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
}
func updateMatch(lr *lr.LogisticRegression, records []creditRecord, protos []*hector.Sample, match [][]float64) {
predictions := make([]float64, len(protos))
for i, proto := range protos {
predictions[i] = lr.Predict(proto)
}
for borrower, dist := range match {
r := records[borrower].returned
nr := records[borrower].borrowed - r
for iuser, gamma := range dist {
match[borrower][iuser] = gamma *
math.Exp(float64(r)*math.Log(1-predictions[iuser])+float64(nr)*math.Log(predictions[iuser]))
}
norm := sum(match[borrower])
for iuser, prob := range match[borrower] {
match[borrower][iuser] = prob / norm
}
}
}
