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| 1 | +module MatlabParser |
| 2 | + |
| 3 | +using Dictionaries |
| 4 | +using ..FuzzyLogic |
| 5 | +using ..FuzzyLogic: FuzzyAnd, FuzzyOr, FuzzyRule, FuzzyRelation, FuzzyNegation, Domain, |
| 6 | + Variable, memberships, AbstractMembershipFunction |
| 7 | + |
| 8 | +export parse_matlabfis, @matlabfis_str |
| 9 | + |
| 10 | +const MATLAB_JULIA = Dict("'mamdani'" => MamdaniFuzzySystem, |
| 11 | + "'sugeno'" => SugenoFuzzySystem, |
| 12 | + "and'min'" => MinAnd(), "and'prod'" => ProdAnd(), |
| 13 | + "or'max'" => MaxOr(), "or'probor'" => ProbSumOr(), |
| 14 | + "imp'min'" => MinImplication(), "imp'prod'" => ProdImplication(), |
| 15 | + "agg'max'" => MaxAggregator(), |
| 16 | + "agg'probor'" => ProbSumAggregator(), |
| 17 | + "'centroid'" => CentroidDefuzzifier(), |
| 18 | + "'bisector'" => BisectorDefuzzifier(), |
| 19 | + "'trapmf'" => TrapezoidalMF, |
| 20 | + "'trimf'" => TriangularMF, |
| 21 | + "'gaussmf'" => GaussianMF, |
| 22 | + "'gbellmf'" => GeneralizedBellMF, |
| 23 | + "'sigmf'" => SigmoidMF, |
| 24 | + "'dsigmf'" => DifferenceSigmoidMF, |
| 25 | + "'psigmf'" => ProductSigmoidMF, |
| 26 | + "'zmf'" => ZShapeMF, |
| 27 | + "'smf'" => SShapeMF, |
| 28 | + "'pimf'" => PiShapeMF, |
| 29 | + "'linzmf'" => LinearMF, |
| 30 | + "'linsmf'" => LinearMF, |
| 31 | + "'constant'" => ConstantSugenoOutput, |
| 32 | + "'linear'" => LinearSugenoOutput) |
| 33 | + |
| 34 | +# Handle special cases where FuzzyLogic.jl and matlab dont store parameters the same way. |
| 35 | +function preprocess_params(mftype, mfparams; inputs = nothing) |
| 36 | + mftype in ("'gaussmf'", "'linzmf'") && return reverse(mfparams) |
| 37 | + mftype == "'linear'" && |
| 38 | + return [Dictionary(inputs, mfparams[1:(end - 1)]), mfparams[end]] |
| 39 | + mfparams |
| 40 | +end |
| 41 | + |
| 42 | +function parse_mf(line::AbstractString; inputs = nothing) |
| 43 | + mfname, mftype, mfparams = split(line, r"[:,]") |
| 44 | + mfname = Symbol(mfname[2:(end - 1)]) |
| 45 | + mfparams = parse.(Float64, split(mfparams[2:(end - 1)])) |
| 46 | + mfparams = preprocess_params(mftype, mfparams; inputs) |
| 47 | + mfname, MATLAB_JULIA[mftype](mfparams...) |
| 48 | +end |
| 49 | + |
| 50 | +function parse_var(var; inputs = nothing) |
| 51 | + dom = Domain(parse.(Float64, split(var["Range"][2:(end - 1)]))...) |
| 52 | + name = Symbol(var["Name"][2:(end - 1)]) |
| 53 | + mfs = map(1:parse(Int, var["NumMFs"])) do i |
| 54 | + mfname, mf = parse_mf(var["MF$i"]; inputs) |
| 55 | + mfname => mf |
| 56 | + end |> dictionary |
| 57 | + name, Variable(dom, mfs) |
| 58 | +end |
| 59 | + |
| 60 | +function parse_rule(line, inputnames, outputnames, inputmfs, outputmfs) |
| 61 | + ants, cons, op = split(line, r"[,:] ") |
| 62 | + antsidx = filter!(!iszero, parse.(Int, split(ants))) |
| 63 | + considx = filter!(!iszero, parse.(Int, split(cons)[1:length(outputnames)])) |
| 64 | + # TODO: weighted rules |
| 65 | + op = op == "1" ? FuzzyAnd : FuzzyOr |
| 66 | + length(antsidx) == 1 && (op = identity) |
