diff --git a/src/algoritmos/LazyGreedy.java b/src/algoritmos/LazyGreedy.java
index 0204b4e37af9ec38413078ad91c1eeb02851f51f..23fbeb85602596aa0e863507ee2744bd1c402c52 100644
--- a/src/algoritmos/LazyGreedy.java
+++ b/src/algoritmos/LazyGreedy.java
@@ -5,8 +5,6 @@ import grafos.Actor;
import grafos.DirectedSocialNetwork;
import interfaces.SeedChooser;
-import java.io.FileWriter;
-import java.io.IOException;
import java.util.HashSet;
import java.util.PriorityQueue;
@@ -15,15 +13,14 @@ public class LazyGreedy implements SeedChooser<Actor> {
private int cont;
private double[] spreadData;
private double[] callData;
-
public LazyGreedy(DirectedSocialNetwork g) {
this.grafo = g;
}
/**
- * Cria uma fila de prioridade por ganho marginal de todos os vértices do grafo
- * e cada vértice é inicializado com ganho de NaN e inválido
+ * Cria uma fila de prioridade por ganho marginal de todos os vértices do
+ * grafo e cada vértice é inicializado com ganho de NaN e inválido
*
* @return uma fila de prioridade com o ganho marginal de todos os vértices
*/
@@ -42,13 +39,13 @@ public class LazyGreedy implements SeedChooser<Actor> {
public HashSet<Actor> escolher(int k) {
HashSet<Actor> semente = new HashSet<>();
cont = 0;
- spreadData = new double[k+1];
- callData = new double[k+1];
+ spreadData = new double[k + 1];
+ callData = new double[k + 1];
// create priority queue of all nodes, with marginal gain delta +inf
PriorityQueue<MarginalGain> fila = priorityQueueOfGains();
- double MaxSpread = 0;
+ double MaxSpread = 0;
while (semente.size() < k) {
// set all gains invalid
@@ -61,9 +58,8 @@ public class LazyGreedy implements SeedChooser<Actor> {
if (max.isValid() == true) {
semente.add(max.getVertice());
MaxSpread = MaxSpread + max.getGain();
- System.out.println(semente.size()+"\t"+MaxSpread);
+// System.out.println(semente.size() + "\t" + MaxSpread);
spreadData[semente.size()] = MaxSpread;
-// callData[semente.size()] = cont;
break;
} else {
double sigma = cascata(max.getVertice(), semente);
@@ -75,55 +71,21 @@ public class LazyGreedy implements SeedChooser<Actor> {
}
}
}
- System.out.println("chamadas: " + cont);
+ System.out.println("Chamadas: " + cont);
+ System.out.println("Chamadas depois da 1a iteracao: " + (cont - grafo.vertexSet().size()));
return semente;
}
-
- public int callToSigma(){
+
+ public int callToSigma() {
return this.cont;
}
- public double[] getSpreadData(){
+
+ public double[] getSpreadData() {
return this.spreadData;
}
-
- public double[] getCallData(){
- return this.callData;
- }
-
- public HashSet<Actor> toFile(int k, FileWriter writer) throws IOException {
- HashSet<Actor> semente = new HashSet<>();
- cont = 0;
-
- // create priority queue of all nodes, with marginal gain delta +inf
- PriorityQueue<MarginalGain> fila = priorityQueueOfGains();
- double MaxSpread = 0;
-
- while (semente.size() < k) {
- // set all gains invalid
- for (MarginalGain mg : fila) {
- mg.setValid(false);
- }
-
- while (true) {
- MarginalGain max = fila.poll();
- if (max.isValid() == true) {
- semente.add(max.getVertice());
- MaxSpread = MaxSpread + max.getGain();
- writer.write(semente.size()+"\t"+MaxSpread);
