using CodingTheory

using Oscar

using LinearAlgebra

using BenchmarkTools

function visit_all_subsets(mutate, g_len, v_weight, subset)

if subset

gi=CodingTheory.SubsetGrayCode(g_len, v_weight)

else

gi=CodingTheory.GrayCode(g_len, v_weight; mutate = mutate)

end

for subset in gi

# BenchmarkTools can elide simple computations so we need to do some nontrivial calculation here.

# In any realistic situation we need to look at least look at all entries of the vector.

# Ill model the task of looking at the entries using the 'all' function

Base.all(i->(i==0), subset)

end

return

end

#=

SubsetGrayCode and GrayCode appear to be in the same ballpark.

GrayCode is faster, SubsetGrayCode uses less memory.

######

# n=25, k=12, mutate=true

######

@benchmark visit_all_subsets(25, 12, false)

BenchmarkTools.Trial: 54 samples with 1 evaluation.

Range (min … max): 66.365 ms … 219.557 ms ┊ GC (min … max): 0.00% … 34.15%

Time (median): 92.912 ms ┊ GC (median): 26.24%

Time (mean ± σ): 93.526 ms ± 27.981 ms ┊ GC (mean ± σ): 19.78% ± 13.74%

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66.4 ms Histogram: frequency by time 181 ms <

Memory estimate: 158.70 MiB, allocs estimate: 5200322.

BenchmarkTools.Trial: 65 samples with 1 evaluation.

Range (min … max): 51.159 ms … 103.931 ms ┊ GC (min … max): 0.00% … 50.88%

Time (median): 77.176 ms ┊ GC (median): 31.98%

Time (mean ± σ): 77.271 ms ± 6.824 ms ┊ GC (mean ± σ): 31.52% ± 7.35%

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51.2 ms Histogram: frequency by time 89.3 ms <

Memory estimate: 238.05 MiB, allocs estimate: 5200308.

=#