class_docstr: Interface for scalar regression metrics 
class_docstr_tok: ["Interface","for","scalar","regression","metrics"]
docstr: Computes a scalar value for the metric
  :param reg: np.array with inferred regression
  :param ref: np array with the reference output
  :return: scalar metric value
docstr_tok: ["Computes","a","scalar","value","for","the","metric",":","param","reg",":","np.array","with","inferred","regression",":","param","ref",":","np","array","with","the","reference","output",":","return",":","scalar","metric","value"]
code: def metric(self, reg, ref):
      """
      Computes a scalar value for the metric
      :param reg: np.array with inferred regression
      :param ref: np array with the reference output
      :return: scalar metric value
      """
      raise NotImplementedError
  
file_path: NiftyNet/niftynet/evaluation/regression_evaluations.py
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import pandas
  from niftynet.evaluation.base_evaluations import BaseEvaluation
label_desc: Use image node_id 2 for calling the BaseRegressionEvaluation obj's underlying member method code with example usage: obj.metric(reg, ref) without return types