// Generative learning with learning rate eta.
func Generative(S spn.SPN, data []map[int]int, eta float64, mode spn.InfType) spn.SPN {
n := len(data)
conv := 1.0
last := 0.0
fmt.Println("Running generative learning.")
// Set root's partial derivative to 1.
S.Rootify("pnode")
S.SetStore(true)
for math.Abs(conv) > 0.01 {
s := 0.0
klast := 0.0
for i := 0; i < n; i++ {
// Call soft inference to store values.
k := S.Value(data[i])
s += k - klast
klast = k
fmt.Println("Computed and stored soft inference values.")
// Backpropagate through SPN.
S.RootDerive("pweight", "pnode", "soft", mode)
fmt.Println("Backpropagated through SPN computing derivatives.")
// Update weights.
S.GenUpdate(eta, "pweight")
fmt.Printf("Weight Updated according to instance %d.\n", i)
S.RResetDP("soft")
}
d := s - last
last = s
conv = d
fmt.Printf("Generative Learning diff: %.5f\n", math.Abs(conv))
}
return S
}
