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@Bin-Cao
Bin-Cao committed Aug 17, 2023
1 parent 033ddd4 commit 2065507
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97 changes: 97 additions & 0 deletions Template/中文示例/单目标实现/.ipynb_checkpoints/未命名-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "65d0b5a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A Bayesian global optimization package\n",
"Bgolearn, Bin CAO, HKUST(GZ)\n",
"Intro : https://bgolearn.netlify.app/\n",
"URL : https://github.com/Bin-Cao/Bgolearn\n",
"Executed on : 2023-08-17 16:16:41 | Have a great day.\n",
"\n",
"██████╗ ██████╗ ██████╗ \n",
"██╔══██╗██╔════╝ ██╔═══██╗\n",
"██████╔╝██║ ███╗██║ ██║\n",
"██╔══██╗██║ ██║██║ ██║\n",
"██████╔╝╚██████╔╝╚██████╔╝\n",
"╚═════╝ ╚═════╝ ╚═════╝ \n",
"\n",
"================================================================================\n",
"The internal model is instantiated with optimized homogenous noise\n",
"current optimal is : 20.72380529\n",
"The next datum recomended by Expected Improvement : \n",
" x = [ 71 300]\n"
]
},
{
"data": {
"text/plain": [
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" array([[ 71, 300]]))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import Bgolearn.BGOsampling as BGOS\n",
"import pandas as pd\n",
"\n",
"data = pd.read_csv('TrainingData.csv')\n",
"x = data.iloc[:,:-1]\n",
"y = data.iloc[:,-1]\n",
"\n",
"dd = pd.read_csv('DesignData.csv') \n",
"\n",
"Bgolearn = BGOS.Bgolearn()\n",
"\n",
"Mymodel = Bgolearn.fit(data_matrix = x, Measured_response = y, virtual_samples = dd)\n",
"\n",
"Mymodel.EI()"
]
},
{
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101 changes: 101 additions & 0 deletions Template/中文示例/单目标实现/DesignData.csv
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component,time
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21 changes: 21 additions & 0 deletions Template/中文示例/单目标实现/TrainingData.csv
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component,time,property
82,157,176.5299641
4,479,29.88606863
36,225,87
29,171,71.07669683
95,152,202.328828
87,479,195.8860686
70,144,152
76,316,169.7763888
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36,514,94.6715681
1,488,24.09072203
21,457,63.37755833
97 changes: 97 additions & 0 deletions Template/中文示例/单目标实现/单目标.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "65d0b5a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A Bayesian global optimization package\n",
"Bgolearn, Bin CAO, HKUST(GZ)\n",
"Intro : https://bgolearn.netlify.app/\n",
"URL : https://github.com/Bin-Cao/Bgolearn\n",
"Executed on : 2023-08-17 16:16:41 | Have a great day.\n",
"\n",
"██████╗ ██████╗ ██████╗ \n",
"██╔══██╗██╔════╝ ██╔═══██╗\n",
"██████╔╝██║ ███╗██║ ██║\n",
"██╔══██╗██║ ██║██║ ██║\n",
"██████╔╝╚██████╔╝╚██████╔╝\n",
"╚═════╝ ╚═════╝ ╚═════╝ \n",
"\n",
"================================================================================\n",
"The internal model is instantiated with optimized homogenous noise\n",
"current optimal is : 20.72380529\n",
"The next datum recomended by Expected Improvement : \n",
" x = [ 71 300]\n"
]
},
{
"data": {
"text/plain": [
"(array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
" array([[ 71, 300]]))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import Bgolearn.BGOsampling as BGOS\n",
"import pandas as pd\n",
"\n",
"data = pd.read_csv('TrainingData.csv')\n",
"x = data.iloc[:,:-1]\n",
"y = data.iloc[:,-1]\n",
"\n",
"dd = pd.read_csv('DesignData.csv') \n",
"\n",
"Bgolearn = BGOS.Bgolearn()\n",
"\n",
"Mymodel = Bgolearn.fit(data_matrix = x, Measured_response = y, virtual_samples = dd)\n",
"\n",
"Mymodel.EI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3f132c4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python3"
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"version": 3
},
"file_extension": ".py",
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