diff --git a/is/UB2/ExerciseSheet2.ipynb b/is/UB2/ExerciseSheet2.ipynb
index c3f7487..13b2642 100644
--- a/is/UB2/ExerciseSheet2.ipynb
+++ b/is/UB2/ExerciseSheet2.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -163,7 +163,7 @@
     "def ineq(wb):\n",
     "    #writing \"return a-b\" would encode the constraint a-b >= 0\n",
     "    ### changed ###\n",
-    "    return t_*(list(map(lambda x:np.dot(wb[0:2].T,x),X_))+wb[2])-1\n",
+    "    return t_*(dot(wb[0:2],X_.T)+wb[2])-1\n",
     "\n",
     "def grad(wb):\n",
     "    return -1*hstack(((-t*X.T).T, -t[:,newaxis]))"
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -225,164 +225,222 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69572e5c88>"
+       "<matplotlib.figure.Figure at 0x106476a90>"
       ]
      },
      "metadata": {},
@@ -416,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -445,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -456,7 +514,7 @@
        "[-3, 3, -3, 3]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -464,94 +522,96 @@
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+       "AABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6957433e10>"
+       "<matplotlib.figure.Figure at 0x105f235d0>"
       ]
      },
      "metadata": {},
@@ -567,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -578,7 +638,7 @@
        "[-1, 6, -1, 6]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -586,100 +646,78 @@
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+       "/n+pqr464ZrGcQXw2SQ/YHYXygcW6+gXeSSpUe5CkaRGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0y\n",
+       "wCWpUQa4JDXq/wHT38XUvMRKQQAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69572f8668>"
+       "<matplotlib.figure.Figure at 0x106c8ddd0>"
       ]
      },
      "metadata": {},
@@ -687,7 +725,8 @@
     }
    ],
    "source": [
-    "X_transformed = 2.5*transpose(vstack((hstack((np.abs(x1),np.abs(x2))),hstack((np.abs(y1),np.abs(y2)))))) ### CHANGE THIS LINE ####\n",
+    "#X_transformed = 2.5*transpose(vstack((hstack((np.abs(x1),np.abs(x2))),hstack((np.abs(y1),np.abs(y2)))))) ### CHANGE THIS LINE ####\n",
+    "X_transformed = transpose(vstack((hstack((sqrt(x1**2+y1**2),sqrt(x2**2+y2**2))),np.zeros(2*N_half))))\n",
     "### prbly other transormation\n",
     "plt.plot(X_transformed[t_c>0,0],X_transformed[t_c>0,1], 'ro')\n",
     "plt.plot(X_transformed[t_c<0,0],X_transformed[t_c<0,1], 'bo')\n",
@@ -696,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -707,7 +746,7 @@
        "(40, 2)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -737,19 +776,20 @@
      "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-37-ae3c6dd9b647>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwb_opt_c\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptimizeSVM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_transformed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt_c\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-12-49e7f2f9b2de>\u001b[0m in \u001b[0;36moptimizeSVM\u001b[1;34m(X_, t_)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     opt_result = optimize.minimize(objective, wb0, jac=jac,constraints=cons, #the optimizer\n\u001b[1;32m---> 15\u001b[1;33m                                      method='SLSQP', options=opt)\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mwb_opt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopt_result\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#result of the optimization\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m/opt/anaconda3/lib/python3.4/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    433\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'slsqp'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m--> 435\u001b[1;33m                                constraints, callback=callback, **options)\n\u001b[0m\u001b[0;32m    436\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dogleg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m         return _minimize_dogleg(fun, x0, args, jac, hess,\n",
-      "\u001b[1;32m/opt/anaconda3/lib/python3.4/site-packages/scipy/optimize/slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[0;32m    402\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m                 \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma_eq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma_ieq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mla\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    406\u001b[0m         \u001b[1;31m# Call SLSQP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-46-6c4875b82fa6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwb_opt_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizeSVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_transformed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#plotSVM(X_transformed,t_c,[-2,2,-1])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-22-49e7f2f9b2de>\u001b[0m in \u001b[0;36moptimizeSVM\u001b[0;34m(X_, t_)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     opt_result = optimize.minimize(objective, wb0, jac=jac,constraints=cons, #the optimizer\n\u001b[0;32m---> 15\u001b[0;31m                                      method='SLSQP', options=opt)\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mwb_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopt_result\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#result of the optimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/Maximus/anaconda/lib/python2.7/site-packages/scipy/optimize/_minimize.pyc\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slsqp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m--> 435\u001b[0;31m                                constraints, callback=callback, **options)\n\u001b[0m\u001b[1;32m    436\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'dogleg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         return _minimize_dogleg(fun, x0, args, jac, hess,\n",
+      "\u001b[0;32m/Users/Maximus/anaconda/lib/python2.7/site-packages/scipy/optimize/slsqp.pyc\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_eq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_ieq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mla\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;31m# Call SLSQP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
      ]
     }
    ],
    "source": [
-    "#wb_opt_c = optimizeSVM(X_transformed,t_c)\n",
-    "#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s"
+    "wb_opt_c = optimizeSVM(X_transformed,t_c)\n",
+    "#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s\n",
+    "#plotSVM(X_transformed,t_c,[-2,2,-1]) "
    ]
   },
   {
@@ -758,6 +798,33 @@
    "source": [
     "Feel free (not mandatory!) to rewrite the function plotSVM to plot the data and the separation curve, not in the transformed space (as above), but in the original space."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -779,21 +846,21 @@
   },
   "celltoolbar": "Edit Metadata",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
   }
  },
  "nbformat": 4,