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

Introduction

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

Prototype

public double get(int index) 

Source Link

Document

Return the attribute at the specified position

Usage

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

License:Apache License

public double getLabel(Instance instance) {
    return instance.get(getLabelId());
}

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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);
    /*//from   w  w w.  j  a v a2s  . c o  m
    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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