LIBSVM Java-Implementierung

Question

Ich versuche, die Java-Bindings für LIBSVM zu verwenden:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 

ich eine 'trivial' Beispiel implementiert haben, die leicht linear trennbar in y ist. Die Daten sind definiert als:

double[][] train = new double[1000][]; 
double[][] test = new double[10][]; 

for (int i = 0; i < train.length; i++){ 
    if (i+1 > (train.length/2)){  // 50% positive 
     double[] vals = {1,0,i+i}; 
     train[i] = vals; 
    } else { 
     double[] vals = {0,0,i-i-i-2}; // 50% negative 
     train[i] = vals; 
    }   
} 

Wo das erste 'Feature' ist die Klasse und das Training-Set ist ähnlich definiert.

Um das Modell zu trainieren:

private svm_model svmTrain() { 
    svm_problem prob = new svm_problem(); 
    int dataCount = train.length; 
    prob.y = new double[dataCount]; 
    prob.l = dataCount; 
    prob.x = new svm_node[dataCount][];  

    for (int i = 0; i < dataCount; i++){    
     double[] features = train[i]; 
     prob.x[i] = new svm_node[features.length-1]; 
     for (int j = 1; j < features.length; j++){ 
      svm_node node = new svm_node(); 
      node.index = j; 
      node.value = features[j]; 
      prob.x[i][j-1] = node; 
     }   
     prob.y[i] = features[0]; 
    }    

    svm_parameter param = new svm_parameter(); 
    param.probability = 1; 
    param.gamma = 0.5; 
    param.nu = 0.5; 
    param.C = 1; 
    param.svm_type = svm_parameter.C_SVC; 
    param.kernel_type = svm_parameter.LINEAR;  
    param.cache_size = 20000; 
    param.eps = 0.001;  

    svm_model model = svm.svm_train(prob, param); 

    return model; 
} 

dann das Modell, das ich verwende, um zu bewerten:

public int evaluate(double[] features) { 
    svm_node node = new svm_node(); 
    for (int i = 1; i < features.length; i++){ 
     node.index = i; 
     node.value = features[i]; 
    } 
    svm_node[] nodes = new svm_node[1]; 
    nodes[0] = node; 

    int totalClasses = 2;  
    int[] labels = new int[totalClasses]; 
    svm.svm_get_labels(_model,labels); 

    double[] prob_estimates = new double[totalClasses]; 
    double v = svm.svm_predict_probability(_model, nodes, prob_estimates); 

    for (int i = 0; i < totalClasses; i++){ 
     System.out.print("(" + labels[i] + ":" + prob_estimates[i] + ")"); 
    } 
    System.out.println("(Actual:" + features[0] + " Prediction:" + v + ")");    

    return (int)v; 
} 

Wenn das übergebene Array ein Punkt aus dem Test-Set ist.

Die Ergebnisse sind immer wiederkehrende Klasse 0. Mit dem genauen Wesen Ergebnisse:

(0:0.9882998314585194)(1:0.011700168541480586)(Actual:0.0 Prediction:0.0) 
(0:0.9883952943701599)(1:0.011604705629839989)(Actual:0.0 Prediction:0.0) 
(0:0.9884899803606306)(1:0.011510019639369528)(Actual:0.0 Prediction:0.0) 
(0:0.9885838957058696)(1:0.011416104294130458)(Actual:0.0 Prediction:0.0) 
(0:0.9886770466322342)(1:0.011322953367765776)(Actual:0.0 Prediction:0.0) 
(0:0.9870913229268679)(1:0.012908677073132284)(Actual:1.0 Prediction:0.0) 
(0:0.9868781382588805)(1:0.013121861741119505)(Actual:1.0 Prediction:0.0) 
(0:0.986661444476744)(1:0.013338555523255982)(Actual:1.0 Prediction:0.0) 
(0:0.9864411843906802)(1:0.013558815609319848)(Actual:1.0 Prediction:0.0) 
(0:0.9862172999068877)(1:0.013782700093112332)(Actual:1.0 Prediction:0.0) 

Kann jemand erklären, warum dieser Klassifikator nicht funktioniert? Gibt es einen Schritt, den ich vermasselt habe, oder einen Schritt, den ich vermisse?

Dank

Answer 1

Es scheint mir, dass Ihre Evaluierungsmethode falsch ist. Sollte so etwas wie diese:

public double evaluate(double[] features, svm_model model) 
{ 
    svm_node[] nodes = new svm_node[features.length-1]; 
    for (int i = 1; i < features.length; i++) 
    { 
     svm_node node = new svm_node(); 
     node.index = i; 
     node.value = features[i]; 

     nodes[i-1] = node; 
    } 

    int totalClasses = 2;  
    int[] labels = new int[totalClasses]; 
    svm.svm_get_labels(model,labels); 

    double[] prob_estimates = new double[totalClasses]; 
    double v = svm.svm_predict_probability(model, nodes, prob_estimates); 

    for (int i = 0; i < totalClasses; i++){ 
     System.out.print("(" + labels[i] + ":" + prob_estimates[i] + ")"); 
    } 
    System.out.println("(Actual:" + features[0] + " Prediction:" + v + ")");    

    return v; 
} 

Answer 2

habe ich eine etwas Refactoring-Version von Java-Implementierung des LibSVM, die Sie einfacher zu bedienen sein können: https://github.com/syeedibnfaiz/libsvm-java-kernel. Werfen Sie einen Blick auf die Demo.java-Klasse, um zu sehen, wie Sie sie verwenden können.

Answer 3

Hier ist eine Überarbeitung des obigen Beispiels, die ich getestet hat Daten aus dem folgenden R Code verwendet: http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf

import libsvm.*; 

public class libsvmTest { 

    public static void main(String [] args) { 

     double[][] xtrain = ... 
     double[][] xtest = ... 
     double[][] ytrain = ... 
     double[][] ytest = ... 

     svm_model m = svmTrain(xtrain,ytrain); 

     double[] ypred = svmPredict(xtest, m); 

     for (int i = 0; i < xtest.length; i++){ 
      System.out.println("(Actual:" + ytest[i][0] + " Prediction:" + ypred[i] + ")"); 
     } 

    } 

    static svm_model svmTrain(double[][] xtrain, double[][] ytrain) { 
     svm_problem prob = new svm_problem(); 
     int recordCount = xtrain.length; 
     int featureCount = xtrain[0].length; 
     prob.y = new double[recordCount]; 
     prob.l = recordCount; 
     prob.x = new svm_node[recordCount][featureCount];  

     for (int i = 0; i < recordCount; i++){    
      double[] features = xtrain[i]; 
      prob.x[i] = new svm_node[features.length]; 
      for (int j = 0; j < features.length; j++){ 
       svm_node node = new svm_node(); 
       node.index = j; 
       node.value = features[j]; 
       prob.x[i][j] = node; 
      }   
      prob.y[i] = ytrain[i][0]; 
     }    

     svm_parameter param = new svm_parameter(); 
     param.probability = 1; 
     param.gamma = 0.5; 
     param.nu = 0.5; 
     param.C = 100; 
     param.svm_type = svm_parameter.C_SVC; 
     param.kernel_type = svm_parameter.LINEAR;  
     param.cache_size = 20000; 
     param.eps = 0.001;  

     svm_model model = svm.svm_train(prob, param); 

     return model; 
    } 

    static double[] svmPredict(double[][] xtest, svm_model model) 
    { 

     double[] yPred = new double[xtest.length]; 

     for(int k = 0; k < xtest.length; k++){ 

     double[] fVector = xtest[k]; 

     svm_node[] nodes = new svm_node[fVector.length]; 
     for (int i = 0; i < fVector.length; i++) 
     { 
      svm_node node = new svm_node(); 
      node.index = i; 
      node.value = fVector[i]; 
      nodes[i] = node; 
     } 

     int totalClasses = 2;  
     int[] labels = new int[totalClasses]; 
     svm.svm_get_labels(model,labels); 

     double[] prob_estimates = new double[totalClasses]; 
     yPred[k] = svm.svm_predict_probability(model, nodes, prob_estimates); 

     } 

     return yPred; 
    } 


} 

Hier ist die Ausgabe:

(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:1.0 Prediction:1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0) 
(Actual:-1.0 Prediction:-1.0)