content: package zemberek.core.embeddings;
  
  import java.io.DataInputStream;
  import java.io.DataOutputStream;
  import java.io.IOException;
  
  public class Args {
  
    public double lr;
    public int lrUpdateRate;
    public int dim;
    public int ws;
    public int epoch;
    public int minCount;
    public int minCountLabel;
    public int neg;
    public int wordNgrams;
    public loss_name loss;
    public model_name model;
    public int bucket;
    public int minn;
    public int maxn;
    public int thread;
    public double t;
    public String label;
    public int verbose;
    public String pretrainedVectors;
    public SubWordHashProvider subWordHashProvider;
  
    public boolean qout = false;
    public boolean retrain = false;
    public boolean qnorm = false;
    public int cutoff = 0;
    public int dsub = 2;
  
    private Args() {
      dim = 100;
      ws = 5;
      epoch = 5;
      minCount = 5;
      minCountLabel = 0;
      neg = 5;
      bucket = 2_000_000;
      thread = Runtime.getRuntime().availableProcessors() / 2;
      lrUpdateRate = 100;
      t = 1e-4;
      label = "__label__";
      verbose = 2;
      pretrainedVectors = "";
    }
  
    public static Args forWordVectors(model_name modelName) {
      Args args = new Args();
      args.minn = 3;
      args.maxn = 6;
      args.subWordHashProvider =
          new EmbeddingHashProviders.CharacterNgramHashProvider(args.minn, args.maxn);
      args.lr = 0.05;
      args.loss = loss_name.negativeSampling;
      args.model = modelName;
      args.wordNgrams = 1;
      return args;
    }
  
    public static Args forSupervised() {
      Args args = new Args();
      args.minn = 0;
      args.maxn = 0;
      args.subWordHashProvider =
          new EmbeddingHashProviders.EmptySubwordHashProvider();
      args.lr = 0.1;
      args.loss = loss_name.softmax;
      args.model = model_name.supervised;
      args.wordNgrams = 2;
      return args;
    }
  
    public static Args load(DataInputStream in) throws IOException {
      Args args = new Args();
      args.dim = in.readInt();
      args.ws = in.readInt();
      args.epoch = in.readInt();
      args.minCount = in.readInt();
      args.neg = in.readInt();
      args.wordNgrams = in.readInt();
      int loss = in.readInt();
      if (loss == loss_name.hierarchicalSoftmax.index) {
        args.loss = loss_name.hierarchicalSoftmax;
      } else if (loss == loss_name.negativeSampling.index) {
        args.loss = loss_name.negativeSampling;
      } else if (loss == loss_name.softmax.index) {
        args.loss = loss_name.softmax;
      } else {
        throw new IllegalStateException("Unknown loss type.");
      }
      int model = in.readInt();
      if (model == model_name.cbow.index) {
        args.model = model_name.cbow;
      } else if (model == model_name.skipGram.index) {
        args.model = model_name.skipGram;
      } else if (model == model_name.supervised.index) {
        args.model = model_name.supervised;
      } else {
        throw new IllegalStateException("Unknown model type.");
      }
      args.bucket = in.readInt();
      args.minn = in.readInt();
      args.maxn = in.readInt();
      args.lrUpdateRate = in.readInt();
      args.t = in.readDouble();
  
      if (args.minn != 0) {
        args.subWordHashProvider = new EmbeddingHashProviders.CharacterNgramHashProvider(args.minn,
            args.maxn);
      } else {
        args.subWordHashProvider = new EmbeddingHashProviders.EmptySubwordHashProvider();
      }
  
      return args;
    }
  
    public void save(DataOutputStream out) throws IOException {
      out.writeInt(dim);
      out.writeInt(ws);
      out.writeInt(epoch);
      out.writeInt(minCount);
      out.writeInt(neg);
      out.writeInt(wordNgrams);
      out.writeInt(loss.index);
      out.writeInt(model.index);
      out.writeInt(bucket);
      out.writeInt(minn);
      out.writeInt(maxn);
      out.writeInt(lrUpdateRate);
      out.writeDouble(t);
    }
  
    public enum model_name {
      cbow(1), skipGram(2), supervised(3);
  
      int index;
  
      model_name(int i) {
        this.index = i;
      }
    }
  
    public enum loss_name {
      hierarchicalSoftmax(1), negativeSampling(2), softmax(3);
  
      int index;
  
      loss_name(int i) {
        this.index = i;
      }
    }
  
  }
  