- break;
- } else {
- double sigma = cascata(max.getVertice(), semente);
- double delta = sigma - MaxSpread;
- max.setGain(delta);
- max.setValid(true);
- fila.add(max);
- cont++;
- }
- }
- }
-// System.out.println("chamadas: " + cont);
- return semente;
+ public double[] getCallData() {
+ return this.callData;
}
private double cascata(Actor v, HashSet<Actor> semente) {
@@ -140,14 +102,13 @@ public class LazyGreedy implements SeedChooser<Actor> {
startTime = System.nanoTime();
HashSet<Actor> seed2 = new LazyGreedy(g).escolher(15);
- System.out.println("LazyGreedy = "
- + g.espectedSpread(seed2, true) + ", tempo: "
- + (System.nanoTime() - startTime) / 1000);
+ System.out.println(
+ "LazyGreedy = " + g.espectedSpread(seed2, true) + ", tempo: " + (System.nanoTime() - startTime) / 1000);
startTime = System.nanoTime();
HashSet<Actor> seed1 = new GreedyIC(g).escolher(10);
- System.out.println("GeneralGreedy = " + g.espectedSpread(seed1, true)
- + ", tempo: " + (System.nanoTime() - startTime) / 1000);
+ System.out.println("GeneralGreedy = " + g.espectedSpread(seed1, true) + ", tempo: "
+ + (System.nanoTime() - startTime) / 1000);
}
}
diff --git a/src/algoritmos/PrevalentSeed.java b/src/algoritmos/PrevalentSeed.java
index f368691966085802a0053f1ceacae874640ac8ae..a2472f028082fad7dbf7f3513640777948f55547 100644
--- a/src/algoritmos/PrevalentSeed.java
+++ b/src/algoritmos/PrevalentSeed.java
@@ -2,7 +2,6 @@ package algoritmos;
import java.util.HashSet;
import java.util.PriorityQueue;
-import java.util.Random;
import java.util.Set;
import util.ComparaPorGrau;
@@ -10,10 +9,10 @@ import util.ComparaPorGrau;
import com.google.common.collect.MinMaxPriorityQueue;
import algoritmos.MarginalGain;
-import geradores.SocialNetworkGenerate;
import grafos.Actor;
import grafos.DirectedSocialNetwork;
import interfaces.SeedChooser;
+import readgraph.GraphReader;
/*De acordo com os experimentos, utilizando o CELF como subrotina
* dentro do conjunto dominante obtem-se um resultado semelhante
@@ -26,7 +25,6 @@ public class PrevalentSeed implements SeedChooser<Actor> {
private int cont; // número de chamadas a sigma
private double[] spreadData; // histograma da propagação esperada
private double[] callData;
-
public PrevalentSeed(DirectedSocialNetwork g) {
this.grafo = g;
@@ -35,10 +33,9 @@ public class PrevalentSeed implements SeedChooser<Actor> {
public HashSet<Actor> preSelecao(DirectedSocialNetwork grafo) {
int n = grafo.vertexSet().size();
- // DS <-- {}
HashSet<Actor> candidatos = new HashSet<>();
- // compare os vertices pelo grau
+ // comparador de vertices pelo grau
ComparaPorGrau comp = new ComparaPorGrau(grafo);
MinMaxPriorityQueue<Actor> heapMinMax = null;
@@ -94,11 +91,11 @@ public class PrevalentSeed implements SeedChooser<Actor> {
public HashSet<Actor> escolher(int k) {
HashSet<Actor> semente = new HashSet<Actor>();
HashSet<Actor> candidatos = new HashSet<Actor>();
- spreadData = new double[k+1];
- callData = new double[k+1];
+ spreadData = new double[k + 1];
+ callData = new double[k + 1];
candidatos = preSelecaoOriginal(grafo);
-// System.out.println("|C| = "+candidatos.size());
