# *****************************************************************************
# Written by Ritchie Lee, ritchie.lee@sv.cmu.edu
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# Reinforcement Learning Encounter Simulator (RLES) includes the following
# third party software. The SISLES.jl package is licensed under the MIT Expat
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"""
Test a given sequence of actions (for example, NMAC rule sequence).
Returns fitness score as well as the expr.
Deprecated: use ExprSearch.RefExprSearch and GrammarExpts.ACASX_Ref
"""
module ACASX_Test_Seq

export setup, playsequence, nmacrule

using ACASXProblem
using DerivationTrees

const NMAC_SEQUENCE = Int64[2, 2, 7, 10, 7, 7, 5, 3, 10]
#results in :(F((D[:,76] .< 100) & (D[:,77] .< 500))) which is 38 chars

function setup(; runtype::Symbol=:nmacs_vs_nonnmacs,
               data::AbstractString="dasc",
               manuals::AbstractString="dasc_manual",
               clusterdataname::AbstractString="",

               maxsteps::Int64=20)

  problem = ACASXClustering(runtype, data, manuals, clusterdataname)

  grammar = get_grammar(problem)
  tree_params = DerivTreeParams(grammar, maxsteps)
  tree = DerivationTree(tree_params)

  return problem, tree
end

function playsequence{T}(problem::ACASXClustering{T}, tree::DerivationTree,
                      sequence::Vector{Int64}=NMAC_SEQUENCE)
  play!(tree, sequence)
  expr = get_expr(tree)
  fitness = get_fitness(problem, expr)

  return tree, fitness, expr
end

function nmacrule()
  problem, tree = setup()
  tree, fitness, expr = playsequence(problem, tree)
  @show fitness
  @show expr
  return tree, fitness, expr
end

end #module
