docstr: Method to compute a similarity matrix from original df_matrix
  
  :param transpose: If True, calculate the similarity in a transpose matrix
  :type transpose: bool, default False
docstr_tok: ["Method","to","compute","a","similarity","matrix","from","original","df_matrix",":","param","transpose",":","If","True",",","calculate","the","similarity","in","a","transpose","matrix",":","type","transpose",":","bool",",","default","False"]
code: def compute_similarity(self, transpose=False):
      """
      Method to compute a similarity matrix from original df_matrix
  
      :param transpose: If True, calculate the similarity in a transpose matrix
      :type transpose: bool, default False
  
      """
  
      # Calculate distance matrix
      if transpose:
          similarity_matrix = np.float32(
              squareform(pdist(self.matrix.T, self.similarity_metric))
          )
      else:
          similarity_matrix = np.float32(
              squareform(pdist(self.matrix, self.similarity_metric))
          )
  
      # Remove NaNs
      similarity_matrix[np.isnan(similarity_matrix)] = 1.0
      # transform distances in similarities. Values in matrix range from 0-1
      similarity_matrix = (
          similarity_matrix.max() - similarity_matrix
      ) / similarity_matrix.max()
  
      return similarity_matrix
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/base_rating_prediction.py
imports: from scipy.spatial.distance import squareform, pdist
  import numpy
  from caserec.evaluation.rating_prediction import RatingPredictionEvaluation
  from caserec.utils.extra_functions import print_header
  from caserec.utils.process_data import ReadFile, WriteFile
label_desc: Use image node_id 4 for calling the BaseRatingPrediction obj's underlying member method code with example usage: obj.compute_similarity(transpose) and returns: similarity_matrix