diff --git a/bayesian/bayesian_optimization.ipynb b/bayesian/bayesian_optimization/bayesian_optimization.ipynb
similarity index 59%
rename from bayesian/bayesian_optimization.ipynb
rename to bayesian/bayesian_optimization/bayesian_optimization.ipynb
index 419d794..040e3a1 100644
--- a/bayesian/bayesian_optimization.ipynb
+++ b/bayesian/bayesian_optimization/bayesian_optimization.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting anastruct\n",
+      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/1e/7ca168dffa4c38cae9e6269726bf1327075c93806c3ac8f3918816716503/anastruct-1.0.2-py3-none-any.whl (57kB)\n",
+      "\u001b[K     |████████████████████████████████| 61kB 6.3MB/s \n",
+      "\u001b[?25hRequirement already satisfied: numpy in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (1.16.4)\n",
+      "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (1.3.0)\n",
+      "Requirement already satisfied: matplotlib in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (3.0.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (2.4.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (2.8.0)\n",
+      "Requirement already satisfied: six in /opt/miniconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->anastruct) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /opt/miniconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->anastruct) (41.0.1)\n",
+      "Installing collected packages: anastruct\n",
+      "Successfully installed anastruct-1.0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install anastruct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Uninstalling anastruct-1.0.2:\n",
+      "  Successfully uninstalled anastruct-1.0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip uninstall anastruct -y"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -65,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,16 +194,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'foo': 4.545454545454545, 'bar': 580}"
+       "{'foo': 1.9090909090909092, 'bar': 440}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -177,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,16 +416,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9ef1f369b0>]"
+       "[<matplotlib.lines.Line2D at 0x7f71598cd908>]"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -409,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,14 +474,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:00<00:00, 2374.36it/s]"
+      "100%|██████████| 2/2 [00:00<00:00, 3763.40it/s]"
      ]
     },
     {
@@ -611,44 +659,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([5.69161243])"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bo._new_proposal()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " /opt/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning:divide by zero encountered in true_divide\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning:invalid value encountered in true_divide\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning:invalid value encountered in less\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1807: RuntimeWarning:invalid value encountered in greater_equal\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:897: RuntimeWarning:invalid value encountered in greater_equal\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:897: RuntimeWarning:invalid value encountered in less_equal\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f9ef1bdc7b8>"
+       "Text(0, 0.5, '$\\\\sigma(x^*)$')"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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55RZ++9vfcv/991NRUcGCBQv47ne/u9VcoSNs90qgiNgtIvbY+B4YDyyOiJOAK4AJmVm/rTG6ivPPP5+rr76a8vLyTdo//vGP893vfrfxhz9//nwA1q5dy/7770+PHj348Y9/3O1uGCVJ6j6qyqtYftly3rvmPZZfttwASJIkdQnve9/7+MxnPsP3v//9xrbx48dz4403Ni4vWLAAgAMOOIBXX32V5557jgMPPJBjjz2WqVOnbjUEeuKJJ/jjH//Ie++9x4wZM5q9hcshhxzC8uXLG+/38+Mf/5iPfOQjjetnzJjR+Hr00UcDcMwxx3DHHXcAUFtb2zjuH/7wB4488kimTJnCvvvuywsvvNDpuUJLpoPtB8yNiKeAJ4D7M/M/gBuBPYCHC4+Ov6UD6+wUgwYN4tJLL92i/etf/zrr169n+PDhDBs2jK9//esAfOELX+D222/nqKOO4ve//z277bZbZ5csSZIkSVK39uUvf3mTp4TdcMMN1NXVMXz4cA477DBuueW/44gjjzyycerWcccdx5/+9Ket3p/36KOP5sorr2TYsGEMHTqU0047bYs+ffr04bbbbuPMM8+kvLycHj16cMEFFzSuf/vttznyyCP5zne+03gT6xtuuIHbbruN4cOH8+Mf/5jvfOc7AHz1q1+lvLycYcOGcfzxxzNixIhOzxWiM59aVVlZmZvf4Gjp0qUceuihnVaDWs6fjSSptSJiXmZWFrsObaq5czBJkramFH4XnD17NlOnTuW+++7b4THKysqoq6tj3333bcfKtq+5n09Lz8F2+BHxkiRJkiRJ6jpa+oh4SZIkSZKkbmHs2LGMHTu2TWN0xaeAeyWQJEmSJEnaRGfeOkYt19afiyGQJEmSJElq1KdPH9asWWMQtJPJTNasWdOmx8g7HUySJEmSJDUaNGgQq1atYvXq1cUuRZvp06cPgwYN2uHtDYEkSZIkSVKj3r17M3To0GKXoQ6w04VA6155nrfffrvdxtt1113p8/4Dt9nnz3/+M5dddhlPPvkku+66K2VlZUybNo2DDz64Vft69NFHueCCC+jduzf3338/l156KT//+c+36Dd27FimTp1KZaVP0JUkSZIkSZ1jpwuB3n77bdb0OaDdxttn3Qtsa7ZcZnLaaadxzjnncMcddwCwYMECXn755VaHQLW1tXzlK1/hvPPOA2g2AJIkSZIkSSqGkr8x9K9//Wt69+7NBRdc0NhWUVHBsccey1e/+lWGDRtGeXk5M2bMAGD27NmMHTuWM844g0MOOYSqqioyk1tvvZWf/exnTJkyhaqqKpYvX86wYcMAeOuttzj77LMZPnw4Z511Fm+99Vbjvh566CGOPvpoRo0axZlnnsmbb74JQFlZGddccw2jRo2ivLycZ555BoA333yT8847j/LycoYPH86dd965zXEkSdLOJyJOiohnI2JZRFzZzPqIiBsK6xdGxKjN1veMiPkRcV/nVS1Jkrq6kg+BFi9ezBFHHLFF+1133cWCBQt46qmnmDlzJl/96ld56aWXAJg/fz7Tpk1jyZIlPP/88/zmN7/h85//PBMmTOBb3/oWtbW1m4x18803069fPxYuXMjkyZOZN28eAK+++irf/OY3mTlzJr/73e+orKzk29/+duN2++67L7/73e+48MILmTp1KgDf+MY36N+/P4sWLWLhwoWceOKJ2x1HkiTtPCKiJ3ATcDJwGDAxIg7brNvJwEGFP9XAzZutvxRY2sGlSpKkbmanmw62s5g7dy4TJ06kZ8+e7LfffnzkIx/hySefZM8992T06NGNd+OuqKhg+fLlHHvssVsda86cOVxyySUADB8+nOHDhwPw+OOPs2TJEsaMGQPAO++8w9FHH9243emnnw7AEUccwV133QXAzJkzG6etAey9997cd9992xxHkiTtVEYDyzLzeYCIuAP4FLCkSZ9PAT/KhmfzPh4Re0XE/pn5UkQMAj4B1ABf6uTaJUlSF1byIdDhhx/e7L17Gs65mrfrrrs2vu/ZsycbNmzY7n4iotl9fOxjH+OnP/3pNvfTdB+ZucVY2xtHkiTtVAYCLzRZXgUc2YI+A4GXgGnA5cAe29pJRFTTcBURgwcPblvFkiSpWyj56WAnnngib7/9Nt/73vca25588kn23ntvZsyYwbvvvsvq1auZM2cOo0eP3qF9HH/88Y1TxBYvXszChQsBOOqoo/jNb37DsmXLAKivr+f3v//9NscaP348N954Y+PyX/7ylx0aR5IkFc2W3wzB5t8+NdsnIk4FXsnMedvbSWZOz8zKzKwcMGDAjtQpSZK6mZ3uSqBdd92Vfda9sP2OrRhvWyKCu+++m8suu4zrr7+ePn36ND4i/s0332TEiBFEBP/8z//MBz7wgcYbNLfGhRdeyHnnncfw4cOpqKhoDJMGDBjAD3/4QyZOnMjbb78NwDe/+c1tPpXsH//xH7nooosYNmwYPXv25JprruH0009v9TiSJKloVgFNH4U6CHixhX3OACZExClAH2DPiPjXzPxsB9YrSZK6idjWtKf2VllZmXV1dZu0LV26lEMPPbTTalDL+bORJLVWRMzLzMpi17Ezi4hewO+BccCfgCeBv8/Mp5v0+QRwMXAKDVPFbsjM0ZuNMxb4Smaeur19NncOJkmSuo+WnoPtdFcCSZIkdWeZuSEiLgYeBHoCP8jMpyPigsL6W4AHaAiAlgH1wHnFqleSJHUfhkCSJEmdLDMfoCHoadp2S5P3CVy0nTFmA7M7oDxJktRNlfyNoSVJ6mpqF9VSNq2MHtf1oGxaGbWLaotdkiRJkroArwSSJKkLqV1US/W91dSvrwdgxdoVVN9bDUBVeVUxS5MkSdJOziuBJEnqQibPmtwYAG1Uv76eybMmF6kiSZIkdRWGQJIkdSEr165sVbskSZK0kSFQBygrK+PVV19t1zEXLFjAAw88sNX1dXV1XHLJJdscY/bs2Zx66nafIitJ2okN7j+4Ve2SJEnSRoZATWQm7733XrHLaNa2QqANGzZQWVnJDTfc0MlVSZI6W824Gvr17rdJW7/e/agZV1OkiiRJktRVdL0QqLYWysqgR4+G19q2PRFl+fLlHHrooXzhC19g1KhRvPDCC1x44YVUVlZy+OGHc8011zT2LSsr45prrmHUqFGUl5fzzDPPALBmzRrGjx/PyJEj+Z//83/S8FTXBt/+9rcZNmwYw4YNY9q0aY37POSQQ/j85z/PsGHDqKqqYubMmYwZM4aDDjqIJ554YpMa33nnHa6++mpmzJhBRUUFM2bM4Nprr6W6uprx48czadKkTa7yeeKJJzjmmGMYOXIkxxxzDM8+++wWn/uRRx6hoqKCiooKRo4cyRtvvNGm4yhJ6hxV5VVM/+R0hvQfQhAM6T+E6Z+c7k2hJUmStF1dKwSqrYXqalixAjIbXqur2xwEPfvss0yaNIn58+czZMgQampqqKurY+HChTzyyCMsXLiwse++++7L7373Oy688EKmTp0KwHXXXcexxx7L/PnzmTBhAitXNtyXYd68edx222389re/5fHHH+d73/se8+fPB2DZsmVceumlLFy4kGeeeYaf/OQnzJ07l6lTp/JP//RPm9S3yy67MGXKFM466ywWLFjAWWed1Tj+Pffcw09+8pNN+h9yyCHMmTOH+fPnM2XKFL72ta9t8ZmnTp3KTTfdxIIFC3j00Ufp27dvm46hJKnzVJVXsfyy5bx3zXssv2y5AZAkSZJapGuFQJMnQ/2mT0Shvr6hvQ2GDBnCUUcd1bj8s5/9jFGjRjFy5EiefvpplixZ0rju9NNPB+CII45g+fLlAMyZM4fPfvazAHziE59g7733BmDu3Lmcdtpp7Lbbbuy+++6cfvrpPProowAMHTqU8vJyevToweGHH864ceOICMrLyxvH3Z4JEyY0G96sXbuWM888k2HDhvHFL36Rp59+eos+Y8aM4Utf+hI33HADr732Gr169WrRPiVJkiRJUtfUtUKglVt58snW2ltot912a3z/xz/+kalTpzJr1iwWLlzIJz7xCdatW9e4ftdddwWgZ8+