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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img  src=\"assets/SVM/header.jpg\" width=\"900\"> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img  style=\"float: left;\" src=\"assets/SVM/logo_NOMAD.png\" width=300>\n",
+    "<img  style=\"float: right;\" src=\"assets/SVM/logo_MPG.png\" width=170> "
+   ]
+  },
+  {
+   "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",
+    "\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",
+    "</div>\n",
+    "\n",
+    "\n",
+    "# Intoduction\n",
+    "\n",
+    "The support vector machine is a generalized form of a classifier called the Maximal Margin Classifier(MMC). This classifier is a simple algorithm that tries to find a separating hyperplane in a p-dimensional feature space to classify the data points. Hyperplanes are middle borders that divide the feature space into different parts. Each data point that lies in each of these specified parts i.e. each side of the hyperplane is assigned to a different class. The major goal of MMC is to find a hyperplane that has the maximum margin. The main shortcoming of MMC is that this method is restricted to the data sets which are separable by a linear boundary. For this reason, MMC was extended to the non-separable and non-linear cases and a more general and robust method, the SVM, was developed which can be applied to a broader range of data sets. \n",
+    "\n",
+    "This tutorial is divided into the following parts:\n",
+    "\n",
+    "- What is a hyperplane? \n",
+    "- Maximal Margin Classifier (MMC)\n",
+    "- Support Vector Classifier (SVC) - linear classifier\n",
+    "- Support Vector Machines (SVM) - nonlinear classifier\n",
+    "- Extensions of support vector machines to the case of more than two classes \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What is a hyperplane?\n",
+    "A hyperplane is a p-1 dimensional flat affine subspace of a p-dimensional space and has a simple mathematical definition. For example, in two dimensions, a hyperplane is considered as a line and is defined as\n",
+    "\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",
+    "\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",
+    "If any $X=(X_1, X_2,..., X_p)^T$ in p-dimensional space satisfies equation (2), then X is a point on the hyperplane. When any X doesn't satisfy equation (2), then it is located on the left or right side of the hyperplane. By calculating the sign of equation (2) for these points, we can easily determine which side of the hyperplane the points are located on. In this way, the hyperplane divides the p-dimensional space into two parts.\n",
+    "\n",
+    "In the script below, a two-dimensional hyperplane, $-5+3X_{1}+2X_{2}=0$, is illustrated. The hyperplane divides the data points into blue and orange classes. The blue points are located at the top of the hyperplane  ($-5+3X_{1}+2X_{2}>0$) and the orange points are located at the bottom of the hyperplane ($-5+3X_{1}+2X_{2}<0$). After determining the hyperplane the test data can be classified based on their positive and negative value(>0 or <0). Here the test data are shown with more colorful points. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "import random\n",
+    "from sklearn import svm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "#define a hyperplane for two dimentions \n",
+    "\n",
+    "X_h=np.linspace(-10, 10, 35)\n",
+    "X=np.linspace(-15, 15, 10)\n",
+    "#X_h=np.random.uniform(-10, 10, 10)\n",
+    "X_test=np.random.uniform(-10, 10, 4)\n",
+    "\n",
+    "def f(X1,X2):\n",
+    "    Y=(+5-3*X1)/2\n",
+    "    Z=-5+3*X1+2*X2\n",
+    "    return(Y,Z)\n",
+    "hyperplane=[f(i,0)[0] for i in X]\n",
+    "\n",
+    "positive_x=[i for i in X_h for j in X_h if f(i,j)[1]>0]\n",
+    "positive_y=[j for i in X_h for j in X_h if f(i,j)[1]>0]\n",
+    "\n",
+    "negative_x=[i for i in X_h for j in X_h if f(i,j)[1]<0]\n",
+    "negative_y=[j for i in X_h for j in X_h if f(i,j)[1]<0]\n",
+    "\n",
+    "test_data_px=[i for i in X_test for j in X_test if f(i,j)[1]>0]\n",
+    "test_data_py=[j for i in X_test for j in X_test if f(i,j)[1]>0]\n",
+    "test_data_nx=[i for i in X_test for j in X_test if f(i,j)[1]<0]\n",
+    "test_data_ny=[j for i in X_test for j in X_test if f(i,j)[1]<0]\n",
+    "\n",
+    "plt.scatter(positive_x,positive_y,10,'b')\n",
+    "plt.scatter(negative_x,negative_y,10,color='orange')\n",
+    "plt.scatter(test_data_px,test_data_py,40,'b')\n",
+    "plt.scatter(test_data_nx,test_data_ny,40,color='orange')\n",
+    "\n",
+    "plt.plot(X,hyperplane,'r')\n",
+    "plt.xlim(-10.5,10.5)\n",
+    "plt.ylim(-10.5,10.5)\n",
+    "plt.title('Hyperplane', size=20)\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(8, 6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Maximal Margin Classifier\n",
+    "\n",
