Package edu.uci.jforestsx.sample

Examples of edu.uci.jforestsx.sample.Sample$BinFreq

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      residuals[instance] = (2 * target) / (1 + Math.exp(2 * target * trainPredictions[d]));
      responseAbs = Math.abs(residuals[instance]);
      weights[instance] = responseAbs * (2 - responseAbs);
    }

    Sample subLearnerSample = curTrainSet.getRandomSubSample(samplingRate, rnd).getClone();
    subLearnerSample.targets = residuals;

    for (int i = 0; i < subLearnerSample.size; i++) {
      subLearnerSampleIndicesInTrainSet[i] = subLearnerSample.indicesInParentSample[i];
    }
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    Ensemble ensemble = new Ensemble();
    subLearner.setTreeWeight(treeWeight / bagCount);
    for (int iteration = 1; iteration <= bagCount; iteration++) {
      System.out.println("Iteration: " + iteration);
      Sample subLearnerTrainSet = trainSet.getRandomSubSample(baggingTrainFraction, rnd);
      //((TreeLearner) subLearner).setRnd();
      Sample subLearnerOutOfTrainSet = trainSet.getOutOfSample(subLearnerTrainSet);
      Sample subLearnerValidSet = (validSet == null || validSet.isEmpty() ? subLearnerOutOfTrainSet : validSet);
      Ensemble subEnsemble = subLearner.learn(subLearnerTrainSet, subLearnerValidSet);

      for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
        Tree tree = subEnsemble.getTreeAt(t);
        double curTreeWeight = subEnsemble.getWeightAt(t);
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    int earlyStoppingIteration = 0;
    int bestIteration = 0;
    int[] treeCounts = new int[numSubModules];
    subLearner.setTreeWeight(treeWeight);
    for (curIteration = 1; curIteration <= numSubModules; curIteration++) {
      Sample subLeanerSample = getSubLearnerSample();
      Ensemble subEnsemble = subLearner.learn(subLeanerSample, validSet);
      if (subEnsemble == null) {
        break;
      }
      for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
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  protected Sample getSubLearnerSample() {
    for (int i = 0; i < curTrainSet.size; i++) {
      residuals[i] = curTrainSet.targets[i] - trainPredictions[i];
    }
    Sample subLearnerSample = curTrainSet.getClone();
    subLearnerSample.targets = residuals;
    subLearnerSample = subLearnerSample.getRandomSubSample(samplingRate, rnd);
    return subLearnerSample;
  }
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    /*
     * Load the data set
     */
    InputStream in = new IOUtils().getInputStream((String) options.valueOf("test-file"));
    Sample sample;
    if (options.has("ranking")) {
      RankingDataset dataset = new RankingDataset();
      RankingDatasetLoader.load(in, dataset);
      sample = new RankingSample(dataset);
    } else {
      Dataset dataset = new Dataset();
      DatasetLoader.load(in, dataset);
      sample = new Sample(dataset);
    }
    in.close();

    double[] predictions = new double[sample.size];
    LearningUtils.updateScores(sample, predictions, ensemble);
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  protected void initDataset(Dataset dataset) throws Exception {
    // Will be overridden by subclasses
  }

  protected Sample createSample(Dataset dataset, boolean trainSample) {
    return new Sample(dataset);
  }
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      }
      System.out.println("Finished loading datasets.");

      Constants.init(maxInstances);

      Sample allTrainSample = createSample(trainDataset, true);
      trainSet = allTrainSample.getRandomSubSample(trainingConfig.trainFraction, rnd);

      if (validDataset != null) {
        validSet = createSample(validDataset, false);
        if (trainingConfig.validFraction < 1.0) {
          validSet = validSet.getRandomSubSample(trainingConfig.validFraction, rnd);
        }
      } else if (trainingConfig.validOutOfTrain) {
        validSet = allTrainSample.getOutOfSample(trainSet);
      }

      init();

      evaluationMetric = getEvaluationMetric(trainingConfig.evaluationMetric);
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Related Classes of edu.uci.jforestsx.sample.Sample$BinFreq

  • edu.uci.jforestsx.applications.ClassificationApp
  • edu.uci.jforestsx.applications.Runner
  • edu.uci.jforestsx.learning.bagging.Bagging
  • edu.uci.jforestsx.learning.boosting.GradientBoosting
  • edu.uci.jforestsx.learning.classification.GradientBoostingBinaryClassifier
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