OptionsSuper.java
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package is2.util;
import java.io.File;
public class OptionsSuper {
public String trainfile = null;
public String testfile = null;
public File trainforest = null;
public String nbframes = null;
public String pbframes = null;
public boolean nopred = false;
public boolean upper = false;
public boolean train = false;
public boolean eval = false;
public boolean test = false;
public boolean keep = false;
public boolean flt = false;
public boolean loadTaggerModels = false;
public String modelName = "prs.mdl";
public String modelTaggerName = null;
public String useMapping = null;
public String device = "C:";
public String tmp = null;
public boolean createForest = true;
public boolean decodeProjective = false;
public double decodeTH = 0.3d;
public String format = "CONLL";
public int formatTask = 9;
public int numIters = 10;
public int best = 1000;
public String outfile = "dp.conll";
public String charset = "UTF-8";
public String phraseTrain = null;
public String phraseTest = null;
public String goldfile = null;
public String gout = "sec23.gld";
public String features = null;
public String lexicon = null;
public int hsize = 0x07ffffff;
public int maxLen = 2000;
public int maxForms = Integer.MAX_VALUE;
public int beam = 4;
public float prune = -100000000;
public String third = "";
public String second = "";
public String first = "";
public int cross = 10;
// public boolean secondOrder = true;
public boolean useRelationalFeatures = false;
public int count = 10000000;
public int cores = Integer.MAX_VALUE;
public int start = 0;
public int minOccureForms = 0;
public int tt = 30; // tagger averaging
public boolean allFeatures = false;
public boolean normalize = false;
public boolean no2nd = false;
public boolean noLemmas = false;
public boolean few2nd = false, noLinear = false, noMorph = false;
public String clusterFile;
// output confidence values
public boolean conf = false;
public String phraseFormat = "penn"; // tiger | penn
public boolean average = true;
public boolean label = false;
public boolean stack = false;
public boolean oneRoot = false;
public String significant1 = null, significant2 = null;
// horizontal stacking
public int minLength = 0, maxLength = Integer.MAX_VALUE;
public boolean overwritegold = false;
public static final int MULTIPLICATIVE = 1, SHIFT = 2;
public int featureCreation = MULTIPLICATIVE;
public OptionsSuper(String[] args, String dummy) {
for (int i = 0; i < args.length; i++) {
i = addOption(args, i);
}
}
public OptionsSuper() {
}
public int addOption(String args[], int i) {
if (args[i].equals("-train")) {
train = true;
trainfile = args[i + 1];
} else if (args[i].equals("-eval")) {
eval = true;
goldfile = args[i + 1];
i++;
} else if (args[i].equals("-gout")) {
gout = args[i + 1];
i++;
} else if (args[i].equals("-test")) {
test = true;
testfile = args[i + 1];
i++;
} else if (args[i].equals("-sig1")) {
significant1 = args[i + 1];
i++;
} else if (args[i].equals("-sig2")) {
significant2 = args[i + 1];
i++;
} else if (args[i].equals("-i")) {
numIters = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-out")) {
outfile = args[i + 1];
i++;
} else if (args[i].equals("-cluster")) {
clusterFile = args[i + 1];
i++;
}
else if (args[i].equals("-count")) {
count = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-model")) {
modelName = args[i + 1];
i++;
} else if (args[i].equals("-tmodel")) {
this.modelTaggerName = args[i + 1];
i++;
} else if (args[i].equals("-nonormalize")) {
normalize = false;
} else if (args[i].equals("-float")) {
flt = true;
} else if (args[i].equals("-hsize")) {
hsize = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-charset")) {
charset = args[++i];
} else if (args[i].equals("-pstrain")) {
this.phraseTrain = args[i + 1];
i++;
} else if (args[i].equals("-pstest")) {
this.phraseTest = args[i + 1];
i++;
} else if (args[i].equals("-len")) {
maxLen = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-cores")) {
cores = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-start")) {
start = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-max")) {
maxLength = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-min")) {
minLength = Integer.parseInt(args[i + 1]);
i++;
} else if (args[i].equals("-noLemmas")) {
noLemmas = true;
} else if (args[i].equals("-noavg")) {
this.average = false;
} else if (args[i].equals("-label")) {
label = true;
} else if (args[i].equals("-stack")) {
stack = true;
} else if (args[i].equals("-overwritegold")) {
overwritegold = true;
} else if (args[i].equals("-format")) {
formatTask = Integer.parseInt(args[++i]);
} else if (args[i].equals("-tt")) {
tt = Integer.parseInt(args[++i]);
} else if (args[i].equals("-min-occure-forms")) {
minOccureForms = Integer.parseInt(args[++i]);
} else if (args[i].equals("-loadTaggerModels")) {
this.loadTaggerModels = true;
;
} else if (args[i].equals("-feature_creation")) {
this.featureCreation = args[++i].equals("shift") ? SHIFT : MULTIPLICATIVE;
}
return i;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("FLAGS [");
sb.append("train-file: " + trainfile);
sb.append(" | ");
sb.append("test-file: " + testfile);
sb.append(" | ");
sb.append("gold-file: " + goldfile);
sb.append(" | ");
sb.append("output-file: " + outfile);
sb.append(" | ");
sb.append("model-name: " + modelName);
sb.append(" | ");
sb.append("train: " + train);
sb.append(" | ");
sb.append("test: " + test);
sb.append(" | ");
sb.append("eval: " + eval);
sb.append(" | ");
sb.append("training-iterations: " + numIters);
sb.append(" | ");
sb.append("decode-type: " + decodeProjective);
sb.append(" | ");
sb.append("create-forest: " + createForest);
sb.append(" | ");
sb.append("format: " + format);
sb.append("]\n");
return sb.toString();
}
}