// A Basic Matrix Factorization learning model which includes the algorithm and estimator
var model go_recommend_me.BasicMF

//  Setup the model parameters
var params go_recommend_me.ModelParameters
params.Dimensionality = 2
params.NumItems = 3
params.NumUsers = 2
params.Steps = 5000
params.Alpha = 0.0002
params.Beta = 0.02
params.TrainingSize = 5

var tset go_recommend_me.TrainingSet

// Initialize the training set with parameters and fill with known user/item ratings
tset.Initialize(params)

tset.SetRating(0, 0, 4)
tset.SetRating(0, 1, 1)
tset.SetRating(0, 2, 6)

tset.SetRating(1, 1, 1)
tset.SetRating(1, 0, 2)

// Factors measure the extent to the extent that an item possess some feature
learned := model.Learn(tset)

// Rating estimation
fmt.Println("user [0] item[0], rating = ", model.EstimateRating(0, 0, learned))
fmt.Println("user [0] item[1], rating = ", model.EstimateRating(0, 1, learned))


fmt.Println("user [1] item[0], rating = ", model.EstimateRating(1, 0, learned))
fmt.Println("user [1] item[1], rating = ", model.EstimateRating(1, 1, learned))
fmt.Println("user [1] item[2], rating = ", model.EstimateRating(1, 2, learned))

go run tests/test.go