Example usage for org.apache.mahout.classifier.sgd OnlineLogisticRegression lambda

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.sgd OnlineLogisticRegression lambda.

Prototype

@Override
    public OnlineLogisticRegression lambda(double lambda) 

Source Link

Usage

From source file:com.sixgroup.samplerecommender.Point.java

public static void main(String[] args) {

    Map<Point, Integer> points = new HashMap<Point, Integer>();

    points.put(new Point(0, 0), 0);
    points.put(new Point(1, 1), 0);
    points.put(new Point(1, 0), 0);
    points.put(new Point(0, 1), 0);
    points.put(new Point(2, 2), 0);

    points.put(new Point(8, 8), 1);
    points.put(new Point(8, 9), 1);
    points.put(new Point(9, 8), 1);
    points.put(new Point(9, 9), 1);

    OnlineLogisticRegression learningAlgo = new OnlineLogisticRegression();
    learningAlgo = new OnlineLogisticRegression(2, 3, new L1());
    learningAlgo.lambda(0.1);
    learningAlgo.learningRate(10);//from  w w w.j a v  a2  s.c  o  m

    System.out.println("training model  \n");

    for (Point point : points.keySet()) {

        Vector v = getVector(point);
        System.out.println(point + " belongs to " + points.get(point));
        learningAlgo.train(points.get(point), v);
    }

    learningAlgo.close();

    Vector v = new RandomAccessSparseVector(3);
    v.set(0, 0.5);
    v.set(1, 0.5);
    v.set(2, 1);

    Vector r = learningAlgo.classifyFull(v);
    System.out.println(r);

    System.out.println("ans = ");
    System.out.println("no of categories = " + learningAlgo.numCategories());
    System.out.println("no of features = " + learningAlgo.numFeatures());
    System.out.println("Probability of cluster 0 = " + r.get(0));
    System.out.println("Probability of cluster 1 = " + r.get(1));

}

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From source file:org.wso2.siddhi.extension.ModelInitializer.java

License:Open Source License

public static OnlineLogisticRegression InitializeLogisticRegression(String modelPath) {
    OnlineLogisticRegression LRmodel = null;
    FileInputStream fileInputStream = null;
    ObjectInputStream objectInputStream = null;
    double[][] modelWeights = null;
    LogisticRegressionModel LRmodelObject;
    try {/*from w  ww  .  ja  va2s . co m*/
        // get the values for hyper-parameters from model file.
        fileInputStream = new FileInputStream(modelPath);
        objectInputStream = new ObjectInputStream(fileInputStream);
        LRmodelObject = (LogisticRegressionModel) objectInputStream.readObject();
        LRmodel = new OnlineLogisticRegression(LRmodelObject.getNumCategories(), LRmodelObject.getNumFeatures(),
                new L2(1));
        LRmodel.learningRate(LRmodelObject.getLearningRate());
        LRmodel.lambda(LRmodelObject.getLambda());
        LRmodel.alpha(LRmodelObject.getAlpha());
        LRmodel.stepOffset(LRmodelObject.getStepOffset());
        LRmodel.decayExponent(LRmodelObject.getDecayExponent());
        modelWeights = LRmodelObject.getWeights();
        fileInputStream.close();
        objectInputStream.close();
        for (int i = 0; i < modelWeights.length; i++) {
            for (int j = 0; j < modelWeights[0].length; j++) {
                LRmodel.setBeta(i, j, modelWeights[i][j]);
            }
        }
    } catch (Exception e) {
        logger.error("Failed to create a Logistic Regression model from the file \"" + modelPath + "\"", e);
    } finally {
        try {
            fileInputStream.close();
            objectInputStream.close();
        } catch (IOException e) {
            logger.error("Failed to close the model input stream!", e);
        }
    }
    logger.info("Logistic Regression model execution plan successfully intialized for \"" + modelPath
            + "\" model file.");
    return LRmodel;
}

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