+    "In the maximal margin classifier method, the perpendicular distance between each data point and a separating hyperplane is calculated and the minimal distance is considered as the margin. Then the main task is to find a hyperplane that has the largest margin i.e. the largest minimum distance between the points and the hyperplane. Afterward, the test data points can be easily classified based on which side of the maximal margin hyperplane they are located (sign of the f(x) function). In the following picture, the MMC concept is illustrated. The black line that splits the data is defined as a hyperplane and the dashed lines show the margin width. There are three data points that have the same distance to the hyperplane. These points which lie on the margin lines (dashed lines) are called **\"support vectors\"** (each data point is a vector in the p-dimensional space). The reason for this naming is that the maximal margin hyperplane is highly dependent on these points. In another word, if the positions of these points are changed the maximal margin hyperplane would be changed as well, but any changes in the positions of other points don't affect the maximal margin hyperplane. \n",
+    "\n",
+    "<img  style=\"float: center;\" src=\"data/SVM/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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "from sklearn.datasets import make_blobs, make_moons\n",
+    "\n",
+    "X=np.linspace(-10, 12, 10) \n",
+    "x_blobs, y_blobs = make_blobs(n_samples=[20,30],random_state=0,cluster_std=[1,0.8],centers=[[4.5,5],[6,8]])\n",
+    "\n",
+    "hyperplane1=-0.4*X+8.65\n",
+    "hyperplane2=-1.3*X+13.8\n",
+    "plt.plot(X,hyperplane1,'r--')\n",
+    "plt.plot(X,hyperplane2,'b--')\n",
+    "\n",
+    "plt.scatter(x_blobs[:,0],x_blobs[:,1],c=y_blobs,s=80,cmap=plt.cm.Paired)\n",
+    "plt.title('dataset', size=20)\n",
+    "plt.xlim(4,8)\n",
+    "plt.ylim(3.5,10)\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(8, 6)\n",
+    "plt.xlabel('X1',fontsize=18)\n",
+    "plt.ylabel('X2',fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Support Vector Classifier\n",
+    "\n",
+    "A hyperplane that separates different classes of a data set perfectly and without any defect is too ideal and it is not achievable in the case of complex data sets and mostly leads to the poor classification of test data. As an alternative, one can think of sacrificing some data points for the sake of better classification of most of the training data points. This concept leads to a new classifier that is called support vector classifier (SVC). In SVC, instead of trying to find a hyperplane that does the classification perfectly, some points are allowed to be misclassified. \n",
+    "\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",
+    "\n",
+    "<center> subject to $ \\sum\\limits_{j=1}^{n} \\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",
+    "\n",
+    "<center> $\\epsilon_i \\geq 0, \\sum\\limits_{i=1}^{m} \\epsilon_i \\leq C$\n",
+    "\n",
+    "\n",
+    "    \n",
+    "Where  $M$ is the margin width which we try to make as large as possible and $C$ is a nonnegative tuning parameter that determines the number of points that can be on the wrong side of the hyperplane or margin. When $C=0$ then there is no misclassified point and the SVC becomes the maximal margin classifier. As $C$ increases more points are allowed to be on the wrong side and then the margin width increases. The $\\epsilon_i ... \\epsilon_m$ variables determine whether a data point is located on the wrong side of the hyperplane or margin. If $\\epsilon_i=0$ then the ith point is located on the right side of the hyperplane. If $\\epsilon_i>0$ then the ith point is located on the wrong side of the margin and If $\\epsilon_i>1$ then it would be on the wrong side of the hyperplane. The definition of support vectors is slightly different in SVC compared to MMC. In SVC, the data points or observations which are located on the margin or the wrong side of the margin are called support vectors. As we explained before, the hyperplane is only dependent on support vectors. \n",
+    "    \n",
+    "In the example below, the SVC is used to classify the previous inseparable data set. As you can see, some data points are allowed to violate the margin. This helps us obtain a margin with a larger width and as a result, be more confident that most of the data points are classified well.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+    "# create data set\n",
+    "centers=[[4.5,5],[6,8]]\n",
+    "n_samples=[20,30]\n",
+    "cluster_std=[1,0.8]\n",
+    "\n",
+    "#plot Gaussian blobs- x_blobs is the generated samples and y_blobs are the labels\n",
+    "xy_blobs, labels = make_blobs(n_samples=n_samples,random_state=0,cluster_std=cluster_std,centers=centers)\n",
+    "\n",
+    "# SVC model and fitting the data points\n",
+    "clf = svm.SVC(kernel=\"linear\", C=1000)\n",
+    "clf.fit(xy_blobs, labels)\n",
+    "\n",
+    "#plot the data set, you can use the colormap list to change the color\n",
+    "plt.scatter(xy_blobs[:, 0], xy_blobs[:, 1], c=labels ,s=80,cmap=plt.get_cmap(\"tab20c\"))\n",
+    "\n",
+    "# colormap=['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',\n",