ebNiwobE9IrYYt+m0sM1tHAegR48ejcs9evTYZNyW1t3U17/+dU444QQWL17Mvffeu0n9G1155ZXceuutvPXWWxx11FGNU9skSZIkSVL31LVCoMFbefLJ1tp3wOuvv85uu+1G//79efnll/nlL3+53W2OP/54agtT0n75y1/yl7/8pbH93//936mvr+evf/0rd999N8cdd9wO1bXHHnu0+L49a9euZeDAgQD88Ic/bLbPH/7wB8rLy7niiiuorKw0BJIkSZIkqZvrWiFQTQ302/SJKPTr19DeTkaMGMHIkSM5/PDDOf/88xkzZsx2t7nmmmuYM2cOo0aN4qGHHmJwIZQaNWoU5557LqNHj+bII4/k85//PCNHjtyhuk444QSWLFnSeGPobbn88su56qqrGDNmDO+++26zfaZNm8awYcMYMWIEffv25eSTT96huiRJkiRJUtcQ25qy1NgpYjnwBvAusCEzKyPiTOBa4FBgdGbWbW+cysrKrKvbtNvSpUs59NBDW15xbW3DPYBWrmy4AqimBqq8IWZHaPXPRpJU8iJiXmZWFrsObaq5czBJktR9tPQcrDV3Az4hM19tsrwYOB34f60trk2qqgx9JEmSJEmSWmmHHwmVmUuh+RsiS5IkSZIkaefS0nsCJfBQRMyLiOrW7CAiqiOiLiLqVq9e3foKJUmSJEmS1GYtDYHGZOYo4GTgoog4vqU7yMzpmVmZmZUDBgzYoSIlSZIkSZLUNi0KgTLzxcLrK8DdwOiOLEqSJEmSJEnta7shUETsFhF7bHwPjKfhptCSJEmSJEnqIlpyJdB+wNyIeAp4Arg/M/8jIk6LiFXA0cD9EfFgRxbakXr27ElFRQUjRoxg1KhR/Od//mfjuqeffpoTTzyRgw8+mIMOOohvfOMbZOYWYyxYsIAHHnhgq/uoq6vjkksu2WYds2fP5tRTT93xDyJJkiRJkrQV2306WGY+D4xopv1uGqaGdXl9+/ZlwYIFADz44INcddVVPPLII7z11ltMmDCBm2++mfHjx1NfX8+nP/1p/u///b9cdNFFm4yxYMEC6urqOOWUU7YYf8OGDVRWVlJZWdkpn0eSJEmSJGlzLb0x9E6jdlEtZdPK6HFdD8qmlVG7qLZdx3/99dfZe++9AfjJT37CmDFjGD9+PAD9+vXjxhtv5Prrr99km3feeYerr76aGTNmUFFRwYwZM7j22muprq5m/PjxTJo0aZOrfJ544gmOOeYYRo4cyTHHHMOzzz67RR2PPPIIFRUVVFRUMHLkSN544412/ZySJEmSJKm0bPdKoJ1J7aJaqu+tpn59PQAr1q6g+t6GJ9ZXlVft8LhvvfUWFRUVrFu3jpdeeolf/epXQMNUsCOOOGKTvn/zN3/Dm2++yeuvv86ee+4JwC677MKUKVOoq6vjxhtvBODaa69l3rx5zJ07l759+zJ79uzGMQ455BDmzJlDr169mDlzJl/72te48847N9nP1KlTuemmmxgzZgxvvvkmffr02eHPJ0mSJEmS1KVCoMmzJjcGQBvVr69n8qzJbQqBmk4He+yxx5g0aRKLFy8mM4mIZrfZWntTEyZMoG/fvlu0r127lnPOOYfnnnuOiGD9+vVb9BkzZgxf+tKXqKqq4vTTT2fQoEGt/FSSJEmSJEn/rUtNB1u5dmWr2nfE0Ucfzauvvsrq1as5/PDDqaur22T9888/z+67784ee+yx3bF22223Ztu//vWvc8IJJ7B48WLuvfde1q1bt0WfK6+8kltvvZW33nqLo446imeeeWbHPpAkSZIkSRJdLAQa3H9wq9p3xDPPPMO7777LPvvsQ1VVFXPnzmXmzJlAw7SxSy65hMsvv3yL7fbYY48W37dn7dq1DBw4EIAf/vCHzfb5wx/+QHl5OVdccQWVlZWGQJIkSZIkqU26VAhUM66Gfr37bdLWr3c/asbVtGncjfcEqqio4KyzzuL222+nZ8+e9O3bl3vuuYdvfvObfOhDH6K8vJwPf/jDXHzxxVuMccIJJ7BkyZLGG0Nvy+WXX85VV13FmDFjePfdd5vYkvvQAAAWgElEQVTtM23aNIYNG8aIESPo27cvJ598cps+oyRJkiRJKm2RmZ22s8rKytx8etXSpUs59NBDWzxG7aJaJs+azMq1KxncfzA142radD8gbV1rfzaSJEXEvMysLHYd2lRz52CSJKn7aOk5WJe6MTQ0PAXM0EeSJEmSJKl1utR0MEmSJEmSJO2YnSIE6swpaWoZfyaSJHWciDgpIp6NiGURcWUz6yMibiisXxgRowrtB0TEryNiaUQ8HRGXdn71kiSpqyp6CNSnTx/WrFlj6LATyUzWrFlDnz59il2KJEndTkT0BG4CTgYOAyZGxGGbdTsZOKjwpxq4udC+AfhyZh4KHAVc1My2kiRJzSr6PYEGDRrEqlWrWL16dbFLURN9+vRh0KBBxS5DkqTuaDSwLDOfB4iIO4BPAUua9PkU8KNs+Jbs8YjYKyL2z8yXgJcAMvONiFgKDNxsW0mSpGYVPQTq3bs3Q4cOLXYZkiRJnWUg8EKT5VXAkS3oM5BCAAQQEWXASOC3HVGkJEnqfoo+HUySJKnERDNtm8+L32afiNgduBO4LDNfb3YnEdURURcRdV5xLUmSwBBIkiSps60CDmiyPAh4saV9IqI3DQFQbWbetbWdZOb0zKzMzMoBAwa0S+GSJKlrMwSSJEnqXE8CB0XE0IjYBTgb+MVmfX4BTCo8JewoYG1mvhQRAXwfWJqZ3+7csiVJUldX9HsCSZIklZLM3BARFwMPAj2BH2Tm0xFxQWH9LcADwCnAMqAeOK+w+Rjgc8CiiFhQaPtaZj7QmZ9BkiR1TYZAkiRJnawQ2jywWdstTd4ncFEz282l+fsFSZIkbZfTwSRJkiRJkkpAaYdAtbVQVgY9ejS81tYWuyJJkiRJkqQOUbrTwWproboa6usbllesaFgGqKoqXl2SJEmSJEkdoHSvBJo8+b8DoI3q6xvaJUmSJEmSupnSDYFWrmxduyRJkiRJUhdWuiHQ4MGta5ckSZIkSerCSjcEqqmBfv02bevXr6FdkiRJkiSpmyndEKiqCqZPhyFDIKLhdfp0bwotSZIkSZK6pdJ9Ohg0BD6GPpIkSZIkqQSU7pVAkiRJkiRJJcQQSJIkSZIkqQQYAkmSJEmSJJWAFoVAEbE8IhZFxIKIqCu0vS8iHo6I5wqve3dsqVtXu6iWsmll9LiuB2XTyqhdVFusUiRJkiRJknZKrbkS6ITMrMjMysLylcCszDwImFVY7nS1i2qpvreaFWtXkCQr1q6g+t5qgyBJkiRJkqQm2jId7FPA7YX3twN/1/ZyWm/yrMnUr6/fpK1+fT2TZ00uRjmSJEmSJEk7pZaGQAk8FBHzIqK60LZfZr4EUHh9f3MbRkR1RNRFRN3q1avbXvFmVq5d2ap2SZIkSZKkUtTSEGhMZo4CTgYuiojjW7qDzJyemZWZWTlgwIAdKnJbBvcf3Kp2SZIkSZKkUtSiECgzXyy8vgLcDYwGXo6I/QEKr690VJHbUjOuhn69+23S1q93P2rG1RSjHEmSJEmSpJ3SdkOgiNgtIvbY+B4YDywGfgGcU+h2DnBPRxW5LVXlVUz/5HSG9B9CEAzpP4Tpn5xOVXlVMcqRJEmSJEnaKfVqQZ/9gLsjYmP/n2Tmf0TEk8DPIuJ/ACuBMzuuzG2rKq8y9JEkSZIkSdqG7YZAmfk8MKKZ9jXAuI4oSpIkSZIkSe2rLY+IlyRJkiRJUhdhCCRJkiRJklQCDIEkSZIkSZJKgCGQJEmSJElSCTAEkiRJkiRJKgGGQJIkSZIkSSXAEEiSJEmSJKkEGAJJkiRJkiSVAEMgSZIkSZKkEmAIJEmSJEmSVAIMgSRJkjpZRJwUEc9GxLKIuLKZ9RERNxTWL4yIUS3dVpIkaWsMgSRJkjpRRPQEbgJOBg4DJkbEYZt1Oxk4qPCnGri5FdtKkiQ1yxBIkiSpc40GlmXm85n5DnAH8KnN+nwK+FE2eBzYKyL2b+G2kiRJzTIEkiRJ6lwDgReaLK8qtLWkT0u2BSAiqiOiLiLqVq9e3eaiJUlS12cIJEmS1LmimbZsYZ+WbNvQmDk9Myszs3LAgAGtLFGSJHVHvYpdgCRJUolZBRzQZHkQ8GIL++zSgm0lSZKa5ZVAkiRJnetJ4KCIGBoRuwBnA7/YrM8vgEmFp4QdBazNzJdauK0kSVKzvBJIkiSpE2Xmhoi4GHgQ6An8IDOfjogLCutvAR4ATgGWAfXAedvatggfQ5IkdUGGQJIkSZ0sMx+gIehp2nZLk/cJXNTSbSVJklrC6WCSJEmSJEklwBBIkiRJkiSpBBgCSZIkSZIklQBDIEmSJEmSpBJgCCRJkiRJklQCDIEkSZIkSZJKgCGQJEmSJElSCTAEkiRJkiRJKgGGQJIkSZIkSSXAEEiSJEmSJKkEtDgEioieETE/Iu4rLI+IiMciYlFE3BsRe3ZcmZIkSZIkSWqL1lwJdCmwtMnyrcCVmVkO3A18tT0LkyRJkiRJUvtpUQgUEYOAT9AQ/Gz0IWBO4f3DwKfbtzRJkoqndlEtZdPK6HFdD8qmlVG7qLbYJUmSJElt0tIrgaYBlwPvNWlbDEwovD8TOKC5DSOiOiLqIqJu9erVO1yoJEmdpXZRLdX3VrNi7QqSZMXaFVTfW20QJEmSpC5tuyFQRJwKvJKZ8zZbdT5wUUTMA/YA3mlu+8ycnpmVmVk5YMCANhcsSVJHmzxrMvXr6zdpq19fz+RZk4tUkSRJktR2vVrQZwwwISJOAfoAe0bEv2bmZ4HxABFxMA3TxSRJ6vJWrl3ZqnZJkiSpK9julUCZeVVmDsrMMuBs4FeZ+dmIeD9ARPQA/hG4pUMrlSSpkwzuP7hV7ZIkSVJX0Jqng21uYkT8HngGeBG4rX1KkiSpuGrG1dCvd79N2vr17kfNuJoiVSRJkiS1XUumgzXKzNnA7ML77wDfaf+SJEkqrqryKqDh3kAr165kcP/B1IyraWyXJEmSuqJWhUCSJJWKqvIqQx9JkiR1K22ZDiZJkiRJkqQuwhBIkiRJkiSpBBgCSZIkSZIklQBDIEmSJEmSpBJgCCRJkiRJklQCDIEkSZIkSZJKgCGQJEmSJElSCTAEkiRJkiRJKgGGQJIkSZIkSSXAEEiSJEmSJKkEGAJJkiR1koh4X0Q8HBHPFV733kq/kyLi2YhYFhFXNmn/VkQ8ExELI+LuiNir86qXJEldnSGQJElS57kSmJWZBwGzCsubiIiewE3AycBhwMSIOKyw+mFgWGYOB34PXNUpVUuSpG7BEEiSJKnzfAq4vfD+duDvmukzGliWmc9n5jvAHYXtyMyHMnNDod/jwKAOrleSJHUjhkCSJEmdZ7/MfAmg8Pr+ZvoMBF5osryq0La584FftnuFkiSp2+pV7AIkSZK6k4iYCXygmVWTWzpEM2252T4mAxuA2m3UUQ1UAwwePLiFu5YkSd2ZIZAkSVI7ysyPbm1dRLwcEftn5ksRsT/wSjPdVgEHNFkeBLzYZIxzgFOBcZmZbEVmTgemA1RWVm61nyRJKh1OB5MkSeo8vwDOKbw/B7inmT5PAgdFxNCI2AU4u7AdEXEScAUwITPrO6FeSZLUjRgCSZIkdZ7rgY9FxHPAxwrLRMQHI+IBgMKNny8GHgSWAj/LzKcL298I7AE8HBELIuKWzv4AkiSp63I6mCRJUifJzDXAuGbaXwROabL8APBAM/3+tkMLlCRJ3ZpXAkmSJEmSJJUAQyBJkiRJkqQSYAgkSZIkSZJUAgyBJEmSJEmSSoAhkCRJkiRJUgkwBJIkSZIkSSoBhkCSJEmSJEklwBBIkiRJkiSpBBgCSZIkSZIklQBDIEmSJEmSpBLQ4hAoInpGxPyIuK+wXBERj0fEgoioi4jRHVemJEmSJEmS2qI1VwJdCixtsvzPwHWZWQFcXViWJEmSJEnSTqhFIVBEDAI+AdzapDmBPQvv+wMvtm9pkiRJkiRJai+9WthvGnA5sEeTtsuAByNiKg1h0jHNbRgR1UA1wODBg3e8UkmSJEmSJO2w7V4JFBGnAq9k5rzNVl0IfDEzDwC+CHy/ue0zc3pmVmZm5YABA9pcsCRJkiRJklqvJVcCjQEmRMQpQB9gz4j4V+CTNNwnCODf2HSqmCRJkiRJknYi270SKDOvysxBmVkGnA38KjM/S8M9gD5S6HYi8FyHVSlJkiRJkqQ2aek9gZrzD8B3IqIXsI7CfX8kSZIkSZK082lVCJSZs4HZhfdzgSPavyRJkiRJkiS1txY9Il6SJEmSJEldmyGQJEmSJElSCTAEkiRJkiRJKgGGQJIkSZIkSSXAEEiSJEmSJKkEGAJJkiRJkiSVAEMgSZIkSZKkEmAIJEmSJEmSVAIMgSRJkiRJkkqAIZAkSZIkSVIJMASSJEmSJEkqAYZAkiRJkiRJJcAQSJIkqZNExPsi4uGIeK7wuvdW+p0UEc9GxLKIuLKZ9V+JiIyIfTu+akmS1F0YAkmSJHWeK4FZmXkQMKuwvImI6AncBJwMHAZMjIjDmqw/APgYsLJTKpYkSd2GIZAkSVLn+RRwe+H97cDfNdNnNLAsM5/PzHeAOwrbbfQvwOVAdmShkiSp+zEEkiRJ6jz7ZeZLAIXX9zfTZyDwQpPlVYU2ImIC8KfMfGp7O4qI6oioi4i61atXt71ySZLU5fUqdgGSJEndSUTMBD7QzKrJLR2imbaMiH6FMca3ZJDMnA5MB6isrPSqIUmSZAgkSZLUnjLzo1tbFxEvR8T+mflSROwPvNJMt1XAAU2WBwEvAn8DDAWeioiN7b+LiNGZ+ed2+wCSJKnbcjqYJElS5/kFcE7h/TnAPc30eRI4KCKGRsQuwNnALzJzUWa+PzPLMrOMhrBolAGQJElqKUMgSZKkznM98LGIeI6GJ3xdDxARH4yIBwAycwNwMfAgsBT4WWY+XaR6JUlSN2IIpOKqrYWyMujRo+G1trbYFUmS1GEyc01mjsvMgwqv/1VofzEzT2nS74HMPDgz/yYza7YyVllmvtpZtUuSpK7PewKpeGproboa6usbllesaFgGqKoqXl2SJEmSJHVDXgmk4pk8+b8DoI3q6xvaJUmSJElSuzIEUvGsXNm6dkmSJEmStMMMgVQ8gwe3rl2SJEmSJO0wQyAVT00N9Ou3aVu/fg3tkiRJkiSpXRkCqXiqqmD6dBgyBCIaXqdP96bQkiRJkiR1AJ8OpuKqqjL0kSRJkiSpE3glkCRJkiRJUglo8ZVAEdETqAP+lJmnRsQM4EOF1XsBr2VmRQfUKEmSJEmSpDZqzZVAlwJLNy5k5lmZWVEIfu4E7mrv4iRpR9QuqqVsWhk9rutB2bQyahfVFrskSZIkSSq6FoVAETEI+ARwazPrAvgM8NP2LU2SWq92US3V91azYu0KkmTF2hVU31ttECRJkiSp5LX0SqBpwOXAe82sOw54OTOfa27DiKiOiLqIqFu9evUOlilJLTN51mTq19dv0la/vp7JsyYXqSJJkiRJ2jlsNwSKiFOBVzJz3la6TGQbVwFl5vTMrMzMygEDBuxgmZLUMivXrmxVuyRJkiSVipZcCTQGmBARy4E7gBMj4l8BIqIXcDowo8MqlKRWGNx/cKvaJUmSJKlUbDcEysyrMnNQZpYBZwO/yszPFlZ/FHgmM1d1YI2S1GI142ro17vfJm39evejZlxNkSqSJEmSpJ1Da54O1pyz8YbQknYiVeVVTP/kdIb0H0IQDOk/hOmfnE5VeVWxS5MkSZKkourVms6ZORuY3WT53PYtR5Larqq8ytBHkiRJkjbT1iuBJEmSJEmS1AUYAkmSJEmSJJUAQyBJkiRJkqQSYAgkSZIkSZJUAgyBJEmSJEmSSoAhkCRJkiRJUgkwBJIkSZIkSSoBkZmdt7OI1cCKDtzFvsCrHTi+NuXx7lwe787l8e48HuvO1dHHe0hmDujA8bUDOvgczH/DbecxbDuPYdt5DNvOY9h2HsMd16JzsE4NgTpaRNRlZmWx6ygVHu/O5fHuXB7vzuOx7lweb7U3/061ncew7TyGbecxbDuPYdt5DDue08EkSZIkSZJKgCGQJEmSJElSCehuIdD0YhdQYjzencvj3bk83p3HY925PN5qb/6dajuPYdt5DNvOY9h2HsO28xh2sG51TyBJkiRJkiQ1r7tdCSRJkiRJkqRmdIsQKCJOiohnI2JZRFxZ7Hq6s4g4ICJ+HRFLI+LpiLi02DWVgojoGRHzI+K+YtfS3UXEXhHx84h4pvD3/Ohi19SdRcQXC/8tWRwRP42IPsWuqTuJiB9ExCsRsbhJ2/si4uGIeK7wuncxa1TX5flX2zX3b1Qt53lp20VEn4h4IiKeKhzD64pdU1fl+XrbRMTyiFgUEQsioq7Y9XRnXT4EioiewE3AycBhwMSIOKy4VXVrG4AvZ+ahwFHARR7vTnEpsLTYRZSI7wD/kZmHACPwuHeYiBgIXAJUZuYwoCdwdnGr6nZ+CJy0WduVwKzMPAiYVViWWsXzr3bzQ7b8N6qW87y07d4GTszMEUAFcFJEHFXkmroqz9fb7oTMrPAR8R2ry4dAwGhgWWY+n5nvAHcAnypyTd1WZr6Umb8rvH+Dhv/QDSxuVd1bRAwCPgHcWuxauruI2BM4Hvg+QGa+k5mvFbeqbq8X0DciegH9gBeLXE+3kplzgP/arPlTwO2F97cDf9epRam78PyrHWzl36hayPPStssGbxYWexf+eNPYVvJ8XV1JdwiBBgIvNFlehf/x7xQRUQaMBH5b3Eq6vWnA5cB7xS6kBBwIrAZuK1zOe2tE7FbsorqrzPwTMBVYCbwErM3Mh4pbVUnYLzNfgoZfoID3F7kedU2ef2mn4nnpjitMY1oAvAI8nJkew9bzfL3tEngoIuZFRHWxi+nOukMIFM20mV53sIjYHbgTuCwzXy92Pd1VRJwKvJKZ84pdS4noBYwCbs7MkcBfcapMhynci+ZTwFDgg8BuEfHZ4lYlqYU8/9JOw/PStsnMdzOzAhgEjI6IYcWuqSvxfL3djMnMUTRMM74oIo4vdkHdVXcIgVYBBzRZHoTTCTpURPSm4X+0tZl5V7Hr6ebGABMiYjkNl9qfGBH/WtySurVVwKom34D9nIZQSB3jo8AfM3N1Zq4H7gKOKXJNpeDliNgfoPD6SpHrUdfk+Zd2Cp6Xtp/CFPjZeJ+q1vJ8vR1k5ouF11eAu2mYdqwO0B1CoCeBgyJiaETsQsNNRX9R5Jq6rYgIGu6XsjQzv13serq7zLwqMwdlZhkNf7d/lZleKdFBMvPPwAsR8aFC0zhgSRFL6u5WAkdFRL/Cf1vG4Q0VO8MvgHMK788B7iliLeq6PP9S0Xle2nYRMSAi9iq870vDFzTPFLeqrsXz9baLiN0iYo+N74HxgE9N7CBdPgTKzA3AxcCDNPzy8LPMfLq4VXVrY4DP0ZBwLyj8OaXYRUnt6H8BtRGxkIanZPxTkevptgpXXP0c+B2wiIb/J00valHdTET8FHgM+FBErIqI/wFcD3wsIp4DPlZYllrF86/2sZV/o2o5z0vbbn/g14XznidpuCeQjzhXZ9sPmBsRTwFPAPdn5n8UuaZuKzKdvi1JkiRJktTddfkrgSRJkiRJkrR9hkCSJEmSJEklwBBIkiRJkiSpBBgCSZIkSZIklQBDIEmSJEmSpBJgCCRJkiRJklQCDIEkSZIkSZJKgCGQJEmSJElSCfj/AUOb92F0XWxtAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1440x288 with 2 Axes>"
+       "<Figure size 504x504 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -658,498 +699,77 @@
     }
    ],
    "source": [
-    "quants = bo.gp.predict_quantiles(x[:, None], quantiles=(10, 90))\n",
-    "mu, var = bo.gp.predict(x[:, None])\n",
+    "delta = np.linspace(0, 1)\n",
+    "sigma = np.linspace(0, 1)[::-1]\n",
+    "# get a square grid w/ the delta and sigma inputs\n",
+    "dd, ss = np.meshgrid(delta, sigma)\n",
     "\n",
-    "ei = expected_improvement(\n",
-    "        f=bo.gp.predict,\n",
-    "        y_current=bo.y.max(),\n",
-    "        x_proposed=x[:, None])\n",
+    "# this is just the EI function\n",
+    "z = dd / ss\n",
+    "unit_norm = stats.norm()\n",
+    "ei = dd * unit_norm.cdf(z) + ss * unit_norm.pdf(z)\n",
+    "ei[np.isnan(ei)] = 0\n",
     "\n",
-    "\n",
-    "plt.figure(figsize=(20, 4))\n",
-    "plt.suptitle(f'Iteration {i}')\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.title('$f(x^*)$')\n",
-    "plt.plot(x, mu, color='C0', label='Mean')\n",
-    "plt.fill_between(x, quants[0].flatten(), quants[1].flatten(), alpha=0.15, edgecolor='C1', label='Confidence')\n",
-    "plt.scatter(bo.x[:bo.random_trials], bo.y[:bo.random_trials], color='r', label='random trials')\n",
-    "\n",
-    "mask = bo.x.sum(axis=1) != 0\n",
-    "plt.scatter(bo.x[mask][bo.random_trials:], bo.y[mask][bo.random_trials:], color='g', label='BO trials')\n",
-    "\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
+    "plt.figure(figsize=(7, 7))\n",
     "plt.title('$EI(x)$')\n",
-    "plt.plot(x, ei, color='C1')\n",
-    "plt.vlines(bo._new_proposal(), 0, ei.max(), linestyle='--', label='New proposal')\n",
-    "plt.legend()"
+    "plt.contourf(delta, sigma, ei)\n",
+    "plt.xlabel('$\\delta$')\n",
+    "plt.ylabel('$\\sigma(x^*)$')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Array 'cov' must be square if it is two dimensional, but cov.shape = (50, 1).",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-206-b3e1a5540454>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultivariate_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrvs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_multivariate.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, mean, cov, allow_singular, seed)\u001b[0m\n\u001b[1;32m    361\u001b[0m         return multivariate_normal_frozen(mean, cov,\n\u001b[1;32m    362\u001b[0m                                           \u001b[0mallow_singular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_singular\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m                                           seed=seed)\n\u001b[0m\u001b[1;32m    364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_process_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_multivariate.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, mean, cov, allow_singular, seed, maxpts, abseps, releps)\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultivariate_normal_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m         self.dim, self.mean, self.cov = self._dist._process_parameters(\n\u001b[0;32m--> 735\u001b[0;31m                                                             None, mean, cov)\n\u001b[0m\u001b[1;32m    736\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_PSD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_singular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmaxpts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/scipy/stats/_multivariate.py\u001b[0m in \u001b[0;36m_process_parameters\u001b[0;34m(self, dim, mean, cov)\u001b[0m\n\u001b[1;32m    419\u001b[0m                        \" but 'mean' is a vector of length %d.\")\n\u001b[1;32m    420\u001b[0m                 \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m             raise ValueError(\"Array 'cov' must be at most two-dimensional,\"\n",
-      "\u001b[0;31mValueError\u001b[0m: Array 'cov' must be square if it is two dimensional, but cov.shape = (50, 1)."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "a = stats.multivariate_normal(mu.flatten(), cov).rvs(75)\n",
-    "\n",
-    "plt.figure(figsize=(16, 4))\n",
-    "for i in range(a.shape[0]):\n",
-    "    plt.plot(x, a[i, :], alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'gpmean': [[<matplotlib.lines.Line2D at 0x7f9ef3c11240>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ae7eb8>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa18d0>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa1160>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa1da0>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa1e80>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa9470>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa97b8>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa9b00>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa9e48>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3aa9f28>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ab2518>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ab2860>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ab2ba8>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ab2ef0>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3ab2fd0>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3abd5c0>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3abd908>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3abdc50>],\n",
