antoninkriz / TimeSeriesClassification.jl

Time Series Classification algorithms in Julia - Bachelor thesis project
MIT License
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Fix formatting for model doc-strings #6

Open ablaom opened 1 year ago

ablaom commented 1 year ago

Part of the output from ?KNNDTWModel:

 Hyperparameters
  =================

  K=1: Number of neighbors weights=:uniform: Either :uniform or :distance based weights of
  the neighbors. - :uniform: All neighbors are weighted equally. - :distance: Each
  neighbor is weighted by it's distance. distance=DTW(): DTW distance struct, for example
  DTWSakoeChiba or pure DTW. - DTW(): Dynamic Time Warping without any constraints. -
  DTWSakoeChiba(): Dynamic Time Warping with Sakoe Chiba bound constraint. - DTWItakura():
  Dynamic Time Warping without Itakura Parallelogram constraint. - You can provide your
  own metric by subtyping DTWType. bounding=LBNone(): Lower bounding of the distance using
  methods like LBKeogh(). - LBNone(): NO-OP, no lower bouning is being done. - LBKeogh():
  Estimating distance lower bound of the distance using the LB_Keogh method
  (https://www.cs.ucr.edu/~eamonn/LB_Keogh.htm). - You can provide your own methofs by
  subtyping LBType.