// AwesomeParsingRerankerFactory.java
public class AwesomeParsingRerankerFactory implements ParsingRerankerFactory {
  public ParsingReranker trainParserReranker(
      Iterable<Pair<KbestList, Tree<String>>> kbestListsAndGoldTrees) {
    return new MyAwesomeParsingReranker(kbestListsAndGoldTrees);
  }
}

// BasicParsingRerankerFactory.java
public class BasicParsingRerankerFactory implements ParsingRerankerFactory {
  public ParsingReranker trainParserReranker(
      Iterable<Pair<KbestList, Tree<String>>> kbestListsAndGoldTrees) {
    return new MyBasicParsingReranker();
  }
}

public class SimpleFeatureExtractor {
  public int[] extractFeatures(KbestList kbestList, int idx,
      Indexer<String> featureIndexer, boolean addFeaturesToIndexer) {
    // TREE PROCESSING
    Tree<String> tree = kbestList.getKbestTrees().get(idx);

    // FEATURE COMPUTATION
    List<Integer> feats = new ArrayList<Integer>();
    // Feature 1: position in the k-best list
    addFeature("Posn=" + idx, feats, featureIndexer, addFeaturesToIndexer);
    // More features. Yours will go here

    int[] featsArr = new int[feats.size()];
    for (int i = 0; i < feats.size(); i++) {
      featsArr[i] = feats.get(i).intValue();
    }
    return featsArr;
  }

  private void addFeature(String feat, List<Integer> feats,
      Indexer<String> featureIndexer, boolean addNew) {
    if (addNew || featureIndexer.contains(feat)) {
      feats.add(featureIndexer.addAndGetIndex(feat));
    }
  }
}

public class Indexer<E> /* ... */ {
  List<E> objects;
  Map<E, Integer> indexes;
}

public class Tree<L> implements Serializable {
  L label;
  List<Tree<L>> children;

  public List<Constituent<L>> toConstituentList() {
    List<Constituent<L>> constituentList = new ArrayList<Constituent<L>>();
    toConstituentCollectionHelper(this, 0, constituentList);
    return constituentList;
  }
}

public class AnchoredTree<L> implements Serializable {
  final L label;
  final int startIdx;
  final int endIdx;
  final List<AnchoredTree<L>> children;

  public static <L> AnchoredTree<L> fromTree(Tree<L> tree) {
    // implementation
  }

  public List<AnchoredTree<L>> toSubTreeList() {
    return getPreOrderTraversal();
  }
}

public class SurfaceHeadFinder {
  private Map<String,Boolean> searchDirections;
  private Map<String,Set<String>> validHeads;

  public SurfaceHeadFinder() {
    this.searchDirections = new HashMap<String,Boolean>();
    searchDirections.put("ADJP", true); // More rules: searchDirections.put(...)

    this.validHeads = new HashMap<String,Set<String>>();
    validHeads.put("ADJP", new HashSet<String>(Arrays.asList(
        new String[] { "NNS", "NN", "$", "JJ", ... }))); // More rules: validHeads.put(...)
  }

  public int findHead(String label, List<String> preterminals) {
    if (label.equals("NP")) {
      return searchFindLastBefore(preterminals, true, npHeads, npBlockers);
    } else if (label.equals("PRN")) {
      return (preterminals.size() > 1 ? 1 : 0);
    } else if (validHeads.containsKey(label)) {
      return searchFindFirst(preterminals, true, validHeads.get(label));
    } else {
      return 0;
    }
  }
}

// Function.java
public interface Function {
  int dimension();
  double valueAt(double[] x);
}

// DifferentiableFunction.java
public interface DifferentiableFunction extends Function {
 double[] derivativeAt(double[] x);
}

// LBFGSMinimizer.java
public class LBFGSMinimizer implements GradientMinimizer, Serializable {
  public double[] minimize(DifferentiableFunction function, double[] initial, double tolerance) {
    // dark magic...
  }
}

// edu/berkeley/nlp/assignments/rerank/PrimalSubgradientSVMLearner.java
public class PrimalSubgradientSVMLearner<D> {
  public PrimalSubgradientSVMLearner(double stepSize, double regConstant, int numFeatures) { ... }

  public IntCounter train(IntCounter initWeights,
      final LossAugmentedLinearModel<D> model, List<D> data, int iters) {
    // More dark magic
    // But you have to read and understand to implement your updates
    // To this soon: LossAugmentedLinearModel<T>

    OnlineMinimizer minimizer = new AdagradMinimizer(stepSize, regConstant, iters);
    return minimizer.minimize(objs, initWeights.toArray(numFeatures), true, null);
  }
}

// edu.berkeley.nlp.assignments.rerank/AdagradMinimizer.java
public class AdagradMinimizer implements OnlineMinimizer {
  public IntCounter minimize(List<DifferentiableFunction> functions, double[] initial,
      boolean verbose, Callback iterCallbackFunction) {
    // dark magic...
    double[] result = new double[initial.length];
    for (int i = 0; i < result.length; ++i) {
      result[i] = lazyResult.getCount(i);
    }
    return IntCounter.wrapArray(result, result.length);
  }
}

// edu/berkeley/nlp/util/IntCounter.java
// Class for vector computation
public class IntCounter {
  public static IntCounter wrapArray(double[] arrayToWrap, int size) {
    return new IntCounter(arrayToWrap, size);
  }

  private IntCounter(double[] arrayToWrap, int size) {
    this.values = arrayToWrap;
    this.keys = null;
    this.size = size;
  }
}

// edu/berkeley/nlp/assignments/rerank/LossAugmentedLinearModel.java
public interface LossAugmentedLinearModel<T> {
  public class UpdateBundle {
    public UpdateBundle(IntCounter goldFeatures,
        IntCounter lossAugGuessFeatures, double lossOfGuess) {
      this.goldFeatures = goldFeatures;
      this.lossAugGuessFeatures = lossAugGuessFeatures;
      this.lossOfGuess = lossOfGuess;
    }

    public final IntCounter goldFeatures;
    public final IntCounter lossAugGuessFeatures;
    public final double lossOfGuess;
  }

  public UpdateBundle getLossAugmentedUpdateBundle(T datum, IntCounter weights);
}