A2_120040039.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.multiclass import OutputCodeClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "def write_file(filename,\\\n",
    "                tr_error,\\\n",
    "                    test_error,\\\n",
    "                        b_s,\\\n",
    "                            support_vector_s,\\\n",
    "                                b_ve,\\\n",
    "                                support_vector_ve,\\\n",
    "                                    b_vi,\\\n",
    "                                    support_vector_vi,\\\n",
    "                                        ):\n",
    "    with open(filename, 'w') as f:\n",
    "        f.write('training error: ' + str(tr_error) + '\\n')\n",
    "        f.write('testing error: ' + str(test_error) + '\\n')\n",
    "        f.write('b_of_setosa: ' + str(b_s) + '\\n')\n",
    "        f.write('support_vector_indices_of_setosa: ' + str(support_vector_s) + '\\n')\n",
    "        f.write('b_of_versicolor: ' +str(b_ve) + '\\n')\n",
    "        f.write('support_vector_indices_of_versicolor: ' +str(support_vector_ve) + '\\n')\n",
    "        f.write('b_of_virginica: ' +str(b_vi) + '\\n')\n",
    "        f.write('support_vector_indices_of_virginica: ' +str(support_vector_vi))\n",
    "\n",
    "\n",
    "class SVM(object):\n",
    "\n",
    "    def __init__(self, training_dataset_, test_dataset_):\n",
    "        self.training_dataset = training_dataset_\n",
    "        self.test_dataset = test_dataset_\n",
    "        self.classes = {}\n",
    "        self.X_train = None\n",
    "        self.Y_train = None\n",
    "        self.X_test = None\n",
    "        self.Y_test = None\n",
    "\n",
    "        self.support_indecies = None\n",
    "        self.train_errors = None\n",
    "        self.val_errors = None\n",
    "        self.train_loss = None\n",
    "        self.val_loss = None\n",
    "  \n",
    "       \n",
    "    def read_data(self):\n",
    "        f = open(self.training_dataset, 'r')\n",
    "        rows = list(re.split(' ', row) for row in re.split('\\n', f.read())[:-1])\n",
    "        names, self.Y_train = np.unique(list(row[-1] for row in rows), return_inverse=True)\n",
    "        self.X_train = np.empty((0,4), float)\n",
    "        f.close()\n",
    "        for row in rows:\n",
    "            self.X_train = np.append(self.X_train, np.array([np.array(row[:-1]).astype(float)]), axis = 0)\n",
    "        f = open(self.test_dataset, 'r')\n",
    "        f.close()\n",
    "        \n",
    "        f = open(self.test_dataset, 'r')\n",
    "        rows = list(re.split(' ', row) for row in re.split('\\n', f.read())[:-1])\n",
    "        names, self.Y_test = np.unique(list(row[-1] for row in rows), return_inverse=True)\n",
    "        self.X_test = np.empty((0,4), float)\n",
    "        f.close()\n",
    "        for row in rows:\n",
    "            self.X_test = np.append(self.X_test, np.array([np.array(row[:-1]).astype(float)]), axis = 0)\n",
    "\n",
    "\n",
    "    def SVM_general(self, C, kernel, degree, gamma, filename):\n",
    "        Y_train_setosa = np.where(self.Y_train == 0, 1, -1)\n",
    "        if gamma == 0: #gamma = 0 means that there's no gamma value\n",
    "            clf_s = svm.SVC(C = C, kernel = kernel, degree = degree, decision_function_shape = 'ovo')\n",
    "        else:\n",
    "            clf_s = svm.SVC(C = C, kernel = kernel, degree = degree, gamma = gamma, decision_function_shape = 'ovo')\n",
    "        clf_s.fit(self.X_train, Y_train_setosa) #fit the training data\n",
    "        b_s = clf_s.intercept_ #retrieve the intercept bs\n",
    "        b_s= ', '.join(str(i) for i in b_s) #string the bs up\n",
    "        support_vector_s = clf_s.support_ #retrieve support vector\n",
    "        support_vector_s = ', '.join(str(i) for i in support_vector_s) #wrap up the support vectors in string\n",
    "        decision_function_s_train = clf_s.decision_function(self.X_train) #retrieve decision function\n",
    "        decision_function_s_test = clf_s.decision_function(self.X_test)\n",
    "        \n",
    "        Y_train_ve = np.where(self.Y_train == 1, 1, -1)\n",
    "        if gamma == 0:\n",
    "            clf_ve = svm.SVC(C = C, kernel = kernel, degree = degree, decision_function_shape = 'ovo')\n",
