Example usage for weka.classifiers.functions LibSVM KERNELTYPE_SIGMOID

Introduction

In this page you can find the example usage for weka.classifiers.functions LibSVM KERNELTYPE_SIGMOID.

Prototype

int KERNELTYPE_SIGMOID

To view the source code for weka.classifiers.functions LibSVM KERNELTYPE_SIGMOID.

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Document

kernel type sigmoid: tanh(gamma*u'*v + coef0).

Usage

From source file:org.jaqpot.algorithm.resource.WekaSVM.java

License:Open Source License

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {//from ww  w  .jav  a  2  s .  co  m
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST).entity(
                    ErrorReportFactory.badRequest("Dataset is empty", "Cannot train model on empty dataset"))
                    .build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().collect(Collectors.toList());

        Instances data = InstanceUtils.createFromDataset(request.getDataset(), request.getPredictionFeature());
        Map<String, Object> parameters = request.getParameters() != null ? request.getParameters()
                : new HashMap<>();

        LibSVM regressor = new LibSVM();
        Double epsilon = Double.parseDouble(parameters.getOrDefault("epsilon", _epsilon).toString());
        Double cacheSize = Double.parseDouble(parameters.getOrDefault("cacheSize", _cacheSize).toString());
        Double gamma = Double.parseDouble(parameters.getOrDefault("gamma", _gamma).toString());
        Double coeff0 = Double.parseDouble(parameters.getOrDefault("coeff0", _coeff0).toString());
        Double cost = Double.parseDouble(parameters.getOrDefault("cost", _cost).toString());
        Double nu = Double.parseDouble(parameters.getOrDefault("nu", _nu).toString());
        Double loss = Double.parseDouble(parameters.getOrDefault("loss", _loss).toString());
        Integer degree = Integer.parseInt(parameters.getOrDefault("degree", _degree).toString());

        regressor.setEps(epsilon);
        regressor.setCacheSize(cacheSize);
        regressor.setDegree(degree);
        regressor.setCost(cost);
        regressor.setGamma(gamma);
        regressor.setCoef0(coeff0);
        regressor.setNu(nu);
        regressor.setLoss(loss);

        Integer svm_kernel = null;
        String kernel = parameters.getOrDefault("kernel", _kernel).toString();
        if (kernel.equalsIgnoreCase("rbf")) {
            svm_kernel = LibSVM.KERNELTYPE_RBF;
        } else if (kernel.equalsIgnoreCase("polynomial")) {
            svm_kernel = LibSVM.KERNELTYPE_POLYNOMIAL;
        } else if (kernel.equalsIgnoreCase("linear")) {
            svm_kernel = LibSVM.KERNELTYPE_LINEAR;
        } else if (kernel.equalsIgnoreCase("sigmoid")) {
            svm_kernel = LibSVM.KERNELTYPE_SIGMOID;
        }
        regressor.setKernelType(new SelectedTag(svm_kernel, LibSVM.TAGS_KERNELTYPE));

        Integer svm_type = null;
        String type = parameters.getOrDefault("type", _type).toString();
        if (type.equalsIgnoreCase("NU_SVR")) {
            svm_type = LibSVM.SVMTYPE_NU_SVR;
        } else if (type.equalsIgnoreCase("NU_SVC")) {
            svm_type = LibSVM.SVMTYPE_NU_SVC;
        } else if (type.equalsIgnoreCase("C_SVC")) {
            svm_type = LibSVM.SVMTYPE_C_SVC;
        } else if (type.equalsIgnoreCase("EPSILON_SVR")) {
            svm_type = LibSVM.SVMTYPE_EPSILON_SVR;
        } else if (type.equalsIgnoreCase("ONE_CLASS_SVM")) {
            svm_type = LibSVM.SVMTYPE_ONE_CLASS_SVM;
        }
        regressor.setSVMType(new SelectedTag(svm_type, LibSVM.TAGS_SVMTYPE));

        regressor.buildClassifier(data);

        WekaModel model = new WekaModel();
        model.setClassifier(regressor);

        Map<String, Double> options = new HashMap<>();
        options.put("gamma", gamma);
        options.put("coeff0", coeff0);
        options.put("degree", new Double(degree.toString()));

        Field modelField = LibSVM.class.getDeclaredField("m_Model");
        modelField.setAccessible(true);
        svm_model svmModel = (svm_model) modelField.get(regressor);
        double[][] coefs = svmModel.sv_coef;
        List<Double> coefsList = IntStream.range(0, coefs[0].length).mapToObj(i -> coefs[0][i])
                .collect(Collectors.toList());

        svm_node[][] nodes = svmModel.SV;

        List<Map<Integer, Double>> vectors = IntStream.range(0, nodes.length).mapToObj(i -> {
            Map<Integer, Double> node = new TreeMap<>();
            Arrays.stream(nodes[i]).forEach(n -> node.put(n.index, n.value));
            return node;
        }).collect(Collectors.toList());

        String pmml = PmmlUtils.createSVMModel(features, request.getPredictionFeature(), "SVM", kernel,
                svm_type, options, coefsList, vectors);
        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        List<String> independentFeatures = features.stream()
                .filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());
        response.setIndependentFeatures(independentFeatures);
        response.setPmmlModel(pmml);
        response.setAdditionalInfo(request.getPredictionFeature());
        response.setPredictedFeatures(
                Arrays.asList("Weka SVM prediction of " + request.getPredictionFeature()));

