79 lines
1.9 KiB
Julia
79 lines
1.9 KiB
Julia
"""
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Fisher's classic iris dataset.
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Measurements from 3 different species of iris: setosa, versicolor and
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virginica. There are 50 examples of each species.
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There are 4 measurements for each example: sepal length, sepal width,
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petal length and petal width. The measurements are in centimeters.
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The module retrieves the data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
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"""
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module Iris
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using DelimitedFiles
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using ..Data: deps, download_and_verify
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# Uncomment if the iris.data file is cached to cache.julialang.org.
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const cache_prefix = "https://cache.julialang.org/"
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function load()
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isfile(deps("iris.data")) && return
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@info "Downloading iris dataset."
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download_and_verify("$(cache_prefix)https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
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deps("iris.data"),
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"6f608b71a7317216319b4d27b4d9bc84e6abd734eda7872b71a458569e2656c0")
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end
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"""
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labels()
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Get the labels of the iris dataset, a 150 element array of strings listing the
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species of each example.
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```jldoctest; setup = :(Flux.Data.Iris.load())
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julia> labels = Flux.Data.Iris.labels();
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julia> summary(labels)
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"150-element Array{String,1}"
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julia> labels[1]
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"Iris-setosa"
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```
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"""
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function labels()
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load()
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iris = readdlm(deps("iris.data"), ',')
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Vector{String}(iris[1:end, end])
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end
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"""
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features()
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Get the features of the iris dataset. This is a 4x150 matrix of Float64
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elements. It has a row for each feature (sepal length, sepal width,
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petal length, petal width) and a column for each example.
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```jldoctest; setup = :(Flux.Data.Iris.load())
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julia> features = Flux.Data.Iris.features();
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julia> summary(features)
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"4×150 Array{Float64,2}"
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julia> features[:, 1]
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4-element Array{Float64,1}:
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5.1
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3.5
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1.4
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0.2
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```
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"""
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function features()
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load()
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iris = readdlm(deps("iris.data"), ',')
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Matrix{Float64}(iris[1:end, 1:4]')
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end
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end
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