master-thesis/EvolutionaryAlgorithm/InitializationPlots.ipynb

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{
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{
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2019-12-01 14:04:07 +01:00
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2019-12-01 14:04:07 +01:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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]
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}
],
"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",
"execution_count": 2,
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"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",
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" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
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" </script>\n",
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"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"
]
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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": {},
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],
"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",
"execution_count": 3,
"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"
]
},
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"cell_type": "code",
"execution_count": 4,
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"metadata": {},
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2019-12-01 14:04:07 +01:00
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2019-12-01 14:04:07 +01:00
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2019-12-01 14:04:07 +01:00
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2019-12-01 14:04:07 +01:00
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2019-12-01 14:04:07 +01:00
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},
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],
"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",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"920"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1531"
]
},
"execution_count": 9,
"metadata": {},
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],
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},
{
"cell_type": "code",
"execution_count": 10,
"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",
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]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
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" 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",
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]
},
{
"cell_type": "code",
"execution_count": 12,
"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",
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]
},
{
"cell_type": "code",
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"metadata": {},
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"iplot([data])\n",
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2019-12-01 14:04:07 +01:00
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},
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],
"source": [
"ing = EA.Ingredient(\"noodle\")\n",
"ing.apply_action(\"cook\")\n",
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"\n",
"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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},
"metadata": {},
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}
],
"source": [
"keys, values = EA.m_grouped_act.get_backward_adjacent(EA.Ingredient(\"onion\").to_json())\n",
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"data = go.Histogram(x=keys, y=values, histfunc=\"sum\")\n",
"iplot([data])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.405195500803428"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prepare_ratio(\"sugar\")"
]
},
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{
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"metadata": {},
"outputs": [],
"source": []
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