55 lines
1.3 KiB
Julia
55 lines
1.3 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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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 breeder(parent)
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end
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function fitness(candidate::DataFrames.DataFrame)
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sum(+, candidate[:Calories])
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end
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function breed(candidates::Array{DataFrames.DataFrame})
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# Truncation selection, top 3 as parents.
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# First, check everyone's fitness.
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# Then, generate new solutions by selecting parents and breeding
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sort!(candidates, by = x -> fitness(x))
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parents = candidates[1:3]
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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()
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[randomCandidate(5) for i = 1:POPSIZE]
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end
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# search(generateInitialPopulation()) |