| 67 | + ant = mapreduce(op, enumerate(antsidx)) do (var, mf) |
| 68 | + if mf > 0 |
| 69 | + FuzzyRelation(inputnames[var], inputmfs[var][mf]) |
| 70 | + else |
| 71 | + FuzzyNegation(inputnames[var], inputmfs[var][-mf]) |
| 72 | + end |
| 73 | + end |
| 74 | + con = map(enumerate(considx)) do (var, mf) |
| 75 | + FuzzyRelation(outputnames[var], outputmfs[var][mf]) |
| 76 | + end |
| 77 | + FuzzyRule(ant, con) |
| 78 | +end |
| 79 | + |
| 80 | +function parse_rules(lines, inputs, outputs) |
| 81 | + inputnames = collect(keys(inputs)) |
| 82 | + outputnames = collect(keys(outputs)) |
| 83 | + inputmfs = collect.(keys.(memberships.(collect(inputs)))) |
| 84 | + outputmfs = collect.(keys.(memberships.(collect(outputs)))) |
| 85 | + FuzzyRule[parse_rule(line, inputnames, outputnames, inputmfs, outputmfs) |
| 86 | + for line in lines] |
| 87 | +end |
| 88 | + |
| 89 | +""" |
| 90 | + parse_matlabfis(s::AbstractString) |
| 91 | +
|
| 92 | +Parse a fuzzy inference system from a string in Matlab FIS format. |
| 93 | +""" |
| 94 | +function parse_matlabfis(s::AbstractString) |
| 95 | + lines = strip.(split(s, "\n")) |
| 96 | + key = "" |
| 97 | + fis = Dict() |
| 98 | + for line in lines |
| 99 | + if occursin(r"\[[a-zA-Z0-9_]+\]", line) |
| 100 | + key = line |
| 101 | + fis[key] = ifelse(key == "[Rules]", [], Dict()) |
| 102 | + elseif !isempty(line) |
| 103 | + if key != "[Rules]" |
| 104 | + k, v = split(line, "=") |
| 105 | + fis[key][k] = v |
| 106 | + else |
| 107 | + push!(fis[key], line) |
| 108 | + end |
| 109 | + end |
| 110 | + end |
| 111 | + sysinfo = fis["[System]"] |
| 112 | + inputs = Dictionary{Symbol, Variable}() |
| 113 | + for i in 1:parse(Int, sysinfo["NumInputs"]) |
| 114 | + varname, var = parse_var(fis["[Input$i]"]) |
| 115 | + insert!(inputs, varname, var) |
| 116 | + end |
| 117 | + outputs = Dictionary{Symbol, Variable}() |
| 118 | + for i in 1:parse(Int, sysinfo["NumOutputs"]) |
| 119 | + varname, var = parse_var(fis["[Output$i]"]; inputs = collect(keys(inputs))) |
| 120 | + insert!(outputs, varname, var) |
| 121 | + end |
| 122 | + rules = parse_rules(fis["[Rules]"], inputs, outputs) |
| 123 | + opts = (; name = Symbol(sysinfo["Name"][2:(end - 1)]), inputs = inputs, |
| 124 | + outputs = outputs, rules = rules, |
| 125 | + and = MATLAB_JULIA["and" * sysinfo["AndMethod"]], |
| 126 | + or = MATLAB_JULIA["or" * sysinfo["OrMethod"]]) |
| 127 | + |
| 128 | + if sysinfo["Type"] == "'mamdani'" |
| 129 | + opts = (; opts..., implication = MATLAB_JULIA["imp" * sysinfo["ImpMethod"]], |
| 130 | + aggregator = MATLAB_JULIA["agg" * sysinfo["AggMethod"]], |
| 131 | + defuzzifier = MATLAB_JULIA[sysinfo["DefuzzMethod"]]) |
| 132 | + end |
| 133 | + |
| 134 | + MATLAB_JULIA[sysinfo["Type"]](; opts...) |
| 135 | +end |
| 136 | + |
| 137 | +""" |
| 138 | +String macro to parse Matlab fis formats. See [`parse_matlabfis`](@ref) for more details. |
| 139 | +""" |
| 140 | +macro matlabfis_str(s::AbstractString) |
| 141 | + parse_matlabfis(s) |
| 142 | +end |
| 143 | + |
| 144 | +end |
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