    "        else:\n",
    "            clf_ve = svm.SVC(C = C, kernel = kernel, degree = degree, gamma = gamma, decision_function_shape = 'ovo')\n",
    "        clf_ve.fit(self.X_train, Y_train_ve)\n",
    "        b_ve = clf_ve.intercept_\n",
    "        b_ve = ', '.join(str(i) for i in b_ve)\n",
    "        support_vector_ve = clf_ve.support_\n",
    "        support_vector_ve = ', '.join(str(i) for i in support_vector_ve)\n",
    "        decision_function_ve_train = clf_ve.decision_function(self.X_train)\n",
    "        decision_function_ve_test = clf_ve.decision_function(self.X_test)\n",
    "\n",
    "        Y_train_vi = np.where(self.Y_train == 2, 1, -1)\n",
    "        if gamma == 0:\n",
    "            clf_vi = svm.SVC(C = C, kernel = kernel, degree = degree, decision_function_shape = 'ovo')\n",
    "        else:\n",
    "            clf_vi = svm.SVC(C = C, kernel = kernel, degree = degree, gamma = gamma, decision_function_shape = 'ovo')\n",
    "        clf_vi.fit(self.X_train, Y_train_vi)\n",
    "        b_vi = clf_vi.intercept_\n",
    "        b_vi = ', '.join(str(i) for i in b_vi)\n",
    "        support_vector_vi = clf_vi.support_\n",
    "        support_vector_vi = ', '.join(str(i) for i in support_vector_vi)\n",
    "        decision_function_vi_train = clf_vi.decision_function(self.X_train)\n",
    "        decision_function_vi_test = clf_vi.decision_function(self.X_test)\n",
    "\n",
    "        predict_class_tr = [] #to store the class of training vectors for prediction\n",
    "        for i in range(len(decision_function_s_train)):\n",
    "            deciList = [decision_function_s_train[i], decision_function_ve_train[i], \\\n",
    "                decision_function_vi_train[i]]\n",
    "            predict_class_tr.append(deciList.index(max(deciList)))\n",
    "        tr_error = 1 - accuracy_score(self.Y_train, predict_class_tr) #error = 1 - accuracy score\n",
    "\n",
    "        predict_class_test = [] #to store the class of testing vectors for prediction\n",
    "        for i in range(len(decision_function_s_test)):\n",
    "            deciList = [decision_function_s_test[i], decision_function_ve_test[i], \\\n",
    "                decision_function_vi_test[i]]\n",
    "            predict_class_test.append(deciList.index(max(deciList)))\n",
    "        test_error = 1 - accuracy_score(self.Y_test, predict_class_test)\n",
    "        \n",
    "        write_file(filename,\\\n",
    "            tr_error,\\\n",
    "            test_error,\\\n",
    "                b_s,\\\n",
    "                    support_vector_s,\\\n",
    "                        b_ve,\\\n",
    "                        support_vector_ve,\\\n",
    "                            b_vi,\\\n",
    "                            support_vector_vi,\\\n",
    "                                )\n",
    "\n",
    "        \n",
    "    def SVM(self):\n",
    "        \n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################\n",
    "\n",
    "        Y_train_setosa = np.where(self.Y_train == 0, 1, -1)\n",
    "        clf_s = svm.SVC(C = 1e5, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_s.fit(self.X_train, Y_train_setosa)\n",
    "        w_s = clf_s.coef_.flatten()\n",
    "        w_s = ', '.join(str(i) for i in w_s)\n",
    "        b_s = clf_s.intercept_\n",
    "        b_s= ', '.join(str(i) for i in b_s)\n",
    "        support_vector_s = clf_s.support_\n",
    "        support_vector_s = ', '.join(str(i) for i in support_vector_s)\n",
    "        decision_function_s_train = clf_s.decision_function(self.X_train)\n",
    "        decision_function_s_test = clf_s.decision_function(self.X_test)\n",
    "\n",
    "        Y_train_ve = np.where(self.Y_train == 1, 1, -1)\n",
    "        clf_ve = svm.SVC(C = 1e5, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_ve.fit(self.X_train, Y_train_ve)\n",
    "        w_ve = clf_ve.coef_.flatten()\n",
    "        w_ve = ', '.join(str(i) for i in w_ve)\n",
    "        b_ve = clf_ve.intercept_\n",
    "        b_ve = ', '.join(str(i) for i in b_ve)\n",
    "        support_vector_ve = clf_ve.support_\n",
    "        support_vector_ve = ', '.join(str(i) for i in support_vector_ve)\n",
    "        decision_function_ve_train = clf_ve.decision_function(self.X_train)\n",
