Examples of org.data2semantics.proppred.learners.liblinear.LibLINEARModel

        targetSplit = LibLINEAR.splitTestTarget(target, linearParms.getSplitFraction());
        
        // If we deal with on RDFWLSubTree, then we show the most used features, based on the featuremap created
        if (kernel instanceof RDFWLSubTreeKernel) {
          RDFWLSubTreeKernel k = (RDFWLSubTreeKernel) kernel;
          LibLINEARModel model = LibLINEAR.trainLinearModel(fv, target, linearParms);
          LibLINEARModel.WeightIndexPair[][] fw = model.getFeatureWeights();
          
          Map<String, String> lm = k.getInverseLabelMap();
          
          System.out.println("Map size: " + lm.size() + " fw length: " + fw[0].length + " fv max index: " + fv[0].getLastIndex());

    }
    linParms.setWeightLabels(wLabels);
    linParms.setWeights(weights);
    
    // Train model on the first 100 instances.
    LibLINEARModel model = LibLINEAR.trainLinearModel(Arrays.copyOfRange(featureVectors, 0, 100), EvaluationUtils.target2Doubles(target.subList(0, 100)), linParms);


    // Test on the rest of the data
    Prediction[] predictions = LibLINEAR.testLinearModel(model, Arrays.copyOfRange(featureVectors, 100, featureVectors.length));
    
    // Print out the predictions and compute the accuracy on the test set.

Related Classes of org.data2semantics.proppred.learners.liblinear.LibLINEARModel

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