        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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From source file:org.jaqpot.algorithms.resource.WekaSVM.java

License:Open Source License

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {/*from  w  w  w  .j  a  va2s  . c o m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST)
                    .entity("Dataset is empty. Cannot train model on empty dataset.").build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().collect(Collectors.toList());

        Instances data = InstanceUtils.createFromDataset(request.getDataset(), request.getPredictionFeature());
        Map<String, Object> parameters = request.getParameters() != null ? request.getParameters()
                : new HashMap<>();

        LibSVM regressor = new LibSVM();
        Double epsilon = Double.parseDouble(parameters.getOrDefault("epsilon", _epsilon).toString());
        Double cacheSize = Double.parseDouble(parameters.getOrDefault("cacheSize", _cacheSize).toString());
        Double gamma = Double.parseDouble(parameters.getOrDefault("gamma", _gamma).toString());
        Double coeff0 = Double.parseDouble(parameters.getOrDefault("coeff0", _coeff0).toString());
        Double cost = Double.parseDouble(parameters.getOrDefault("cost", _cost).toString());
        Double nu = Double.parseDouble(parameters.getOrDefault("nu", _nu).toString());
        Double loss = Double.parseDouble(parameters.getOrDefault("loss", _loss).toString());
        Integer degree = Integer.parseInt(parameters.getOrDefault("degree", _degree).toString());

        regressor.setEps(epsilon);
        regressor.setCacheSize(cacheSize);
        regressor.setDegree(degree);
        regressor.setCost(cost);
        regressor.setGamma(gamma);
        regressor.setCoef0(coeff0);
        regressor.setNu(nu);
        regressor.setLoss(loss);

        Integer svm_kernel = null;
        String kernel = parameters.getOrDefault("kernel", _kernel).toString();
        if (kernel.equalsIgnoreCase("rbf")) {
            svm_kernel = LibSVM.KERNELTYPE_RBF;
        } else if (kernel.equalsIgnoreCase("polynomial")) {
            svm_kernel = LibSVM.KERNELTYPE_POLYNOMIAL;
        } else if (kernel.equalsIgnoreCase("linear")) {
            svm_kernel = LibSVM.KERNELTYPE_LINEAR;
        } else if (kernel.equalsIgnoreCase("sigmoid")) {
            svm_kernel = LibSVM.KERNELTYPE_SIGMOID;
        }
        regressor.setKernelType(new SelectedTag(svm_kernel, LibSVM.TAGS_KERNELTYPE));

        Integer svm_type = null;
        String type = parameters.getOrDefault("type", _type).toString();
        if (type.equalsIgnoreCase("NU_SVR")) {
            svm_type = LibSVM.SVMTYPE_NU_SVR;
        } else if (type.equalsIgnoreCase("NU_SVC")) {
            svm_type = LibSVM.SVMTYPE_NU_SVC;
        } else if (type.equalsIgnoreCase("C_SVC")) {
            svm_type = LibSVM.SVMTYPE_C_SVC;
        } else if (type.equalsIgnoreCase("EPSILON_SVR")) {
            svm_type = LibSVM.SVMTYPE_EPSILON_SVR;
        } else if (type.equalsIgnoreCase("ONE_CLASS_SVM")) {
            svm_type = LibSVM.SVMTYPE_ONE_CLASS_SVM;
        }
        regressor.setSVMType(new SelectedTag(svm_type, LibSVM.TAGS_SVMTYPE));

        regressor.buildClassifier(data);

        WekaModel model = new WekaModel();
        model.setClassifier(regressor);

        Map<String, Double> options = new HashMap<>();
        options.put("gamma", gamma);
        options.put("coeff0", coeff0);
        options.put("degree", new Double(degree.toString()));

        Field modelField = LibSVM.class.getDeclaredField("m_Model");
        modelField.setAccessible(true);
        svm_model svmModel = (svm_model) modelField.get(regressor);
        double[][] coefs = svmModel.sv_coef;
        List<Double> coefsList = IntStream.range(0, coefs[0].length).mapToObj(i -> coefs[0][i])
                .collect(Collectors.toList());

        svm_node[][] nodes = svmModel.SV;

        List<Map<Integer, Double>> vectors = IntStream.range(0, nodes.length).mapToObj(i -> {
            Map<Integer, Double> node = new TreeMap<>();
            Arrays.stream(nodes[i]).forEach(n -> node.put(n.index, n.value));
            return node;
        }).collect(Collectors.toList());

        String pmml = PmmlUtils.createSVMModel(features, request.getPredictionFeature(), "SVM", kernel,
                svm_type, options, coefsList, vectors);
        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        List<String> independentFeatures = features.stream()
                .filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());
        response.setIndependentFeatures(independentFeatures);
        response.setPmmlModel(pmml);
        response.setAdditionalInfo(request.getPredictionFeature());
        response.setPredictedFeatures(
                Arrays.asList("Weka SVM prediction of " + request.getPredictionFeature()));

        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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