diff --git a/svm_classification.ipynb b/svm_classification.ipynb
index b64ba131bb40c7a5e3f322ffe65a00059f37dd02..98623d62d7df48d4220507113baae9cfddf94090 100644
--- a/svm_classification.ipynb
+++ b/svm_classification.ipynb
@@ -19,12 +19,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we introduce the support vector machine (SVM) (or support-vector networks), a well-known supervised learning method for both classification and regression problems. This method is one of the most powerful prediction methods which produces high accuracy with less computational expenses. It was developed by Vladimir Vapnik and his colleagues in the 1990s and nowadays it is extensively used in the scientific community, mostly for data classification. \n",
+    "In this tutorial, we introduce the support vector machine (SVM) (or support-vector networks), a well-known supervised learning method for both classification and regression problems. This method is one of the most powerful prediction methods which produces high accuracy with less computational expenses. It was developed by Vladimir Vapnik and his colleagues in the 1990s and nowadays it is extensively used in the scientific community, mostly for data classification. This tutorial is inspired by chapter 9 of the book \"An Indtroduction to Statistical Learning\" written by G. James, D. Witten, T. Hastie, R. Tibshirani. Therefore, we especially appreciate the authors of this book. \n",
     "\n",
     "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\n",
-    "Corinna Cortes, Vladimir Vapnik : <span style=\"font-style: italic;\">Support-vector networks</span>, Mach Learn 20, 273–297 (1995) <a href=\"https://link.springer.com/article/10.1007%2FBF00994018\" target=\"_blank\">[PDF]</a> .\n",
+    "Cortes, Corinna and Vapnik, Vladimir: <span style=\"font-style: italic;\">Support-vector networks</span>, Mach Learn 20, 273–297 (1995) <a href=\"https://link.springer.com/article/10.1007%2FBF00994018\" target=\"_blank\">[PDF]</a> .\n",
     "</div>\n",
     "\n",
+    "<div style=\"padding: 1ex; margin-top: 1ex; margin-bottom: 1ex; border-style: dotted; border-width: 1pt; border-color: blue; border-radius: 3px;\">\n",
+    "James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert: <span style=\"font-style: italic;\">An Introduction to Statistical Learning: with Applications in R</span>, Springer New York, 2014, 1461471370, 9781461471370 <a href=\"https://link.springer.com/book/10.1007/978-1-4614-7138-7#authorsandaffiliationsbook\" target=\"_blank\">[PDF]</a> .\n",
+    "</div>\n",
+    "\n",
+    "\n",
     "\n",
     "# Intoduction\n",
     "\n",
@@ -48,7 +53,7 @@
     "\n",
     "$\\beta_0+\\beta_1 X_1+\\beta_2 X_2=0$ &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;(1)\n",
     "\n",
-    "where $\\beta_0$,$\\beta_1$,$\\beta_2$ are the intercept and slope of the line. Then one can easily extents equation (1) and obtains an equation for a p-dimentional hyperplane:\n",
+    "where $\\beta_0$,$\\beta_1$,$\\beta_2$ are the intercept and slope of the line and X1 and X2 are the features for each data point. Since we have two features for each point then the points (observations) are located in a two dimensional feature space. Then one can easily extents equation (1) and obtains an equation for a linear p-dimentional hyperplane:\n",
     "\n",
     "$\\beta_0+\\beta_1 X_1+\\beta_2 X_2+...+\\beta_p X_n=0$ &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; (2)\n",
     "\n",
@@ -66,7 +71,10 @@
     "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
     "import random\n",
-    "from sklearn import svm"
+    "from sklearn import svm\n",
+    "from sklearn import metrics\n",
+    "import pandas as pd\n",
+    "from itertools import combinations"
    ]
   },
   {
@@ -76,7 +84,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAF6CAYAAAANsLFbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABDHklEQVR4nO2dd5gV5dmH74cFdBE7oIAKii2xJAFiRWM00hQrBnvBRkQ+xcQWjdEYo8ZE7FEU1ORTsYtSxa74GYMFSxQsQUVUEBsb+u7z/fEeZFlO291zdsr+7uua6+yZed9zPzOzu++ZOTO/Y+6OEEIIIZJLi6gLEEIIIUTj0GAuhBBCJBwN5kIIIUTC0WAuhBBCJBwN5kIIIUTC0WAuhBBCJBwN5kKIJsXMZpnZrKjrECJNaDAXooSYmZtZ3vCGzGDmZta1icoSQqQcDeZCCCFEwtFgLoQQQiQcDeZCRIyZrW9mC83sAzOzHG3GZU7N98g875p5foeZbWtmj5jZV2b2XzN7wcx65/EdYWZPm9nXZrbYzN4xswvNbI0sbd3MnjGzjc3sNjP71Myqzez4zPI7Mm22MLOzzOzdzGvONrMRZrZOkdtgXTM728yeyvRdambzzOxRM9slR58VtbUzs5Fm9pmZLTGzt83shDyuPmY2wcy+zLT/wMyuMrP1iqlViDiiwVyIiHH3r4ExwBbAL+ouN7NNgL7AK+7+Sp3FmwP/B2wI3ALcD/QAJprZoCyvNQq4G9gSeAi4EfgKuBSYZGYts5S4AfASsEumzw3AF3XajAB+BzwLXAt8CZwJPGVma+bdAIEfAJcBNcB44GpgCrA38LyZ9c3Rbz1gKrAr8ADwd6ATMNrMjqvb2MwuAiYBO2c81wHvA78Bphb75kOI2OHumjRpKtEEeGa6OM/0TaZN11r9embmPZDlNS/OLDu51ryutVxX1WnfE1gGfA2sU2v+8Zn2DwGVORxn5FifvwMts9R2R2b5l0CXWvNbAA9mlv2uTp9ZwKw689YF2mV5/U2AOcA7ebb1bUBFrfk/BJYD/67T/ueZ9i8C69VZtmLbjIj6d0iTpoZM5q5vTROiVBS6kr0Om7v7rFp9/wX8GNjU3T/PzKsgDH7rAp3cvSozvyvwH+DbTPsFdeq4AzgOON7d78zMew3YHmjv7t/UaV9BONr+0N13qrM+SzOOuVnWd4XnIne/tM6yLYD3gI/dffNa82cBuHvXAttnRfvrgGGENwsf16ltIdDR3b+r0+dZYE/Cm5kFmXkPAwcB27v721k8rwGd3b1DMXUJESeynVITQjQSd8/62Td8P5h1ybLoJmA0MBj4U2Zef8LR6d9WDOR1eLXuQJ7hGcIg+xPgTjNrA/yIzOnvHB/NLyGc7q7LrGwDeR2erTvD3T80s0+Arma2Xt03EHUxs92BMwinzDsAres06Qx8XGfee3UH8gyfZB7XA1Zsn10JZywOM7PDsvRpDbQ3sw3dfX6+WoWIGxrMhYgPY4C/Aieb2RXuXgOcmll2S44+dT+7XsHnmcd1M4/rAwa0B35fz7o+L9wkbx1dMnV8k6uzmR1M+Mx7MeGz8g+A/xI+Q98L+Bmw2gV6eV5zeeaxota8DQn/8wqtf1tAg7lIFBrMhYgJ7r4oc9p6ONDbzN4iXPj2T3efnqPbRjnmb5x5/LbO42vu3r2+pRXRZiNgRhF15OJSwun8nu7+Tu0FZnYLYTBvLN8CLdx9gxK8lhCxQlezCxEv/kYYPE8FTiIcWeY6KgfobmZrZ5m/V+bxNYDMKfq3ge3MrByD2WqDbeYz800Jp+m/KdB/S8IFa3UH8hZArxLV+BKwvpltV6LXEyI2aDAXIka4+3vAk8D+wBDCaeR783RZF7io9gwz6wkcRTgSfbjWoqsJnwuPznZPdeZ+9/oeta/gDDP7/jqAzCB8FeF/zO1F9J8FbGVmnWq9hhFOif+wgTXVZUTm8dbanlq+tXLd0y5E3NFpdiHix02E+803Aq5394V52j4HnGRmOxPut+4IDCIMoqfWvjjM3UdnQmdOAz4ws8mEC8o2INyvvidh4B3SgJqnAq+b2b2ENxF9CBfcvQL8uYj+I4CbgdfM7EHChWq7Ewbyx4ABDahpFdz9STM7D7gceM/MJhDuCGhL+Fz/Z8ALhI82hEgUGsyFiB+PEq46b0f+U+wQBqMhwBWZxzWAV4E/uPvkuo3dfaiZTcy0/QXhau+vCIP6VcD/NrDm4cDBwMmEe+DnE8JjLnL3xYU6u/stZraEEDRzHLAIeB44ATiUEgzmGc+VZjYV+B/C6fsDCW8+PgVGEgJ1hEgcus9ciJiR+az5fWCqu++Ro01XwkB+p7sf33TVrVbHHYTBd5V75oUQTYs+MxcifvyGcBvZDVEXIoRIBjrNLkQMMLPNgCOBrQinlqcTctaFEKIgGsyFiAdbEC7MWkgITflVJjRGCCEKos/MhRBCiISjz8yFEEKIhJPI0+zt2rXzrl27Rl2GqA+ffw6ffgrt28Nmm0VdjRBCJIpXXnnlS3dvn2t5Igfzrl27Mm3atKjLEPXl7LPhL3+BYcPgd7+LuhohhEgMZvZRvuWJHMxFQrnySpg7Fy66KByhD2lI0JgQQoi6aDAXTUeLFnDbbTB/Ppx2GrRrBwMHRl2VEEIknpJcAGdmo81sbuYrG1fM28DMppjZe5nH9XP07WtmM8zs/UxuskgzrVrBfffBrrvCUUfBU09FXZEQQiSeUl3NfgerfznBecCT7r4V4VugVhuozawCuBHoR/hChSPMrFTfkASzH4V/nR4ey9FejoY5vnoCxo2DrbaCAw+EV17J2vzRR+H008NjsdS3jxxyyCFHUhx5cfeSTIQvV3ir1vMZQMfMzx2BGVn67ApMrvX8fOD8Qq4ePXp4QT4Z6z6mjftdhMdPxpa2vRyNd8ye7b7ZZu7t27vPnLlK87Fj3du0cYfwOLYIRX37yCGHHHIkxQFM8zzjYjnvM9/I3T/LvGH4DOiQpU1n4JNaz2dn5q2GmZ1iZtPMbNq8efMK2z97HKoz3xxZvTA8L2V7ORrv6NwZHn8c3KF3b5gz5/vmjz8OCzPNFy4MzwtR3z5yyCGHHElxFCLq0BjLMi9rJJ27j3T3nu7es337nLfaraRjb6hoE36uaBOel7K9HKVxbLMNTJgA8+ZB377wzTdAGNvbZJq3aROeF6K+feSQQw45kuIoSL7D9vpMxO00u3s4lfvy0OJOAzekvRylczz+uHurVu577OG+cKG7h9NOQ4cWd8pqBfXtI4cccsiRBAcFTrOblyibPfP9yuPcffvM86uA+e5+ReYq9Q3c/Zw6fVoCM4F9gE+BfwFHuvvb+Vw9e/Z0hcakkHvvhSOOgAED4MEHoaXunBRCCAAze8Xde+ZaXqpb0+4B/g/Yxsxmm9mJwBXAvmb2HrBv5jlm1snMJgC4+3LgdGAy8A5wX6GBXKSYQYPguuvCpZ2nnho+SxdCCFGQkhz6uPsRORbtk6XtHKB/recTgAmlqEOkgNNPDylxl14KHTrA5ZdHXZEQQsQenccU8eOSS8KAfsUVYUAfPjzqioQQItZoMBfxwwxuvBG+/BLOOivkuB99dNRVCSFEbIn61rTykrbktObkqKiAK4+AHp3hhONh4sSiFHFMbZJDDjnkKFWfnOS71D2uU7NKgJs1xv2eNZO/Hg113Ip7V3Nfs7X7iy/m7dKQ1KbKytC+sjKeyVCVle433eS+YEH5HHFJuJJDjiQ5FixwnzEjPKY9AS5akp6cVrMcpg2DF4+CmsUr+8yZlKz1aKyjDXCOQ7s1Yb/94O3cNzvUJ1Fp+XK48EJYtCg8X7QoPF++PH9ZTZ0MtWgRnHFGuHRg2LDs9aUl4UoOOZLgWL48/C126AA9eoTHCy9UAlz5SHpy2qvD4YPR4NWrzv/vx6WtqSF9mtqxQRsYcxWssQb06QMfZ98G9UlUGj4cZs5cdd7MmYWvtWuqZKjat9gvWxYG9dGjs9eXloQrOeRIgmP48PC3uGgRVFWFx5kzV/7NJj4Brimn1CfALV3gPqYynJKuO42pDMuTsB6ldrz+uvs667hvu637vHlZuxSTqLRgwcrT63Wnysr8p7SLdTSm/YIF7q1b16++NCRcySFH3B35/ne0bu1+8skpSIBrSlKfAPfdTJjUA5ZXrb6sZVvo+wqss3XT1xUHnnsuvIX90Y/gySehbdt6v8TMmeH0WFWWzdu2bfhG1q0j3Lxxr0+I5kqUf5tNkgAnSkxlp9VPr6/Aq8Py5sqee4bY12nTYOBAWLq03i/RqRNU59i81dVheZTEvT4hmitx/tvUYB5HWrWFbieu/Mx4BRVtwvxW9T8aTRUHHggjR8LkyXD88VBTU6/ubdvCiSeu/LxqBW3ahPkNONgvKXGvT4jmSpz/NhUaE1e6jwiPH4wCqwhH5N0Gr5zf3DnxxPC1qeefH0JlrrkmhM0UyYjMZhw1KtzSXl0NgwevnB81ca9PiOZKXP82031kHrcglPq0b9ESel4Ph8yFH/8Zuh4NG+8b5pe6pob0iYPj3HPhzDPDl7NkMtyLDWFo2RKuvx5uvz0c6N9+e3hezBe1NUWYxIQJ4bKa228Pn8PNnZu/vrSEYsghR9wdK/53zJ276t/mhAkKjSnP1exxDkKRo3SO6mr3o45yB39t6MhYhEnIIYcccpTagUJjiF8Qihylc7RoEQ5f+/Vjx5uG0Hvhw0B6AyvkkEOO5ukoRHoH87gHochRuvatWsH99/PN1jtxD0ewJ8+mNrBCDjnkaJ6OguQ7bI/rlPrQGDka1v7LL/27TX7gC1uv40+PeK2oLkkKrJBDDjmarwOFxohmxSefwG67hQzUqVOhW7eoKxJCiEaj0BjRvNh00/Dh07Jl4bzV559HXZEQQpQdDeYiffzgB+E+kc8/h3794Ntvo65ICCHKigZzkU523hkefBDeeivcSL54cdQVCSFE2Uj3YJ7EIBQ5Sufo2xfuvBOefRaOPDJrqHLSAivkkEOO5uvIS76r4+I6KTRGjnr1ueaakMxw8snuNTXfN09LmIQccsiRfgcKjSF5QShylNZxxhnw29/CrbfC7373ffO0hEnIIYcc6XcUIr2DedKDUOQoreOPf4STToLLLgtZ7qQnTEIOOeRIv6Mg+Q7b4zopNEaOBvVZtsz9oIPCea2773b3dIRJyCGHHOl3oNAYIWqxeHG4MG7qVBg3Dvr0iboiIYQoiEJjhKjNmmvC2LGw3XZw6KHw8stRVySEEI1Gg7lofqy7LkyaBBttBP37w7vvRl2REEI0Cg3monmy8cbh8tGKinDlyezZUVckhBANJt2DeRqDUOQoXZ9u3eDO8+Grz2Gf3eCrr4pSxDFMQg455Ei/Iy/5ro5r7ARsA7xea/oOOLNOm72Ab2u1uajQ6yo0Ro6SOn6Le0vce2zjXlWVt0scwyTkkEOO9DuIMjTG3We4+4/d/cdAD2Ah8HCWps+vaOfufyiJPO1BKHKUzrEdcDrw2kw47LDwjWs5iGOYhBxyyJF+RyGa8jT7PsAH7v5Rk9iiDimRI1mOXdrAn34FEyfC4MFQU5O1SxzDJOSQQ470OwqS77C9lBMwGjg9y/y9gPnAdGAisF2O/qcA04Bpm222WeFzGO7Rh5TIkTzHpZeG815nnbVKjntt4hYmIYcccqTfQRxCY8ysNTAnM1B/UWfZOkCNu1eZWX/gWnffKt/rKTRGlA33kOV+/fVw5ZVwzjlRVySEELEJjekHvFp3IAdw9+/cvSrz8wSglZm1a6K6hFgVM7jmGjj8cDj3XBg9OuqKhBCiIC2byHMEcE+2BWa2MfCFu7uZ7UR4gzG/ieoSYnVatAjfg/7VV3DyydCuHRxwQNRVCSFETsp+ZG5mbYB9gYdqzRtiZkMyTwcCb5nZdOA64HBvinP/QuSjdWt48EHo2RMGDYLnn4+6IiGEyEnZB3N3X+juG7r7t7Xm3ezuN2d+vsHdt3P3H7n7Lu7+YsnkcQspkSNZjrZtYfx46NIFBgyAN94A4hkmIYcccqTfkZd8V8fFdVJojBxN6pg1y71zZ/eOHX3KyA9jFyYhhxxypN9BlKExkRLnkBI5kuXo0gUmT4bFi9nx7N6stXAuEJ8wCTnkkCP9jkKkdzCPe0iJHMlybLcdjBvHBos+ZbL1Y22+i02YhBxyyJF+R0HyHbbHdSrqNLt7MkJK5EiWY9w4r25R4e923tsfe2BxUV3SEFghhxxyROsgDqExpUahMSJS/v53OO44GDgQxowJX6MqhBBlJC6hMUKkh2OPhb/+FR54IFyKmsA3xEKIdNFUoTFCpIuzzoIvvoA//xk22gguvjjqioQQzRgN5kI0lCuugHnz4JJLoEMHOO20qCsSQjRT0n2aPWkhJXIky/HpYzByZAiUOf10uO++rM3TElghhxxyROvIS76r4+I6KTRGjlg5/vtf91693Fu1cp8yZZXmaQmskEMOOaJ1oNAYkhdSIkeyHG3ahLfW22wDBx8Mte60SEtghRxyyBGtoxDpHcyTHlIiR7Ic668fUuI23BD69YOZM4H0BFbIIYcc0ToKku+wPa6TQmPkiK1jxgz39u3du3Rx//RTd09HYIUccsgRrQOFxgjRxLzyCuy1F3TtCs89F47ahRCiESg0RoimpkcPeOSRcKp9wICVH4wJIUSZ0GAuRDnYZx/43/+FF1+EQYNg2bKoKxJCpBgN5kKUi8MOgxtvhHHj4JRTFPsqhCgb6R7M0xZSIkfyHL/6FZx1BNxxBwwdWLQijoEVcsghR7SOvOS7Oi6uk0Jj5EiU455K918Q0iEuPKGgIo6BFXLIIUe0DhQaQ/pCSuRIlqNmERwH7Az88fbwFap5iGNghRxyyBGtoxDpHczTHlIiR7IcLYDTK6HXjjB4MIwfn7NLHAMr5JBDjmgdBcl32B7XSaExciTW8d137j16uFdWuk+dmrNL3AIr5JBDjmgdKDRGiJgxdy706hW+PvX552H77aOuSAgRcxQaI0Tc6NAh5LhXVkKfPvDRR1FXJIRIOBrMhYiCzTcPA/rCheHDsnnzoq5ICJFgNJgLERU77ACPPQYffwz9+8OCBVFXJIRIKOkezOMWICKHHHXp1Qvuuw9eew0OOQSWLgXiGVghhxxyROvIS76r4+I6KTRGjtQ5Ro8O6RGDBvnYh6tjF1ghhxxyROtAoTHEK0BEDjmyccIJcOWVcO+9rHvRGSxcGO4yiUtghRxyyBGtoxDpHczjHCAihxzZOPts+PWv+dmbN3Bxq8uA+ARWyCGHHNE6CpLvsL0UEzALeBN4nSynCQADrgPeB94Auhd6TYXGyJFaR3W1+zHHuIPf