code: def knapsack_greedy(items, maxweight):
      r
      items_subset = []
      total_weight = 0
      total_value = 0
      for item in items:
          value, weight = item[0:2]
          if total_weight + weight > maxweight:
              continue
          else:
              items_subset.append(item)
              total_weight += weight
              total_value += value
      return total_value, items_subset
docs: r"""
      non-optimal greedy version of knapsack algorithm
      does not sort input. Sort the input by largest value
      first if desired.
  
      Args:
          `items` (tuple): is a sequence of tuples `(value, weight, id_)`, where `value`
              is a scalar and `weight` is a non-negative integer, and `id_` is an
              item identifier.
  
          `maxweight` (scalar):  is a non-negative integer.
  
      CommandLine:
          python -m utool.util_alg --exec-knapsack_greedy
  
      Example:
          >>> # ENABLE_DOCTEST
          >>> from utool.util_alg import *  # NOQA
          >>> items = [(4, 12, 0), (2, 1, 1), (6, 4, 2), (1, 1, 3), (2, 2, 4)]
          >>> maxweight = 15
          >>> total_value, items_subset = knapsack_greedy(items, maxweight)
          >>> result =  'total_value = %r\n' % (total_value,)
          >>> result += 'items_subset = %r' % (items_subset,)
          >>> print(result)
          total_value = 7
          items_subset = [(4, 12, 0), (2, 1, 1), (1, 1, 3)]