gnn/main.go

package main

import (
	"fmt"
	"math/rand"
	"os"
)

const (
	Node    int     = 2708
	Edge    int     = 5429
	Labeled float64 = 0.05
)

var (
	labelName = []string{
		"Case_Based",
		"Genetic_Algorithms",
		"Neural_Networks",
		"Probabilistic_Methods",
		"Reinforcement_Learning",
		"Rule_Learning",
		"Theory",
	}

	nodeId = make([]int, 0)
)

func main() {
	file, err := os.Open("cora/cora.content")
	if err != nil {
		panic(err)
	}
	G := MakeGraph()
	for i := 0; i < Node; i++ {
		if i%100 == 0 {
			fmt.Println("reading node", i)
		}
		var u int
		fmt.Fscan(file, &u)
		V := MakeVector(Input)
		for j := 0; j < Input; j++ {
			fmt.Fscan(file, &V[j])
		}
		var label string
		fmt.Fscan(file, &label)
		for j := 0; j < Output; j++ {
			if labelName[j] == label {
				if rand.Float64() < Labeled {
					G.AddNode(u, V, j)
					nodeId = append(nodeId, u)
				} else {
					G.AddNode(u, V, j+Output)
				}
				break
			}
		}
	}
	file.Close()
	file, err = os.Open("cora/cora.cites")
	if err != nil {
		panic(err)
	}
	for i := 0; i < Edge; i++ {
		var u, v int
		fmt.Fscan(file, &u, &v)
		G.AddEdge(v, u)
	}
	file.Close()
	l := Train(G)
	cnt1, cnt2 := 0, 0
	for u := range G.X {
		GetEmbedding(G, u, 2, l, false)
		id, _ := l[2].E[0].Max()
		fmt.Println(u, id)
		if G.L[u] == id {
			cnt1++
		}
		if G.L[u] == id+Output {
			cnt2++
		}
	}
	fmt.Println(cnt1, "/", len(nodeId), ",", cnt2, "/", Node-len(nodeId))
	fmt.Println(100.*cnt1/len(nodeId), ",", 100.*cnt2/(Node-len(nodeId)))
}