/bObYxNYIYccckTryDZ+1p7MyxwaY2azgJ7u/mWO5f2BYUB/Qnr1te6+c77XVGiMSDXLlsFBB8GkSeHiuEMPjboiIUTEJCE05kDg75k3Hy8B65lZx6iLEiIyWrWC+++HnXeGI4+Ep5+OuiIhRMxpisHcgcfN7BUzOyXL8s7AJ7Wez87MWwUzO8XMppnZtHkK2BBpp00bGDcOttwSDjww3LomhBA5aIrBfHd37w70A4aa2Z51lluWPqud+3f3ke7e0917tm/fvhx1ChEvNtggpMSttx707Qvvvx91RUKImFL2wdzd52Qe5wIPAzvVaTIb2LTW802AOeWuS4hEsMkm4Z6V6upwuetnn0VdkRAihpR1MDeztcxs7RU/A72Bt+o0exQ41gK7AN+6e2n+YyUtDUwOObL12XZbmDAhfNtav37wzTerNU9LwpUccsjRQPJd6t7YCdgCmJ6Z3gYuyMwfAgzxlbem3Qh8QLiFrWeh11UCnBzN0jF5snurVu577um+cOH3zdOScCWHHHLkhigT4Nz9Q3f/UWbazt0vy8y/2d1vzvzs7j7U3bu5+w7uXpp7zpKcBiaHHNn69O4Nd94ZvgP9iCNg+XIgPQlXcsghR8OJw61p5SHpaWByyJGtzxFHwLXXwtixMGQIuKcm4UoOOeRoBPkO2+M6KQFOjmbvuPDCcH7u/PPdPR0JV3LIIUduiDoBrhwoAU40e9zDkfnIkTBiBJx5ZtQVCSHKSKEEuJZNWYwQokSYwU03wZdfwvDh0L49HHVU1FUJISIivZ+ZC5F2Kirgrrtgr73g+ONDlrsQolmiwVyIJLPmmuFiuO23D1/I8tJLUVckhIiAdA/maQwQkUOOuqyzDoweDhu0hH694Z13ilLEMRRDDjnkaCD5ro6L66TQGDnkyNJ+BO7r4t5xQ/ePP87bJY6hGHLIIUduiDI0JlLSHiAihxx123cAzgG+WxBuWp0/P2eXOIZiyCGHHA0nvYN51OEecsgRhaNbG7j99/Cf/8B++8F//5u1SxxDMeSQQ45GkO+wPa6TQmPkkKNA+4cfdm/Rwr1vX/elS7N2iVsohhxyyJEbFBojRDPlttvg5JPD/ed//zu0SO+JOCHSjkJjhGiunHRS+NrUCy6Adu1CUpxZ1FUJIcqABnMh0sz554cB/dprYaONwnMhROrQYC5EmjGDq6+GefPgt78Nsa8nnRR1VUKIEpPuD9HiFu4hhxxROFq0gNtvhz594NRT4ZFHgHiGYsghhxwNJN/VcXGdFBojhxwNaF9V5b7zzu5rrOHP/+nZ2IViyCGHHLlBoTHEL9xDDjmicKy1FowfD5tvTo+LB7DlwulAfEIx5JBDjoaT3sE87uEecsgRhWPDDWHyZFh7HR6nD5vzYWxCMeSQQ45GkO+wPa6TQmPkkKOR7d9+25esvYHPXaebT7rz86K6pCF4Qw45kupAoTFCiKz83//BL34B22wDzzwTvn1NCBFLCoXGpPc0uxAiP7vuCg88AG++CQcdBIsXR12REKKBaDAXojnTrx/ccQc8/XSIfa2ujroiIUQD0GAuRHPnqKNC1OtDD8HQoZDAj96EaO6kezBPWriHHHJE5TjzTDjvPLjlFvj977M2T0vwhhxyJNWRl3xXx8V1UmiMHHKUwVFT4z54cEixuO66VZqnJXhDDjmS6kChMSQv3EMOOaJwmIUj8wMPhDPOgDFjvm+eluANOeRIqqMQ6R3Mkx7uIYccUThatoR77oFeveDYY2HKFCA9wRtyyJFUR0HyHbbHdVJojBxylNnx9dfuO+7ovtZa7i+/7O7pCN6QQ46kOogyNMbMNgX+DmwM1AAj3f3aOm32AsYC/8nMesjd/5DvdRUaI0QT8NlnsPvu8N138MILsO22UVckRLMl6tCY5cCv3f0HwC7AUDP7YZZ2z7v7jzNT3oFcCNFEdOwYctwrKsLXp86eHXVFQogclHUwd/fP3P3VzM8LgHeAzuV0CiFKyFZbwcSJ8PXX0LcvfPVV1BUJIbLQZBfAmVlX4CfAP7Ms3tXMppvZRDPbrqlqEkIUQffu8Mgj8N57MGDAyktwhRCxoUkGczNrCzwInOnu39VZ/CrQxd1/BFwPPJLjNU4xs2lmNm3evHnFidMY7iGHHFE49t4brh8O//ciHPAzWLasKEUcgzfkkCOpjrzkuzquFBPQCpgMnFVk+1lAu3xtFBojhxwROQYTUi4G/ty9ujpvlzgGb8ghR1IdRBkaY2YGjALecferc7TZONMOM9uJcLZgfqPlaQ73kEOOqBz7AAOBB56Gc8/N2yWOwRtyyJFURyHKfZp9d+AYYG8zez0z9TezIWY2JNNmIPCWmU0HrgMOz7wLaRxpD/eQQ46oHIdWwnH94S9/gauuytkljsEbcsiRVEdB8h22x3VSaIwcckTsqK52HzQonCO8/facXeIWvCGHHEl1EGVoTLlQaIwQMWDJEth///Bd6A8/HK50F0KUhahDY4QQaWWNNcJ3oP/kJ/DLX4aUOCFEJGgwF0I0nLXXhgkTYLPNwpH5m29GXZEQzRIN5kKIxtG+fbgUt02bEPs6a1bUFQnR7Ej3YB634A055Eiro0uXkOO+aFG4LHfuXCCewRtyyJFUR17yXR0X10mhMXLIEVPHCy+4r7mme48ePu6e72IXvCGHHEl1EGVoTKTEOXhDDjnS6th9d7j/fnj9dbqdfTDLFy4B4hO8IYccSXUUIr2DedyDN+SQI62O/feHUaPYdvaT3F1xDC2ojk3whhxyJNVRkHyH7XGdFBojhxwJcFx1lTv4s9uf5mMfqSmqSxrCPeSQoxwOFBojhIiMc84Jka+XXAIXXRR1NUIklkKhMS2bshghRDPjyivDle2//324he1Xv4q6IiFSiQZzIUT5MINbb4X582HoUGjXDg47LOqqhEgd6b0ATggRD1q1gnvvhd12g6OPhiefjLoiIVJHugfzpAVvyCFHWh1fPQGPPQZbbw0HHQSvvJK1eVrCPeSQo1x9cpLv6ri4TgqNkUOOhDpmz3bv0sW9fXv3mTNXaZ6WcA855FBoTClJcvCGHHKk1dG5c0jHcA831s6Z833ztIR7yCGHQmNKSdKDN+SQI62OrbeGiRPhyy+hb1/45hsgPeEecsih0BiFxsghR/NxTJni3qqVe69e7gsXuns6wj3kkKMcDhQaI4SILffdB4cfHiJgH3oIWupuWSGyUSg0Jr2n2YUQ8eeXv4Trrw9Xup9ySvgsXQhRb/Q2WAgRLUOHhpS4P/wBOnSAK66IuiIhEocGcyFE9Fx8cRjQr7wyDOhnnRV1RUIkCg3mQojoMYMbbghXuP/61yHH/Zhjoq5KiMSQ7s/M05aiJYccaXZUVMAVh0PPznDC8TBhQlGKOCZ1ySFHufrkJN+l7nGdlAAnhxwpdtyK++bmvmZr9xdfzNsljkldcsihBLhSkuYULTnkSLOjDXC2Q/tK2G8/ePvtnF3imNQlhxxKgCslaU/RkkOONDs2aANjroI11oA+feDjj7N2iWNSlxxyKAFOCXByyCFH7fbTp7uvu677Ntu4z5uXtUvckrrkkKMcDpQAJ4RINM8/Hw5bdtwxfBd627ZRVyREk6MEOCFEstljD7j3Xpg2DQ49FJYujboiIWJH2QdzM+trZjPM7H0zOy/LcjOz6zLL3zCz7uWuSQiRMA44AG69NVwldPzxUFMTdUVCxIqyhsaYWQVwI7AvMBv4l5k96u7/rtWsH7BVZtoZ+FvmUQghVjJ4cEiJO/98aNcOrr02hM0IIcp+ZL4T8L67f+juS4ExwIF12hwI/D3zGf9LwHpm1rEk9riFYsghhxyNa3/uuTB8ePhylj/9CYhnuIcccpSrT07yXR3X2AkYCNxW6/kxwA112owDetV6/iTQM9/rKjRGDjmasaO62v3oo93BXxs6MnbhHnLIkcbQmGznwOpePl9MG8zsFDObZmbT5s2bV9gc51AMOeSQo+HtW7SA0aOhXz92vGkIfRY+BMQn3EMOOdIYGjMb2LTW802AOQ1og7uPdPee7t6zffv2hc1xD8WQQw45Gt6+VSu4/36+2Xon7uZIfsYzsQn3kEOO1IXGEC6w+xDYHGgNTAe2q9NmP2Ai4Qh9F+DlQq+r0Bg55JDD3d2//NK/2+QHvrDV2v70iFeL6pKGABE5mp+DqENjzKw/cA1QAYx298vMbEjmjcTNZmbADUBfYCFwgrvnTYRRaIwQ4ns++QR23z3cfz51KnTrFnVFQpScQqExZR/My4EGcyHEKrz7LvTqBeuuGwb0jTeOuiIhSooS4IQQ6WfbbWH8ePj8c+jbF779NuqKhGhSNJgLIdLBzjvDQw+Fr0w98EBYvDjqioRoMtI9mCcxFEMOOeRouKNPH7jzTnj2WTjiCFi+fLXmaQkQkaP5OfKS7+q4uE4KjZFDDjny9rn22pDGcdJJ7jU13zdPS4CIHM3PQcShMdGR5FAMOeSQo3GO//kfuOACuO02+N3vvm+elgAROZqfoxDpHcyTHoohhxxyNM5x6aVw8slw2WVw3XVAegJE5Gh+joLkO2yP66TQGDnkkKOoPsuWuR98cDiXeddd7p6OABE5mp+DqENjyoHuMxdCFM3ixeF2talTYdy4cJGcEAlD95kLIZo3a64JY8fCdtvBoYfCP/8ZdUVClBwN5kKI9LPuujBpEmy0Eey3H7zzTtQVCVFSNJgLIZoHG28cLhlu2TKcav/kk6grEqJkpHswT2MohhxyyNHwPt26wR3nwVdfwD67wfz5RSniGCAiR/Nz5CXf1XFxnRQaI4cccjTKcQHurXDvvo17VVXeLnEMEJGj+TlQaAzpDMWQQw45Gu74ITAUeH0mHHYYLFuWs0scA0TkaH6OQqR3MI86sEIOOeSIt2OXNnD5aTBxIgweDDU1WbvEMUBEjubnKEi+w/a4TgqNkUMOOUrm+OMfw7nO4cNXyXGvTdwCRORofg4UGpNwllXBojlQ2QlatY26mvQR9+0b9/rSgDuceWaIfL3iCjj33KgrEgmgqgrmzIFOnaBtE/xpFgqNaVn+EkSDqFkOrw6HD0aBVYBXQ7cTofsIaKHd1mjivn3jXl+aMIMRI2DePDjvPGjfPpx2FyILy5fD8OEwahRUVEB1NZx4YvgVahnhn6b+K8SVV4fDB6OhetHKeR+MDo89r4+mpjQR9+0b9/rSRosWcMcd4Va1k0+Gdu3ggAOirkrEkOHDYfRoWFTrT3N05k/z+gj/NHWaPY4sq4KHOqz6j3wFFZVwyFydcm0Mcd++ca8vzVRVwT77wBtvhMuL99gj6opEjKiqgg4dVh3IV1BZCXPnlu+Ue/POZo9bYEWx7RfNCadWs2EVYXkpa2pInyQ7mnr71rf9ojlAjjfZuepL8v6Ik6NtWxg/Hrp0gQEDwqBOPANE5Gh6x5w54dR6NtxhyBCFxpT+avY4B1YUar90gfuYytCu7nRP67A8CesRV0e+7TumsrTbtyHrMWtM9tpy1Zf0/RFHx0cfuXfu7L7xxj5l5IexCxCRIxrHggXulZVhfq5JoTGlJs6BFYXat2obLnZacT9sbdbeJv8p1jitR1wdK7av1blkxFqG+aXcvg1Zj7nPZ59f0SZ7fUnfH3F0bLZZOM2+ZAk7nt2btRbOBeITICJHNI62bcPFbivuD19B7QvfFBpTauIeWFGoffcR0G0wtGhda2ZL2PGS0tbUkD5pcHQfARvX+V7rjfuE+aWsq6Hr0aJy5fOKNcNn5d0GZ68vDfsjjo4f/hDGj2eDRZ8y2fqxNt/FJkBEjugcI0aEmx0qK8PgXlkZvrensrK861GQfIftcZ2aVWjM0gXuM25yf+nkZK9HXB2zxri/cFR4LJejMesxa4z7tzPyn/pvrCNO+yOOjvHjvbpFhb/beW9/7IHFRXVJQ0iJHPn7LFjgPmNGeCyXozYoNEYIIRrJP/4Bxx4LAwfCmDG5r4ISokw076vZhRCiFBxzDFx9NTzwQLj8OIEHQSLdKDRGCCGKYfhw+OILuPJK2GgjuPjiqCsS4ns0mAshRLFcfnmIfb3kkhD7OnRo1BUJAaT9NHvSAivkkEOOeDs+fQxuuSVEvQ4bBvfdl7V5koJQ5EiOIy/5ro5rzARcBbwLvAE8DKyXo90s4E3gdQpcrbdiSn1ojBxyyBFvx8KF7r16ubdq5T5lyirNkxaEIkcyHIXGx3IemU8Btnf3HYGZwPl52v7c3X/sea7UqzdJDqyQQw454u2orITHHoNtt4WDDoJad9ckLQhFjmQ4ClG2wdzdH3f35ZmnLwGblMuVlaQHVsghhxzxdqy3HkyeHD4779cPZs4EkhmEIkf8HQXJd9heqgl4DDg6x7L/AK8CrwCnFPN6zSo0Rg455Ii3Y+ZM9/bt3bt0cZ89292TG4QiR3wdlDM0xsyeADbOsugCdx+baXMB0BM4xLPIzKyTu88xsw6EU/PD3P25LO1OAU4B2GyzzXp89NFHDa5bCCFKyiuvwF57Qdeu8NxzsP76UVckUkah0JhGDeZFyI8DhgD7uPvCItpfDFS5+1/ytVMCnBAidjz1VDjd/tOfhg9A634bhxCNILIEODPrC5wLHJBrIDeztcxs7RU/A72Bt8pVkxBClI2994a77oIXX4RBg2DZsqgrEs2Icl7NfgOwNjDFzF43s5shnFY3swmZNhsBL5jZdOBlYLy7TypjTUIIUT4GDoSbboJx4+DkkxX7KpqMcl7NvqW7b+rhlrMfu/uQzPw57t4/8/OH7v6jzLSdu19W0iLSFlghhxxyxN8xZAj8+gi480447dCiFXEMKZEjXo685Ls6Lq6TQmPkkEOOWDvuqXTfl5AIcsHxBRVxDCmRI14OIgyNiZY0B1bIIYcc8XbULIJjgZ2By+4IR+l5iGNIiRzxchQivYN52gMr5JBDjng7WgCnV0KvHeHEE8Pn6DmIY0iJHPFyFKKst6aVi6JvTZv9aHiX3LE3bHJA6dvLIYccchRqv+7P4ec/h3//G6ZMgd13z9rl0UfD0Vnv3uF7XApR3/ZyJNsR6X3m5UL3mQshEsW8edCrF8ydC88/D9tvH3VFImFEdp+5EEKIDO3bhxz3Nm2gTx9QgqUoMRrMhRCiKejaFSZNClc79e4djtaFKBEazOPOsir4bmZ4FKUn7ts37vWJ+rHDDuGrUz/+GPr3hwULoq5INJCqqvBFeVUx+dPUYB5XapbDtGHwUAeY1CM8ThsW5ovGE/ftG/f6RMPp1Qvuuw9eew0OOQSWLIm6IlEPli+HYcOgQwfo0SM8DhsW5kdJugfzuCVD1af9q8Phg9FQvQiWV4XH928N80tdU0P6JN2Rbft+MLo827ch6/HsQfDezcXXl/T90dwcAwbAqFHwxBNw3HFQUxPLxDE5Vu8zfDiMHg2LFoWj8kWLwvODDlICnBLg6rJ0gfuYytCu7nRP67A8CesRV0e+7TumsrTbtyHrMWtM9tpy1Zf0/dGcHX/+szv4B/ud7m0qa2KVOCbH6n0WLHCvrAzzc01KgCs1cU2GKqb9ojlgFdmXuYflpaqpIX2S7si3fa2itNu3Iesx+7Hcy7LVl/T90ZwdZ58Nv/kNW4y/gbMW/RGIT+KYHKv3mTMHKnL861iBEuBKTZyToQq1r+wEXp19mVlYXqqaGtIn6Y5829erS7t9G7IemwzIvSxbfUnfH83dceWVfPLzY7mUiziFW2KTOCbH6n06dYLqHP86VqAEuHrQLBLgpg3LfKa7cOW8FmvAlidDz+uTsx5xdWTbvhVtoNvg0m/fhqzH0/vD55PBa11Vk6++pO+P5u5YtozPdzuYDtMm8Mo59/HTKwcW7JKGVLMkOoYNC5+RL6z1r6NNmxDy17WrEuDqRbNIgKtZnrlIa1Q4terV0O1E6D4CWrSMurrkE/ftG/f6ROlZuBD23RemTYOJE2HvvaOuSGRh+fJwEdyoUeGUe3V1iN4fMQJalvFPU4N50llWFT4jrewErdpGXU36iPv2jXt9orR8/TXssUe4D/2ZZ6B796grEjmoqgqfoXfqBG2b4E9Tg7kQQiSJTz+F3XaDxYth6lTYcsuoKxIxQNnsQgiRJDp3Dh+k1tSED1M/+yzqikQCSPdgnsQwCTnkkEOObbaBCRPCt6z17QvffLNa86SFrchRmj45yXcTelyn1IfGyCGHHHK4uz/+uHurVu577um+cOH3zZMWtiJH4/ug0BiSGSYhhxxyyLHvvvCPf4TvQD/iiO8DwJMWtiJHafrkI72DeRrCJOSQQw45Bg2C666DsWPh1FPBPXFhK3KUpk9e8h22x3Uq6jS7ezhV9fLQ4k5zNaS9HHLIIUdTOX73u3BO9vzz3T2clh06tLhTuiuobx854uOgwGl2c92aJoQQ8ccdfvUruOWWkFBy5plRVySakEK3pilKSgghkoAZ3HgjfPlliCBr3x6OOirqqkRM0GAuhBBJoaIC7roLvvoKjj8eNtgA+vWLuioRA9J7AZwQQqSRNdaARx6BHXaAgQPhpZeirkjEgHQP5mkMk5BDDjnkWGcdGHUmbNAS+vWGf/+7KEUcg1DkUGhMftIeJiGHHHLIMQL39XDvuKH7xx/n7RLHIBQ5FBpTmLSHScghhxxydADOBRYsCDcqz5+fs0scg1DkqF+ffJRtMDezi83sUzN7PTP1z9Gur5nNMLP3zey8khUQddCDHHLIIUdTODZvA7dfDP/5D+y3H/z3v1m7xDEIRY769clLvsP2xkzAxcBvCrSpAD4AtgBaA9OBHxZ6bYXGyCGHHHLUaf/II+4tWrj36eO+ZEnWLnELQpEjAaExZnYxUOXuf8nTZlfgYnfvk3l+fuYNxuX5XluhMUIIkYVRo+Ckk+DII0Ome4v0fpLa3Ij6+8xPN7M3zGy0ma2fZXln4JNaz2dn5q2GmZ1iZtPMbNq8efPKUasQQiSbE0+Eyy+Hu++Gs84KqXGiWdCowdzMnjCzt7JMBwJ/A7oBPwY+A/6a7SWyzMv62+fuI929p7v3bN++fWPKFkKI9HLuuSHq9dpr4Yoroq5GNBGNSoBz918U087MbgXGZVk0G9i01vNNgDmNqUkIIZo1ZvDXv8K8efDb34bY15NOiroqUWbKeTV7x1pPDwbeytLsX8BWZra5mbUGDgdKcft8IG5BD3LIIYccTeFo0QJuvz1EvZ56akiMI55BKHLEPDQG+AfwJvAGYYDumJnfCZhQq11/YCbhqvYLinlthcbIIYccchTRvqrKfZdd3NdYw1+47JnYBaHIkYDQGHc/xt13cPcd3f0Ad/8sM3+Ou/ev1W6Cu2/t7t3c/bKSFRDnoAc55JBDjqZwrLUWjBsHW2xB90sOYKuFrwPxCUKRo3598pHe+xbiHvQghxxyyNEUjg03hMmTYe11mExfNufD2AShyFG/Pvko233m5aTo+8xnPxresXbsDZscUPr2csghhxxJcbzzDkt37sW3tj6vXj+VPsduVLDLo4+GI8beveGAIlejvn3kKK5PofvM0z2YCyGEWMk//wl77w1bbw3PPAPrrht1RaJIog6NEUIIERd23hkeegjeegsOOggWL466IlEiNJgLIURzok8fuPPOcGR+1FFQXR11RaIEaDAXQojmxpFHwjXXhKP0005T7GsKSPdgnrSgBznkkEOOpnKccUZIiBs5Ei66KGvztIStpMWRl3w3ocd1UmiMHHLIIUcJHDU17iedFJJLrrtuleZpCVtJi4OoQmMiJ8lBD3LIIYccTeEwg7/9LVwM9z//A/fc833ztIStpMVRiPQO5kkPepBDDjnkaApHy5ZhEP/Zz+C4474fVdIStpIWRyHSfZ95GoIe5JBDDjmawvHtt2FAf/99eOop2GmnVIStpMWh0BghhBDF8fnnsPvuYWB/4QXYdtuoKxIZFBojhBCiODbeOBwqVlSE+9Fnz466IlEkGsyFEEKspFs3mDQJvv46DOhffRV1RaIINJgLIYRYlZ/8BMaODZ+f77//ysuuRWxJ92CexqAHOeSQQ46mcPz853DDWfDPl2DAnrBsWVGKOIatpMWRl3w3ocd1UmiMHHLIIUcTOU4kJJscupd7dXXeLnEMW0mLA4XGkL6gBznkkEOOpnLsDQwEHnwGzj47b457HMNW0uIoRHoH87QHPcghhxxyNJXj0Eo4YT+4+mq46qqcXeIYtpIWR0HyHbbHdSrqNLt7OE308tDiTkE1pL0ccsghR3NxVFe7H354OC88enTOLmPHug8dWtyp5oa0b64OCpxmN1dojBBCiGJYuhQGDIAnnwxfn1ps1JloNAqNEUIIURpat4YHH4QePWDQIHj++agrEhk0mAshhCietm1h/Hjo0iUcpb/xRtQVCTSYCyGEqC/t2sHkyWFg79sX/vOfqCtq9qR7MI9bCIMccsghR1ocXbqEAX3x4hD7OncuEM+wlbQ48pLv6ri4TgqNkUMOOeSIiWPqVPfKSvfu3X38Pd/GLmwlLQ4UGkP8QhjkkEMOOdLi2G03uP9+mD6dbr85mOULlwDxCVtJi6MQ6R3M4x7CIIcccsiRFsd++8Ho0Wzz6VPcU3E0LaiOTdhKWhwFyXfYHtdJoTFyyCGHHDF0/PWv7uDPbv8rH/tITVFd0hDo0hQOFBojhBCiyTj3XPjzn+Hii+H3v4+6mtRQKDSmZRnF9wLbZJ6uB3zj7j/O0m4WsACoBpbnK1YIIUTMueIKmDcvDObt28Npp0VdUbOgbIO5uw9a8bOZ/RX4Nk/zn7v7l+WqRQghRBNhBiNHwpdfhvuu2rWDX/4y6qpST9kvgDMzA34J3FNuVypZVgXfzQyPovTEffvGvT4hstGyJYwZA7vvDkcfDU88EXVFJaeqCmbODI9xoCmuZt8D+MLd38ux3IHHzewVMzsl14uY2SlmNs3Mps2bN684c9JCGGpTsxymDYOHOsDEH8ED68HT+4f5pa6pIX2S7lixfR/cEMZvHx6nDSvP9m3IenzyMIzfMdQ1qUf4PchXX9L3hxzpc3z1REhD2XZbOPhgyHGdU9ICXZYvh2HDoEOHEFHfoUN4/vDDCQ6NAZ4A3soyHVirzd+AX+d5jU6Zxw7AdGDPQt5mERrzr9NXtq09PbVfstYjro5/ne5+zxqrbtt71gjz47Aed7Vcfd+PaZO9vjTsDznS6/j0U/euXd3bt3efMWOV5kkMdDn99JXLVkxrrOHesmWCQ2Pc/Rfuvn2WaSyAmbUEDgHuzfMaczKPc4GHgZ0aU9P3JDmEYVkVfDBqZdvafD4p/ynXOK1HXB0rtm/NklXn1ywJ80u5fRuyHp+OA7IcgVcvzF5f0veHHOl2dOoUYl8h3Ew9Z873zZMW6FJVBaNGrVy2giVLwhF7OdejEOU+zf4L4F13n51toZmtZWZrr/gZ6E04sm88SQ5hWDQHrCL7shatwvJS1dSQPkl35Nu+VlHa7duQ9Vj/J7mXZasv6ftDjvQ7tt4aJk6E+fNDjvvXXwPJC3SZMwcqcvzrWEFUoTFlvc/czO4AXnL3m2vN6wTc5u79zWwLwtE4hCvr73b3ywq9btH3mc9+NLwz7NgbNjmg9O3L5VhWFT4jrV60+rKKSjhkLrRqG//1iKujqbdvfdsvqwqfldcsLb6+JO8POZqP48knoX9/+OlPw6FomzY8+mj4sXdvOKBIRX37lMpRVRU+I1+U5V9H69Zw3HGw//7lWY9C95krNCauTBsGH4xe9VR7RRvoNhh6Xh9dXWkh7ts37vUJ0VDuvx8GDQoRsA8/HK58TxDDhsHo0aueam/TBgYPhuvL+KcZWWiMaCTdR4THD0aFU6teHf6Rr5gvGkfct2/c6xOioRx2WLgH/bTT4OSTw8hoFnVVRTMi8yc4alQ45V5dHQbyERH/aerIPO4sqwqfkVZ2yn/qVzSMuG/fuNcnREO55JKQEnfOOXDllVFXU2+qqsJn6J06Qdsm+NPUkXnSadUWWm0ddRXpJe7bN+71CdFQLroI5s4NOe4dOsCvfx11RfWibdtwXV9c0GAuhBCi6TGD664LOe6/+U3IcT/22KirSizp/T5zSF+ikhxyyCFHmhwVFXD5IPjpJjD4BBg/vihFlAlwUTryki9RJq5Ts0iAk0MOOeRoLo7bcN+ihfuard2nTs3bJeoEuKgclDMBLtakOVFJDjnkkCNNjkrgNzXQvjLcqP322zm7RJkAF6WjEOkdzNOeqCSHHHLIkSbHBm1gzFWw5pohJe6jj7J2iTIBLkpHIdJ9a1rUaUdyyCGHHHLUr/2bb8Kee4Yr3F94IVwYV4eoEuCidCgBTgghRLJ44QXYd1/YYQd46qmmuZE75hQazNN7ml0IIUQy6dUL7rsPXn0VDjkEli6NuqLYo8FcCCFE/BgwAG69FaZMCd9gUlMTdUWxRqExQggh4skJJ4RQmXPPhXbtQshMgnLcm5J0H5nHLSBBDjnkkEOO+rU/++wQ9XrDDXBZ+IbsOAa6KDRGoTFyyCGHHHLka19d7X7MMe7gr//q5tgFuig0ppzEOSBBDjnkkEOO4tu3aBG+c7R/f3a4+TT6LnwQiE+gi0JjykncAxLkkEMOOeQovn2rVnD//Xyz9c7czZHsxdOxCXRRaEwDUWiMHHLIIUczdXz1Fd/9eA9aff4J//zzs+x15k8KdlFoTExRaIwQQjRjZs+G3XaDJUtg6lTYcsuoKyo7Co0RQgiRLjbZJBzSVleHw9rPPou6osjRYC6EECJ5bLstTJgAc+dCv37wzTdRVxQpGsyFEEIkk512gocegn//Gw48EBYtirqiyEj3YJ7EgAQ55JBDDjmK79O7N9x5Jzz/PBx5JCxfvlpzhcbEdFJojBxyyCGHHKv0ue66kMBy4onuNTXfN1doTNJJckCCHHLIIYcc9eszbBhceGEIl7nggu+bKzQm6SQ9IEEOOeSQQ4769fnDH+CUU+Dyy+GaawCFxsQahcbIIYcccsiRtU91Nfzyl+HCuP/9XzjqKIXGxBWFxgghhMjJ4sXhdrUXXoDHHoO+faOuqNEoNEYIIUTzYs01YexY2H57OPRQeOmlqCsqOxrMhRBCpI911oFJk6BjR9hvP3jnnagrKiuNGszN7DAze9vMasysZ51l55vZ+2Y2w8z65Oi/gZlNMbP3Mo/rN6YeIYQQ4ns22ih8KN2qVfhg+pNPoq6obDT2yPwt4BDgudozzeyHwOHAdkBf4CYzq8jS/zzgSXffCngy87x0pDEgQQ455JBDjuL7bLEF3HkefD0X9t4N5s8vStEsQ2OAZ4CetZ6fD5xf6/lkYNcs/WYAHTM/dwRmFONTaIwccsghhxz1clyIeyvcf7K1e1VV3i4KjVlJZ6D2+YzZmXl12cjdPwPIPHbI9YJmdoqZTTOzafPmzStcQdoDEuSQQw455Cje8QPgdGD6ezBwICxblrNLKkNjzOwJM3sry3Rgvm5Z5jXqHjh3H+nuPd29Z/v27Qt3iDq8QA455JBDjng5dm4DV5wWLow74QSoqcnaJYmhMek9ze4eTq+8PLS4UzcNaS+HHHLIIUfyHJddFs5vn3HGKjnutRk71n3o0OJOmTekfX37UOA0u3kJQmPM7BngN+4+LfN8O+BuYCegE+Hitq3cvbpOv6uA+e5+hZmdB2zg7ucU8ik0RgghRINxh+HD4dpr4U9/gvPPj7qigpQ1NMbMDjaz2cCuwHgzmwzg7m8D9wH/BiYBQ1cM5GZ2W63b2K4A9jWz94B9M8+FEEKI8mEGV18dvjL1t7+F226LuqJGU5Ij86ZGR+ZCCCEazdKlIRR9yhR48EE46KCoK8qJ4lyFEEKIbLRuHQbxn/4UDj8cnnuucJ+YosFcCCFE82WttWD8+BAuM2AATJ8edUUNIpGn2c1sHvBRkc3bAV+WsZwo0DolA61TMtA6JYc0rlex69TF3XPel53Iwbw+mNm0fJ8zJBGtUzLQOiUDrVNySON6lWqddJpdCCGESDgazIUQQoiE0xwG85FRF1AGtE7JQOuUDLROySGN61WSdUr9Z+ZCCCFE2mkOR+ZCCCFEqkn8YG5mh5nZ22ZWUysmdsWy883sfTObYWZ9cvTfwMymmNl7mcf1m6by4jGze83s9cw0y8xez9Fulpm9mWkX64g8M7vYzD6ttV79c7Trm9l/72fy+2OLmV1lZu+a2Rtm9rCZrZejXez3U6HtboHrMsvfMLPuUdRZLGa2qZk9bWbvZP5fnJGlzV5m9m2t38mLoqi1PhT6XUrgftqm1vZ/3cy+M7Mz67RJxH4ys9FmNtfM3qo1r6jxpkH/9/J9C0sSJsK31G7D6t/c9kNgOrAGsDnwAVCRpf+fgfMyP58HXBn1OhVY378CF+VYNgtoF3WNRa7HxYQv58nXpiKz37YAWmf25w+jrj1Pvb2Blpmfr8z1uxT3/VTMdgf6AxMJX3e8C/DPqOsusE4dge6Zn9cGZmZZp72AcVHXWs/1yvu7lLT9VKf2CuBzwv3VidtPwJ5Ad+CtWvMKjjcN/b+X+CNzd3/H3WdkWXQgMMbdl7j7f4D3Cd/ilq3dnZmf7wQOKkuhJcDMDPglcE/UtTQROwHvu/uH7r4UGEPYX7HE3R939+WZpy8Bm0RZTyMoZrsfCPzdAy8B65lZx6YutFjc/TN3fzXz8wLgHaBztFU1CYnaT3XYB/jA3YsNCIsV7v4c8FWd2cWMNw36v5f4wTwPnYFPaj2fTfY/3o3c/TMIf/BAhyaoraHsAXzh7u/lWO7A42b2ipmd0oR1NZTTM6f+Ruc43VTsPowjgwlHRNmI+34qZrsndt+YWVfgJ8A/syze1cymm9lEC1/lHHcK/S4ldj8Bh5P7wCVp+2kFxYw3DdpnLUtSXpkxsyeAjbMsusDdx+bqlmVebC/dL3IdjyD/Ufnu7j7HzDoAU8zs3cy7w0jIt07A34BLCfvkUsLHB4PrvkSWvpHuw2L2k5ldACwH7srxMrHaT1koZrvHbt8Ug5m1BR4EznT37+osfpVwSrcqcw3HI8BWTVxifSn0u5TU/dQaOADI9kXjSdxP9aFB+ywRg7m7/6IB3WYDm9Z6vgkwJ0u7L8yso7t/ljn9NLchNTaWQutoZi2BQ4AeeV5jTuZxrpk9TDhdE9kgUex+M7NbgXFZFhW7D5uMIvbTccD+wD6e+QAsy2vEaj9loZjtHrt9Uwgza0UYyO9y94fqLq89uLv7BDO7yczauXtss8CL+F1K3H7K0A941d2/qLsgifupFsWMNw3aZ2k+zf4ocLiZrWFmmxPeub2co91xmZ+PA3Id6UfNL4B33X12toVmtpaZrb3iZ8LFWG9laxsH6nxudzDZa/0XsJWZbZ55p344YX/FEjPrC5wLHODuC3O0ScJ+Kma7Pwocm7laehfg2xWnD+NI5nqTUcA77n51jjYbZ9phZjsR/j/Ob7oq60eRv0uJ2k+1yHkWMmn7qQ7FjDcN+78X9RV/jZ0IA8FsYAnwBTC51rILCFcFzgD61Zp/G5kr34ENgSeB9zKPG0S9TjnW8w5gSJ15nYAJmZ+3IFz1OB14m3DaN/K686zPP4A3gTcyv6gd665T5nl/wpXHHyRgnd4nfNb1ema6Oan7Kdt2B4as+B0knAq8MbP8TWrdSRLHCehFOFX5Rq3907/OOp2e2SfTCRcw7hZ13QXWKevvUpL3U6bmNoTBed1a8xK3nwhvRj4DlmXGqBNzjTel+L+nBDghhBAi4aT5NLsQQgjRLNBgLoQQQiQcDeZCCCFEwtFgLoQQQiQcDeZCCCFEwtFgLoQQQiQcDeZCCCFEwtFgLoQQQiSc/wfsgtW+hhsqUgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -137,7 +145,7 @@
     "<img  style=\"float: center;\" src=\"data/svm_classification/MMC11.png\" width=\"500\"> \n",
     " \n",
     "\n",
-    "Although this method is a simple and useful method, in most of the classification problems in real life there isn't a separating hyperplane and it is not possible to find a maximal margin classifier. A sample of such data set is plotted at the following cell. As you can see for this data set a separating hyperplane can not be defined and considering any line will misclassify some points. In addition, MMC is too much sensitive to support vectors and when these points are close to the hyperplane the margin would not be satisfactory. This may lead to overfitting of the training data and higher error rates in the classification of test data."
