/**
* @param unusedLocale the wanted locale (actually unused).
* @throws MavenReportException if any
*/
protected void executeReport(Locale unusedLocale) throws MavenReportException {
File config = buildConfigurationFile();
Commandline cli = new Commandline();
cli.setWorkingDirectory(getBasedir().getAbsolutePath());
cli.setExecutable(getExecutablePath());
cli.createArgument().setValue(config.getAbsolutePath());
Writer stringWriter = new StringWriter();
StreamConsumer out = new WriterStreamConsumer(stringWriter);
StreamConsumer err = new WriterStreamConsumer(stringWriter);
try {
int returnCode = CommandLineUtils.executeCommandLine(cli, out, err);
if (!isQuiet()) {
// Get all output from doxygen and put it to the log out of Maven.
String[] lines = stringWriter.toString().split("\n");
for (int i = 0; i < lines.length; i++) {
lines[i] = lines[i].replaceAll("\n|\r", "");
getLog().info("doxygen: " + lines[i]);
}
}
if (returnCode != 0) {
throw new MavenReportException("Failed to generate Doxygen documentation.");
}
} catch (CommandLineException ex) {
throw new MavenReportException("Error while executing Doxygen.", ex);
}
}
public static void main(String[] args) throws IOException {
// Parse command line flags and arguments
Map<String,String> argMap = CommandLineUtils.simpleCommandLineParser(args);
// Set up default parameters and settings
String basePath = ".";
int maxTrainingSentences = 0;
int maxIterations = 20;
boolean verbose = false;
boolean initialize = false;
String dataset = "mini";
String model = "baseline";
// Update defaults using command line specifications
if (argMap.containsKey("-path")) {
basePath = argMap.get("-path");
System.out.println("Using base path: "+basePath);
}
if (argMap.containsKey("-sentences")) {
maxTrainingSentences = Integer.parseInt(argMap.get("-sentences"));
System.out.println("Using an additional "+maxTrainingSentences+" training sentences.");
}
if (argMap.containsKey("-data")) {
dataset = argMap.get("-data");
System.out.println("Running with data: "+dataset);
} else {
System.out.println("No data set specified. Use -data [miniTest, validate].");
}
if (argMap.containsKey("-model")) {
model = argMap.get("-model");
System.out.println("Running with model: "+model);
} else {
System.out.println("No model specified. Use -model modelname.");
}
if (argMap.containsKey("-verbose")) {
verbose = true;
}
if (argMap.containsKey("-iterations")) {
maxIterations = Integer.parseInt(argMap.get("-iterations"));
}
if (argMap.containsKey("-initialize")) {
initialize = true;
}
// Read appropriate training and testing sets.
List<SentencePair> trainingSentencePairs = new ArrayList<SentencePair>();
if (! (dataset.equals("miniTest") || dataset.equals("mini")) && maxTrainingSentences > 0)
trainingSentencePairs = readSentencePairs(basePath+"/training", maxTrainingSentences);
List<SentencePair> testSentencePairs = new ArrayList<SentencePair>();
Map<Integer,Alignment> testAlignments = new HashMap<Integer, Alignment>();
if (dataset.equalsIgnoreCase("validate")) {
testSentencePairs = readSentencePairs(basePath+"/trial", Integer.MAX_VALUE);
testAlignments = readAlignments(basePath+"/trial/trial.wa");
} else if (dataset.equals("miniTest") || dataset.equals("mini")) {
testSentencePairs = readSentencePairs(basePath+"/mini", Integer.MAX_VALUE);
testAlignments = readAlignments(basePath+"/mini/mini.wa");
} else {
throw new RuntimeException("Bad data set mode: "+ dataset+", use validate or miniTest.");
}
trainingSentencePairs.addAll(testSentencePairs);
// Build model
WordAligner wordAligner = null;
if (model.equalsIgnoreCase("baseline")) {
wordAligner = new BaselineWordAligner();
}
// TODO : build other alignment models
else if (model.equalsIgnoreCase("heuristic")) {
wordAligner = new HeuristicWordAligner(trainingSentencePairs);
}
else if (model.equalsIgnoreCase("dice")) {
wordAligner = new DiceWordAligner(trainingSentencePairs);
}
else if (model.equalsIgnoreCase("ibm1") || model.equalsIgnoreCase("ibmModel1")) {
wordAligner = new IBMmodel1WordAligner(trainingSentencePairs, maxIterations, initialize);
}
else if (model.equalsIgnoreCase("ibm2") || model.equalsIgnoreCase("ibmModel2")) {
wordAligner = new IBMmodel2WordAligner(trainingSentencePairs, maxIterations, initialize);
}
// Test model
test(wordAligner, testSentencePairs, testAlignments, verbose);
// Generate file for submission //can comment out if not ready for submission
testSentencePairs = readSentencePairs(basePath+"/test", Integer.MAX_VALUE);
predict(wordAligner, testSentencePairs, basePath+"/"+model+".out");
}