+ System.out.println("|C| = " + candidatos.size());
if (candidatos.size() < k) {
System.out.println("Erro: o cojunto de candidatos é menor que K");
@@ -123,8 +120,7 @@ public class PrevalentSeed implements SeedChooser<Actor> {
if (max.isValid() == true) {
semente.add(max.getVertice());
maxSpread = maxSpread + max.getGain();
- System.out.println(semente.size()+"\t"+maxSpread);
-// callData[semente.size()] = cont;
+// System.out.println(semente.size() + "\t" + maxSpread);
spreadData[semente.size()] = maxSpread;
break;
} else {
@@ -137,70 +133,27 @@ public class PrevalentSeed implements SeedChooser<Actor> {
}
}
}
- System.out.println("chamadas: "+cont);
+ System.out.println("Chamadas: " + cont);
+ System.out.println("Chamadas depois da 1a iteracao: " + (cont - candidatos.size()));
return semente;
}
-
- public int callToSigma(){
+
+ public int callToSigma() {
return this.cont;
}
- public double[] getSpreadData(){
+
+ public double[] getSpreadData() {
return this.spreadData;
}
-
- public double[] getCallData(){
- return this.callData;
- }
-
- public HashSet<Actor> toFile(int k) {
- HashSet<Actor> semente = new HashSet<Actor>();
- HashSet<Actor> candidatos = new HashSet<Actor>();
- cont = 0;
-
- candidatos = preSelecaoOriginal(grafo);
-// System.out.println("|C| = "+candidatos.size());
-
- if (candidatos.size() < k) {
- System.out.println("Erro: o cojunto de candidatos é menor que K");
- return null;
- }
-
- // create priority queue of all nodes, with marginal gain delta +inf
- PriorityQueue<MarginalGain> fila = priorityQueueOfGains(candidatos);
-
- double maxSpread = 0;
-
- while (semente.size() < k) {
- // set all gains invalid
- for (MarginalGain mg : fila) {
- mg.setValid(false);
- }
- while (true) {
- MarginalGain max = fila.poll();
- if (max.isValid() == true) {
- semente.add(max.getVertice());
- maxSpread = maxSpread + max.getGain();
- System.out.println(semente.size()+"\t"+maxSpread);
- break;
- } else {
- double sigma = cascata(max.getVertice(), semente);
- double delta = sigma - maxSpread;
- max.setGain(delta);
- max.setValid(true);
- fila.add(max);
- cont++; // Conta o numero de chamadas à sigma
- }
- }
- }
- System.out.println("chamadas: "+cont);
- return semente;
+ public double[] getCallData() {
+ return this.callData;
}
public HashSet<Actor> escolher2(int k) {
HashSet<Actor> semente = new HashSet<Actor>();
HashSet<Actor> minSet = new HashSet<Actor>();
- cont=0;
+ cont = 0;
MinDominatingSet ds = new MinDominatingSet();
minSet = ds.fastGreedy2(grafo);
@@ -227,7 +180,7 @@ public class PrevalentSeed implements SeedChooser<Actor> {
if (max.isValid() == true) {
semente.add(max.getVertice());
MaxSpread = MaxSpread + max.getGain();
- System.out.println("|S| = "+semente.size()+", |A| = "+MaxSpread);
+ System.out.println("|S| = " + semente.size() + ", |A| = " + MaxSpread);
break;
} else {
double sigma = cascata(max.getVertice(), semente);
@@ -239,7 +192,7 @@ public class PrevalentSeed implements SeedChooser<Actor> {
}
}
}
- System.out.println("chamadas: "+cont);
+ System.out.println("chamadas: " + cont);
return semente;
}
@@ -313,35 +266,60 @@ public class PrevalentSeed implements SeedChooser<Actor> {
