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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 |
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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))) |
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