    "        decision_function_ve_test = clf_ve.decision_function(self.X_test)\n",
    "\n",
    "        Y_train_vi = np.where(self.Y_train == 2, 1, -1)\n",
    "        clf_vi = svm.SVC(C = 1e5, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_vi.fit(self.X_train, Y_train_vi)\n",
    "        w_vi = clf_vi.coef_.flatten()\n",
    "        w_vi = ', '.join(str(i) for i in w_vi)\n",
    "        b_vi = clf_vi.intercept_\n",
    "        b_vi = ', '.join(str(i) for i in b_vi)\n",
    "        support_vector_vi = clf_vi.support_\n",
    "        support_vector_vi = ', '.join(str(i) for i in support_vector_vi)\n",
    "        decision_function_vi_train = clf_vi.decision_function(self.X_train)\n",
    "        decision_function_vi_test = clf_vi.decision_function(self.X_test)\n",
    "\n",
    "        predict_class_tr = []\n",
    "        for i in range(len(decision_function_s_train)):\n",
    "            deciList = [decision_function_s_train[i], decision_function_ve_train[i], \\\n",
    "                decision_function_vi_train[i]]\n",
    "            predict_class_tr.append(deciList.index(max(deciList)))\n",
    "        tr_error = 1 - accuracy_score(self.Y_train, predict_class_tr)\n",
    "\n",
    "        predict_class_test = []\n",
    "        for i in range(len(decision_function_s_test)):\n",
    "            deciList = [decision_function_s_test[i], decision_function_ve_test[i], \\\n",
    "                decision_function_vi_test[i]]\n",
    "            predict_class_test.append(deciList.index(max(deciList)))\n",
    "        test_error = 1 - accuracy_score(self.Y_test, predict_class_test)\n",
    "\n",
    "        with open(\"SVM_linear.txt\", \"w\") as f:\n",
    "            f.write('training error: ' + str(tr_error) + '\\n')\n",
    "            f.write('testing error: ' + str(test_error) + '\\n')\n",
    "            f.write('w_of_setosa: ' + str(w_s) + '\\n')\n",
    "            f.write('b_of_setosa: ' + str(b_s) + '\\n')\n",
    "            f.write('support_vector_indices_of_setosa: ' + str(support_vector_s) + '\\n')\n",
    "            f.write('w_of_versicolor: ' +str(w_ve) + '\\n')\n",
    "            f.write('b_of_versicolor: ' +str(b_ve) + '\\n')\n",
    "            f.write('support_vector_indices_of_versicolor: ' +str(support_vector_ve) + '\\n')\n",
    "            f.write('w_of_virginica: ' +str(w_vi) + '\\n')\n",
    "            f.write('b_of_virginica: ' +str(b_vi) + '\\n')\n",
    "            f.write('support_vector_indices_of_virginica: ' +str(support_vector_vi))\n",
    "\n",
    "        # return train_loss, test_loss, support_vectors\n",
    "    \n",
    "    def SVM_slack(self, C):\n",
    "    \n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################  \n",
    "          \n",
    "        Y_train_setosa = np.where(self.Y_train == 0, 1, -1)\n",
    "        clf_s = svm.SVC(C = C, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_s.fit(self.X_train, Y_train_setosa)\n",
    "        w_s = clf_s.coef_.flatten()\n",
    "        w_s = ', '.join(str(i) for i in w_s)\n",
    "        b_s = clf_s.intercept_\n",
    "        b_s= ', '.join(str(i) for i in b_s)\n",
    "        support_vector_s = clf_s.support_\n",
    "        support_vector_s = ', '.join(str(i) for i in support_vector_s)\n",
    "        decision_function_s_train = clf_s.decision_function(self.X_train)\n",
    "        decision_function_s_test = clf_s.decision_function(self.X_test)\n",
    "\n",
    "        Y_train_ve = np.where(self.Y_train == 1, 1, -1)\n",
    "        clf_ve = svm.SVC(C = C, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_ve.fit(self.X_train, Y_train_ve)\n",
    "        w_ve = clf_ve.coef_.flatten()\n",
    "        w_ve = ', '.join(str(i) for i in w_ve)\n",
    "        b_ve = clf_ve.intercept_\n",
    "        b_ve = ', '.join(str(i) for i in b_ve)\n",
    "        support_vector_ve = clf_ve.support_\n",
    "        support_vector_ve = ', '.join(str(i) for i in support_vector_ve)\n",
    "        decision_function_ve_train = clf_ve.decision_function(self.X_train)\n",
    "        decision_function_ve_test = clf_ve.decision_function(self.X_test)\n",
    "\n",
    "        Y_train_vi = np.where(self.Y_train == 2, 1, -1)\n",
    "        clf_vi = svm.SVC(C = C, kernel = 'linear', decision_function_shape = 'ovo')\n",
