diff --git a/exercise.ipynb b/exercise.ipynb
index cc377a2..97be01d 100644
--- a/exercise.ipynb
+++ b/exercise.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {
     "scrolled": true
    },
@@ -522,9 +522,15 @@
     "    :return k-nearest x values [k x input_dimension], k-nearest y values [k x target_dimension]\n",
     "    \"\"\"\n",
     "\n",
-    "    distance = []\n",
+    "    distances = []\n",
     "    for i in range(x_data.shape[0]):\n",
-    "        distance.append(np.linalg.norm(x_data[i]-query_point))\n",
+    "        distances.append(np.linalg.norm(x_data[i]-query_point))\n",
+    "   \n",
+    "    data = np.column_stack((x_data,y_data,distances))\n",
+    "    data = data[data[:,-1].argsort()]\n",
+    "    nearest_x = data[0:k]\n",
+    "    nearest_y = data[0:k,-2]\n",
+    "    return nearest_x, nearest_y\n",
     "#        point = np.array([x_data[i], y_data[i]])\n",
     "#        dist = 0\n",
     "#        for j in range(len(point[0])):\n",
@@ -537,15 +543,15 @@
     "    #idx = np.argpartition(distance, k)\n",
     "    #idx = idx[:k]\n",
     "\n",
-    "    idx = sorted(range(len(distance)), key=lambda i: distance[i])[:k]\n",
-    "\n",
-    "    nearest_x = []\n",
-    "    nearest_y = []\n",
-    "    for j in range(len(idx)):\n",
-    "        nearest_x.append(x_data[idx[j]])\n",
-    "        nearest_y.append(y_data[idx[j]])\n",
-    "\n",
-    "    return nearest_x, nearest_y\n",
+    "#    idx = sorted(range(len(distance)), key=lambda i: distance[i])[:k]\n",
+    "#\n",
+    "#    nearest_x = []\n",
+    "#    nearest_y = []\n",
+    "#    for j in range(len(idx)):\n",
+    "#        nearest_x.append(x_data[idx[j]])\n",
+    "#        nearest_y.append(y_data[idx[j]])\n",
+    "#\n",
+    "#    return nearest_x, nearest_y\n",
     "\n",
     "\n",
     "\n",
@@ -566,17 +572,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 97,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  1.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "k = 5\n",
     "predictions = np.zeros(test_features.shape[0])\n",
@@ -1321,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {