我正在尝试为libsvm使用Java绑定：

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

我实现了一个“平凡的”示例，该示例很容易在y中线性分离。数据定义为：

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;
    }           
}

第一个“功能”是班级，并且训练集也类似地定义。

训练模型：

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;
}

然后评估我使用的模型：

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;
}

传递的数组是测试集中的一点。

结果始终返回类0。确切的结果是：

(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)

有人可以解释为什么该分类器不起作用吗？有没有弄乱我的步骤，或者我错过了一个步骤？

谢谢