diff --git a/assets/SVM/Huberlin-logo.svg.png b/assets/svm_classification/Huberlin-logo.svg.png
similarity index 100%
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diff --git a/assets/SVM/fairmat_logo.png b/assets/svm_classification/fairmat_logo.png
similarity index 100%
rename from assets/SVM/fairmat_logo.png
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diff --git a/assets/SVM/logo_MPG.png b/assets/svm_classification/logo_MPG.png
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diff --git a/data/SVM/MMC.pdf b/data/svm_classification/MMC.pdf
similarity index 100%
rename from data/SVM/MMC.pdf
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diff --git a/data/SVM/MMC.png b/data/svm_classification/MMC.png
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diff --git a/data/SVM/MMC11.png b/data/svm_classification/MMC11.png
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diff --git a/setup.py b/setup.py
index 2f366a81b3d1c0d9d9348ff6c41e83c1e14d2111..4598d43f8dd2f32c349e55d1b24d2e26a35fbaae 100644
--- a/setup.py
+++ b/setup.py
@@ -13,5 +13,5 @@ setup(
     description=metainfo['title'],
     long_description=metainfo['description'],
     packages=find_packages(),
-    install_requires=['numpy', 'matplotlib', 'pandas', 'seaborn', 'sklearn'],
+    install_requires=['numpy', 'matplotlib', 'pandas', 'seaborn', 'sklearn','random'],
 )
diff --git a/svm_classification.ipynb b/svm_classification.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b64ba131bb40c7a5e3f322ffe65a00059f37dd02
--- /dev/null
+++ b/svm_classification.ipynb
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img  src=\"assets/svm_classification/header.jpg\" width=\"900\"> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img  style=\"float: left;\" src=\"assets/svm_classification/logo_NOMAD.png\" width=300>\n",
+    "<img  style=\"float: right;\" src=\"assets/svm_classification/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_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."
+   ]
+  },
+  {
+   "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": 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",
+    "# 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": 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": [
+    "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": 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": [
+    "\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": []
+  }
+ ],
+ "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
+}