original_docstring: Normalize the array Y using statistics or its range.
      In case Y is effectively a 2d-array normalize by column
  
      :param Y: array that you want to normalize.
      :param normalization_type: String specifying the kind of normalization
      to use. Options are 'stats' to use mean and standard deviation,
      or 'maxmin' to use the range of function values.
      :param target
      
      :return Y_normalized: The normalized vector.
      
docstring: Normalize the array Y using statistics or its range.
  In case Y is effectively a 2d-array normalize by column
  
  :param Y: array that you want to normalize.
  :param normalization_type: String specifying the kind of normalization
  to use. Options are 'stats' to use mean and standard deviation,
  or 'maxmin' to use the range of function values.
  :param target
  
  :return Y_normalized: The normalized vector.
docstring_tokens: Normalize the array Y using statistics or its range . In case Y is effectively a 2d - array normalize by column : param Y : array that you want to normalize . : param normalization_type : String specifying the kind of normalization to use . Options are ' stats ' to use mean and standard deviation or ' maxmin ' to use the range of function values . : param target : return Y_normalized : The normalized vector .
code: def invert_normalize(Y, normalization_type='stats', norm_args={}, target = None):
      """Normalize the array Y using statistics or its range.
      In case Y is effectively a 2d-array normalize by column
  
      :param Y: array that you want to normalize.
      :param normalization_type: String specifying the kind of normalization
      to use. Options are 'stats' to use mean and standard deviation,
      or 'maxmin' to use the range of function values.
      :param target
      
      :return Y_normalized: The normalized vector.
      """
      Y = np.asarray(Y, dtype=float)
  
      #if np.max(Y.shape) != Y.size:
      #    raise NotImplementedError('Only 1-dimensional arrays are supported.')
  
      # Only normalize with non null sdev (divide by zero). For only one
      # data point both std and ptp return 0.
      if normalization_type == 'stats':
          mean = norm_args['mean']
          std = norm_args['std']
          Y_invert = Y * std + mean
  
      else:
          raise ValueError('Unknown normalization type: {}'.format(normalization_type))
  
      return Y_invert
code_tokens: def invert_normalize ( Y , normalization_type = 'stats' , norm_args = { } , target = None ) : Y = np . asarray ( Y , dtype = float ) if normalization_type == 'stats' : mean = norm_args [ 'mean' ] std = norm_args [ 'std' ] Y_invert = Y * std + mean else : raise ValueError ( 'Unknown normalization type: {}' . format ( normalization_type ) ) return Y_invert
short_docstring: Normalize the array Y using statistics or its range.
short_docstring_tokens: Normalize the array Y using statistics or its range .
comment: """Normalize the array Y using statistics or its range.
      In case Y is effectively a 2d-array normalize by column
  
      :param Y: array that you want to normalize.
      :param normalization_type: String specifying the kind of normalization
      to use. Options are 'stats' to use mean and standard deviation,
      or 'maxmin' to use the range of function values.
      :param target
      
      :return Y_normalized: The normalized vector.
      """$SEP$#if np.max(Y.shape) != Y.size:$SEP$#    raise NotImplementedError('Only 1-dimensional arrays are supported.')$SEP$# Only normalize with non null sdev (divide by zero). For only one$SEP$# data point both std and ptp return 0.