+    "Although this method is a simple and useful method, in most of the classification problems in real life there isn't a separating hyperplane and it is not possible to find a maximal margin classifier. A sample of such data set is plotted at the following cell. "
    ]
   },
   {
@@ -184,7 +192,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, MMC can be extended to the non-separable data sets by defining a new separating hyperplane that doesn't perfectly separate the classes. This new classifier is called soft margin or support vector classifier which is the subject of the next section. "
+    "As you can see for this data set a separating hyperplane can not be defined and considering any line will misclassify some points. In addition, MMC is too much sensitive to support vectors and when these points are close to the hyperplane the margin would not be satisfactory. This may lead to overfitting of the training data and higher error rates in the classification of test data. However, MMC can be extended to the non-separable data sets by defining a new separating hyperplane that doesn't perfectly separate the classes. This new classifier is called soft margin or support vector classifier which is the subject of the next section. "
    ]
   },
   {
@@ -197,11 +205,11 @@
     "\n",
     "Then for $n$ training observations $x_1,...,x_n \\in \\mathbb{R}^{p}$ and associated class labels $y_1,...,y_n$, SVC is an optimization problem and the solution to this problem would be a seperating hyperplane as follows: \n",
     "<p>\n",
-    "<center> $\\underset{\\beta_0,\\beta_1,...,\\beta_n,\\varepsilon_0,\\varepsilon_1,...,\\varepsilon_n}{Maximize}$   M\n",
+    "<center> $\\underset{\\beta_0,\\beta_1,...,\\beta_p,\\varepsilon_0,\\varepsilon_1,...,\\varepsilon_n}{Maximize}$   M\n",
     "\n",
-    "<center> subject to $ \\sum\\limits_{j=1}^{n} \\beta_j^2 =1$\n",
+    "<center> subject to $ \\sum\\limits_{j=1}^{p} \\beta_j^2 =1$\n",
     "\n",
-    "<center> $y_i(\\beta_0+\\beta_{1}x_{i1}+\\beta_{2}x_{i2}+,...,+\\beta_{p}x_{ip}) \\geq M(1-\\epsilon_i)$\n",
+    "<center> $y_i(\\beta_0+\\sum\\limits_{j=1}^{p}\\beta_{j}x_{ij}) \\geq M(1-\\epsilon_i)$\n",
     "\n",
     "<center> $\\epsilon_i \\geq 0, \\sum\\limits_{i=1}^{m} \\epsilon_i \\leq C$\n",
     "\n",
@@ -305,11 +313,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -343,7 +353,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, SVC fails to provide a correct classification for this data set. However, occasionally we face such datasets in which the class boundaries are not linear and for them, support vector classifier is not a good choice."
+    "As you can see, SVC fails to provide a correct classification for this data set. However, occasionally we face such datasets in which the class boundaries are not linear and for them, support vector classifier is not a good choice. For these data sets, we should further extend the support vector calssifier to come up with a new elegent way to deal with this nonlinearity. "
    ]
   },
   {
@@ -352,9 +362,9 @@
    "source": [
     "# Support vector machines\n",
     "\n",
-    "For non-linear datasets, we need to tackle the nonlinearity of the boundaries by extending the features space ($X_1, X_2,..., X_p$) using a nonlinear function such as the cubic and quadratic functions. In this higher dimensional feature space the data points become separable and the new hyperplane function would be linear.\n",
+    "For non-linear datasets, we need to tackle the nonlinearity of the boundaries by extending the features space ($X_1, X_2,..., X_p$) using a nonlinear function such as the cubic and quadratic functions. For instance, instead of using p features $X_{1}, X_{2},..., X_{p}$, we can use 2p features $X_{1}, X_{1}^{2}, X_{2}, X_{2}^{2},..., X_{p}, X_{P}^{2}$ to fit a support vector classifier. In this higher dimensional feature space the data points become separable and the new hyperplane function would be linear.\n",
     "\n",
-    "In practice, this can be done through the kernels. For explaining the kernel, we need to go back to the SVC concept. For solving the SVC problem we only need to calculate the inner products of the data points (observations) and then the classifier can be rewritten as follows:\n",
+    "In practice, this can be done through kernels. For explaining the kernel, we need to go back to the SVC concept. We did not explain how we solve the SVC algorithm becuase it is a bit tachnical and out of the scope of this tutorial but the point is that for solving the SVC problem we only need to calculate the inner products of the data points (observations) and then the classifier can be rewritten as follows:\n",
     "\n",
     "$f(x)=\\beta_0+ \\sum\\limits_{i=1}^{n} \\alpha_i<x,x_{i}>$\n",
     "\n",
@@ -362,21 +372,23 @@
     "\n",
     "$K(x,x^{'})=exp(-\\frac{\\lVert x-x^{'} \\rVert^2}{2\\sigma^2})$\n",
     "\n",
-    "and the classifier with this Kernel is written as:\n",
+    "and the classifier with this Kernel or any other kernal is written as:\n",
     "\n",
     "$f(x)=\\beta_0+ \\sum\\limits_{i=1}^{n} \\alpha_i K(x,x_{i})$\n",
     "\n",
-    "In the next cell, we use the radial basis function (RBF) to classifiy the previous data set. "
+    "In the next cell, we use the radial basis function (RBF) to classifiy the previous data set. As you can see, applying the kernal to new datset and incearsing the dimention  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
@@ -388,12 +400,6 @@
     }
    ],
    "source": [
-    "\n",
-    "set1=np.concatenate((np.random.normal([1,1], 0.25, (100, 2)), np.random.normal([-1,1], 0.25, (100, 2))))\n",
-    "#set1=np.random.normal([1,1], 0.25, (100, 2))\n",
-    "set2=np.random.normal([0,1], 0.25, (100, 2))\n",
-    "set12=np.concatenate((set1,set2))\n",
-    "\n",
     "plt.plot(set1[:,0],set1[:,1],'mo',markersize=8)\n",
     "plt.plot(set2[:,0],set2[:,1],'co',markersize=8)\n",
     "\n",
@@ -414,19 +420,10 @@
     "xy = np.vstack([grid_x.ravel(), grid_y.ravel()]).T\n",
     "Z = clf.decision_function(xy).reshape(grid_x.shape)\n",
     "\n",
-    "plt.contour(\n",
-    "    grid_x, grid_y, Z, colors=\"k\", levels=[-1, 0, 1],alpha=0.8, linestyles=[\"--\", \"-\", \"--\"]\n",
-    ")\n",
+    "plt.contour(grid_x, grid_y, Z, colors=\"k\", levels=[-1, 0, 1],alpha=0.8, linestyles=[\"--\", \"-\", \"--\"])\n",
     "\n",
     "# plot support vectors\n",
-    "plt.scatter(\n",
-    "    clf.support_vectors_[:, 0],\n",
-    "    clf.support_vectors_[:, 1],\n",
-    "    s=80,\n",
-    "    linewidth=1.5,\n",
-    "    facecolors=\"none\",\n",
-    "    edgecolors=\"k\",\n",
-    ")\n",
+    "plt.scatter(clf.support_vectors_[:, 0],clf.support_vectors_[:, 1],s=80,linewidth=1.5,facecolors=\"none\",edgecolors=\"k\")\n",
     "\n",
     "plt.title('dataset', size=20)\n",
     "#plt.xlim(-2,2)\n",
@@ -438,6 +435,1887 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilization of SVM for real datasets\n",
+    "\n",
+    "For more realistic problems, we can now use SVM to classify a real dataset of materials. Below, you can find a dataset of 576 materials that are experimentally characterized as perovskite and non-pervoskite at ambient conditions by $exp$_$label$ label that is a Boolean variable, for which the value 1.0 (0.0) means stable (unstable) as perovskite. For these materials some common properties such as Shannon ionic radii (r), oxidation satates of ions(n), nuclear charges(Z), HOMO, LUMO, electron affinity(EA), ionization potential(IP) (columns of the dataframes) etc are calculated with DFT-PBE using the FHI-aims all-electron full-potential code. Here we consider these properties as the primary features and try to classify the dataset using SVM. Below the perovskite data set and the chosen primary features are domentrated as a dataframe. \n",
+    "\n",
+    "We have already classified this data set with other machine learning techniques (SISSO and decision tree classifier) which for more details you can visit the folowing link:\n",
+    "\n",
+    "[Finding a tolerance factor to predict perovskite stability with SISSO](https://nomad-lab.eu/dev/analytics/staging/user/8aae636a-3dc1-4620-8cff-e109c16f27f8/notebooks/tutorials/perovskites_tolerance_factor.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>exp_label</th>\n",
+       "      <th>rA</th>\n",
+       "      <th>rB</th>\n",
+       "      <th>rX</th>\n",
+       "      <th>nA</th>\n",
+       "      <th>nB</th>\n",
+       "      <th>nX</th>\n",
+       "      <th>rA_rB_ratio</th>\n",
+       "      <th>rA_rX_ratio</th>\n",
+       "      <th>rB_rX_ratio</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LUMO_B</th>\n",
+       "      <th>EA_B</th>\n",
+       "      <th>IP_B</th>\n",
+       "      <th>rS_X</th>\n",
+       "      <th>rP_X</th>\n",
+       "      <th>Z_X</th>\n",
+       "      <th>HOMO_X</th>\n",
+       "      <th>LUMO_X</th>\n",
+       "      <th>EA_X</th>\n",
+       "      <th>IP_X</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>material</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AgBrO3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>4.12903</td>\n",
+       "      <td>0.914286</td>\n",
+       "      <td>0.221429</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.055110</td>\n",
+       "      <td>-3.678151</td>\n",
+       "      <td>12.554312</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AgCdBr3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>1.96</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.34737</td>\n",
+       "      <td>0.653061</td>\n",
+       "      <td>0.484694</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.157118</td>\n",
+       "      <td>0.948262</td>\n",
+       "      <td>9.271930</td>\n",
+       "      <td>0.7514</td>\n",
+       "      <td>0.8834</td>\n",
+       "      <td>35</td>\n",
+       "      <td>-7.858439</td>\n",
+       "      <td>0.055110</td>\n",
+       "      <td>-3.678151</td>\n",
+       "      <td>12.554312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PbAgBr3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.49</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.96</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.29565</td>\n",
+       "      <td>0.760204</td>\n",
+       "      <td>0.586735</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.246293</td>\n",
+       "      <td>-1.475587</td>\n",
+       "      <td>7.755963</td>\n",
+       "      <td>0.7514</td>\n",
+       "      <td>0.8834</td>\n",
+       "      <td>35</td>\n",
+       "      <td>-7.858439</td>\n",
+       "      <td>0.055110</td>\n",
+       "      <td>-3.678151</td>\n",
+       "      <td>12.554312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AgCaCl3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.81</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.28000</td>\n",
+       "      <td>0.707182</td>\n",
+       "      <td>0.552486</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.945848</td>\n",
+       "      <td>0.149995</td>\n",
+       "      <td>6.309260</td>\n",
+       "      <td>0.6785</td>\n",
+       "      <td>0.7567</td>\n",
+       "      <td>17</td>\n",
+       "      <td>-8.594666</td>\n",
+       "      <td>0.019724</td>\n",
+       "      <td>-3.935230</td>\n",
+       "      <td>13.876021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AgClO3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>10.66670</td>\n",
+       "      <td>0.914286</td>\n",
+       "      <td>0.085714</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.019724</td>\n",
+       "      <td>-3.935230</td>\n",
+       "      <td>13.876021</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RbUO3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>2.26316</td>\n",
+       "      <td>1.228570</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.995273</td>\n",
+       "      <td>0.546862</td>\n",
+       "      <td>5.590258</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SmTiO3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.24</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>1.85075</td>\n",
+       "      <td>0.885714</td>\n",
+       "      <td>0.478571</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-4.219539</td>\n",
