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POlibsvm java implementation
 

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    <p>I am trying to use the java bindings for libsvm:</p> <pre><code>http://www.csie.ntu.edu.tw/~cjlin/libsvm/ </code></pre> <p>I have implemented a 'trivial' example which is easily linearly separable in y. The data is defined as:</p> <pre><code>double[][] train = new double[1000][]; double[][] test = new double[10][]; for (int i = 0; i &lt; train.length; i++){ if (i+1 &gt; (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; } } </code></pre> <p>Where the first 'feature' is the class and the training set is similarly defined.</p> <p>To train the model:</p> <pre><code>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 &lt; dataCount; i++){ double[] features = train[i]; prob.x[i] = new svm_node[features.length-1]; for (int j = 1; j &lt; 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; } </code></pre> <p>Then to evaluate the model I use:</p> <pre><code>public int evaluate(double[] features) { svm_node node = new svm_node(); for (int i = 1; i &lt; 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 &lt; totalClasses; i++){ System.out.print("(" + labels[i] + ":" + prob_estimates[i] + ")"); } System.out.println("(Actual:" + features[0] + " Prediction:" + v + ")"); return (int)v; } </code></pre> <p>Where the passed array is a point from the testing set.</p> <p>The results are always returning class 0. With the exact results being:</p> <pre><code>(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) </code></pre> <p>Can someone explain why this classifier is not working? Is there a step I have messed up, or a step I am missing?</p> <p>Thanks</p>
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