-       "  [<matplotlib.lines.Line2D at 0x7f9ef3abdf98>]]}"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bo.gp.plot_samples(samples=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2/2 [00:00<00:00, 1279.92it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting 2 random trials...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/rbf.py:54: RuntimeWarning:invalid value encountered in multiply\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9f04d91860>]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " /opt/miniconda3/lib/python3.7/site-packages/matplotlib/figure.py:2369: UserWarning:This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(3)\n",
-    "bo = BayesOpt(param_ranges, \n",
-    "              evaluate_params, \n",
-    "             random_trials=2)\n",
-    "\n",
-    "bo._random_search()\n",
-    "bo._fit_gp()\n",
-    "bo.gp.plot(plot_density=0, figsize=(16, 4))\n",
-    "plt.scatter(bo.x[:bo.random_trials], bo.y[:bo.random_trials], color='r')\n",
-    "plt.plot(x, func(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2/2 [00:00<00:00, 1929.75it/s]\n",
-      "  0%|          | 0/5 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting 2 random trials...\n",
-      "Starting 5 optimization trials...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 1/5 [00:00<00:01,  2.33it/s] /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/rbf.py:51: RuntimeWarning:overflow encountered in square\n",
-      "100%|██████████| 5/5 [00:01<00:00,  3.23it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f9f04a61400>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# np.random.seed(3)\n",
-    "bo = BayesOpt(param_ranges, evaluate_params, random_trials=2, optimization_trials=5)\n",
-    "bo.fit()\n",
-    "bo.gp.plot(plot_density=0, figsize=(16, 4))\n",
-    "plt.plot(x, func(x))\n",
-    "plt.scatter(bo.x[:bo.random_trials], bo.y[:bo.random_trials], color='r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'x': 10.0}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bo.best_params"
+    "from water_accumulation import determine_max_water_cubic"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([9.82811278])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "bo._new_proposal(n=25)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'dataplot': [<matplotlib.collections.PathCollection at 0x7f7eb343a908>],\n",
-       " 'gpmean': [[<matplotlib.lines.Line2D at 0x7f7eb343a8d0>]],\n",
-       " 'gpconfidence': [<matplotlib.collections.PolyCollection at 0x7f7eb343add8>]}"
-      ]
-     },
-     "execution_count": 398,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bo._fit_gp()\n",
-    "bo.gp.plot(plot_density=0, figsize=(16, 4))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 13648.89it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting 10 random trials...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# np.random.seed(1)\n",
-    "bo = BayesOpt(param_ranges, evaluate_params, xi=0.05, random_trials=10)\n",
-    "bo._random_search()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f7eb48fa9e8>"
-      ]
-     },
-     "execution_count": 364,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bo._fit_gp()\n",
-    "bo.gp.plot(plot_density=0, figsize=(16, 4), plot_limits=[0, 10])\n",
-    "plt.plot(x, func(x), ls='--')\n",
-    "plt.scatter(bo.x[:bo.random_trials], bo.y[:bo.random_trials], color='r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.])"
-      ]
-     },
-     "execution_count": 356,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bo._new_proposal(n=25)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 385,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x7f7eb361ee80>"
-      ]
-     },
-     "execution_count": 385,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ei = expected_improvement(\n",
-    "            f=bo.gp.predict,\n",
-    "            y_current=bo.y.max(),\n",
-    "            x_proposed=x[:, None],\n",
-    "            xi=0\n",
-    ")\n",
+    "maximum_factor = 1.3\n",
     "\n",
-    "plt.plot(x, ei)\n",
-    "plt.vlines(bo._new_proposal(), 0, max(ei))\n",
-    "plt.vlines(bo.x[bo.y.argmax()], 0, max(ei), linestyle='--')"
+    "param_ranges = {\n",
+    "    'mp_span_1': {\n",
+    "        'range': [70, int(70 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'mp_span_2': {\n",
+    "        'range': [240, int(240 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'mp_span_3': {\n",
+    "        'range': [25, int(25 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'k1': {\n",
+    "        'range': [2000, int(2000 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'k2': {\n",
+    "        'range': [3000, int(3000 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    }\n",
+    "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def optim_f(x):\n",
-    "    return -expected_improvement(\n",
-    "        f=bo.gp.predict,\n",
-    "        y_current=bo.y.max(),\n",
-    "        x_proposed=x[None, :],\n",
-    "        xi=bo.xi)\n",
-    "\n",
-    "\n",
-    "x0 = np.random.uniform(\n",
-    "    low=bo.bounds[:, 0],\n",
-    "    high=bo.bounds[:, 1],\n",
-    "    size=(25, bo.x.shape[1])\n",
-    ")\n",
-    "proposal = None\n",
-    "best_ei = np.inf\n",
-    "for x0_ in x0:\n",
-    "    res = optimize.minimize(optim_f, \n",
-    "                            x0_, \n",
-    "                            bounds=bo.bounds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.05296022])"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ei_along_axis([8.94396671])"
+    "bo = BayesOpt(param_ranges, \n",
+    "              determine_max_water_cubic, \n",
+    "              random_trials=5, \n",
+    "              optimization_trials=20, \n",
+    "              kernel=GPy.kern.Matern52(input_dim=3) + GPy.kern.Matern32(input_dim=3))\n",
+    "bo.fit()"
    ]
   },
   {
@@ -1161,278 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details', 'categories', 'url'])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fetch = fetch_openml('SpeedDating')\n",
-    "fetch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>has_null</th>\n",
-       "      <th>wave</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>d_importance_same_race</th>\n",
-       "      <th>d_importance_same_religion</th>\n",
-       "      <th>d_pref_o_intelligence</th>\n",
-       "      <th>d_pref_o_funny</th>\n",
-       "      <th>d_pref_o_ambitious</th>\n",
-       "      <th>d_pref_o_shared_interests</th>\n",
-       "      <th>d_sinsere_o</th>\n",
-       "      <th>...</th>\n",
-       "      <th>d_theater</th>\n",
-       "      <th>d_movies</th>\n",
-       "      <th>d_concerts</th>\n",
-       "      <th>d_music</th>\n",
-       "      <th>d_shopping</th>\n",
-       "      <th>d_yoga</th>\n",
-       "      <th>d_expected_happy_with_sd_people</th>\n",
-       "      <th>d_expected_num_interested_in_me</th>\n",
-       "      <th>d_expected_num_matches</th>\n",
-       "      <th>met</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
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-       "      <td>1.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 55 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   has_null  wave  gender  d_importance_same_race  d_importance_same_religion  \\\n",
-       "0       0.0   1.0     0.0                     0.0                         0.0   \n",
-       "1       0.0   1.0     0.0                     0.0                         0.0   \n",
-       "2       1.0   1.0     0.0                     0.0                         0.0   \n",
-       "3       0.0   1.0     0.0                     0.0                         0.0   \n",
-       "4       0.0   1.0     0.0                     0.0                         0.0   \n",