}
public static void main(String[] args) {
- DirectedSocialNetwork g = new SocialNetworkGenerate().gerarGrafo(2000, 2.5);
- System.out.println("|V(G)| = " + g.vertexSet().size());
- long startTime = 0;
- startTime = System.nanoTime();
- HashSet<Actor> seed = new PrevalentSeed(g).escolher2(15);
- System.out.println("Tempo: " + (System.nanoTime() - startTime) / 1000);
-
- HashSet<Actor> ativos = g.indepCascadeDiffusion(seed);
-
- // System.out.println("|S| = " + seed.size());
- System.out.println("|A| = " + ativos.size());
- g.deactivate(ativos);
- // g.activate(seed);
- // g.visualize();
-
- startTime = System.nanoTime();
- HashSet<Actor> seed2 = new PrevalentSeed(g).escolher(15);
- System.out.println("Tempo: " + (System.nanoTime() - startTime) / 1000);
-
- HashSet<Actor> ativos2 = g.indepCascadeDiffusion(seed2);
- // System.out.println("\n|S| = " + seed2.size());
- System.out.println("|A| = " + ativos2.size());
- g.deactivate(ativos2);
-
- // HashSet<Actor> seed3 = new DominatingSeed(g).escolherRandom(15);
- //
- // HashSet<Actor> ativos3 = g.indepCascadeDiffusion(seed3);
- //// System.out.println("\n|S| = " + seed3.size());
- // System.out.println("|A| = "+ativos3.size());
+ /*
+ * DirectedSocialNetwork g = new
+ * SocialNetworkGenerate().gerarGrafo(2000, 2.5);
+ * System.out.println("|V(G)| = " + g.vertexSet().size()); long
+ * startTime = 0; startTime = System.nanoTime(); HashSet<Actor> seed =
+ * new PrevalentSeed(g).escolher2(15); System.out.println("Tempo: " +
+ * (System.nanoTime() - startTime) / 1000);
+ *
+ * HashSet<Actor> ativos = g.indepCascadeDiffusion(seed);
+ *
+ * // System.out.println("|S| = " + seed.size());
+ * System.out.println("|A| = " + ativos.size()); g.deactivate(ativos);
+ * // g.activate(seed); // g.visualize();
+ *
+ * startTime = System.nanoTime(); HashSet<Actor> seed2 = new
+ * PrevalentSeed(g).escolher(15); System.out.println("Tempo: " +
+ * (System.nanoTime() - startTime) / 1000);
+ *
+ * HashSet<Actor> ativos2 = g.indepCascadeDiffusion(seed2); //
+ * System.out.println("\n|S| = " + seed2.size());
+ * System.out.println("|A| = " + ativos2.size()); g.deactivate(ativos2);
+ *
+ * // HashSet<Actor> seed3 = new DominatingSeed(g).escolherRandom(15);
+ * // // HashSet<Actor> ativos3 = g.indepCascadeDiffusion(seed3); ////
+ * System.out.println("\n|S| = " + seed3.size()); //
+ * System.out.println("|A| = "+ativos3.size());
+ */
+
+ DirectedSocialNetwork g;
+ PrevalentSeed ps = new PrevalentSeed(null);
+
+ System.out.println("enron.txt");
+ g = new GraphReader().enron();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
+
+ System.out.println("epinions.txt");
+ g = new GraphReader().readEpinions();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
+
+ System.out.println("dblp.txt");
+ g = new GraphReader().readDblp();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
+
+ System.out.println("phy.txt");
+ g = new GraphReader().readPhy();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
+
+ System.out.println("hep.txt");
+ g = new GraphReader().readHep();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
