/// <summary>
/// Convert a dependency graph to a format expected as input to
/// <see cref="IWriter.Set(string, object)"/>
/// .
/// </summary>
private static object BuildDependencyTree(SemanticGraph graph)
{
// It's lying; we need the "redundant" casts (as of 2014-09-08)
if (graph != null)
{
return(IStream.Concat(graph.GetRoots().Stream().Map(null), graph.EdgeListSorted().Stream().Map(null)));
}
else
{
// Roots
// Regular edges
return(null);
}
}
/// <exception cref="System.IO.IOException"/>
public static void Main(string[] args)
{
RedwoodConfiguration.Standard().Apply();
Redwood.Util.StartTrack("main");
// Read the data
IStream <SimpleSentiment.SentimentDatum> data = IStream.Concat(IStream.Concat(IStream.Concat(Imdb("/users/gabor/tmp/aclImdb/train/pos", SentimentClass.Positive), Imdb("/users/gabor/tmp/aclImdb/train/neg", SentimentClass.Negative)), IStream.Concat
(Imdb("/users/gabor/tmp/aclImdb/test/pos", SentimentClass.Positive), Imdb("/users/gabor/tmp/aclImdb/test/neg", SentimentClass.Negative))), IStream.Concat(IStream.Concat(Stanford("/users/gabor/tmp/train.tsv"), Stanford("/users/gabor/tmp/test.tsv"
)), IStream.Concat(Twitter("/users/gabor/tmp/twitter.csv"), Unlabelled("/users/gabor/tmp/wikipedia"))));
// Train the model
OutputStream stream = IOUtils.GetFileOutputStream("/users/gabor/tmp/model.ser.gz");
SimpleSentiment classifier = SimpleSentiment.Train(data, Optional.Of(stream));
stream.Close();
log.Info(classifier.Classify("I think life is great"));
Redwood.Util.EndTrack("main");
}
