Example usage for org.apache.mahout.classifier.df.data Data get

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.df.data Data get.

Prototype

public Instance get(int index) 

Source Link

Document

Returns the element at the specified position

Usage

From source file:com.wsc.myexample.decisionForest.MyDecisionForest.java

License:Apache License

/**
 * Classifies the data and calls callback for each classification
 *//*w  w w .j  a v  a 2  s  .  c o  m*/
public void classify(Data data, double[] predictions) {
    Preconditions.checkArgument(data.size() == predictions.length,
            "predictions.length must be equal to data.size()");

    if (data.isEmpty()) {
        return; // nothing to classify
    }

    for (Node tree : trees) {
        for (int index = 0; index < data.size(); index++) {
            predictions[index] = tree.classify(data.get(index));
        }
    }
}

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From source file:javaapplication3.runRandomForest.java

public static void main(String[] args) throws InterruptedException, IOException, ClassNotFoundException {

    String outputFile = "data/lule24";
    String inputFile = "data/DataFraud1MTest.csv";
    String modelFile = "data/forest.seq";
    String infoFile = "data/DataFraud1M.info";

    Path dataPath = new Path(inputFile); // test data path
    Path datasetPath = new Path(infoFile);
    Path modelPath = new Path(modelFile); // path where the forest is stored
    Path outputPath = new Path(outputFile); // path to predictions file, if null do not output the predictions

    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);
    /*/*w  ww .  ja v a2  s  . com*/
    p = Runtime.getRuntime().exec("bash /home/ivan/hadoop-1.2.1/bin/start-all.sh");
    p.waitFor();*/

    if (outputPath == null) {
        throw new IllegalArgumentException(
                "You must specify the ouputPath when using the mapreduce implementation");
    }

    Classifier classifier = new Classifier(modelPath, dataPath, datasetPath, outputPath, conf);

    classifier.run();

    double[][] results = classifier.getResults();

    if (results != null) {

        Dataset dataset = Dataset.load(conf, datasetPath);
        Data data = DataLoader.loadData(dataset, fs, dataPath);

        Instance inst;

        for (int i = 0; i < data.size(); i++) {
            inst = data.get(i);

            //System.out.println("Prediction:"+inst.get(7)+" Real value:"+results[i][1]);
            System.out.println(inst.get(0) + " " + inst.get(1) + " " + inst.get(2) + " " + inst.get(3) + " "
                    + inst.get(4) + " " + inst.get(5) + " " + inst.get(6) + " " + inst.get(7) + " ");
        }

        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");

        for (double[] res : results) {
            analyzer.addInstance(dataset.getLabelString(res[0]),
                    new ClassifierResult(dataset.getLabelString(res[1]), 1.0));
            System.out.println("Prvi shit:" + res[0] + " Drugi Shit" + res[1]);
        }

        System.out.println(analyzer.toString());

    }

}

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