+       "      <td>-0.313899</td>\n",
+       "      <td>7.119307</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SrTeO3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.44</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>1.48454</td>\n",
+       "      <td>1.028570</td>\n",
+       "      <td>0.692857</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.193946</td>\n",
+       "      <td>-2.575489</td>\n",
+       "      <td>9.729526</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SrTiO3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.44</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>2.40000</td>\n",
+       "      <td>1.028570</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-4.219539</td>\n",
+       "      <td>-0.313899</td>\n",
+       "      <td>7.119307</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YTmO3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.08</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>1.22727</td>\n",
+       "      <td>0.771429</td>\n",
+       "      <td>0.628571</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.072406</td>\n",
+       "      <td>0.522244</td>\n",
+       "      <td>6.424662</td>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-9.030485</td>\n",
+       "      <td>-0.068724</td>\n",
+       "      <td>-3.078804</td>\n",
+       "      <td>16.431366</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>576 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          exp_label    rA    rB    rX   nA  nB  nX  rA_rB_ratio  rA_rX_ratio  \\\n",
+       "material                                                                       \n",
+       "AgBrO3          0.0  1.28  0.31  1.40  1.0   5  -2      4.12903     0.914286   \n",
+       "AgCdBr3         0.0  1.28  0.95  1.96  1.0   2  -1      1.34737     0.653061   \n",
+       "PbAgBr3         0.0  1.49  1.15  1.96  2.0   1  -1      1.29565     0.760204   \n",
+       "AgCaCl3         0.0  1.28  1.00  1.81  1.0   2  -1      1.28000     0.707182   \n",
+       "AgClO3          0.0  1.28  0.12  1.40  1.0   5  -2     10.66670     0.914286   \n",
+       "...             ...   ...   ...   ...  ...  ..  ..          ...          ...   \n",
+       "RbUO3           1.0  1.72  0.76  1.40  1.0   5  -2      2.26316     1.228570   \n",
+       "SmTiO3          1.0  1.24  0.67  1.40  3.0   3  -2      1.85075     0.885714   \n",
+       "SrTeO3          0.0  1.44  0.97  1.40  2.0   4  -2      1.48454     1.028570   \n",
+       "SrTiO3          1.0  1.44  0.60  1.40  2.0   4  -2      2.40000     1.028570   \n",
+       "YTmO3           0.0  1.08  0.88  1.40  3.0   3  -2      1.22727     0.771429   \n",
+       "\n",
+       "          rB_rX_ratio  ...    LUMO_B      EA_B       IP_B    rS_X    rP_X  \\\n",
+       "material               ...                                                  \n",
+       "AgBrO3       0.221429  ...  0.055110 -3.678151  12.554312  0.4608  0.4333   \n",
+       "AgCdBr3      0.484694  ... -1.157118  0.948262   9.271930  0.7514  0.8834   \n",
+       "PbAgBr3      0.586735  ... -0.246293 -1.475587   7.755963  0.7514  0.8834   \n",
+       "AgCaCl3      0.552486  ... -1.945848  0.149995   6.309260  0.6785  0.7567   \n",
+       "AgClO3       0.085714  ...  0.019724 -3.935230  13.876021  0.4608  0.4333   \n",
+       "...               ...  ...       ...       ...        ...     ...     ...   \n",
+       "RbUO3        0.542857  ... -1.995273  0.546862   5.590258  0.4608  0.4333   \n",
+       "SmTiO3       0.478571  ... -4.219539 -0.313899   7.119307  0.4608  0.4333   \n",
+       "SrTeO3       0.692857  ...  0.193946 -2.575489   9.729526  0.4608  0.4333   \n",
+       "SrTiO3       0.428571  ... -4.219539 -0.313899   7.119307  0.4608  0.4333   \n",
+       "YTmO3        0.628571  ... -1.072406  0.522244   6.424662  0.4608  0.4333   \n",
+       "\n",
+       "          Z_X    HOMO_X    LUMO_X      EA_X       IP_X  \n",
+       "material                                                \n",
+       "AgBrO3      8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "AgCdBr3    35 -7.858439  0.055110 -3.678151  12.554312  \n",
+       "PbAgBr3    35 -7.858439  0.055110 -3.678151  12.554312  \n",
+       "AgCaCl3    17 -8.594666  0.019724 -3.935230  13.876021  \n",
+       "AgClO3      8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "...       ...       ...       ...       ...        ...  \n",
+       "RbUO3       8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "SmTiO3      8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "SrTeO3      8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "SrTiO3      8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "YTmO3       8 -9.030485 -0.068724 -3.078804  16.431366  \n",
+       "\n",
+       "[576 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load data\n",
+    "df = pd.read_csv(\"data/svm_classification/data_perovskite.csv\", index_col=0)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we first perform SVM in a 2D feature space (two features are chosen). In this regard, we consider all possible pairs of the chosen primary features. Then we divide the dataset into training set and test set and then use the training set to fit the SVM model. For each pair, we fit the SVM and obtain the prediction accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting dataframe to training and test sets\n",
+    "\n",
+    "# 80 percent of materials for training set\n",
+    "training_df = df.sample(frac = 0.8)\n",
+    " \n",
+    "# the rest for the test set\n",
+    "test_df = df.drop(training_df.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Training Accuracy</th>\n",
+       "      <th>Test Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(rA, rB)</td>\n",
+       "      <td>0.845987</td>\n",
+       "      <td>0.904348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(rA, rX)</td>\n",
+       "      <td>0.800434</td>\n",
+       "      <td>0.747826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(rA, nA)</td>\n",
+       "      <td>0.707158</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(rA, nB)</td>\n",
+       "      <td>0.741866</td>\n",
+       "      <td>0.678261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(rA, nX)</td>\n",
+       "      <td>0.694143</td>\n",
+       "      <td>0.634783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>(HOMO_X, EA_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>(HOMO_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>(LUMO_X, EA_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>(LUMO_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>(EA_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>435 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Features  Training Accuracy  Test Accuracy\n",
+       "0          (rA, rB)           0.845987       0.904348\n",
+       "1          (rA, rX)           0.800434       0.747826\n",
+       "2          (rA, nA)           0.707158       0.652174\n",
+       "3          (rA, nB)           0.741866       0.678261\n",
+       "4          (rA, nX)           0.694143       0.634783\n",
+       "..              ...                ...            ...\n",
+       "430  (HOMO_X, EA_X)           0.691974       0.652174\n",
+       "431  (HOMO_X, IP_X)           0.691974       0.652174\n",
+       "432  (LUMO_X, EA_X)           0.691974       0.652174\n",
+       "433  (LUMO_X, IP_X)           0.691974       0.652174\n",
+       "434    (EA_X, IP_X)           0.691974       0.652174\n",
+       "\n",
+       "[435 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "n=2  #dimension \n",
+    "primary_features_list =training_df.columns.values[:]\n",
+    "primary_features_list =np.delete(primary_features_list, 0)\n",
+    "all_pairs_combinations=[comb for comb in combinations(primary_features_list, n)]\n",
+    "\n",
+    "train_labels=training_df[\"exp_label\"].values\n",
+    "test_labels=test_df[\"exp_label\"].values\n",
+    "accuracy_training=[]\n",
+    "accuracy_test=[]\n",
+    "for i in range(len(all_pairs_combinations)):\n",
+    "    primary_feature_pairs_train=np.array(list(zip(training_df[all_pairs_combinations[i][0]].values, training_df[all_pairs_combinations[i][1]].values)))\n",
+    "    primary_feature_pairs_test=np.array(list(zip(test_df[all_pairs_combinations[i][0]].values, test_df[all_pairs_combinations[i][1]].values)))\n",
+    "    clf = svm.SVC(kernel=\"rbf\", C=10)              # kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable’\n",
+    "    clf.fit(primary_feature_pairs_train, train_labels)\n",
+    "    pred_test_labels=clf.predict(primary_feature_pairs_test)\n",
+    "    accuracy_training.append(clf.score(primary_feature_pairs_train,train_labels))\n",
+    "    accuracy_test.append(metrics.accuracy_score(test_labels, pred_test_labels))  \n",
+    "\n",
+    "features_and_accuracy_list=[[all_pairs_combinations[i],accuracy_training[i],accuracy_test[i]] for i in range(len(accuracy_training))]\n",
+    "combinations_df= pd.DataFrame(data=features_and_accuracy_list,columns=['Features','Training Accuracy','Test Accuracy'])\n",
+    "combinations_df.style.set_properties(subset=['Features'], **{'width': '300px'})\n",
+    "combinations_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we choose the pair with the highest prediction accuracy for the training and test sets and then fit the SVM model for this pair and plot the hyperplane, margin and support vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The 1st combination has the highest accuracy (training accuracy * test accuracy) with the following features :\n",
+      " ('rA', 'rB') \n",
+      "\n",
+      "SVM highest accuracy for the training set: 0.845987.\n",
+      "SVM highest accuracy for the test set: 0.904348.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "product_list=[accuracy_training[i]*accuracy_test[i] for i in range(len(accuracy_training))]  \n",
+    "product_list_max_index = product_list.index(max(product_list)) \n",
+    "\n",
+    "if product_list_max_index+1==1:\n",
+    "    print(\"The %.fst combination has the highest accuracy (training accuracy * test accuracy) with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "elif product_list_max_index+1==2:\n",
+    "    print(\"The %.fsd combination has the highest accuracy (training accuracy * test accuracy) with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "else :\n",
+    "    print(\"The %.fth combination has the highest accuracy (training accuracy * test accuracy) with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "\n",
+    "train_feature_1=training_df[all_pairs_combinations[product_list_max_index][0]].values\n",
+    "train_feature_2=training_df[all_pairs_combinations[product_list_max_index][1]].values\n",
+    "\n",
+    "plt.scatter(train_feature_1,train_feature_2, c=train_labels ,s=80,cmap=plt.get_cmap(\"tab20c\"))\n",
+    "\n",
+    "primary_feature_pairs_train=np.array(list(zip(train_feature_1, train_feature_2)))\n",
+    "primary_feature_pairs_test=np.array(list(zip(test_df[all_pairs_combinations[product_list_max_index][0]].values, test_df[all_pairs_combinations[product_list_max_index][1]].values)))\n",
+    "\n",
+    "clf = svm.SVC(kernel=\"rbf\", C=10)              # kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable’\n",
+    "#clf = svm.NuSVC(gamma=\"auto\")\n",
+    "#clf = svm.LinearSVC()\n",
+    "clf.fit(primary_feature_pairs_train, train_labels)\n",
+    "pred_test_labels=clf.predict(primary_feature_pairs_test)\n",
+    "\n",
+    "# create grid for plotting the hyperplane and margin lines\n",
+    "xmin, xmax = plt.xlim()\n",
+    "ymin, ymax = plt.ylim()\n",
+    "X=np.linspace(xmin,xmax, 400) \n",
+    "Y=np.linspace(ymin,ymax, 400)\n",
+    "grid_x, grid_y = np.meshgrid(X,Y)\n",
+    "xy = np.vstack([grid_x.ravel(), grid_y.ravel()]).T\n",
+    "Z = clf.decision_function(xy).reshape(grid_x.shape)\n",
+    "\n",
+    "print('SVM highest accuracy for the training set: %f.' % clf.score(primary_feature_pairs_train,train_labels))\n",
+    "print('SVM highest accuracy for the test set: %f.' % metrics.accuracy_score(test_labels, pred_test_labels))\n",
+    "\n",
+    "plt.contour(grid_x, grid_y, Z, colors=\"k\", levels=[-1, 0, 1],alpha=0.8, linestyles=[\"--\", \"-\", \"--\"])\n",
