code: def generateVectors(numVectors=100, length=500, activity=50):
    
  
    vectors = []
    coinc = numpy.zeros(length, dtype='int32')
    indexList = range(length)
  
    for i in xrange(numVectors):
        coinc[:] = 0
        coinc[random.sample(indexList, activity)] = 1
        vectors.append(coinc.copy())
  
    return vectors
docs: Generate a list of random sparse distributed vectors.  This is used to generate
    training vectors to the spatial or temporal learner and to compare the predicted
    output against.
  
    It generates a list of 'numVectors' elements, each element has length 'length'
    and has a total of 'activity' bits on.
  
    Parameters:
    -----------------------------------------------
    numVectors:    the number of vectors to generate
    length:        the length of each row
    activity:      the number of ones to put into each row.