+    "#           'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',\n",
+    "#           'tab20c'])\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, 40) \n",
+    "Y=np.linspace(ymin,ymax, 40)\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",
+    "plt.contour(\n",
+    "    grid_x, grid_y, Z, colors=\"k\", levels=[-1, 0, 1], alpha=0.8, linestyles=[\"--\", \"-\", \"--\"]\n",
+    ")\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",
+    "\n",
+    "#plot setting\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(8, 6)\n",
+    "plt.xlabel('X1',fontsize=18)\n",
+    "plt.ylabel('X2',fontsize=18)\n",
+    "\n",
+    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
+    "plt.tick_params(which='minor',length=5,width=3.0)\n",
+    "plt.tick_params(which='major',length=5.0, width=3.00)\n",
+    "#plt.tick_params(axis='y',which='minor',right='off')\n",
+    "#plt.tick_params(axis='x',which='major',top='off')\n",
+    "#plt.tick_params(axis='x',which='minor',top='off')\n",
+    "#plt.tick_params(axis='y',which='major',right='off')\n",
+    "plt.tick_params(labelsize=20)\n",
+    "#plt.rcParams['figure.figsize'] = [9.5, 7]\n",
+    "#plt.locator_params(axis=\"x\", nbins=6)\n",
+    "#plt.legend(prop={'size': 13},bbox_to_anchor=(1.132, 1.10))\n",
+    "#fig.savefig('MMC11.png', dpi=600)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SVC works well for the data sets which are separable by a linear hyperplane. But what if the boundary between two classes is not linear? Below, a nonlinear data set is shown. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "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",
+    "plt.title('dataset', 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('X1',fontsize=18)\n",
+    "plt.ylabel('X2',fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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",
+    "\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",
+    "\n",
+    "$f(x)=\\beta_0+ \\sum\\limits_{i=1}^{n} \\alpha_i<x,x_{i}>$\n",
+    "\n",
+    "where $\\alpha_i $ are n parameters per each data point. Then we can generalize the inner product term and substitute it with some functions such as radial basis function, polynomial functions, cubic function, etc. This new term expresses the similarity of two observations and is called the kernel. For example, radial basis function kernel has the following form: \n",
+    "\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",
+    "\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. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+    "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",
+    "set1_label=np.zeros(len(set1))\n",
+    "set2_label=np.full((1, len(set2)), 1, dtype=int)\n",
+    "set12_label=np.concatenate((set1_label,set2_label[0]))\n",
+    "\n",
+    "clf = svm.SVC(kernel=\"rbf\", C=10)\n",
+    "#clf = svm.NuSVC(gamma=\"auto\")\n",
+    "#clf = svm.LinearSVC()\n",
+    "clf.fit(set12, set12_label)\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",
+    "plt.contour(\n",
+    "    grid_x, grid_y, Z, colors=\"k\", levels=[-1, 0, 1],alpha=0.8, linestyles=[\"--\", \"-\", \"--\"]\n",
+    ")\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",
+    "\n",
+    "plt.title('dataset', 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('X1',fontsize=18)\n",
+    "plt.ylabel('X2',fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assets/Huberlin-logo.svg.png b/assets/SVM/Huberlin-logo.svg.png
similarity index 100%
rename from assets/Huberlin-logo.svg.png
rename to assets/SVM/Huberlin-logo.svg.png
diff --git a/assets/fairmat_logo.png b/assets/SVM/fairmat_logo.png
similarity index 100%
rename from assets/fairmat_logo.png
rename to assets/SVM/fairmat_logo.png
diff --git a/assets/Logo_MPG.png b/assets/SVM/logo_MPG.png
similarity index 100%
rename from assets/Logo_MPG.png
rename to assets/SVM/logo_MPG.png
diff --git a/assets/Logo_NOMAD.png b/assets/SVM/logo_NOMAD.png
similarity index 100%
rename from assets/Logo_NOMAD.png
rename to assets/SVM/logo_NOMAD.png
diff --git a/data/MMC.pdf b/data/SVM/MMC.pdf
similarity index 100%
rename from data/MMC.pdf
rename to data/SVM/MMC.pdf
diff --git a/data/MMC.png b/data/SVM/MMC.png
similarity index 100%
rename from data/MMC.png
rename to data/SVM/MMC.png
diff --git a/data/MMC11.png b/data/SVM/MMC11.png
similarity index 100%
rename from data/MMC11.png
rename to data/SVM/MMC11.png
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..536a19f9c62ccfda63a6954ac207caec80a845b0
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,17 @@
+import json
+from setuptools import setup, find_packages
+
+with open('metainfo.json') as file:
+    metainfo = json.load(file)
+
+setup(
+    name='perovskites_tolerance_factor',
+    version='1.0',
+    author=', '.join(metainfo['authors']),
+    author_email=metainfo['email'],
+    url=metainfo['url'],
+    description=metainfo['title'],
+    long_description=metainfo['description'],
+    packages=find_packages(),
+    install_requires=['numpy', 'matplotlib', 'pandas', 'seaborn', 'sklearn'],
+)