+
+ System.out.println("amazon.txt");
+ g = new GraphReader().amazon();
+ System.out.println("|C| = " + ps.preSelecaoOriginal(g).size() + "\n");
}
diff --git a/src/grafos/DirectedSocialNetwork.java b/src/grafos/DirectedSocialNetwork.java
index 166a1a12dccc65ae339701de5d1b8af802b6cf59..b63e227ec2a59ecb95cd81cacf4983b18a402bcc 100644
--- a/src/grafos/DirectedSocialNetwork.java
+++ b/src/grafos/DirectedSocialNetwork.java
@@ -186,7 +186,7 @@ public class DirectedSocialNetwork extends
public double espectedSpread(HashSet<Actor> seed, boolean ic) {
double media = 0;
int soma = 0;
- int repeticoes =100;
+ int repeticoes =500;
if (!ic) {
HashSet<Actor> ativados = linearThresholdDiffusion(seed);
diff --git a/src/plot/MeuPlot.java b/src/plot/MeuPlot.java
index 15eb0b02b7d4c58519036c295a0bde0a6d5182c1..6f8ec543d680d751eafc52dd0a8d15afbae86dc5 100644
--- a/src/plot/MeuPlot.java
+++ b/src/plot/MeuPlot.java
@@ -10,38 +10,38 @@ public class MeuPlot extends JGnuplot {
// String output = null;
// String beforeStyle="linewidth=4";
-// public void plotPropagacao(double[] x, double[] y1, double[] y2, double[] y3, double[] y4, String outDir){
-// double[] x2 = suavisalinhas(x);
-// double[] z1 = suavisalinhas(y1);
-// double[] z2 = suavisalinhas(y2);
-// double[] z3 = suavisalinhas(y3);
-// double[] z4 = suavisalinhas(y4);
-//
-// JGnuplot plot = new JGnuplot();
-// Plot plotPropagacao = new Plot("Propagação Esperada") {
-// {
-// xlabel = "'|S|'";
-// ylabel = "'sigma(S)'";
-// extra = "set key top left";
-// }
-// };
-//
-// DataTableSet dts = plotPropagacao.addNewDataTableSet("Propagação Esperada");
-// dts.addNewDataTable("PrevalentSeed", x2, z1);
-// dts.addNewDataTable("CELF", x2, z2);
-// dts.addNewDataTable("HighDegree", x2, z3);
-// dts.addNewDataTable("RandomSeed", x2, z4);
-//
-// plotPropagacao.add(dts);
-// String saida = outDir+"propagacao_esperada.plt";
-// plot.compile(plotPropagacao, plot.plot2d, saida);
-//
-//// this.terminal = "epslatex color colortext dashed";
-//// this.output = outDir+"propagacao.tex'";
-// this.terminal = "eps color dashed";
-// this.output = outDir+"propagacao.eps";
-// this.execute(plotPropagacao, this.plot2d);
-// }
+ public void plotPropagacao(double[] x, double[] y1, double[] y2, double[] y3, double[] y4, String outDir){
+ double[] x2 = suavisalinhas(x);
+ double[] z1 = suavisalinhas(y1);
+ double[] z2 = suavisalinhas(y2);
+ double[] z3 = suavisalinhas(y3);
+ double[] z4 = suavisalinhas(y4);
+
+ JGnuplot plot = new JGnuplot();
+ Plot plotPropagacao = new Plot("Propagação Esperada") {
+ {
+ xlabel = "'|S|'";
+ ylabel = "'sigma(S)'";
+ extra = "set key top left";
+ }
+ };
+
+ DataTableSet dts = plotPropagacao.addNewDataTableSet("Propagação Esperada");
+ dts.addNewDataTable("PrevalentSeed", x2, z1);
+ dts.addNewDataTable("CELF", x2, z2);
+ dts.addNewDataTable("HighDegree", x2, z3);
+ dts.addNewDataTable("RandomSeed", x2, z4);
+
+ plotPropagacao.add(dts);
+ String saida = outDir+"propagacao_esperada.plt";
+ plot.compile(plotPropagacao, plot.plot2d, saida);
+
+// this.terminal = "epslatex color colortext dashed";
+// this.output = outDir+"propagacao.tex'";
