public void ArbitraryPrediction_Test_With_Named_Iterator()
{
var data = ArbitraryPrediction.GetDataUsingNamedIterator();
var description = Descriptor.Create<ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 10.0m,
ThirdTestFeature = 1.2m
};
ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 57.0m,
ThirdTestFeature = 1.2m
};
var expectedMinimum = model.Predict<ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel;
var expectedMaximum = model.Predict<ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel;
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum);
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum);
}
static void Main(string[] args)
{
var descriptor = Descriptor.For <Tennis>()
.With(t => t.Outlook)
.With(t => t.Temperature)
.With(t => t.Windy)
.With(t => t.Humidity)
.Learn(t => t.Play);
Console.WriteLine(descriptor);
var generator = new DecisionTreeGenerator(descriptor);
var model = generator.Generate(Tennis.GetData());
Console.WriteLine($"Model:\n{model}");
var tn = new Tennis {
Outlook = Outlook.Sunny,
Temperature = Temperature.Cool,
Humidity = Humidity.High,
Windy = true
};
var tennis = model.PredictValue(tn);
Console.WriteLine($"Play? {tennis}");
Console.ReadKey();
}
public void Decision_Tree_Study_Category_Test()
{
var categoryA = Guid.NewGuid();
var categoryB = Guid.NewGuid();
var data = new[] {
new { Study = 2.0, Category = categoryA, Passed = false },
new { Study = 3.0, Category = categoryA, Passed = false },
new { Study = 1.0, Category = categoryB, Passed = false },
new { Study = 4.0, Category = categoryA, Passed = false },
new { Study = 6.0, Category = categoryA, Passed = true },
new { Study = 8.0, Category = categoryB, Passed = true },
new { Study = 12.0, Category = categoryA, Passed = true },
new { Study = 3.0, Category = categoryB, Passed = true }
};
var descriptor = Descriptor.New("Student")
.With("Study").As(typeof(double))
.With("Category").AsGuid()
.Learn("Passed").As(typeof(bool));
DecisionTreeGenerator generator = new DecisionTreeGenerator()
{
Descriptor = descriptor,
NormalizeFeatures = true
};
var model = Learner.Learn(data, 0.8, 10, generator).Model;
var test = model.PredictValue(new { Study = 7.0, Category = categoryA, Passed = false });
Assert.Equal(true, test);
}
public void ArbitraryPrediction_Test_With_Enum_Label()
{
var data = ArbitraryPrediction.GetData();
var description = Descriptor.Create <ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 10.0m,
ThirdTestFeature = 1.2m
};
ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 55.0m,
ThirdTestFeature = 1.2m
};
var expectedMinimum = model.Predict <ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel;
var expectedMaximum = model.Predict <ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel;
//The value should be minimum, however, maximum is returned
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum);
//Maximum is returned as expected
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum);
}
public void ArbitraryPrediction_Test_With_Enum_Label()
{
var data = ArbitraryPrediction.GetData();
var description = Descriptor.Create<ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 10.0m,
ThirdTestFeature = 1.2m
};
ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 55.0m,
ThirdTestFeature = 1.2m
};
var expectedMinimum = model.Predict<ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel;
var expectedMaximum = model.Predict<ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel;
//The value should be minimum, however, maximum is returned
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum);
//Maximum is returned as expected
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum);
}
public void ArbitraryPrediction_Test_With_Named_Iterator()
{
var data = ArbitraryPrediction.GetDataUsingNamedIterator();
var description = Descriptor.Create <ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 10.0m,
ThirdTestFeature = 1.2m
};
ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 57.0m,
ThirdTestFeature = 1.2m
};
var expectedMinimum = model.Predict <ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel;
var expectedMaximum = model.Predict <ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel;
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum);
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum);
}
public void Train(IEnumerable <Game> games)
{
_description = Descriptor.Create <Record>();
var generator = new DecisionTreeGenerator();
var data = games.Select(z => new Record(z));
_model = generator.Generate(_description, data);
}
public static void Main(string[] args)
{
// Step 1: Data
Console.WriteLine("Step 1: Data");
Tennis[] tennis = Tennis.GetData();
var descriptor = Descriptor.Create <Tennis>();
// Step 2: Maths
Console.WriteLine("\nStep 2: Maths");
var data = descriptor.ToExamples(tennis);
var x = data.Item1;
var y = data.Item2;
Console.WriteLine($"Mathematical feature representation:{x}");
