outputs": [
{
"data": {
changed signatures to have also a main ingredient set
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Better Initialization
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"text/plain": [
changed signatures to have also a main ingredient set
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"<Figure size 432x288 with 1 Axes>"
Better Initialization
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Data to plot\n",
"labels = 'Python', 'C++', 'Ruby', 'Java'\n",
"sizes = [215, 130, 245, 210]\n",
"colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']\n",
"explode = (0.1, 0, 0, 0) # explode 1st slice\n",
"\n",
"# Plot\n",
"plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
"autopct='%1.1f%%', shadow=True, startangle=140)\n",
"\n",
"plt.axis('equal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization Plots for Thesis"
]
},
{
"cell_type": "code",
changed signatures to have also a main ingredient set
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"execution_count": 2,
Better Initialization
2019-12-01 14:04:07 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
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"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jonas/Dokumente/gitRepos/master_thesis/EvolutionaryAlgorithm/EvolutionaryAlgorithm.py:58: TqdmExperimentalWarning:\n",
"\n",
"Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
"\n"
]
},
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import EvolutionaryAlgorithm as EA\n",
"import ea_tools as ET\n",
"import plotly\n",
"plotly.__version__\n",
"import plotly.graph_objs as go\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 3,
changed signatures to have also a main ingredient set
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"metadata": {},
"outputs": [],
"source": [
"def forward_normalized_score(key, matrix):\n",
" sum_key = matrix.get_fw_sum(key)\n",
" keys, values = matrix.get_forward_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_bw_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]\n",
"\n",
"def backward_normalized_score(key, matrix):\n",
" sum_key = matrix.get_bw_sum(key)\n",
" keys, values = matrix.get_backward_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_fw_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]\n",
"\n",
"def normalized_score(key, matrix):\n",
" sum_key = matrix.get_sum(key)\n",
" keys, values = matrix.get_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]\n"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 4,
Better Initialization
2019-12-01 14:04:07 +01:00
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": false
},
"data": [
{
"histfunc": "sum",
"type": "histogram",
"x": [
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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"onion",
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"banana",
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"roast red pepper",
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"shiitake mushroom",
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"grapefruit",
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"eggs",
"apples",
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"yeast",
"cherry tomato",
"cilantro",
"turkey",
"chicken breast fillet",
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"orange marmalade",
"potatoes",
"celery",
"soy sauce",
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"mustard",
"green",
"asparagus",
"lime juice",
"fig",
"portobello mushroom",
"honey",
"oregano",
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"spinach leaf",
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"garlic salt",
"beef stock",
"pecans",
"fruit",
"caraway seed",
"cherry",
"salt butter",
"green bean",
"wine",
"flaked coconut",
"cayenne pepper",
"ice water",
"gingerroot",
"thyme leaf",
"sage",
"pie shell",
"nut",
"black peppercorn",
"mushroom soup",
"flat-leaf parsley",
"cracker",
"molasses",
"fish sauce",
"applesauce",
"ground ginger",
"chocolate chips",
"apple juice",
"raspberry",
"allspice",
"vanilla bean",
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"vanilla pudding",
"maple syrup",
"vegetable shortening",
"oil",
"parsley leaf",
"caper",
"juice",
"apple cider vinegar",
"clove",
"bean",
"rhubarb",
"celery rib",
"seasoning",
"oat",
"syrup",
"hot sauce",
"chili",
"corn syrup",
"sesame oil",
"chicken broth",
"pumpkin",
"rice vinegar",
"ground allspice",
"blueberry",
"corn",
"graham cracker crumb",
"pea",
"ground clove",
"rum",
"hot water",
"noodle",
"chicken stock",
"roll oat",
"peanut oil",
"all-purpose flour",
"curry",
"cumin"
Better Initialization
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],
"y": [
changed signatures to have also a main ingredient set
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Better Initialization
2019-12-01 14:04:07 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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Better Initialization
2019-12-01 14:04:07 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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Better Initialization
2019-12-01 14:04:07 +01:00
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"title": {
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"title": {
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334.5
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" <script type=\"text/javascript\">\n",
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new initialization functions in test notebook
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" Plotly.newPlot(\n",
new initialization functions in test notebook
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" 'a8685fe6-00f5-42af-bd68-9bf7597de794',\n",
changed signatures to have also a main ingredient set
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" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
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"var gd = document.getElementById('a8685fe6-00f5-42af-bd68-9bf7597de794');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
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" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
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},
"metadata": {},
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}
],
"source": [
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"keys, values = forward_normalized_score(\"slice\", EA.m_base_act)\n",
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"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": false
},
"data": [
{
"histfunc": "sum",
"type": "histogram",
"x": [
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"sugar",
"butter",
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"salt",
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"onion",
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"olive oil",
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"water",
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"flour",
"egg",
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"milk",
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"garlic clove",
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"clove garlic",
