new_contents: from numpy import array
  from scipy.cluster.vq import vq, kmeans, whiten
  import math
  
  # TK: Load data from file as array and assign to bobross
   
  # Normalizes according to st.dev.                
  whitened = whiten(bobross)
  output = kmeans(whitened,10)
  print output
  
  # Determines distance between each of 403 vectors and each centroid, and finds closest neighbor
  for i in range(0,403):
  	print i+1 
  	# Dist between centroid 0 and vector
  	distance = math.sqrt(sum((whitened[i] - output[0][0]) ** 2)) 
  	# Group is the centroid it is closest to so far, set initally to centroid 0
  	group = 0
  	# Combo combines distance and group into a single entity
  	combo = (distance, group)
  	# Tests the vector i against the 10 centroids, finds nearest neighbor:
  	for x in range (0,10):
  		distance_temp = math.sqrt(sum((whitened[i] - output[0][x]) ** 2))
  		if distance_temp < combo[0]:
  			combo = (distance_temp,x)
  	print combo	
  
subject: add bob ross clustering code