+ this.terminal = "eps color dashed";
+ this.output = outDir+"propagacao.eps";
+ this.execute(plotPropagacao, this.plot2d);
+ }
public void plotPropagacao(double[] x, double[] y1, double[] y2, String outDir){
double[] x2 = suavisalinhas(x);
diff --git a/src/readgraph/GraphReader.java b/src/readgraph/GraphReader.java
index d853657a429e59281d5de68aeb56bc2f013c186d..8b4c3e80e227825a912d4d5cbeddeba391fa86d9 100644
--- a/src/readgraph/GraphReader.java
+++ b/src/readgraph/GraphReader.java
@@ -87,9 +87,9 @@ public class GraphReader {
// uniform IC model
// return (double)10/100;
-// return 0.001;
+// return 0.025;
// return Math.random()/4;
- return 0.0025;
+ return 0.001;
}
public DirectedSocialNetwork readEpinions() {
diff --git a/src/simulacao/Experimentacao.java b/src/simulacao/Experimentacao.java
index 8d3e0a32cb0f87f010ec1d3361fbd08f5d26fddf..4bbbbdeb41af451884bce43af05176be461e6b97 100644
--- a/src/simulacao/Experimentacao.java
+++ b/src/simulacao/Experimentacao.java
@@ -1,11 +1,5 @@
package simulacao;
-import java.io.File;
-import java.io.FileWriter;
-import java.io.IOException;
-
-import org.jgrapht.graph.DefaultWeightedEdge;
-
import algoritmos.HightDegree;
import algoritmos.LazyGreedy;
import algoritmos.PrevalentSeed;
@@ -18,19 +12,23 @@ import readgraph.GraphReader;
public class Experimentacao {
public static void main(String[] args) {
- int k = 50;
-// sinteticGraphSimulate(k);
+ int k = 30;
+ sinteticGraphSimulate(k);
//Simulações a serem feita com probabilidade 0.01
-// System.out.println("Simulaçoes realizadas para p = 0.025");
+ //System.out.println("Simulaçoes realizadas para p = 0.025");
// System.out.println("Grafos: HEP, PHY e DBLP");
+ //System.out.println("Grafos: AMAZON");
// simularHep(k);
-// simularPhy(k);
-// simularDblp(k);
-// Simulações a serem feita com probabilidade 0.0025
- System.out.println("Simulaçoes realizadas para p = 0.0025");
- System.out.println("Grafos: ENRON e EPINIONS");
- simularEnron(k);
- simularEpinions(k);
+ //simularPhy(k);
+ //simularAmazon(k);
+
+ //Simulações a serem feita com probabilidade 0.0025
+ //System.out.println("Simulaçoes realizadas para p = 0.0025");
+ //System.out.println("Grafos: ENRON e EPINIONS");
+ //System.out.println("Grafos: Epinions");
+ //simularEnron(k);
+ //simularEpinions(k);
+ //simularDblp(k);
}
private static void simularHep(int k) {
@@ -55,7 +53,6 @@ public class Experimentacao {
monteCarloSimulation(g, out, k);
}
-
private static void simularDblp(int k) {
System.out.println("dblp.txt");
DirectedSocialNetwork g;
@@ -158,7 +155,7 @@ public class Experimentacao {
private static void sinteticGraphSimulate(int k) {
int n = 2;
- while (n <= 64) {
+ while (n <= 16) {
String out = "plots/rand/" + n + "/";
sinteticGraph(n, out, k);
System.out.println("\t----------");
@@ -168,7 +165,7 @@ public class Experimentacao {
/**
* Faz um conjunto de simulações para grafos aleatórios de tamanho n e gera
- * grafos com os resultados da simulação
+ * graficos com os resultados da simulação
*
* @param n
* multiplo de 1000 para o tamanho dos grafos gerados
@@ -184,53 +181,56 @@ public class Experimentacao {
