public void TestPredictMethod()
{
TestTrainValidData();
object predicted = NaiveBayesModel.Predict(new List <object> {
"Sunny"
});
string predictedRes = predicted as string;
Assert.AreEqual(predictedRes, "Yes");
}
public void Deveria_prever_com_cem_porcento_de_probabildade_quando_dado_fornecido_for_igual_ao_dado_treinado()
{
var play = new PlayBasketball
{
HasBall = true,
NumberOfPlayers = 4,
Rain = false,
Windy = false,
CanPlay = true
};
_naiveBayes.Add(play);
_naiveBayes.Fit();
decimal probability = _naiveBayes.Predict(play);
probability.Should().Be(1);
}
public List <string> PredictKeywords(List <List <string> > sentencePosTokensIn)
{
List <string> retKeywords = new List <string>();
var examples = SplitInputIntoExamples(sentencePosTokensIn);
int currIndex = 1; //the element directly after the START token
foreach (List <string> example in examples)
{
List <object> objExample = new List <object>();
foreach (string x in example)
{
objExample.Add(x);
}
object res = Model.Predict(objExample);
if ((bool)res)
{
retKeywords.Add(sentencePosTokensIn[currIndex][0]);
}
currIndex++;
}
return(retKeywords);
}