original_docstring: 
          This function selects the cluster corresponing to the most frequently
          occuring label in the point cloud
          
docstring: This function selects the cluster corresponing to the most frequently
  occuring label in the point cloud
docstring_tokens: This function selects the cluster corresponing to the most frequently occuring label in the point cloud
code: def selectCluster(self,point_cloud_array,label_array,verbose=True):
          """
          This function selects the cluster corresponing to the most frequently
          occuring label in the point cloud
          """
  
          # Remove the negative labels
          if verbose: print(label_array.shape,label_array)
          label_clean = label_array[label_array>=0]
          if verbose: print(label_clean.shape,label_clean)
  
          # Determine the Biggest Cluster
          max_label = np.bincount(label_clean).argmax()
          if verbose: print("Most Frequent Label:",max_label)
  
          # Store Internally as the evaluation data
          self.x_eval = point_cloud_array[label_array==max_label]
          self.y_eval = np.full(self.x_eval.shape[0],max_label)
  
          # Return the max label
          return max_label
code_tokens: def selectCluster ( self , point_cloud_array , label_array , verbose = True ) : if verbose : print ( label_array . shape , label_array ) label_clean = label_array [ label_array >= 0 ] if verbose : print ( label_clean . shape , label_clean ) max_label = np . bincount ( label_clean ) . argmax ( ) if verbose : print ( "Most Frequent Label:" , max_label ) self . x_eval = point_cloud_array [ label_array == max_label ] self . y_eval = np . full ( self . x_eval . shape [ 0 ] , max_label ) return max_label
short_docstring: This function selects the cluster corresponing to the most frequently
  occuring label in the point cloud
short_docstring_tokens: This function selects the cluster corresponing to the most frequently occuring label in the point cloud
comment: """
          This function selects the cluster corresponing to the most frequently
          occuring label in the point cloud
          """$SEP$# Remove the negative labels$SEP$# Determine the Biggest Cluster$SEP$# Store Internally as the evaluation data$SEP$# Return the max label