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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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"""
Draw random samples and compare get_fitness() on full and pruned versions.
Every update to bestfit should be simultaneous. Produce counterexample if they differ.
Main entry: acasx_prunecheck()
"""
module ACASX_PruneCheck

export setup, runtest, acasx_prunecheck

using ACASXProblem, DerivationTrees

function setup(;data::AbstractString="libcas098small_10K",
               maxsteps::Int64=20)

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

  return problem, tree
end

function runtest{T}(problem::ACASXClustering{T}, tree::DerivationTree,
                    n_samples::Int64=50000, earlystop_div::Int64=10)
  bestfit = realmax(Float64)
  for i = 1:n_samples
    rand!(tree)
    expr = get_expr(tree)

    #side-by-side comparison
    fullfit = get_fitness(problem, expr)
    prunefit = get_fitness(problem, expr, bestfit, realmax(Float64), earlystop_div)

    println("iter=$i of $(n_samples), bestfit=$(signif(bestfit, 4)), fullfit=$(signif(fullfit, 4)), prunefit=$(signif(prunefit, 4)), expr=$(string(expr))")
    if fullfit < bestfit
      bestfit = fullfit

      #on updates, these should be the same. If not, return counterexample
      if prunefit != fullfit
        return tree, expr, fullfit, prunefit
      end
    end
  end
  return tree, nothing, -1.0, -1.0
end

function acasx_prunecheck(; seed=1)
  srand(seed)
  problem, tree = setup()
  tree, expr, fullfit, prunefit = runtest(problem, tree, 50000, 10)
  @show fullfit
  @show prunefit
  @show expr
  return tree, expr, fullfit, prunefit
end

end #module
