class_docstr: Grid experiment design.
  Uses random design for non-continuous variables, and square grid for continuous ones
class_docstr_tok: ["Grid","experiment","design",".","Uses","random","design","for","non-continuous","variables",",","and","square","grid","for","continuous","ones"]
docstr: This method may return less points than requested.
  The total number of generated points is the smallest closest integer of n^d to the selected amount of points.
docstr_tok: ["This","method","may","return","less","points","than","requested",".","The","total","number","of","generated","points","is","the","smallest","closest","integer","of","n^d","to","the","selected","amount","of","points","."]
code: def get_samples(self, init_points_count):
      """
      This method may return less points than requested.
      The total number of generated points is the smallest closest integer of n^d to the selected amount of points.
      """
  
      init_points_count = self._adjust_init_points_count(
          init_points_count
      )
      samples = np.empty((init_points_count, self.space.dimensionality))
  
      # Use random design to fill non-continuous variables
      random_design = RandomDesign(self.space)
      random_design.fill_noncontinous_variables(samples)
  
      if self.space.has_continuous():
          X_design = multigrid(
              self.space.get_continuous_bounds(),
              self.data_per_dimension,
          )
          samples[:, self.space.get_continuous_dims()] = X_design
  
      return samples
  
file_path: GPyOpt/GPyOpt/experiment_design/grid_design.py
imports: import numpy
  from ..core.errors import InvalidConfigError
  from .base import ExperimentDesign
  from .random_design import RandomDesign
label_desc: Use image node_id 3 for calling the GridDesign obj's underlying member method code with example usage: obj.get_samples(init_points_count) and returns: samples