// Copyright 2015, University of Freiburg,
// Chair of Algorithms and Data Structures.
// Author: Hannah Bast <bast@cs.uni-freiburg.de>.

// NOTE: this file contains specifications and design suggestions in
// pseudo-code. It is not supposed to be compilable in any language. The
// specifications are mandatory, the design suggestions are not.

// Class for a simple inverted index. Copy your code from sheet-01 and extend it
// by BM25 scores, following the explanations in the lecture. In particular,
// consider the implementation advice on slide 17.
class InvertedIndex

// Class for evaluating a given benchmark.
class EvaluateBenchmark

  // Compute the P@k for a given result list and a given set of relevant docs.
  // See the explanation in the lecture, i.p. the example on slide 21.
  // Note that this can also be used to compute P@R by simply taking k as the
  // number of relevant ids.
  double precision_at_k(Array<int> results_ids, Set relevant_ids, int k)
 
  // Compute the AP (avergae precision) of a given result list and a given set
  // of relevant docs. See the explanation in the lecture, i.p. the example on
  // slide 22.
  double average_precision(Array<int> results_ids, Set relevant_ids)
  
  // Evaluate the given benchmark (in a file in the format of the one from the
  // Wiki) and return the requested measures (MP@3, MP@R, MAP).
  Tripe<double, double, double> evaluate_benchmark(String file_name)