diff --git a/es.ipynb b/es.ipynb
index 97792d8..231e590 100644
--- a/es.ipynb
+++ b/es.ipynb
@@ -54,14 +54,14 @@
     "# generate a toy 2D regression dataset\n",
     "sz = 100\n",
     "X,Y = np.meshgrid(np.linspace(-1,1,sz),np.linspace(-1,1,sz))\n",
-    "mux,muy,sigma=0.3,-0.3,4\n",
-    "G1 = np.exp(-((X-mux)**2+(Y-muy)**2)/2.0*sigma**2)\n",
-    "mux,muy,sigma=-0.3,0.3,2\n",
-    "G2 = np.exp(-((X-mux)**2+(Y-muy)**2)/2.0*sigma**2)\n",
-    "mux,muy,sigma=0.6,0.6,2\n",
-    "G3 = np.exp(-((X-mux)**2+(Y-muy)**2)/2.0*sigma**2)\n",
-    "mux,muy,sigma=-0.4,-0.2,3\n",
-    "G4 = np.exp(-((X-mux)**2+(Y-muy)**2)/2.0*sigma**2)\n",
+    "mux,muy,sigma=0.3,-0.3,0.4\n",
+    "G1 = np.exp(-((X-mux)**2+(Y-muy)**2)/(2.0*sigma**2))\n",
+    "mux,muy,sigma=-0.4,0.4,0.4\n",
+    "G2 = np.exp(-((X-mux)**2+(Y-muy)**2)/(2.0*sigma**2))\n",
+    "mux,muy,sigma=0.6,0.6,0.2\n",
+    "G3 = np.exp(-((X-mux)**2+(Y-muy)**2)/(2.0*sigma**2))\n",
+    "mux,muy,sigma=-0.4,-0.2,0.3\n",
+    "G4 = np.exp(-((X-mux)**2+(Y-muy)**2)/(2.0*sigma**2))\n",
     "G = G1 + G2 - G3 - G4\n",
     "fig,ax = plt.subplots()\n",
     "im = ax.imshow(G, vmin=-1, vmax=1, cmap='jet')\n",
@@ -119,7 +119,7 @@
     "    u = alpha * g\n",
     "    plt.arrow(w[0], w[1], u[0], u[1], head_width=3, head_length=5, fc='w', ec='w')\n",
     "    plt.axis('off')\n",
-    "    plt.title('iteration %d, reward %.2f' % (q+1, G[int(w[0]), int(w[1])]))\n",
+    "    plt.title('iteration %d, reward %.2f' % (q+1, G[int(w[1]), int(w[0])]))\n",
     "    \n",
     "    # draw the history of optimization as a white line\n",
     "    prevx.append(w[0])\n",