public void RetrainingSetsCorrectDictionaryKeyLength(int retrainDepth)
{
var model = new StringMarkov();
model.Learn(ExampleData);
model.Retrain(retrainDepth);
Assert.AreEqual(retrainDepth, model.Chain.ChainDictionary.Max(a => a.Key.Ngrams.Length));
}
public void RetrainingSetsCorrectLevel(int retrainDepth)
{
var model = new StringMarkov();
model.Learn(ExampleData);
Assert.AreEqual(2, model.Level);
model.Retrain(retrainDepth);
Assert.AreEqual(retrainDepth, model.Level);
}
public void RetrainValueMustBePositiveInteger(int retrainDepth)
{
var model = new StringMarkov();
model.Learn(ExampleData);
var ex = Assert.Throws <ArgumentException>(() => model.Retrain(retrainDepth));
Assert.AreEqual("Invalid argument - retrain level must be a positive integer\r\nParameter name: newLevel", ex.Message);
}
public void SourceLinesAreSameAfterRetrained(int retrainDepth)
{
var model = new StringMarkov();
model.Learn(ExampleData);
var oldLines = new List <string>(model.SourcePhrases);
model.Retrain(retrainDepth);
CollectionAssert.AreEquivalent(oldLines, model.SourcePhrases);
}
public void RetrainedModelIsNotSameIfLevelIsDifferent(int retrainDepth, bool expectSameModel)
{
var model = new StringMarkov();
model.Learn(ExampleData);
var dict = new ConcurrentDictionary <NgramContainer <string>, List <string> >(model.Chain.ChainDictionary); // this will break for non string type models during testing until fixed
model.Retrain(retrainDepth);
if (expectSameModel)
{
//CollectionAssert.AreEquivalent(dict, model.Model);
Assert.AreEqual(dict.Sum(a => a.Key.Ngrams.Count()), model.Chain.ChainDictionary.Sum(a => a.Key.Ngrams.Count()));
}
else
{
//CollectionAssert.AreNotEquivalent(dict, model.Model);
Assert.AreNotEqual(dict.Sum(a => a.Key.Ngrams.Count()), model.Chain.ChainDictionary.Sum(a => a.Key.Ngrams.Count()));
}
}