class_docstr: The naive greedy algorithm for optimization.
  
  This optimization approach is the naive greedy algorithm. At each iteration
  of selection it will simply calculate the gain one would get from adding
  each example, and then will select the example that has the highest gain.
  This algorithm is conceptually simple and easy to parallelize and put on a
  GPU, but can be slower than other alternatives because it involves
  repeatedly evaluating examples that are not likely to be selected next.
  
  Parameters
  ----------
  self.function : base.BaseSelection
          A submodular function that implements the `_calculate_gains` and
          `_select_next` methods. This is the function that will be
          optimized.
  
  self.verbose : bool
          Whether to display a progress bar during the optimization process.
  
  
  Attributes
  ----------
  self.function : base.BaseSelection
          A submodular function that implements the `_calculate_gains` and
          `_select_next` methods. This is the function that will be
          optimized.
  
  self.verbose : bool
          Whether to display a progress bar during the optimization process.
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code: def select(self, X, k, sample_cost=None):
      cost = 0.0
      if sample_cost is None:
          sample_cost = numpy.ones(X.shape[0], dtype="float64")
  
      i = 0
      while cost < k:
          idx = self.random_state.choice(self.random.idxs)
  
          if cost + sample_cost[idx] > k:
              continue
  
          cost += sample_cost[idx]
          gain = self.function._calculate_gains(
              X, idxs=numpy.array([idx])
          )[0]
  
          self.function._select_next(X[idx], gain, idx)
  
          if self.verbose == True:
              self.function.pbar.update(round(sample_cost[idx], 2))
  
imports: import os
  import copy
  import numpy
  import scipy
  from tqdm import tqdm
  from .utils import PriorityQueue
  from .utils import check_random_state
  from .utils import _calculate_pairwise_distances
label_desc: Use image node_id 2 for calling the RandomGreedy obj's underlying member method code with example usage: obj.select(X, k, sample_cost) without return types