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"garlic",
"vegetable oil",
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"black pepper",
"tomato",
"cream",
"bacon",
"carrot",
"cheese",
changed signatures to have also a main ingredient set
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"oil",
new initialization functions in test notebook
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"chicken",
"vanilla extract",
"chicken breast",
"vanilla",
"kosher salt",
"cream cheese",
"rice",
"celery",
"ginger",
"soy sauce",
"potatoes",
"potato",
"parsley",
"pepper",
"sauce",
"honey",
"chocolate",
"cinnamon",
"coconut",
"oregano",
"eggs",
"almond",
"lemon juice",
"apple",
"corn syrup",
"red onion",
"red bell pepper",
"baking soda",
"ground cinnamon",
"egg yolk",
"sesame oil",
"nut",
changed signatures to have also a main ingredient set
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"curry",
new initialization functions in test notebook
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"vinegar"
Better Initialization
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],
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new initialization functions in test notebook
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143,
137,
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130,
new initialization functions in test notebook
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99,
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98,
changed signatures to have also a main ingredient set
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75,
new initialization functions in test notebook
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71,
65,
59,
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59,
51,
39,
changed signatures to have also a main ingredient set
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33,
27,
25,
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24,
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21,
21,
18,
18,
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15,
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15,
changed signatures to have also a main ingredient set
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14,
13,
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12,
12,
12,
12,
changed signatures to have also a main ingredient set
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11,
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11,
11,
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10,
10,
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10,
10,
10,
10,
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10
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changed signatures to have also a main ingredient set
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changed signatures to have also a main ingredient set
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changed signatures to have also a main ingredient set
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new initialization functions in test notebook
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new initialization functions in test notebook
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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Better Initialization
2019-12-01 14:04:07 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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Better Initialization
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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Better Initialization
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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Better Initialization
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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Better Initialization
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new initialization functions in test notebook
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"keys, values = EA.m_base_act.get_forward_adjacent('burn')\n",
Better Initialization
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"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
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"execution_count": 6,
Better Initialization
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"metadata": {},
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"x": [
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"egg",
"butter",
"salt",
"milk",
"onion",
"cheese",
"sugar",
"olive oil",
"tomato",
"garlic clove",
"black pepper",
"water",
"cream",
"parsley",
"clove garlic",
"bacon",
"mustard",
"pepper",
"vanilla extract",
"garlic",
"cinnamon",
"thyme",
"flour",
"cream cheese",
"raisin",
"nutmeg",
"ham",
"celery",
"lemon juice",
"vanilla",
"ground beef",
"sauce",
"mushroom",
"tablespoon butter",
"ground black pepper",
"vegetable oil",
"apple",
"dijon mustard",
"swiss cheese",
"ground cinnamon",
"oregano",
"basil",
"red onion",
"avocado",
"potato",
"peanut butter",
"chicken broth",
"eggs",
"spinach",
"chicken breast",
"red pepper",
"chicken",
"sausage",
"margarine",
"flat-leaf parsley",
"pineapple",
"banana",
"carrot",
"egg yolk",
"yeast",
"paprika",
"ketchup",
"kosher salt",
"wine",
"lemon",
"honey",
"goat cheese",
"shrimp",
"zucchini",
"almond",
"ground pepper",
"chicken stock",
"vinegar",
"pork",
"extra-virgin olive oil",
"cucumber",
"leaf",
"mozzarella cheese",
"cayenne pepper",
"parsley leaf",
"sage",
"plum tomato",
"seasoning",
"turkey breast",
"oil",
"green onion",
"turkey",
"tuna",
"lemon zest",
"green pepper",
"walnut",
"all-purpose flour",
"red bell pepper",
"broccoli",
"jack cheese",
"cilantro",
"maple syrup",
"crabmeat",
"pecan"
],
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Better Initialization
2019-12-01 14:04:07 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
10
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2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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},
"metadata": {},
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],
"source": [
"keys, values = EA.m_base_mix.get_adjacent('bread')\n",
"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 7,
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"metadata": {},
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Better Initialization
2019-12-01 14:04:07 +01:00
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Better Initialization
2019-12-01 14:04:07 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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"var gd = document.getElementById('008311ea-131c-4a65-b51e-7950a0941222');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" });\n",
" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ing = EA.Ingredient(\"rice\")\n",
"ing.apply_action(\"cook\")\n",
"\n",
"keys, values = EA.m_mix.get_adjacent(ing.to_json())\n",
"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"920"
]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"EA.np.sum(values)"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1531"
]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"EA.m_base_mix.get_sum('noodle')"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
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"execution_count": 10,
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"metadata": {},
"outputs": [],
"source": [
"def normalized_score(key, matrix):\n",
" sum_key = matrix.get_sum(key)\n",
" keys, values = matrix.get_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]\n",
" "
]
},
{
"cell_type": "code",
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"execution_count": 11,
changed signatures to have also a main ingredient set
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"metadata": {},
"outputs": [],
"source": [
"def forward_normalized_score(key, matrix):\n",
" sum_key = matrix.get_fw_sum(key)\n",
" keys, values = matrix.get_forward_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_bw_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
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"execution_count": 12,
changed signatures to have also a main ingredient set
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"metadata": {},
"outputs": [],
"source": [
"def backward_normalized_score(key, matrix):\n",
" sum_key = matrix.get_bw_sum(key)\n",
" keys, values = matrix.get_backward_adjacent(key)\n",
" normalized_values = EA.np.array([(values[i] / matrix.get_fw_sum(keys[i])) * (values[i] / sum_key) for i in range(len(keys))])\n",
" sort = EA.np.argsort(-normalized_values)\n",
" return keys[sort], normalized_values[sort]"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
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"execution_count": 21,
changed signatures to have also a main ingredient set
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"metadata": {},
"outputs": [
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
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"name": "stdout",
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"text": [
"6\n"
]
}
],
"source": [
"keys, values = normalized_score('whiskey', EA.m_base_mix)\n",
"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])\n",
"print(len(keys))"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 14,
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"metadata": {},
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"config": {
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"plotlyServerURL": "https://plot.ly",
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"data": [
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"histfunc": "sum",
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"x": [
"{\"base\": \"ricotta cheese\", \"actions\": []}",
"{\"base\": \"mozzarella cheese\", \"actions\": []}",
"{\"base\": \"cheese\", \"actions\": []}",
"{\"base\": \"cheese\", \"actions\": [\"grate\"]}"
],
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0.005435964340073929,
0.0034655371582595304,
0.0003054101221640489,
0.0002708911234396671
]
}
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"error_x": {
"color": "#2a3f5f"
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"error_y": {
"color": "#2a3f5f"
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"width": 0.5
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}
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"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
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"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
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],
"pie": [
{
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"type": "pie"
}
],
Better Initialization
2019-12-01 14:04:07 +01:00
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new initialization functions in test notebook
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changed signatures to have also a main ingredient set
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"ing = EA.Ingredient(\"noodle\")\n",
"ing.apply_action(\"cook\")\n",
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"\n",
changed signatures to have also a main ingredient set
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"keys, values = normalized_score(ing.to_json(), EA.m_mix)\n",
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"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
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"execution_count": 15,
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changed signatures to have also a main ingredient set
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changed signatures to have also a main ingredient set
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new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
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Better Initialization
2019-12-01 14:04:07 +01:00
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" \n",
" \n",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
" <div id=\"9afc4c11-0fa0-4591-bc07-dca2d4d6318e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
Better Initialization
2019-12-01 14:04:07 +01:00
" <script type=\"text/javascript\">\n",
" require([\"plotly\"], function(Plotly) {\n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
" \n",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
" if (document.getElementById(\"9afc4c11-0fa0-4591-bc07-dca2d4d6318e\")) {\n",
Better Initialization
2019-12-01 14:04:07 +01:00
" Plotly.newPlot(\n",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
" '9afc4c11-0fa0-4591-bc07-dca2d4d6318e',\n",
" [{\"histfunc\": \"sum\", \"type\": \"histogram\", \"x\": [\"heat\", \"prepare\", null, \"cool\"], \"y\": [1696, 1395, 446, 103]}],\n",
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
" {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl
Better Initialization
2019-12-01 14:04:07 +01:00
" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
new initialization functions in test notebook
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"var gd = document.getElementById('9afc4c11-0fa0-4591-bc07-dca2d4d6318e');\n",
Better Initialization
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"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" });\n",
" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"keys, values = EA.m_grouped_act.get_backward_adjacent(EA.Ingredient(\"onion\").to_json())\n",
Better Initialization
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"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 16,
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"metadata": {},
"outputs": [],
"source": [
"def prepare_ratio(ing:str):\n",
" keys, values = EA.m_grouped_act.get_backward_adjacent(EA.Ingredient(ing).to_json())\n",
" action_dict = dict(zip(keys,values))\n",
" return action_dict['prepare'] / action_dict['heat']"
]
},
{
"cell_type": "code",
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 17,
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.405195500803428"
]
},
new initialization functions in test notebook
2019-12-27 11:52:10 +01:00
"execution_count": 17,
changed signatures to have also a main ingredient set
2019-12-12 09:19:38 +01:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prepare_ratio(\"sugar\")"
]
},
Better Initialization
2019-12-01 14:04:07 +01:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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