func (algo *EPLogisticRegression) Predict(sample *core.Sample) float64 {
s := util.Gaussian{Mean: 0.0, Vari: 0.0}
for _, feature := range sample.Features {
if feature.Value == 0.0 {
continue
}
wi, ok := algo.Model[feature.Id]
if !ok {
wi = &(util.Gaussian{Mean: 0.0, Vari: algo.params.init_var})
}
s.Mean += feature.Value * wi.Mean
s.Vari += feature.Value * feature.Value * wi.Vari
}
t := s
t.Vari += algo.params.beta
return t.Integral(t.Mean / math.Sqrt(t.Vari))
}
func (algo *EPLogisticRegression) Train(dataset *core.DataSet) {
for _, sample := range dataset.Samples {
s := util.Gaussian{Mean: 0.0, Vari: 0.0}
for _, feature := range sample.Features {
if feature.Value == 0.0 {
continue
}
wi, ok := algo.Model[feature.Id]
if !ok {
wi = &(util.Gaussian{Mean: 0.0, Vari: algo.params.init_var})
algo.Model[feature.Id] = wi
}
s.Mean += feature.Value * wi.Mean
s.Vari += feature.Value * feature.Value * wi.Vari
}
t := s
t.Vari += algo.params.beta
t2 := util.Gaussian{Mean: 0.0, Vari: 0.0}
if sample.Label > 0.0 {
t2.UpperTruncateGaussian(t.Mean, t.Vari, 0.0)
} else {
t2.LowerTruncateGaussian(t.Mean, t.Vari, 0.0)
}
t.MultGaussian(&t2)
s2 := t
s2.Vari += algo.params.beta
s0 := s
s.MultGaussian(&s2)
for _, feature := range sample.Features {
if feature.Value == 0.0 {
continue
}
wi0 := util.Gaussian{Mean: 0.0, Vari: algo.params.init_var}
w2 := util.Gaussian{Mean: 0.0, Vari: 0.0}
wi, _ := algo.Model[feature.Id]
w2.Mean = (s.Mean - (s0.Mean - wi.Mean*feature.Value)) / feature.Value
w2.Vari = (s.Vari + (s0.Vari - wi.Vari*feature.Value*feature.Value)) / (feature.Value * feature.Value)
wi.MultGaussian(&w2)
wi_vari := wi.Vari
wi_new_vari := wi_vari * wi0.Vari / (0.99*wi0.Vari + 0.01*wi.Vari)
wi.Vari = wi_new_vari
wi.Mean = wi.Vari * (0.99*wi.Mean/wi_vari + 0.01*wi0.Mean/wi.Vari)
if wi.Vari < algo.params.init_var*0.01 {
wi.Vari = algo.params.init_var * 0.01
}
algo.Model[feature.Id] = wi
}
}
}
