Versions in this module Expand all Collapse all v0 v0.1.1 Sep 30, 2023 Changes in this version type Option + func Alpha(alpha float32) Option v0.1.0 Sep 29, 2023 Changes in this version + type Dataset struct + func LoadMovieLens() (*Dataset[int, string], error) + func NewDataset() *Dataset[T, U] + func (d *Dataset[T, U]) Len() int + func (d *Dataset[T, U]) Push(userId T, itemId U, value float32) + func (d *Dataset[T, U]) SplitRandom(p float32) (*Dataset[T, U], *Dataset[T, U]) + type FitInfo struct + Iteration int + TrainLoss float32 + ValidLoss float32 + type Id interface + type Option func(*config) + func Callback(callback func(info FitInfo)) Option + func Factors(factors int) Option + func Iterations(iterations int) Option + func LearningRate(learningRate float32) Option + func Regularization(regularization float32) Option + func Seed(seed int64) Option + type Rec struct + Id T + Score float32 + type Recommender struct + func FitEvalExplicit(trainSet *Dataset[T, U], validSet *Dataset[T, U], options ...Option) (*Recommender[T, U], error) + func FitExplicit(trainSet *Dataset[T, U], options ...Option) (*Recommender[T, U], error) + func FitImplicit(trainSet *Dataset[T, U], options ...Option) (*Recommender[T, U], error) + func (r *Recommender[T, U]) GlobalMean() float32 + func (r *Recommender[T, U]) ItemFactors(itemId U) []float32 + func (r *Recommender[T, U]) ItemIds() []U + func (r *Recommender[T, U]) ItemRecs(itemId U, count int) []Rec[U] + func (r *Recommender[T, U]) Predict(userId T, itemId U) float32 + func (r *Recommender[T, U]) Rmse(data *Dataset[T, U]) float32 + func (r *Recommender[T, U]) SimilarUsers(userId T, count int) []Rec[T] + func (r *Recommender[T, U]) UserFactors(userId T) []float32 + func (r *Recommender[T, U]) UserIds() []T + func (r *Recommender[T, U]) UserRecs(userId T, count int) []Rec[U]