public void TestWords(Log log)
{
var net = new SNeuralNet(6, 2, 300, 1);
var wordBag = WordBag.CreateToWords(string.Join(". ", Configuration.RawDataList), 1);
var trainSets = new List <Tuple <double[], double[]> >();
var wordsHistory = new List <string>();
var vocab = new LRVocab().Create(Configuration.VocabularyPath, (string s) => log(s));
log("Prepare tests list");
foreach (var word in wordBag.Read())
{
var w = word[0];
if (!vocab.Vocabulary.ContainsWord(w))
{
continue;
}
wordsHistory.Add(w);
if (wordsHistory.Count < 4)
{
continue;
}
if (wordsHistory.Count > 4)
{
wordsHistory.RemoveAt(0);
}
double[] input = new double[6];
double[] correct = new double[1];
input[0] = vocab.Vocabulary.GetRepresentationOrNullFor(wordsHistory[0]).MetricLength;
input[1] = vocab.Vocabulary.GetRepresentationOrNullFor(wordsHistory[1]).MetricLength;
input[2] = vocab.Vocabulary.GetRepresentationOrNullFor(wordsHistory[2]).MetricLength;
input[3] = vocab.Vocabulary.GetSummRepresentationOrNullForPhrase(wordsHistory.Take(2).ToArray())?.MetricLength ?? 0d;
input[4] = vocab.Vocabulary.GetSummRepresentationOrNullForPhrase(wordsHistory.Skip(1).Take(2).ToArray())?.MetricLength ?? 0d;
input[5] = vocab.Vocabulary.GetSummRepresentationOrNullForPhrase(wordsHistory.Take(3).ToArray())?.MetricLength ?? 0d;
correct = new double[] { vocab.Vocabulary.GetRepresentationFor(wordsHistory[3]).MetricLength };
trainSets.Add(new Tuple <double[], double[]>(input, correct));
}
if (trainSets.Count == 0)
{
log("No train sets");
return;
}
log($"Train sets count: {trainSets.Count}");
log($"Train starts");
var trainer = new NeuralNetTrainer()
.SetDataSets(trainSets.ToArray())
.SetNet(net);
trainer.EpochsCount = 150;
trainer.LearnRate = 0.001;
trainer.SimpleTrain();
log("Train end");
foreach (var set in trainSets)
{
log($"Input: {set.Item1[0]} {set.Item1[1]} {set.Item1[2]}\t Result: {net.Activate(set.Item1)}");
}
}