    "        clf_vi.fit(self.X_train, Y_train_vi)\n",
    "        w_vi = clf_vi.coef_.flatten()\n",
    "        w_vi = ', '.join(str(i) for i in w_vi)\n",
    "        b_vi = clf_vi.intercept_\n",
    "        b_vi = ', '.join(str(i) for i in b_vi)\n",
    "        support_vector_vi = clf_vi.support_\n",
    "        support_vector_vi = ', '.join(str(i) for i in support_vector_vi)\n",
    "        decision_function_vi_train = clf_vi.decision_function(self.X_train)\n",
    "        decision_function_vi_test = clf_vi.decision_function(self.X_test)\n",
    "\n",
    "        predict_class_tr = []\n",
    "        for i in range(len(decision_function_s_train)):\n",
    "            deciList = [decision_function_s_train[i], decision_function_ve_train[i], \\\n",
    "                decision_function_vi_train[i]]\n",
    "            predict_class_tr.append(deciList.index(max(deciList)))\n",
    "        tr_error = 1 - accuracy_score(self.Y_train, predict_class_tr)\n",
    "\n",
    "        predict_class_test = []\n",
    "        for i in range(len(decision_function_s_test)):\n",
    "            deciList = [decision_function_s_test[i], decision_function_ve_test[i], \\\n",
    "                decision_function_vi_test[i]]\n",
    "            predict_class_test.append(deciList.index(max(deciList)))\n",
    "        test_error = 1 - accuracy_score(self.Y_test, predict_class_test)\n",
    "\n",
    "        with open(\"SVM_slack_{}.txt\".format(round(C,2)), \"w\") as f:\n",
    "            f.write('training error: ' + str(tr_error) + '\\n')\n",
    "            f.write('testing error: ' + str(test_error) + '\\n')\n",
    "            f.write('w_of_setosa: ' + str(w_s) + '\\n')\n",
    "            f.write('b_of_setosa: ' + str(b_s) + '\\n')\n",
    "            f.write('support_vector_indices_of_setosa: ' + str(support_vector_s) + '\\n')\n",
    "            f.write('w_of_versicolor: ' +str(w_ve) + '\\n')\n",
    "            f.write('b_of_versicolor: ' +str(b_ve) + '\\n')\n",
    "            f.write('support_vector_indices_of_versicolor: ' +str(support_vector_ve) + '\\n')\n",
    "            f.write('w_of_virginica: ' +str(w_vi) + '\\n')\n",
    "            f.write('b_of_virginica: ' +str(b_vi) + '\\n')\n",
    "            f.write('support_vector_indices_of_virginica: ' +str(support_vector_vi))\n",
    "        # return train_loss, test_loss, support_vectors\n",
    "    \n",
    "    def SVM_kernel_poly2(self, C):\n",
    "\n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################    \n",
    "\n",
    "        self.SVM_general(C, 'poly', 2, 0, \"SVM_poly2.txt\")\n",
    "\n",
    "\n",
    "        # return train_loss, test_loss, support_vectors\n",
    "    \n",
    "    def SVM_kernel_poly3(self, C):\n",
    "    \n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################  \n",
    "\n",
    "        self.SVM_general(C, 'poly', 3, 0, \"SVM_poly3.txt\")\n",
    "        \n",
    "        # return train_loss, test_loss, support_vectors\n",
    "    \n",
    "    def SVM_kernel_rbf(self, C):\n",
    "    \n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################  \n",
    "\n",
    "        self.SVM_general(C, 'rbf', 3, 0.5, \"SVM_rbf.txt\")\n",
    "\n",
    "        \n",
    "        # return train_loss, test_loss, support_vectors\n",
    "    \n",
    "    def SVM_kernel_sigmoid(self, C):\n",
    "    \n",
    "        #########################\n",
    "        ## WRITE YOUR CODE HERE##\n",
    "        #########################    \n",
    "\n",
    "        self.SVM_general(C, 'sigmoid', 3, 0.25, \"SVM_sigmoid.txt\")\n",
    "\n",
    "    \n",
    "        # return train_loss, test_loss, support_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n"
     ]
    }
   ],
   "source": [
    "svm1 = SVM(\"train.txt\", \"test.txt\")\n",
    "svm1.read_data()\n",
    "print(svm1.X_train)\n",
    "\n",
    "svm1.SVM()\n",
    "\n",
    "for i in range(1, 11):\n",
    "    svm1.SVM_slack(0.1 * i)\n",
    "\n",
    "svm1.SVM_kernel_poly2(1)\n",
    "svm1.SVM_kernel_poly3(1)\n",
    "svm1.SVM_kernel_rbf(1)\n",
    "svm1.SVM_kernel_sigmoid(1)\n",
    "\n",
    "#####################################\n",
    "## Call different SVM with value C ##\n",
    "#####################################"
   ]
  }
 ],
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