func train(records []creditRecord, iusers []internetUser, match [][]float64) (*lr.LogisticRegression, []string) { lr := new(lr.LogisticRegression) protos, id2f := constructFeatureVectors(iusers) borrower2iuser := make([]int, len(records)) for iter := 0; iter < 100; iter++ { // M-step: sampleBorrower2IUser(match, borrower2iuser) dataset := constructTrainingData(records, borrower2iuser, protos) lr.Init(map[string]string{"learning-rate": "0.1", "regularization": "1.0", "steps": "20"}) lr.Train(dataset) // E-step: updateMatch(lr, records, protos, match) } return lr, id2f }