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mi_amor.jl
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mi_amor.jl
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using FIGlet
using CSV
using DataFrames
# Import CoreFunctions module
include("core_functions.jl")
# Import ConjectureType module
include("conjecture_type.jl")
# Place custom properties in this Array
custum_properties = [
["regular", "not_K_n"],
["cubic", "not_K_n"],
["sub_cubic", "not_K_n"],
["cubic", "claw_free"],
["cubic", "triangle_free"],
["regular", "claw_free"],
]
# Read invariant data from csv
data = CSV.File("data.csv")
# Convert data to a DataFrame type
data = DataFrames.DataFrame(data)
# Collect property names from the DataFrame
properties = CoreFunctions.get_properties(data)
# Collect invariant names from the DataFrame
invariants = CoreFunctions.get_invariants(data)
# Add optional custom properties
for prop in custum_properties
push!(properties, prop)
end
FIGlet.render("Christy.jl", "slant")
println("-----------------------------------------------")
println("Version 0.01")
println("Copyright 2022 Randy Davila")
println("-----------------------------------------------")
println()
function Input(prompt)
print(prompt)
println()
readline()
end
for (i, invariant) in enumerate(invariants)
println("$(i). $(invariant)")
end
println()
n = Input("Enter the number of the target invariant: ")
n = parse(Int64, n)
println("Target Invariant: $(invariants[n]).")
conjs = CoreFunctions.make_conjectures(data, [invariants[n]], invariants, properties, "connected_graph")
conjs = CoreFunctions.filter(conjs)
FIGlet.render("Christy.jl", "slant")
println("-----------------------------------------------")
println("Version 0.01")
println("Copyright 2022 Randy Davila")
println("-----------------------------------------------")
println()
println("CONJECTURES: \n")
for i in 1:50
println("Conjecture $(i). ", ConjectureType.conj_string(conjs[i]))
println("touch number = $(conjs[i].touch_number) \n")
end