R S raryo

## 0526.ipynb

      
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                raryo
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              June 9, 2020 04:26
            
          
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## naive_julia.jl
using LinearAlgebra

function main()
    # input data
    train_data = read_data("./ml-100k/u.data")
    n_user = length(unique([t[1] for t in train_data]))
    n_item = length(unique([t[2] for t in train_data]))

    # parameters
    P, Q = fit(n_user, n_item, train_data)

## mf_type.jl
function main()
    # input data
    ui_array, r_array = read_data("./ml-100k/u.data")
    n_user = length(unique([ui[1] for ui in ui_array]))
    n_item = length(unique([ui[2] for ui in ui_array]))

    γ::Float64 = 0.07
    λ::Float64 = 0.01
    # parameters
    P, Q = fit(n_user, n_item, ui_array, r_array, 50, 5, γ, λ)
	using LinearAlgebra

	function main()
	# input data
	train_data = read_data("./ml-100k/u.data")
	n_user = length(unique([t[1] for t in train_data]))
	n_item = length(unique([t[2] for t in train_data]))

	# parameters
	P, Q = fit(n_user, n_item, train_data)
	function main()
	# input data
	ui_array, r_array = read_data("./ml-100k/u.data")
	n_user = length(unique([ui[1] for ui in ui_array]))
	n_item = length(unique([ui[2] for ui in ui_array]))

	γ::Float64 = 0.07
	λ::Float64 = 0.01
	# parameters
	P, Q = fit(n_user, n_item, ui_array, r_array, 50, 5, γ, λ)