+    "\n",
+    "# plot support vectors\n",
+    "plt.scatter(clf.support_vectors_[:, 0],clf.support_vectors_[:, 1],s=80,linewidth=1.5,facecolors=\"none\",edgecolors=\"k\")\n",
+    "\n",
+    "plt.title('Perovskite data set', size=20)\n",
+    "#plt.xlim(-2,2)\n",
+    "#plt.ylim(-2,2)\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(8, 6)\n",
+    "plt.xlabel('%s' % all_pairs_combinations[product_list_max_index][0],fontsize=18)\n",
+    "plt.ylabel('%s' % all_pairs_combinations[product_list_max_index][1],fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to the accuracy of different pairs of features, one can easily realize that the selection of the feature is a key step not even for fitting the SVM model but also for other machine learning models. In fact, these features control the distribution of data points, which is why they are so important in classifying materials.\n",
+    "\n",
+    "The features are not limited to the primary features and one can even combine them by applying some mathematical operators such as $+, -, \\times, ^2, ^3, \\sqrt{}$, etc and produce new and more efficient features for classification of materials. A novel machine learning approach, SISSO, uses this method for the creation of the feature space, and in the mentioned [notebook](https://nomad-lab.eu/dev/analytics/staging/user/8aae636a-3dc1-4620-8cff-e109c16f27f8/notebooks/tutorials/perovskites_tolerance_factor.ipynb) you can find the best-selected feature by SISSO and decision tree classifier for the perovskite dataset. It should be noted that in this notebook the purpose is to find one unique feature in one-dimensional feature space as a tolerance factor for the classification of perovskite.\n",
+    "\n",
+    "Another essential factor to increase the accuracy of SVM is to increase the dimension of the feature space. However, caution should always be exercised in increasing the dimension becuase it might lead to overfitting, especially when the ratio of the degrees of freedom to the number of data points are close to one or higher. \n",
+    "\n",
+    "Now, we train the SVM by increasing the dimension and considering the triple combinations of primary features. In this way, we observe that the prediction accuracy for training and test sets increases drastically as we increase the degrees of freedom of the data points. The training and test accuracy would be more that 90 percent which shows that SVM is a powerful method for classification of materials. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Training Accuracy</th>\n",
+       "      <th>Test Accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(rA, rB, rX)</td>\n",
+       "      <td>0.926247</td>\n",
+       "      <td>0.886957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(rA, rB, nA)</td>\n",
+       "      <td>0.813449</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(rA, rB, nB)</td>\n",
+       "      <td>0.800434</td>\n",
+       "      <td>0.721739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(rA, rB, nX)</td>\n",
+       "      <td>0.780911</td>\n",
+       "      <td>0.695652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(rA, rB, rA_rB_ratio)</td>\n",
+       "      <td>0.772234</td>\n",
+       "      <td>0.852174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4055</th>\n",
+       "      <td>(Z_X, EA_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4056</th>\n",
+       "      <td>(HOMO_X, LUMO_X, EA_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4057</th>\n",
+       "      <td>(HOMO_X, LUMO_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4058</th>\n",
+       "      <td>(HOMO_X, EA_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4059</th>\n",
+       "      <td>(LUMO_X, EA_X, IP_X)</td>\n",
+       "      <td>0.691974</td>\n",
+       "      <td>0.652174</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4060 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Features  Training Accuracy  Test Accuracy\n",
+       "0               (rA, rB, rX)           0.926247       0.886957\n",
+       "1               (rA, rB, nA)           0.813449       0.800000\n",
+       "2               (rA, rB, nB)           0.800434       0.721739\n",
+       "3               (rA, rB, nX)           0.780911       0.695652\n",
+       "4      (rA, rB, rA_rB_ratio)           0.772234       0.852174\n",
+       "...                      ...                ...            ...\n",
+       "4055       (Z_X, EA_X, IP_X)           0.691974       0.652174\n",
+       "4056  (HOMO_X, LUMO_X, EA_X)           0.691974       0.652174\n",
+       "4057  (HOMO_X, LUMO_X, IP_X)           0.691974       0.652174\n",
+       "4058    (HOMO_X, EA_X, IP_X)           0.691974       0.652174\n",
+       "4059    (LUMO_X, EA_X, IP_X)           0.691974       0.652174\n",
+       "\n",
+       "[4060 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "n=3  #dimension \n",
+    "primary_features_list =training_df.columns.values[:]\n",
+    "primary_features_list =np.delete(primary_features_list, 0)\n",
+    "all_pairs_combinations=[comb for comb in combinations(primary_features_list, n)]\n",
+    "\n",
+    "train_labels=training_df[\"exp_label\"].values\n",
+    "test_labels=test_df[\"exp_label\"].values\n",
+    "accuracy_training=[]\n",
+    "accuracy_test=[]\n",
+    "\n",
+    "Matrix_train = [[training_df[all_pairs_combinations[j][i]].values for i in range(len(all_pairs_combinations[j]))] for j in range(len(all_pairs_combinations)) ] \n",
+    "Matrix_test = [[test_df[all_pairs_combinations[j][i]].values for i in range(len(all_pairs_combinations[j]))] for j in range(len(all_pairs_combinations)) ] \n",
+    "\n",
+    "for i in range(len(all_pairs_combinations)):\n",
+    "    feature_train_combined=np.array(Matrix_train[i]).transpose()\n",
+    "    feature_test_combined=np.array(Matrix_test[i]).transpose()\n",
+    "    clf = svm.SVC(kernel=\"rbf\", C=10)              # kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable’\n",
+    "    clf.fit(feature_train_combined, train_labels)\n",
+    "    pred_test_labels=clf.predict(feature_test_combined)\n",
+    "    accuracy_training.append(clf.score(feature_train_combined,train_labels))\n",
+    "    accuracy_test.append(metrics.accuracy_score(test_labels, pred_test_labels))   \n",
+    "    \n",
+    "features_and_accuracy_list=[[all_pairs_combinations[i],accuracy_training[i],accuracy_test[i]] for i in range(len(accuracy_training))]\n",
+    "combinations_df= pd.DataFrame(data=features_and_accuracy_list,columns=['Features','Training Accuracy','Test Accuracy'])\n",
+    "combinations_df.style.set_properties(subset=['Features'], **{'width': '300px'})\n",
+    "combinations_df     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The 548th combination has the highest accuracy with the following features :\n",
+      " ('rB', 'rA_rX_ratio', 'rP_X') \n",
+      "\n",
+      "SVM highest accuracy for the training set: 0.930586.\n",
+      "SVM highest accuracy for the test set: 0.939130.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "/* global mpl */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function () {\n",
+       "    if (typeof WebSocket !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert(\n",
+       "            'Your browser does not have WebSocket support. ' +\n",
+       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "                'Firefox 4 and 5 are also supported but you ' +\n",
+       "                'have to enable WebSockets in about:config.'\n",
+       "        );\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById('mpl-warnings');\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent =\n",
+       "                'This browser does not support binary websocket messages. ' +\n",
+       "                'Performance may be slow.';\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = document.createElement('div');\n",
+       "    this.root.setAttribute('style', 'display: inline-block');\n",
+       "    this._root_extra_style(this.root);\n",
+       "\n",
+       "    parent_element.appendChild(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen = function () {\n",
+       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+       "        fig.send_message('send_image_mode', {});\n",
+       "        if (fig.ratio !== 1) {\n",
+       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
+       "        }\n",
+       "        fig.send_message('refresh', {});\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onload = function () {\n",
+       "        if (fig.image_mode === 'full') {\n",
+       "            // Full images could contain transparency (where diff images\n",
+       "            // almost always do), so we need to clear the canvas so that\n",
+       "            // there is no ghosting.\n",
+       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        }\n",
+       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onunload = function () {\n",
+       "        fig.ws.close();\n",
+       "    };\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function () {\n",
+       "    var titlebar = document.createElement('div');\n",
+       "    titlebar.classList =\n",
+       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+       "    var titletext = document.createElement('div');\n",
+       "    titletext.classList = 'ui-dialog-title';\n",
+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = 'image/png';\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.which === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.which;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which !== 17) {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    if (event.altKey && event.which !== 18) {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    if (event.shiftKey && event.which !== 16) {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data']);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"data:image/png;base64,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\" width=\"799.6767676767677\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib notebook\n",
+    "\n",
+    "product_list=[accuracy_training[i]*accuracy_test[i] for i in range(len(accuracy_training))]  \n",
+    "product_list_max_index = product_list.index(max(product_list)) \n",
+    "\n",
+    "if product_list_max_index+1==1:\n",
+    "    print(\"The %.fst combination has the highest accuracy with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "elif product_list_max_index+1==2:\n",
+    "    print(\"The %.fsd combination has the highest accuracy with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "else :\n",
+    "    print(\"The %.fth combination has the highest accuracy with the following features :\\n\" % (product_list_max_index+1),all_pairs_combinations[product_list_max_index],'\\n')\n",
+    "\n",
+    "feature_train_combined=np.array(Matrix_train[product_list_max_index]).transpose()\n",
+    "feature_test_combined=np.array(Matrix_test[product_list_max_index]).transpose()\n",
+    "\n",
+    "clf = svm.SVC(kernel=\"rbf\", C=10)              # kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable’\n",
+    "clf.fit(feature_train_combined, train_labels)\n",
+    "pred_test_labels=clf.predict(feature_test_combined)\n",
+    "\n",
+    "print('SVM highest accuracy for the training set: %f.' % clf.score(feature_train_combined,train_labels))\n",
+    "print('SVM highest accuracy for the test set: %f.' % metrics.accuracy_score(test_labels, pred_test_labels))\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(Matrix_train[product_list_max_index][0],Matrix_train[product_list_max_index][1],Matrix_train[product_list_max_index][2], c=train_labels ,s=80,cmap=plt.get_cmap(\"tab20c\"))\n",
+    "\n",
+    "plt.title('Perovskite data set', size=20)\n",
+    "\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(8, 6)\n",
+    "\n",
+    "ax.set_xlabel('   %s' % all_pairs_combinations[product_list_max_index][0],fontsize=18)\n",
+    "ax.set_ylabel('   %s' % all_pairs_combinations[product_list_max_index][1],fontsize=18)\n",
+    "ax.set_zlabel('   %s' % all_pairs_combinations[product_list_max_index][2],fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,