update

Template/regression.ipynb

@@ -6,7 +6,10 @@
    "source": [
     "Thank you for choosing Bgolearn for material design. \n",
     "Bgolearn is developed to facilitate the application of machine learning in research.\n",
-    "Bgolearn is designed for optimizing single-target material properties. \n",
+    "Bgolearn is designed for optimizing single-target material properties.\n",
+    "The BgoKit package is being developed to facilitate multi-task design.\n",
+    "\n",
+    "\n",
     "If you need to perform multi-target optimization, here are two important reminders:\n",
     "\n",
     "1. Multi-tasks can be converted into a single task using domain knowledge. \n",
@@ -56,19 +59,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A Bayesian global optimization package\n",
-      "Bgolearn, Bin CAO, HKUST(GZ)\n",
+      " ____                 _                           \n",
+      "| __ )   __ _   ___  | |  ___   __ _  _ __  _ __  \n",
+      "|  _ \\  / _` | / _ \\ | | / _ \\ / _` || '__|| '_ \\ \n",
+      "| |_) || (_| || (_) || ||  __/| (_| || |   | | | |\n",
+      "|____/  \\__, | \\___/ |_| \\___| \\__,_||_|   |_| |_|\n",
+      "        |___/                                     \n",
+      "\n",
+      "Package Name : Bgolearn\n",
+      "Author : Bin CAO, HKUST(GZ)\n",
       "Intro : https://bgolearn.netlify.app/\n",
       "URL : https://github.com/Bin-Cao/Bgolearn\n",
-      "Executed on : 2023-12-27 15:37:35  | Have a great day.\n",
-      "\n",
-      "██████╗  ██████╗  ██████╗ \n",
-      "██╔══██╗██╔════╝ ██╔═══██╗\n",
-      "██████╔╝██║  ███╗██║   ██║\n",
-      "██╔══██╗██║   ██║██║   ██║\n",
-      "██████╔╝╚██████╔╝╚██████╔╝\n",
-      "╚═════╝  ╚═════╝  ╚═════╝ \n",
-      "\n",
+      "Citation Format Suggestion:\n",
+      "[Bin CAO et al]. \"Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys\" [Journal], [2023], [DOI : xx].\n",
+      "Executed on : 2023-12-28 15:38:49  | Have a great day.\n",
       "================================================================================\n"
      ]
     }
@@ -472,7 +476,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x177ec35e0>"
+       "<matplotlib.legend.Legend at 0x14f46c520>"
       ]
      },
      "execution_count": 9,
@@ -510,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -521,20 +525,27 @@
       "current optimal is : 4.4140625\n",
       "The 1-th datum recomended by Expected Improvement : \n",
       " x =  [8.2]\n",
-      "[8.2]\n",
+      "The predictions of Bgolearn are : \n",
+      " y =  4.338227476898954\n",
       "The 2-th datum recomended by Expected Improvement : \n",
       " x =  [8.3]\n",
-      "[8.3]\n",
+      "The predictions of Bgolearn are : \n",
+      " y =  4.294587329706257\n",
       "The 3-th datum recomended by Expected Improvement : \n",
       " x =  [8.1]\n",
-      "[8.1]\n"
+      "The predictions of Bgolearn are : \n",
+      " y =  4.397302263077634\n",
+      "The 4-th datum recomended by Expected Improvement : \n",
+      " x =  [8.4]\n",
+      "The predictions of Bgolearn are : \n",
+      " y =  4.271564943821996\n"
      ]
     }
    ],
    "source": [
     "Bgolearn = BGOS.Bgolearn()\n",
     "\n",
-    "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y , virtual_samples = vs,opt_num=3)\n",
+    "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y ,virtual_samples = vs,opt_num=4)\n",
     "\n",
     "# Expected Improvement method\n",
     "score, data = Mymodel.EI()"
@@ -924,16 +935,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "The internal model is instantiated with optimized homogenous noise\n",
+      "Execution using a single process\n",
       "The next datum recomended by Knowledge Gradient : \n",
-      " x =  [8.2]\n"
+      " x =  [6.9]\n",
+      "The predictions of Bgolearn are : \n",
+      " y =  5.0823131435026045\n"
      ]
     }
    ],