public void TrainModel2()
{
var trainer = BrightWireProvider.CreateMarkovTrainer2 <string>();
_Train(trainer);
var model = trainer.Build().AsDictionary;
// generate some text
var rand = new Random();
string prev = default(string), curr = default(string);
var output = new List <string>();
for (var i = 0; i < 1024; i++)
{
var transitions = model.GetTransitions(prev, curr);
var distribution = new Categorical(transitions.Select(d => Convert.ToDouble(d.Probability)).ToArray());
var next = transitions[distribution.Sample()].NextState;
output.Add(next);
if (SimpleTokeniser.IsEndOfSentence(next))
{
break;
}
prev = curr;
curr = next;
}
Assert.IsTrue(output.Count < 1024);
}
public void TrainModel2()
{
var trainer = BrightWireProvider.CreateMarkovTrainer2 <string>();
_Train(trainer);
// test serialisation/deserialisation
using (var buffer = new MemoryStream())
{
trainer.SerialiseTo(buffer);
buffer.Position = 0;
trainer.DeserialiseFrom(buffer, true);
}
var dictionary = trainer.Build().AsDictionary;
// generate some text
var rand = new Random();
string prev = default, curr = default;
