public static Iris[] Load()
{
if (File.Exists("Data\\iris.data"))
{
List <Iris> data = new List <Iris>();
using (var reader = File.OpenText("Data\\iris.data"))
{
while (!reader.EndOfStream)
{
var tokens = reader.ReadLine().Split(',');
if (tokens.Length == 5)
{
Iris f = new Iris
{
SepalLength = decimal.Parse(tokens[0]),
SepalWidth = decimal.Parse(tokens[1]),
PetalLength = decimal.Parse(tokens[2]),
PetalWidth = decimal.Parse(tokens[3]),
Class = tokens[4],
};
data.Add(f);
}
}
}
return(data.ToArray());
}
else
{
return new Iris[] { }
};
}
}
public static Iris[] Load()
{
if (File.Exists("Data\\iris.data"))
{
List<Iris> data = new List<Iris>();
using (var reader = File.OpenText("Data\\iris.data"))
{
while (!reader.EndOfStream)
{
var tokens = reader.ReadLine().Split(',');
if (tokens.Length == 5)
{
Iris f = new Iris
{
SepalLength = decimal.Parse(tokens[0]),
SepalWidth = decimal.Parse(tokens[1]),
PetalLength = decimal.Parse(tokens[2]),
PetalWidth = decimal.Parse(tokens[3]),
Class = tokens[4],
};
data.Add(f);
}
}
}
return data.ToArray();
}
else
return new Iris[] { };
}
public void Iris_DT_and_Prediction()
{
var data = Iris.Load();
var description = Descriptor.Create<Iris>();
var generator = new NaiveBayesGenerator(2);
var model = generator.Generate(description, data);
// should be Iris-Setosa
Iris iris = new Iris
{
SepalLength = 2.1m,
SepalWidth = 2.2m,
PetalWidth = 0.5m,
PetalLength = 2.3m,
};
iris = model.Predict<Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}
public void Iris_DT_and_Prediction_with_Learner()
{
var data = Iris.Load();
var generator = new DecisionTreeGenerator(50)
{
Descriptor = Descriptor.Create<Iris>(),
Hint = 0
};
var lmodel = Learner.Learn(data, .80, 1000, generator);
// should be Iris-Setosa
Iris iris = new Iris
{
PetalWidth = 0.5m,
PetalLength = 2.3m,
SepalLength = 2.1m,
SepalWidth = 2.1m
};
iris = lmodel.Model.Predict<Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}