Rework classify0 into knn_classify

Fix pep8 issues along with renaming
the variables for better quality.

Ch02/kNN.py

@@ -17,24 +17,33 @@
 from numpy import tile, array, zeros, shape
 
 
-def classify0(inX, dataSet, labels, k):
-    dataSetSize = dataSet.shape[0]
-    diffMat = tile(inX, (dataSetSize, 1)) - dataSet
-    sqDiffMat = diffMat ** 2
-    sqDistances = sqDiffMat.sum(axis=1)
-    distances = sqDistances ** 0.5
-    sortedDistIndicies = distances.argsort()
-    classCount = {}
+def knn_classify(input_vector, training_set, labels, k):
+    """Classify input vector using k nearest neighbors.
+
+    Args:
+        input_vector: The input vector to classify.
+        training_set: The matrix of training examples.
+        labels: The class labels.
+        k: The number of neighbors t ouse.
+
+    """
+    training_set_size = training_set.shape[0]
+    diff_matrix = tile(input_vector, (training_set_size, 1)) - training_set
+    diff_matrix_squared = diff_matrix ** 2
+    distances_squared = diff_matrix_squared.sum(axis=1)
+    distances = distances_squared ** 0.5
+    sorted_distance_indices = distances.argsort()
+    class_count = {}
     for i in range(k):
-        voteIlabel = labels[sortedDistIndicies[i]]
-        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
+        current_label = labels[sorted_distance_indices[i]]
+        class_count[current_label] = class_count.get(current_label, 0) + 1
 
-    sortedClassCount = sorted(classCount.iteritems(),
+    sorted_class_count = sorted(class_count.iteritems(),
                               key=operator.itemgetter(1), reverse=True)
-    return sortedClassCount[0][0]
+    return sorted_class_count[0][0]
 
 
-def createDataSet():
+def create_data_set():
     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
     labels = ['A', 'A', 'B', 'B']
     return group, labels
@@ -68,15 +77,16 @@ def autoNorm(dataSet):
     return normDataSet, ranges, minVals
 
 
-def datingClassTest():
-    hoRatio = 0.50      #hold out 10%
+def dating_class_test():
+    # Hold out 10%.
+    hoRatio = 0.50
     datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
     normMat, ranges, minVals = autoNorm(datingDataMat)
     m = normMat.shape[0]
     numTestVecs = int(m * hoRatio)
     errorCount = 0.0
     for i in range(numTestVecs):
-        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:],
+        classifierResult = knn_classify(normMat[i,:], normMat[numTestVecs:m,:],
                                         datingLabels[numTestVecs:m], 3)
         print "the classifier came back with: %d, the real answer is: %d" % \
             (classifierResult, datingLabels[i])
@@ -115,7 +125,7 @@ def handwritingClassTest():
         fileStr = fileNameStr.split('.')[0]     #take off .txt
         classNumStr = int(fileStr.split('_')[0])
         vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
-        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
+        classifierResult = knn_classify(vectorUnderTest, trainingMat, hwLabels, 3)
         print "the classifier came back with: %d, the real answer is: %d" % \
             (classifierResult, classNumStr)
         if classifierResult != classNumStr: