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

Introduction

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

Prototype

public OnlineLogisticRegression() 

Source Link

Usage

From source file:chapter4.src.logistic.LogisticModelParametersPredict.java

License:Apache License

public void readFields(DataInput in) throws IOException {
    targetVariable = in.readUTF();//from ww w  .  j av a 2 s . c om
    int typeMapSize = in.readInt();
    typeMap = Maps.newHashMapWithExpectedSize(typeMapSize);
    for (int i = 0; i < typeMapSize; i++) {
        String key = in.readUTF();
        String value = in.readUTF();
        typeMap.put(key, value);
    }
    numFeatures = in.readInt();
    useBias = in.readBoolean();
    maxTargetCategories = in.readInt();
    int targetCategoriesSize = in.readInt();
    targetCategories = Lists.newArrayListWithCapacity(targetCategoriesSize);
    for (int i = 0; i < targetCategoriesSize; i++) {
        targetCategories.add(in.readUTF());
    }
    lambda = in.readDouble();
    learningRate = in.readDouble();
    csv = null;
    lr = new OnlineLogisticRegression();
    lr.readFields(in);
}

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From source file:com.ml.ira.algos.LogisticModelParameters.java

License:Apache License

@Override
public void readFields(DataInput in) throws IOException {
    fieldNames = in.readUTF();/*from w  w w.  ja  va 2s .com*/
    targetVariable = in.readUTF();
    int typeMapSize = in.readInt();
    typeMap = Maps.newHashMapWithExpectedSize(typeMapSize);
    for (int i = 0; i < typeMapSize; i++) {
        String key = in.readUTF();
        String value = in.readUTF();
        typeMap.put(key, value);
    }
    numFeatures = in.readInt();
    useBias = in.readBoolean();
    maxTargetCategories = in.readInt();
    int targetCategoriesSize = in.readInt();
    targetCategories = Lists.newArrayListWithCapacity(targetCategoriesSize);
    for (int i = 0; i < targetCategoriesSize; i++) {
        targetCategories.add(in.readUTF());
    }
    lambda = in.readDouble();
    learningRate = in.readDouble();
    csv = null;
    lr = new OnlineLogisticRegression();
    lr.readFields(in);
}

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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);// ww w  .jav  a  2s.c  o m
    learningAlgo.learningRate(10);

    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:edu.isi.karma.cleaning.features.LogisticModelParameters.java

License:Apache License

@Override
public void readFields(DataInput in) throws IOException {
    targetVariable = in.readUTF();/*  w  w w  .j  av a2 s .co m*/
    int typeMapSize = in.readInt();
    typeMap = Maps.newHashMapWithExpectedSize(typeMapSize);
    for (int i = 0; i < typeMapSize; i++) {
        String key = in.readUTF();
        String value = in.readUTF();
        typeMap.put(key, value);
    }
    numFeatures = in.readInt();
    useBias = in.readBoolean();
    maxTargetCategories = in.readInt();
    int targetCategoriesSize = in.readInt();
    targetCategories = Lists.newArrayListWithCapacity(targetCategoriesSize);
    for (int i = 0; i < targetCategoriesSize; i++) {
        targetCategories.add(in.readUTF());
    }
    lambda = in.readDouble();
    learningRate = in.readDouble();
    csv = null;
    lr = new OnlineLogisticRegression();
    lr.readFields(in);
}

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