Moved to use sklearn preprocessing for scaling

notebooks/lab2.ipynb

 {
  "metadata": {
   "name": "",
-  "signature": "sha256:75f989dd2ab73ca4005602cfbe578941785ac582da4dd30251ada2d364be673a"
+  "signature": "sha256:0949f131655e064cf3e40a0f5c298b8bd0662c1299e6aa2be192c8ce2d4c160a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -106,6 +106,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
+      "from sklearn import cross_validation\n",
+      "from sklearn.neighbors import KNeighborsClassifier\n",
+      "from sklearn import preprocessing\n",
+      "\n",
       "def crossValidateKNN(features, labels):\n",
       "    \"\"\"\n",
       "    This code is provided as a template for your cross-validation\n",
@@ -130,15 +135,14 @@
       "        print(\"TEST: %s\" % test_index)\n",
       "    \n",
       "        # SCALE\n",
-      "        trainingFeatures, mf, sf = scale(features.take(train_index, 0))\n",
+      "        scaler = preprocessing.MinMaxScaler(feature_range = (-1, 1))\n",
+      "        trainingFeatures = scaler.fit_transform(features.take(train_index, 0))\n",
       "        # BUILD NEW MODEL - ADD YOUR MODEL BUILDING CODE HERE...\n",
-      "        # model = knn(numFeatures, 2, 3, trainingFeatures, labels[train_index, :]) \n",
       "        model = KNeighborsClassifier(n_neighbors = 3)\n",
       "        model.fit(trainingFeatures, labels.take(train_index, 0))\n",
       "        # RESCALE TEST DATA TO TRAINING SCALE SPACE\n",
-      "        testingFeatures = rescale(features.take(test_index, 0), mf, sf)\n",
+      "        testingFeatures = scaler.transform(features.take(test_index, 0))\n",
       "        # EVALUATE WITH TEST DATA - ADD YOUR MODEL EVALUATION CODE HERE\n",
-      "        # voting, model_output = knnfwd(model, testingFeatures)\n",
       "        model_output = model.predict(testingFeatures)\n",
       "        print(\"KNN prediction %s\" % model_output) # Debugging.\n",
       "        # CONVERT labels(test,:) LABELS TO SAME FORMAT TO COMPUTE ERROR \n",
@@ -147,7 +151,7 @@
       "        matches = model_output != labels_test\n",
       "        errors[foldIndex] = matches.mean()\n",
       "    print('cross validation error: %f' % errors.mean())\n",
-      "    print('cross validation accuracy: %f' % (1.0 - errors.mean()))"
+      "    print('cross validation accuracy: %f' % (1.0 - errors.mean()))\n"
      ],
      "language": "python",
      "metadata": {},