-       "\n",
-       "   d_pref_o_intelligence  d_pref_o_funny  d_pref_o_ambitious  \\\n",
-       "0                    0.0             0.0                 0.0   \n",
-       "1                    1.0             1.0                 0.0   \n",
-       "2                    0.0             0.0                 0.0   \n",
-       "3                    1.0             1.0                 0.0   \n",
-       "4                    0.0             2.0                 0.0   \n",
-       "\n",
-       "   d_pref_o_shared_interests  d_sinsere_o  ...  d_theater  d_movies  \\\n",
-       "0                        0.0          0.0  ...        0.0       0.0   \n",
-       "1                        0.0          0.0  ...        0.0       0.0   \n",
-       "2                        0.0          1.0  ...        0.0       0.0   \n",
-       "3                        0.0          0.0  ...        0.0       0.0   \n",
-       "4                        1.0          0.0  ...        0.0       0.0   \n",
-       "\n",
-       "   d_concerts  d_music  d_shopping  d_yoga  d_expected_happy_with_sd_people  \\\n",
-       "0         0.0      0.0         0.0     0.0                              0.0   \n",
-       "1         0.0      0.0         0.0     0.0                              0.0   \n",
-       "2         0.0      0.0         0.0     0.0                              0.0   \n",
-       "3         0.0      0.0         0.0     0.0                              0.0   \n",
-       "4         0.0      0.0         0.0     0.0                              0.0   \n",
-       "\n",
-       "   d_expected_num_interested_in_me  d_expected_num_matches  met  \n",
-       "0                              0.0                     0.0  0.0  \n",
-       "1                              0.0                     0.0  1.0  \n",
-       "2                              0.0                     0.0  1.0  \n",
-       "3                              0.0                     0.0  0.0  \n",
-       "4                              0.0                     0.0  0.0  \n",
-       "\n",
-       "[5 rows x 55 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(fetch.data, columns=fetch.feature_names)\n",
-    "# make it a bit harder\n",
-    "df = df.drop([\n",
-    "    'samerace', 'sincere_important', 'age_o', 'shared_interests_important', 'pref_o_attractive', 'exercise', 'd_like', 'hiking', 'art', 'concerts', 'sports', 'pref_o_intelligence', 'pref_o_sincere', 'pref_o_ambitious', 'intellicence_important', 'pref_o_funny', 'funny_partner', 'attractive_important', 'interests_correlate', 'guess_prob_liked', 'funny_o', 'expected_num_matches', 'shared_interests_o', 'attractive_partner', 'attractive_o', 'like',\n",
-    "    'clubbing', 'expected_happy_with_sd_people', 'importance_same_race', 'attractive', 'ambtition_important', 'd_d_age', 'race', 'reading', 'expected_num_interested_in_me', 'd_ambitous_o', 'd_interests_correlate', 'sincere', 'intelligence', 'movies', 'd_pref_o_sincere', 'importance_same_religion', 'd_funny_partner', 'd_pref_o_attractive', 'sincere_partner', 'tvsports', 'race_o', 'museums', 'funny_important', 'gaming', 'intelligence_o', 'ambitous_o', 'd_shared_interests_o', 'sinsere_o', 'intelligence_partner', 'field', 'ambition_partner', 'd_age', 'age', 'd_guess_prob_liked', 'pref_o_shared_interests', 'd_funny_o', 'd_attractive_partner', 'd_attractive_o', 'shared_interests_partner']\n",
-    ", axis=1)\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train, x_test, y_train, y_test = train_test_split(df, fetch.target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "imputer = preprocessing.Imputer(strategy='mean')\n",
-    "x_train = imputer.fit_transform(x_train)\n",
-    "x_test = imputer.transform(x_test)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1452,27 +801,209 @@
     "    }\n",
     "}\n",
     "\n",
-    "# nn\n",
-    "param_ranges = {\n",
-    "    'hidden_layer_sizes': {\n",
-    "        'range': [1, 10],\n",
-    "        'type': 'int'\n",
-    "    },\n",
-    "    'batch_size': {\n",
-    "        'range': [10, 100],\n",
-    "        'type': 'int'\n",
-    "    },\n",
-    "    'learning_rate_init': {\n",
-    "        'range': [0.00001, 0.01],\n",
-    "        'type': 'float'\n",
-    "    }\n",
-    "}\n",
     "\n",
     "def evaluate_params(hyperparams):\n",
-    "    m = MLPClassifier(**hyperparams)\n",
+    "    m = DecisionTreeClassifier(**hyperparams)\n",
     "    m.fit(x_train, y_train)\n",
     "    return m.score(x_test, y_test)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'sklearn.datasets.california_housing' from '/opt/miniconda3/lib/python3.7/site-packages/sklearn/datasets/california_housing.py'>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasets.load_diabetes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 2/10 [00:00<00:00, 18.64it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting 10 random trials...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:00<00:00, 19.36it/s]\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting 20 optimization trials...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  5%|▌         | 1/20 [00:00<00:05,  3.55it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
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+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:551: RuntimeWarning:invalid value encountered in multiply\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:454: RuntimeWarning:invalid value encountered in multiply\n",
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+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:551: RuntimeWarning:invalid value encountered in multiply\n",
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+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:554: RuntimeWarning:invalid value encountered in subtract\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:457: RuntimeWarning:invalid value encountered in multiply\n",
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+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:454: RuntimeWarning:invalid value encountered in multiply\n",
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+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 60%|██████    | 12/20 [00:03<00:03,  2.64it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 65%|██████▌   | 13/20 [00:04<00:02,  2.52it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 70%|███████   | 14/20 [00:04<00:02,  2.54it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 75%|███████▌  | 15/20 [00:05<00:01,  2.52it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 80%|████████  | 16/20 [00:05<00:01,  2.54it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 85%|████████▌ | 17/20 [00:05<00:01,  2.49it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 90%|█████████ | 18/20 [00:06<00:00,  2.48it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      " 95%|█████████▌| 19/20 [00:06<00:00,  2.48it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
+      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
+      "100%|██████████| 20/20 [00:07<00:00,  2.51it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bo = BayesOpt(param_ranges, \n",
+    "              evaluate_params, \n",
+    "              random_trials=10, \n",
+    "              optimization_trials=20, \n",
+    "              kernel=GPy.kern.Matern52(input_dim=3) + GPy.kern.Matern32(input_dim=3) + GPy.kern.a)\n",
+    "bo.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8276849642004773"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bo.y.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8276849642004773"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bo.y.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/bayesian/bayesian_optimization/bayesopt_water_accumulation.ipynb b/bayesian/bayesian_optimization/bayesopt_water_accumulation.ipynb
new file mode 100644
index 0000000..40f6437
--- /dev/null
+++ b/bayesian/bayesian_optimization/bayesopt_water_accumulation.ipynb
@@ -0,0 +1,837 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn import preprocessing\n",
+    "import numpy as np\n",
+    "import GPy\n",
+    "from scipy import stats\n",
+    "from scipy import optimize\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "from functools import partial\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from anastruct.basic import converge\n",
+    "from anastruct.material.profile import HEA, IPE\n",
+    "from anastruct.fem.system import SystemElements, Vertex\n",
+    "from anastruct.material.units import to_kNm2, to_kN\n",
+    "import anastruct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# anastruct version == anastruct==1.1.0a1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bayesian Optimization for water accumulation analysis\n",
+    "\n",
+    "See these posts:\n",
+    "\n",
+    "* [water accumulation](https://www.ritchievink.com/blog/2017/08/23/a-nonlinear-water-accumulation-analysis-in-python/)\n",
+    "* [bayesian optimization](https://www.ritchievink.com/blog/2019/08/25/algorithm-breakdown-bayesian-optimization/)\n",
+    "\n",
+    "For optimization purposes we make the `structure` function take the following inputs:\n",
+    "\n",
+    "* mp_span_1\n",
+    "* mp_span_2\n",
+    "* mp_span_3\n",
+    "* k1\n",
+    "* k2\n",
+    "\n",
+    "These parameters will be optimized within the following boundaries:\n",
+    "\n",
+    "```python\n",
+    "{'mp_span_1': {'range': [70, 91], 'type': 'float'},\n",
+    " 'mp_span_2': {'range': [240, 312], 'type': 'float'},\n",
+    " 'mp_span_3': {'range': [25, 32], 'type': 'float'},\n",
+    " 'k1': {'range': [2000, 2600], 'type': 'float'},\n",
+    " 'k2': {'range': [3000, 3900], 'type': 'float'}}\n",
+    "```\n",
+    "\n",
+    "The stiffness of the rotation is assumed to be linearly increasing with `mp`. See the `structure` function;\n",
+    "\n",
+    "```python\n",
+    "    first = dict(spring={1: 9e3 / 70 * mp_span_1}, mp={1: mp_span_1})\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# constants\n",
+    "E = 2.1e5 / 1.3\n",
+    "b = 5 * 1.09\n",
+    "q_water = 10\n",
+    "\n",
+    "# axes height levels\n",
+    "h_1 = 0\n",
+    "h_2 = 0.258\n",
+    "h_3 = 0.046\n",
+    "h_4 = 0.274\n",
+    "h_5 = 0.032\n",
+    "h_6 = 0.15\n",
+    "\n",
+    "# beam spans\n",
+    "span_1 = span_2 = 21.9\n",
+    "span_3 = 8.9\n",
+    "\n",
+    "# Vertices at the axes\n",
+    "p1 = Vertex(0, h_1)\n",
+    "p2 = Vertex(span_1 * 0.5, h_2)\n",
+    "p3 = Vertex(span_1, h_3)\n",
+    "p4 = Vertex(span_1 + span_2 * 0.5, h_4)\n",
+    "p5 = Vertex(span_1 + span_2, h_5)\n",
+    "p6 = Vertex(span_1 + span_2 + span_3, h_6)\n",
+    "\n",
+    "\n",
+    "def structure(\n",
+    "    mp_span_1=70,\n",
+    "    mp_span_2=240,\n",
+    "    mp_span_3=25,\n",
+    "    k1=2e3,\n",
+    "    k2=3e3,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Build the structure from left to right, starting at axis 1.\n",
+    "\n",
+    "    variables:\n",
+    "    EA = Young's modulus * Area\n",
+    "    EI = Young's modulus * moment of Inertia\n",
+    "    g = Weight [kN/ m]\n",
+    "    elements = reference of the element id's that were created\n",
+    "    dl = c.t.c distance different nodes.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dl = 0.2\n",
+    "\n",
+    "    ## SPAN 1 AND 2\n",
+    "\n",
+    "    # The elements between axis 1 and 3 are an IPE 450 member.\n",
+    "    EA = to_kN(E * IPE[450][\"A\"])  # Y\n",
+    "    EI = to_kNm2(E * IPE[450][\"Iy\"])\n",
+    "    g = IPE[450][\"G\"] / 100\n",
+    "\n",
+    "    # New system.\n",
+    "    ss = SystemElements(mesh=3)\n",
+    "\n",
+    "    # span 1\n",
+    "    first = dict(spring={1: 9e3 / 70 * mp_span_1}, mp={1: mp_span_1})\n",
+    "\n",
+    "    elements = ss.add_multiple_elements(\n",
+    "        location=[p1, p2], dl=dl, first=first, EA=EA, EI=EI, g=g\n",
+    "    )\n",
+    "    elements += ss.add_multiple_elements(location=p3, dl=dl, EA=EA, EI=EI, g=g)\n",
+    "\n",
+    "    # span 2\n",
+    "    first = dict(spring={1: 40e3 / 240 * mp_span_2}, mp={1: mp_span_2})\n",
+    "    elements += ss.add_multiple_elements(\n",
+    "        location=p4, dl=dl, first=first, EA=EA, EI=EI, g=g\n",
+    "    )\n",
+    "    elements += ss.add_multiple_elements(location=p5, dl=dl, EA=EA, EI=EI, g=g)\n",
+    "\n",
+    "    ## SPAN 3\n",
+    "\n",
+    "    # span 3\n",
+    "    # different IPE\n",
+    "    g = IPE[240][\"G\"] / 100\n",
+    "    EA = to_kN(E * IPE[240][\"A\"])\n",
+    "    EI = to_kNm2(E * IPE[240][\"Iy\"])\n",
+    "    first = dict(spring={1: 15e3 / 25 * mp_span_3}, mp={1: mp_span_3})\n",
+    "\n",
+    "    elements += ss.add_multiple_elements(\n",
+    "        location=p6, first=first, dl=dl, EA=EA, EI=EI, g=g\n",
+    "    )\n",
+    "\n",
+    "    # Add a dead load of -2 kN/m to all elements.\n",
+    "    ss.q_load(-2, elements, direction=\"y\")\n",
+    "\n",
+    "    ## COLUMNS\n",
+    "\n",
+    "    # column height\n",
+    "    h = 7.2\n",
+    "\n",
+    "    # left column\n",
+    "    EA = to_kN(E * IPE[220][\"A\"])\n",
+    "    EI = to_kNm2(E * HEA[220][\"Iy\"])\n",
+    "    left = ss.add_element([[0, 0], [0, -h]], EA=EA, EI=EI)\n",
+    "\n",
+    "    # right column\n",
+    "    EA = to_kN(E * IPE[180][\"A\"])\n",
+    "    EI = to_kNm2(E * HEA[180][\"Iy\"])\n",
+    "    right = ss.add_element([p6, Vertex(p6.x, -h)], EA=EA, EI=EI)\n",
+    "\n",
+    "    ## SUPPORTS\n",
+    "\n",
+    "    # node ids for the support\n",
+    "    id_left = max(ss.element_map[left].node_map.keys())\n",
+    "    id_top_right = min(ss.element_map[right].node_map.keys())\n",
+    "    id_btm_right = max(ss.element_map[right].node_map.keys())\n",
+    "\n",
+    "    # Add supports. The location of the supports is defined with the nodes id.\n",
+    "    ss.add_support_hinged((id_left, id_btm_right))\n",
+    "\n",
+    "    # Retrieve the node ids at axis 2 and 3\n",
+    "    id_p3 = ss.find_node_id(p3)\n",
+    "    id_p5 = ss.find_node_id(p5)\n",
+    "\n",
+    "    ss.add_support_roll(id_top_right, direction=1)\n",
+    "\n",
+    "    # Add translational spring supports at axes 2 and 3\n",
+    "    ss.add_support_spring(id_p3, translation=2, k=k1, roll=True)\n",
+    "    ss.add_support_spring(id_p5, translation=2, k=k2, roll=True)\n",
+    "    return ss\n",
+    "\n",
+    "\n",
+    "def water_load(ss, water_height, deflection=None):\n",
+    "    \"\"\"\n",
+    "    :param ss: (SystemElements) object.\n",
+    "    :param water_height: (flt) Water level.\n",
+    "    :param deflection: (array) Computed deflection.\n",
+    "    :return (flt) The cubic meters of water on the structure\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # The horizontal distance between the nodes.\n",
+    "    dl = np.diff(ss.nodes_range(\"x\"))\n",
+    "\n",
+    "    if deflection is None:\n",
+    "        deflection = np.zeros(len(ss.node_map))\n",
+    "\n",
+    "    # Height of the nodes\n",
+    "    y = np.array(ss.nodes_range(\"y\"))\n",
+    "\n",
+    "    # An array with point loads.\n",
+    "    # cubic meters * weight water\n",
+    "    force_water = (water_height - y[:-3] - deflection[:-3]) * q_water * b * dl[:-2]\n",
+    "\n",
