update

Template/regression.ipynb

@@ -60,7 +60,7 @@
       "Bgolearn, Bin CAO, HKUST(GZ)\n",
       "Intro : https://bgolearn.netlify.app/\n",
       "URL : https://github.com/Bin-Cao/Bgolearn\n",
-      "Executed on : 2023-08-15 10:54:43  | Have a great day.\n",
+      "Executed on : 2023-12-27 15:37:35  | Have a great day.\n",
       "\n",
       "██████╗  ██████╗  ██████╗ \n",
       "██╔══██╗██╔════╝ ██╔═══██╗\n",
@@ -472,7 +472,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x13fd33fd0>"
+       "<matplotlib.legend.Legend at 0x177ec35e0>"
       ]
      },
      "execution_count": 9,
@@ -519,23 +519,30 @@
      "text": [
       "The internal model is instantiated with optimized homogenous noise\n",
       "current optimal is : 4.4140625\n",
-      "The next datum recomended by Expected Improvement : \n",
-      " x =  [8.2]\n"
+      "The 1-th datum recomended by Expected Improvement : \n",
+      " x =  [8.2]\n",
+      "[8.2]\n",
+      "The 2-th datum recomended by Expected Improvement : \n",
+      " x =  [8.3]\n",
+      "[8.3]\n",
+      "The 3-th datum recomended by Expected Improvement : \n",
+      " x =  [8.1]\n",
+      "[8.1]\n"
      ]
     }
    ],
    "source": [
     "Bgolearn = BGOS.Bgolearn()\n",
     "\n",
-    "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y , virtual_samples = vs)\n",
+    "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y , virtual_samples = vs,opt_num=3)\n",
     "\n",
     "# Expected Improvement method\n",
     "score, data = Mymodel.EI()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -571,7 +578,7 @@
        "       5.57666455e-04, 1.73940117e-03, 4.36038172e-03])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -917,31 +924,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "The internal model is instantiated with optimized homogenous noise\n",
-      "The 2000-th Monte carol simulation\n",
-      "The 4000-th Monte carol simulation\n",
       "The next datum recomended by Knowledge Gradient : \n",
-      " x =  [8.1]\n"
+      " x =  [8.2]\n"
      ]
     }
    ],
    "source": [
     "Bgolearn = BGOS.Bgolearn()\n",
     "\n",
     "Mymodel = Bgolearn.fit(\n",
-    "    data_matrix = x,Measured_response = y , virtual_samples = vs,\n",
+    "    data_matrix = x,Measured_response = y ,virtual_samples = vs,\n",
     "\n",
     ")\n",
     "\n",
     "# Knowledge Gradient method\n",
-    "score, data = Mymodel.Knowledge_G()"
+    "score, data = Mymodel.Knowledge_G(MC_num=4,)"
    ]
   },
   {