content: #!usr/bin/env/python
  
  import numpy as np
  import pylab as pl
  from matplotlib.colors import ListedColormap
  from sklearn import neighbors
  
  def kNNGen(trainfile, testfile):
  	features = np.genfromtxt(trainfile, delimiter=' ', usecols=(0, 1, 2))
  	labels = np.genfromtxt(trainfile, delimiter=' ', usecols=(-1))
  	tests = np.genfromtxt(testfile, delimiter=' ', usecols=(0, 1, 2))
  	testlabels = np.genfromtxt(testfile, delimiter=' ', usecols=(-1))
  
  	n_neighbors = 10
  
  	h = 0.02
  	accuracyScores = []
  	for weights in ['uniform', 'distance']:
  		clf = neighbors.KNeighborsClassifier(n_neighbors, leaf_size=20, weights=weights)
  		clf.fit(features, labels)
  		accuracyScores.append(clf.score(tests, testlabels))
  	return accuracyScores
  
  if __name__ == "__main__":
  	FILEPATH = ".\\SeparatedTrainTest\\"
  	accuracyVals = []
  	for i in range(0, 10):
  		accuracyVals.append(kNNGen(FILEPATH + "trainDataSet" + str(i) + ".csv", FILEPATH + "testDataSet" + str(i) + ".csv"))
  	uniformScore = 0
  	distanceScore = 0
  	with open("kNNAverageAccuracy.txt", 'w') as results:
  		for element in accuracyVals:
  			results.write(str(element) + '\n')
  			uniformScore += element[0]
  			distanceScore += element[1]
  
  		results.write("Uniform kNN Accuracy: " + str(uniformScore / 10.0) + '\n')
  		results.write("Distance kNN Accuracy: " + str(distanceScore / 10.0) + '\n')