93 lines
2.2 KiB
Julia
93 lines
2.2 KiB
Julia
import ExcelReaders
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import DataFrames
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POPSIZE = 5
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mu = 1
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lambda = 4
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GENLIMIT = 20
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TARGETCALORIES = 1000
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data = ExcelReaders.readxlsheet("./data/nutrional_information_5917.xlsx", "Sheet2", skipstartrows=1)
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header = ExcelReaders.readxlsheet("./data/nutrional_information_5917.xlsx", "Sheet2", nrows=1)
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# Convert to symbols to build header row.
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for i = 1:length(header)
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tmp = header[i]
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tmp = Symbol(tmp)
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header[i] = tmp
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end
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header = dropdims(reshape(header, :, 1), dims=2)
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df = DataFrames.DataFrame()
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# Finally, construct our dataframe
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for i = 1:length(header)
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df[header[i]] = data[2:end, i]
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end
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function mutate(parent)
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randomCandidate(4)
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end
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"""
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fitness(candidate::DataFrames.DataFrame)
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Calculate the fitness of the candidate, which is the
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absolute value of the difference of TARGETCALORIES and the sum
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of all calories in the meal.
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"""
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function fitness(candidate::DataFrames.DataFrame)
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abs(TARGETCALORIES - sum(+, candidate[:Calories]))
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end
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function randRow()
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# Generate a random row index
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abs(rand(Int) % size(df, 1))
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end
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function randomCandidate(n::Integer)
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# Select n random rows from the dataset.
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rows = [randRow() for i = 1:n]
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df[rows, :]
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end
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function generateInitialPopulation(lambda::Integer, candidateSize::Integer)
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[randomCandidate(candidateSize) for i = 1:lambda]
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end
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function main()
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# Generate the initial population.
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pop = generateInitialPopulation(lambda, 4)
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best = nothing
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generationNum = 0
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fit = nothing
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parents = nothing
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while generationNum != GENLIMIT || (best != nothing && fitness(best) != 0)
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# Assess the fitness of parents
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for parent in pop
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fit = fitness(parent)
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if best === nothing || fit < fitness(best)
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best = parent
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end
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end
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bestFitness = fitness(best)
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sort!(pop, by = x -> fitness(x))
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parents = pop[1:mu]
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pop = parents
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for p in parents
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for i = 1:(lambda/mu)
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push!(pop, mutate(p))
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end
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end
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println("Generation $generationNum, best $best, fitness $bestFitness")
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generationNum += 1
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end
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end
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# search(generateInitialPopulation()) |