double[] spreadRS = new double[k + 1];
double[] mediaCelf = new double[k + 1], mediaPS = new double[k + 1], mediaHD = new double[k + 1],
mediaRS = new double[k + 1];
+ double timePS = 0, timeCelf = 0, timeHD = 0, timeRS = 0, time;
double[] x;
- int numSimulacoes = 10;
+ int numSimulacoes = 4;
for (int i = 0; i < numSimulacoes; i++) {
- DirectedSocialNetwork g = new SocialNetworkGenerate().gerarGrafo(n * 1000, 2.5);
+ DirectedSocialNetwork g = new SocialNetworkGenerate().gerarGrafo(n * 1000, 2);
System.out.println("\nGrafo n. " + (i + 1));
mostrarTamanho(g);
LazyGreedy celf = new LazyGreedy(g);
System.out.println("#Celf");
+ time = System.currentTimeMillis() * -1;
celf.escolher(k);
+ time += System.currentTimeMillis();
spreadCelf = somaVetores(spreadCelf, celf.getSpreadData());
- // spreadCelf = celf.getSpreadData();
- // sigmaCalls2 = celf.getCallData();
- // for (int i = 0; i < spreadData2.length; i++) {
- // System.out.println(i + "\t" + spreadData2[i]);
- // }
+ timeCelf = timeCelf + (time / 1000.0f);
PrevalentSeed ps = new PrevalentSeed(g);
System.out.println("#PrevalentSeed");
+ time = System.currentTimeMillis() * -1;
ps.escolher(k);
+ time += System.currentTimeMillis();
spreadPS = somaVetores(spreadPS, ps.getSpreadData());
- // sigmaCalls = ps.getCallData();
- // for (int i = 0; i < spreadData1.length; i++) {
- // System.out.println(i + "\t" + spreadData1[i]);
- // }
+ timePS = timePS + (time / 1000.0f);
// Testa a heuristica de grau
HightDegree hd = new HightDegree(g);
System.out.println("#HightDegree");
+ time = System.currentTimeMillis() * -1;
hd.escolher(k);
+ time += System.currentTimeMillis();
spreadHD = somaVetores(spreadHD, hd.getSpreadData());
- // for (int i = 0; i < spreadData3.length; i++) {
- // System.out.println(i + "\t" + spreadData3[i]);
- // }
-
+ timeHD = timeHD + (time / 1000.0f);
+
// Testa a heurística semente aleatória
RandomSeed rs = new RandomSeed(g);
System.out.println("#RandomSeed");
+ time = System.currentTimeMillis() * -1;
rs.escolher(k);
+ time += System.currentTimeMillis();
spreadRS = somaVetores(spreadRS, rs.getSpreadData());
- // for (int i = 0; i < spreadData4.length; i++) {
- // System.out.println(i + "\t" + spreadData4[i]);
- // }
+ timeRS = timeRS + (time / 1000.0f);
}
+
+ System.out.println("Tempo Celf: "+timeCelf/numSimulacoes);
+ System.out.println("Tempo PrevalentSeed: "+timePS/numSimulacoes);
+ System.out.println("Tempo HighDegree: "+timeHD/numSimulacoes);
+ System.out.println("Tempo RandomSeed: "+timeRS/numSimulacoes);
- // TODO tirar a média das 20 simulações e plotar a media
+ // tirar a média das 20 simulações e plotar a media
for (int i = 0; i < spreadPS.length; i++) {
mediaPS[i] = spreadPS[i] / numSimulacoes;
mediaCelf[i] = spreadCelf[i] / numSimulacoes;
@@ -240,7 +240,8 @@ public class Experimentacao {
x = eixox(k + 1); // preenche o vetor do eixo x
MeuPlot meuplot = new MeuPlot();
-// meuplot.plotPropagacao(x, mediaPS, mediaCelf, mediaHD, mediaRS, out);
+ meuplot.plotPropagacao(x, mediaPS, mediaCelf, mediaHD, mediaRS, out);
+// meuplot.plotPropagacao(x, mediaPS, mediaCelf, out);
// meuplot.plotChamadas(sigmaCalls, sigmaCalls2, "'");
}