+    "    cubics = 0\n",
+    "    n = force_water.shape[0]\n",
+    "    for k in ss.node_map:\n",
+    "        if k > n:\n",
+    "            break\n",
+    "        point_load = force_water[k - 1]\n",
+    "\n",
+    "        if point_load > 0:\n",
+    "            ss.point_load(k, Fx=0, Fy=-point_load)\n",
+    "            cubics += point_load / q_water\n",
+    "\n",
+    "    return cubics\n",
+    "\n",
+    "\n",
+    "def det_water_height(c, deflection=None, hyperparams={}):\n",
+    "    \"\"\"\n",
+    "    :param c: (flt) Cubic meters.\n",
+    "    :param deflection: (array) Node deflection values.\n",
+    "    :return (SystemElement, flt) The structure and the redistributed water level is returned.\n",
+    "    \"\"\"\n",
+    "    wh = 0.1\n",
+    "\n",
+    "    while True:\n",
+    "        ss = structure(**hyperparams)\n",
+    "        cubics = water_load(ss, wh, deflection)\n",
+    "\n",
+    "        factor = converge(cubics, c)\n",
+    "        if 0.9999 <= factor <= 1.0001:\n",
+    "            return ss, wh\n",
+    "\n",
+    "        wh *= factor\n",
+    "\n",
+    "\n",
+    "def determine_max_water_cubic(hyper_params, return_ss=False):\n",
+    "    cubics = [0]\n",
+    "    water_heights = [0]\n",
+    "\n",
+    "    deflection = None\n",
+    "    max_water_level = 0\n",
+    "\n",
+    "    # Iterate from 8 m3 to 15 m3 of water.\n",
+    "    for cubic in reversed(\n",
+    "        (14 - np.logspace(0, 1, 35)[:-10])\n",
+    "    ):  # This loop computes the results per m3 of storaged water.\n",
+    "        wh = 0.05\n",
+    "        lastwh = 0.2\n",
+    "\n",
+    "        print(f\"Starting analysis of {cubic} m3\")\n",
+    "\n",
+    "        c = 1\n",
+    "        for _ in range(100):  # This loop redistributes the water until the water level converges.\n",
+    "\n",
+    "            # redistribute the water\n",
+    "            ss, wh = det_water_height(cubic, deflection, hyper_params)\n",
+    "\n",
+    "            # Do a non linear calculation!!\n",
+    "            ss.solve(max_iter=100, verbosity=1)\n",
+    "            deflection = ss.get_node_result_range(\"uy\")\n",
+    "\n",
+    "            # Some breaking conditions\n",
+    "            if min(deflection) < -1:\n",
+    "                print(min(deflection), \"Breaking due to exceeding max deflection\")\n",
+    "                break\n",
+    "            if 0.9999 < lastwh / wh < 1.001:\n",
+    "                print(f\"Convergence in {c} iterations.\")\n",
+    "                cubics.append(cubic)\n",
+    "                water_heights.append(wh)\n",
+    "                break\n",
+    "\n",
+    "            lastwh = wh\n",
+    "            c += 1\n",
+    "\n",
+    "        if wh > max_water_level:\n",
+    "            max_water_level = wh\n",
+    "        else:\n",
+    "            print(\"Breaking. Water level isn't rising.\")\n",
+    "            break\n",
+    "    \n",
+    "    if return_ss:\n",
+    "        return ss, cubics, water_heights\n",
+    "    \n",
+    "    return max_water_level\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def select_hyperparams_random(param_ranges):\n",
+    "    \"\"\"\n",
+    "    Select hyperparameters at random.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    param_ranges : dict\n",
+    "        Named parameter ranges.\n",
+    "\n",
+    "        Example:\n",
+    "        \n",
+    "        {\n",
+    "            'foo': {\n",
+    "                'range': [1, 10],\n",
+    "                'type': 'float'\n",
+    "            }\n",
+    "            'bar': {\n",
+    "                'range': [10, 1000],\n",
+    "                'type': 'int'\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    selection : dict\n",
+    "        Randomly selected hyperparameters within given boundaries.\n",
+    "        \n",
+    "        Example:\n",
+    "        {'foo': 4.213, 'bar': 935}\n",
+    "\n",
+    "    \"\"\"\n",
+    "    selection = {}\n",
+    "    for k in param_ranges:\n",
+    "        val = np.random.choice(\n",
+    "            np.linspace(*param_ranges[k]['range'], num=100)\n",
+    "        )\n",
+    "\n",
+    "        dtype = param_ranges[k]['type']\n",
+    "        if dtype is 'int':\n",
+    "            val = int(val)\n",
+    "        selection[k] = val\n",
+    "    return selection\n",
+    "\n",
+    "\n",
+    "def expected_improvement(f, y_current, x_proposed):\n",
+    "    \"\"\"\n",
+    "    Return E(max(f_proposed - f_current), 0)\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    f : GP predict function\n",
+    "    y_current : float\n",
+    "        Current best evaluation f(x+)\n",
+    "    x_proposed : np.array\n",
+    "        Proposal parameters. Shape: (1, 1)\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    expected_improvement : float\n",
+    "        E(max(f_proposed - f_current), 0)\n",
+    "    \"\"\"\n",
+    "    mu, var = f(x_proposed)\n",
+    "    std = var ** 0.5\n",
+    "    delta = mu - y_current\n",
+    "\n",
+    "    # x / inf = 0\n",
+    "    std[std == 0] = np.inf\n",
+    "    z = delta / std\n",
+    "    unit_norm = stats.norm()\n",
+    "    return delta * unit_norm.cdf(z) + std * unit_norm.pdf(z)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BayesOpt:\n",
+    "    def __init__(\n",
+    "        self, param_ranges, f, random_trials=5, optimization_trials=20, kernel=None\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "\n",
+    "        param_ranges : dict\n",
+    "        f : function\n",
+    "            black box function to evaluate\n",
+    "        random_trials : int\n",
+    "            Number of random trials to run before optimization starts\n",
+    "        optimization_trials : int\n",
+    "            Number of optimization trials to run.\n",
+    "            Together with the random_trials this is the total budget\n",
+    "        kernel: GPy.kern.src.kern.Kern\n",
+    "            GPy kernel for the Gaussian Process.\n",
+    "            If None given, RBF kernel is used\n",
+    "        \"\"\"\n",
+    "        self.param_ranges = param_ranges\n",
+    "        self.f = f\n",
+    "        self.random_trials = random_trials\n",
+    "        self.optimization_trials = optimization_trials\n",
+    "        self.n_trials = random_trials + optimization_trials\n",
+    "        self.x = np.zeros((self.n_trials, len(param_ranges)))\n",
+    "        self.y = np.zeros((self.n_trials, 1))\n",
+    "\n",
+    "        if kernel is None:\n",
+    "            self.kernel = GPy.kern.RBF(\n",
+    "                input_dim=self.x.shape[1], variance=1, lengthscale=1\n",
+    "            )\n",
+    "        else:\n",
+    "            self.kernel = kernel\n",
+    "        self.gp = None\n",
+    "        self.bounds = np.array([pr[\"range\"] for pr in param_ranges.values()])\n",
+    "\n",
+    "    @property\n",
+    "    def best_params(self):\n",
+    "        \"\"\"\n",
+    "        Select best parameters.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        best_parameters : dict\n",
+    "        \"\"\"\n",
+    "        return self._prepare_kwargs(self.x[self.y.argmax()])\n",
+    "\n",
+    "    def fit(self):\n",
+    "        self._random_search()\n",
+    "        self._bayesian_search()\n",
+    "\n",
+    "    def _random_search(self):\n",
+    "        \"\"\"\n",
+    "        Run the random trials budget\n",
+    "        \"\"\"\n",
+    "        print(f\"Starting {self.random_trials} random trials...\")\n",
+    "        for i in tqdm(range(self.random_trials)):\n",
+    "            hp = select_hyperparams_random(self.param_ranges)\n",
