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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def HTM_style_initialize_on_data(self, data, labels):
 """ Uses a style of initialization inspired by the temporal memory. When a new positive example is found, a dendrite is chosen and a number of synapses are created to the example. This works intelligently with an amount of data larger than the number of available dendrites. In this case, data is clustered, and then similar datapoints are allotted to shared dendrites, with as many overlapping bits as possible chosen. In practice, it is still better to simply allocate enough dendrites to have one per datapoint, but this method at least allows initialization to work on larger amounts of data. """

    current_dendrite = 0
    self.dendrites = SM32()
    self.dendrites.reshape(self.dim, self.num_dendrites)

    # We want to avoid training on any negative examples
    data = copy.deepcopy(data)
    data.deleteRows([i for i, v in enumerate(labels) if v != 1])

    if data.nRows() > self.num_dendrites:
      print "Neuron using clustering to initialize dendrites"
      data = (data.toDense())
      model = KMeans(n_clusters = self.num_dendrites, n_jobs=1)
      clusters = model.fit_predict(data)
      multisets = [[Counter(), []] for i in range(self.num_dendrites)]
      sparse_data = [[i for i, d in enumerate(datapoint) if d == 1] for datapoint in data]

      for datapoint, cluster in zip(sparse_data, clusters):
        multisets[cluster][0] = multisets[cluster][0] + Counter(datapoint)
        multisets[cluster][1].append(set(datapoint))

      for i, multiset in enumerate(multisets):
        shared_elements = set(map(lambda x: x[0], filter(lambda x: x[1] > 1, multiset[0].most_common(self.dendrite_length))))
        dendrite_connections = shared_elements
        while len(shared_elements) < self.dendrite_length:
          most_distant_point = multiset[1][numpy.argmin([len(dendrite_connections.intersection(point)) for point in multiset[1]])]
          new_connection = random.sample(most_distant_point - dendrite_connections, 1)[0]
          dendrite_connections.add(new_connection)

        for synapse in dendrite_connections:
          self.dendrites[synapse, current_dendrite] = 1.
        current_dendrite += 1

    else:
      for i in range(data.nRows()):
        ones = data.rowNonZeros(i)[0]
        dendrite_connections = numpy.random.choice(ones, size = self.dendrite_length, replace = False)
        for synapse in dendrite_connections:
          self.dendrites[synapse, current_dendrite] = 1.

        current_dendrite += 1

    self.initialize_permanences()