ガウス過程と機械学習 第1章 linear.py version.02

202004292200.jl

# ==================================================================================================================================
# ISBN 978-4-06-152926-7
# ガウス過程と機械学習
# 
# [reference]
# http://chasen.org/~daiti-m/gpbook/python/linear.py
# http://chasen.org/~daiti-m/gpbook/data/linear.dat
# https://nbviewer.jupyter.org/gist/genkuroki/a37894d5669ad13b4cd27da16096bfd2
# 
# [How to execute]
# Base.MainInclude.include("?\\GitHub\\ISBN9784061529267\\julia\\chap02\\linear_v02.jl")
# ==================================================================================================================================


module ISBN9784061529267

    import CSV    # v0.6.1
    import HTTP   # v0.8.14
    import Printf
    


    export main


    struct ObservedData
        size :: Core.Int64
        inpt :: Base.Vector{Core.Float64} # observed input
        otpt :: Base.Vector{Core.Float64} # observed output
    end


    mutable struct LearnedData
        weight :: Base.Vector{Core.Float64} # weight vector of linear regression
        otpt   :: Base.Vector{Core.Float64} # estimated output
    end


    function read_data(file_path)

        # STEP.01
        # get the sample data from online
        obj_CSV = CSV.read(HTTP.get(file_path).body; delim="\t", header=[:inpt, :otpt], footerskip=1)

        # STEP.02
        # convert the read data to the array on Julia
        data_size   = Base.size(obj_CSV.inpt, 1)
        buffer_inpt = Base.zeros(data_size)
        buffer_otpt = Base.zeros(data_size)

        for itr in 1:1:data_size
            buffer_inpt[itr] = obj_CSV.inpt[itr]
            buffer_otpt[itr] = obj_CSV.otpt[itr]
        end

        # STEP.END
        return ObservedData(data_size, buffer_inpt, buffer_otpt)

    end


    function compute_learned_data(observed_data::ISBN9784061529267.ObservedData)

        design_matrix             = [ Base.ones(observed_data.size) observed_data.inpt ]
        design_matrix_transposed  = Base.transpose(design_matrix)
        buffer_weight             = Base.inv( design_matrix_transposed * design_matrix ) * design_matrix_transposed * observed_data.otpt
        buffer_otpt_estimated     = buffer_weight[1] .+ buffer_weight[2] .* observed_data.inpt

        return LearnedData(buffer_weight, buffer_otpt_estimated)

    end


    function main()

        # STEP.01
        # get the sample data from online

        obj_observed_data = ISBN9784061529267.read_data("http://chasen.org/~daiti-m/gpbook/data/linear.dat")
        obj_learned_data  = ISBN9784061529267.compute_learned_data(obj_observed_data)

        Base.println("")

        Base.println(obj_learned_data.weight)
        Base.println("")

        Printf.@printf("%3s %23s %23s %23s\n", "No.", "input", "output(obs)", "output(est)")

        for itr in 1:1:obj_observed_data.size
            Printf.@printf("%3d %23.15e %23.15e %23.15e\n", itr, obj_observed_data.inpt[itr], obj_observed_data.otpt[itr], obj_learned_data.otpt[itr])
        end

    end

end


# ==================================================================================================================================


@time ISBN9784061529267.main()


# ==================================================================================================================================
# EOF
# ==================================================================================================================================

result.txt

julia> Base.MainInclude.include("?\\GitHub\\ISBN9784061529267\\julia\\chap02\\linear_v02.jl")
WARNING: replacing module ISBN9784061529267.

[1.2161236702127658, 0.38065159574468094]

No.                   input             output(obs)             output(est)
  1  -2.000000000000000e+00   1.200000000000000e+00   4.548204787234039e-01
  2  -9.000000000000000e-01  -3.000000000000000e-01   8.735372340425529e-01
  3   4.000000000000000e-01   1.700000000000000e+00   1.368384308510638e+00
  4   2.000000000000000e+00   2.500000000000000e+00   1.977426861702128e+00
  5   2.500000000000000e+00   1.100000000000000e+00   2.167752659574468e+00
  6   3.000000000000000e+00   3.000000000000000e+00   2.358078457446808e+00
  0.232875 seconds (1.13 k allocations: 108.984 KiB)

julia>