+    "            self.x[i] = np.array(list(hp.values()))\n",
+    "            self.y[i] = self.f(hp)\n",
+    "            print(f'HP: {hp}\\t y:{self.y[i]}')\n",
+    "\n",
+    "    def _bayesian_search(self):\n",
+    "        \"\"\"\n",
+    "        Run the Bayesian Optimization budget\n",
+    "        \"\"\"\n",
+    "        print(f\"Starting {self.optimization_trials} optimization trials...\")\n",
+    "        for i in tqdm(\n",
+    "            range(self.random_trials, self.random_trials + self.optimization_trials)\n",
+    "        ):\n",
+    "            self.x[i], self.y[i] = self._single_iter()\n",
+    "\n",
+    "    def _single_iter(self, x=None):\n",
+    "        \"\"\"\n",
+    "        Fit a GP and retrieve and evaluate a new\n",
+    "        parameter proposal.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        out : tuple[np.array[flt], np.array[flt]]\n",
+    "            (x, f(x))\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self._fit_gp()\n",
+    "        if x is None:\n",
+    "            x = self._new_proposal()\n",
+    "        y = self.f(self._prepare_kwargs(x))\n",
+    "        print(f'HP: {self._prepare_kwargs(x)}\\t y:{y}')\n",
+    "        return x, y\n",
+    "\n",
+    "    def _fit_gp(self, noise_var=0):\n",
+    "        \"\"\"\n",
+    "        Fit a GP on the currently observed data points.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        noise_var : flt\n",
+    "            GPY argmument noise_var\n",
+    "        \"\"\"\n",
+    "        mask = self.x.sum(axis=1) != 0\n",
+    "        self.gp = GPy.models.GPRegression(\n",
+    "            self.x[mask],\n",
+    "            self.y[mask],\n",
+    "            normalizer=True,\n",
+    "            kernel=self.kernel,\n",
+    "            noise_var=noise_var,\n",
+    "        )\n",
+    "        self.gp.optimize()\n",
+    "\n",
+    "    def _new_proposal(self, n=25):\n",
+    "        \"\"\"\n",
+    "        Get a new parameter proposal by maximizing\n",
+    "        the acquisition function.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        n : int\n",
+    "            Number of retries.\n",
+    "            Each new retry the optimization is\n",
+    "            started in another parameter location.\n",
+    "            This improves the chance of finding a global optimum.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        proposal : dict\n",
+    "            Example:\n",
+    "           {'foo': 4.213, 'bar': 935}\n",
+    "        \"\"\"\n",
+    "\n",
+    "        def f(x):\n",
+    "            return -expected_improvement(\n",
+    "                f=self.gp.predict, y_current=self.y.max(), x_proposed=x[None, :]\n",
+    "            )\n",
+    "\n",
+    "        x0 = np.random.uniform(\n",
+    "            low=self.bounds[:, 0], high=self.bounds[:, 1], size=(n, self.x.shape[1])\n",
+    "        )\n",
+    "        proposal = None\n",
+    "        best_ei = np.inf\n",
+    "        for x0_ in x0:\n",
+    "            res = optimize.minimize(f, x0_, bounds=self.bounds)\n",
+    "            if res.success and res.fun < best_ei:\n",
+    "                best_ei = res.fun\n",
+    "                proposal = res.x\n",
+    "            if np.isnan(res.fun):\n",
+    "                raise ValueError(\"NaN within bounds\")\n",
+    "        return proposal\n",
+    "\n",
+    "    def _prepare_kwargs(self, x):\n",
+    "        \"\"\"\n",
+    "        Create a dictionary with named parameters\n",
+    "        and the proper python types.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x : np.array\n",
+    "            Example:\n",
+    "            [4.213, 935.03]\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        hyperparameters : dict\n",
+    "\n",
+    "            Example:\n",
+    "            {'foo': 4.213, 'bar': 935}\n",
+    "        \"\"\"\n",
+    "        # create hyper parameter dict\n",
+    "        hp = dict(zip(self.param_ranges.keys(), x))\n",
+    "        # cast values\n",
+    "        for k in self.param_ranges:\n",
+    "            if self.param_ranges[k][\"type\"] == \"int\":\n",
+    "                hp[k] = int(hp[k])\n",
+    "            elif self.param_ranges[k][\"type\"] == \"float\":\n",
+    "                hp[k] = float(hp[k])\n",
+    "            else:\n",
+    "                raise ValueError(\"Parameter type not known\")\n",
+    "        return hp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maximum_factor = 1.3\n",
+    "\n",
+    "param_ranges = {\n",
+    "    'mp_span_1': {\n",
+    "        'range': [70, int(70 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'mp_span_2': {\n",
+    "        'range': [240, int(240 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'mp_span_3': {\n",
+    "        'range': [25, int(25 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'k1': {\n",
+    "        'range': [2000, int(2000 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    },\n",
+    "    'k2': {\n",
+    "        'range': [3000, int(3000 * maximum_factor)],\n",
+    "        'type': 'float'\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bo = BayesOpt(param_ranges, \n",
+    "              determine_max_water_cubic, \n",
+    "              random_trials=5, \n",
+    "              optimization_trials=15, \n",
+    "              kernel=GPy.kern.Matern52(input_dim=3) + GPy.kern.Matern32(input_dim=3))\n",
+    "bo.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mp_span_1': 79.73448251348799,\n",
+       " 'mp_span_2': 265.11052593961267,\n",
+       " 'mp_span_3': 29.237956757987163,\n",
+       " 'k1': 2570.259044964325,\n",
+       " 'k2': 3443.585144096509}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bo.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'iterations')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title('Maximum value after n iterations')\n",
+    "plt.plot(np.maximum.accumulate(bo.y))\n",
+    "plt.ylabel('Reached water level')\n",
+    "plt.xlabel('iterations')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7ff625609358>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(np.arange(bo.y.shape[0]), bo.y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting analysis of 8.919781953086979 m3\n",
+      "Convergence in 5 iterations.\n",
+      "Starting analysis of 9.252438621002577 m3\n",
+      "Convergence in 3 iterations.\n",
+      "Starting analysis of 9.563312669021387 m3\n",
+      "Convergence in 3 iterations.\n",
+      "Starting analysis of 9.85383044020318 m3\n",
+      "Convergence in 3 iterations.\n",
+      "Starting analysis of 10.125324879543868 m3\n",
+      "Convergence in 3 iterations.\n",
+      "Starting analysis of 10.379041649754082 m3\n",
+      "Convergence in 4 iterations.\n",
+      "Starting analysis of 10.616144846571766 m3\n",
+      "Convergence in 39 iterations.\n",
+      "Breaking. Water level isn't rising.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ss, cubics, water_heights = determine_max_water_cubic(bo.best_params, return_ss=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff62427fc18>]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(cubics, water_heights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ss.nodes_range('x')[:-2], ss.nodes_range('y')[:-2])\n",
+    "plt.plot(ss.nodes_range('x')[:-2], [a + b for a, b in zip(ss.nodes_range('y')[:-2], ss.get_node_result_range(\"uy\")[:-2])])\n",
+    "\n",
+    "plt.ylabel(\"Height level roof when accumulating [m]\")\n",
+    "plt.xlabel(\"Span [m]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}