No conversion possible and could not execute command error on default datasets for LSH about benchmarks HOT 6 CLOSED

[FATAL] Could not execute command: ['/usr/local/bin/mlpack_lsh', '-r','datasets/wine.csv', '-v', '-k', '3', '-s', '42']

[DEBUG] Compiled with debugging symbols.
[FATAL] Both --query_file and --k must be specified if search is to be done!
terminate called after throwing an instance of 'std::runtime_error'
  what():  fatal error; see Log::Fatal output
Aborted (core dumped)

[DEBUG] Compiled with debugging symbols.
[INFO ] Using LSH with 10 projections (K) and 30 tables (L) with default hash width.
[INFO ] Loading 'datasets/wine.csv' as CSV data.  Size is 13 x 178.
[INFO ] Loaded reference data from 'datasets/wine.csv' (13 x 178).
[INFO ] Hash width chosen as: 19.4285
[INFO ] Final hash table size: (5051 x 4)
[INFO ] Computing 5 distance approximate nearest neighbors.
[INFO ] Loading 'datasets/wines_query_point.csv' as CSV data.  Size is 13 x 1.
[INFO ] Loaded query data from 'datasets/wines_query_point.csv' (13 x 1).
[INFO ] 1 distinct indices returned on average.
[INFO ] Neighbors computed.
[INFO ] Saving CSV data to 'distances.csv'.
[INFO ] Saving CSV data to 'neighbours.csv'.
[INFO ] 
[INFO ] Execution parameters:
[INFO ]   bucket_size: 500
[INFO ]   distances_file: distances.csv
[INFO ]   hash_width: 0
[INFO ]   help: false
[INFO ]   info: ""
[INFO ]   input_model_file: ""
[INFO ]   k: 5
[INFO ]   neighbors_file: neighbours.csv
[INFO ]   output_model_file: ""
[INFO ]   projections: 10
[INFO ]   query_file: datasets/wines_query_point.csv
[INFO ]   reference_file: datasets/wine.csv
[INFO ]   second_hash_size: 99901
[INFO ]   seed: 42
[INFO ]   tables: 30
[INFO ]   verbose: true
[INFO ]   version: false
[INFO ] 
[INFO ] Program timers:
[INFO ]   computing_neighbors: 0.000079s
[INFO ]   hash_building: 0.143197s
[INFO ]   loading_data: 0.001300s
[INFO ]   saving_data: 0.000163s
[INFO ]   total_time: 0.146325s

"You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set."

cmd = shlex.split(self.path + "mlpack_lsh -r " + self.dataset + " -v " + options)

cmd = shlex.split(self.path + "mlpack_lsh -r " + self.dataset + " -q " + self.dataset + " -v " + options)