Corrected variable naming to PEP-8

notebooks/knn.ipynb

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  "metadata": {
   "name": "",
-  "signature": "sha256:0194aa9777566a17c15433d0bd70d45727431649e26d09e6c3b091ec6018a537"
+  "signature": "sha256:71d5625f02dc9a007822fd62b88f49f1c98c1cf810f2adee13ab71053bbf59d5"
  },
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@@ -41,7 +41,7 @@
       "import numpy as np\n",
       "\n",
       "scaler = preprocessing.MinMaxScaler(feature_range = (-1, 1))\n",
-      "trainingFeatures = scaler.fit_transform(np.concatenate(featuresSnare, featuresKick))"
+      "training_features = scaler.fit_transform(np.concatenate(features_snare, features_kick))"
      ],
      "language": "python",
      "metadata": {},
@@ -120,7 +120,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "model_snare.fit(trainingFeatures, labels)"
+      "model_snare.fit(training_features, labels)"
      ],
      "language": "python",
      "metadata": {},
@@ -160,7 +160,7 @@
      "collapsed": false,
      "input": [
       "# This uses the previous calculated linear scaling parameters to adjust the incoming features to the same range.  \n",
-      "featuresScaled = scaler.transform(features)"
+      "features_scaled = scaler.transform(testing_features)"
      ],
      "language": "python",
      "metadata": {},
@@ -189,7 +189,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "model_output = model_snare.predict(featuresScaled)"
+      "model_output = model_snare.predict(features_scaled)"
      ],
      "language": "python",
      "metadata": {},

notebooks/spectral_features.ipynb

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   "name": "",
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@@ -35,7 +35,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "First, we want to analyze and feature extract a small collection of audio samples - storing their feature data as our \"training data\".  The commands below read all of the drum example .wav files from the MIR web site into an array, snareFileList.  \n",
+      "First, we want to analyze and feature extract a small collection of audio samples - storing their feature data as our \"training data\".  The commands below read all of the drum example .wav files from the MIR web site into an array, `snare_file_list`.  \n",
       "\n",
       "Let's define a function to retrieve a list of URLs from a text file."
      ]