Package org.data2semantics.proppred.learners.evaluation

Examples of org.data2semantics.proppred.learners.evaluation.Error


    double[] cs = {1, 10, 100, 1000, 10000}; // 100000, 1000000

    // --------------
    // Learning Algorithm settings
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
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    double[] cs = {0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000}

    // --------------
    // Learning Algorithm settings
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
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    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Error());
    linParms.setDoCrossValidation(false);
    linParms.setNumFolds(10);

    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
    int[] wLabels = new int[counts.size()];
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    double[] cs = {1, 10, 100, 1000, 10000, 100000, 1000000}

    // --------------
    // Learning Algorithm settings
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
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