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# regonnregonn

View SF-PCA.jl
 using MultivariateStats # PCA using PlotlyJS # データプロット用 Plotly.jl ではなく js実装を呼ぶ PlotlyJS.jl こちらの方がメンテされている using CSV, DataFrames # CSV と DataFrames が扱えるように df = CSV.File("SF.csv") |> DataFrame reciprocals = 1 ./ Matrix(df[:,2:8]) # 逆数にして1番目の性格の値が大きくなるように top5 = coalesce.(reciprocals, 0.0) # missing を zero で埋める M = fit(PCA, top5; maxoutdim=2) components = MultivariateStats.transform(M, top5) # 説明変数情報 projections = projection(M) # 主成分にProjection
View CADDi 2018 for Beginners C - Product and GCD code.ipynb
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View AtCoder Beginner Contest 115 D Christmas.ipynb
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View Hyperopt.jl
 using Hyperopt using DecisionTree using MLDatasets using Statistics train_x, train_y = MNIST.traindata(Float32) test_x, test_y = MNIST.testdata(Float32) train_features = Array(transpose(MNIST.convert2features(train_x))) test_features = Array(transpose(MNIST.convert2features(test_x)))
View 2018-03-29-1-1.ipynb
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View 2018-04-03-2-2.ipynb