Console.WriteLine($"\nMathematical label representation:{y.T}");
// Step 3: Generate Model
Console.WriteLine("\nStep 3: Generate Model");
var generator = new DecisionTreeGenerator();
var model = generator.Generate <Tennis>(descriptor, tennis);
Console.WriteLine(model);
// Step 4: Prediction
Console.WriteLine("\nStep 4: Prediction");
Tennis t = new Tennis
{
Outlook = Outlook.Sunny,
Temperature = Temperature.Hot,
Windy = false
};
Tennis p = model.Predict(t);
Console.WriteLine($"Play: {p.Play}");
// Step 5: Testing Accuracy
Console.WriteLine("\nStep 5: Testing Accuracy");
((DecisionTreeGenerator)generator).SetHint(0);
var learned = Learner.Learn(tennis, 0.80, 100, generator);
var m = learned.Model;
double accuracy = learned.Accuracy;
Console.WriteLine($"Learned {m.GetType().Name} model with {accuracy*100}% accuracy");
Console.WriteLine(m);
Tennis h = new Tennis
{
Outlook = Outlook.Sunny,
Temperature = Temperature.Hot,
Windy = false
};
Tennis o = m.Predict(h);
Console.WriteLine($"Play: {o.Play}");
}
async Task GenericTree()
{
var data = BombDecisionTree.GetData();
var d = Descriptor.Create <BombDecisionTree>();
var g = new DecisionTreeGenerator(d);
g.SetHint(false);
_model = Learner.Learn(data, 0.80, 10000, g);
}
public void Save_And_Load_Iris_DT()
{
var data = Iris.Load();
var description = Descriptor.Create<Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize<DecisionTreeModel>();
Assert.AreEqual(model.Hint, lmodel.Hint);
AreEqual(model.Tree, lmodel.Tree, false);
}
static void Main(string[] args)
{
Tennis[] data = Tennis.GetData();
var d = Descriptor.Create <Tennis>();
var g = new DecisionTreeGenerator(d);
g.SetHint(false);
var model = Learner.Learn(data, 0.8, 1000, g);
Console.WriteLine(model);
Console.ReadKey();
}
public double LearnFromData(int iterations)
{
var d = Descriptor.Create <ScreenData>();
var g = new DecisionTreeGenerator(d);
g.SetHint(false);
var mdl = Learner.Learn(data, 0.80, iterations, g);
model = (DecisionTreeModel)mdl.Model;
return(mdl.Accuracy);
}
static void NumlTest()
{
Console.WriteLine("Hello World!");
var description = Descriptor.Create <Iris>();
Console.WriteLine(description);
var generator = new DecisionTreeGenerator();
var data = Iris.Load();
var model = generator.Generate(description, data);
Console.WriteLine("Generated model:");
Console.WriteLine(model);
}
public void Save_And_Load_Iris_DT()
{
var data = Iris.Load();
var description = Descriptor.Create <Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize <DecisionTreeModel>();
Assert.AreEqual(model.Hint, lmodel.Hint);
AreEqual(model.Tree, lmodel.Tree, false);
}
public void Save_And_Load_HouseDT()
{
var data = House.GetData();
var description = Descriptor.Create<House>();
var generator = new DecisionTreeGenerator { Depth = 50 };
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize<DecisionTreeModel>();
Assert.AreEqual(model.Hint, lmodel.Hint);
AreEqual(model.Tree, lmodel.Tree, false);
}
public override void CreateClassifier()
{
// this.Normalizator = new Normalizator.Normalizator(this.Stats);
// this.Stats = this.Normalizator.Normalize(this.Stats);
var testSet = this.Stats;
var descriptor = Descriptor.Create <FeatureVector>();
var decisionTreeModel = new DecisionTreeGenerator(descriptor.VectorLength, 2, descriptor, null, 0.0);
// decisionTreeModel.SetHint(false);
decisionTreeModel.Generate((IEnumerable <object>)testSet);
var learnDecisionTree = Learner.Learn(testSet, 0.90, 100, decisionTreeModel);
this.ClassifierModel = learnDecisionTree.Model;
}
private void Basla(string AnaIlanSayfasi, int okunacakIlanSayisi, int offset)
{
try
{
int sayfaSayisi = okunacakIlanSayisi / 50;
bool bit = false;
int i = 0;
int eskiOffset;
while (!bit && i < sayfaSayisi)
{
eskiOffset = offset;
List <string> linkler = IlanlariAl(AnaIlanSayfasi.Replace("pagingOffset=" + eskiOffset, "pagingOffset=" + offset).ToString());
offset += 50;;
for (int j = 0; j < linkler.Count; j++)
{
lblSite.Text = linkler[j];
Application.DoEvents();
OtomobilEkle(linkler[j]);
Thread.Sleep(500);
if (Otomobiller.Count >= okunacakIlanSayisi)
{
bit = true;
}
}
i++;
}
if (cbSerialize.Checked && Otomobiller.Count > 1)
{
SerializeEt();
lblSite.Text = "Serialize edildi";
}
var d = Descriptor.Create <Otomobil>();
var g = new DecisionTreeGenerator(d);
g.SetHint(true);
var model = Learner.Learn(Otomobiller, 0.80, 1000, g);
lblSite.ForeColor = Color.Green;
lblSite.Text = Otomobiller.Count + " adet otomobil için Model çıkarıldı.";
}
catch (Exception e)
{
lblSite.ForeColor = Color.Red;
lblSite.Text = "Veriler alınamadı!";
}
}
public void Save_And_Load_Iris_DT_Json()
{
var data = Iris.Load();
var description = Descriptor.Create <Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data) as DecisionTreeModel;
var file = GetPath();
Register.Type <Iris>();
var lmodel = SaveAndLoadJson(model);
Assert.Equal(model.Descriptor, lmodel.Descriptor);
Assert.Equal(model.Hint, lmodel.Hint);
Assert.Equal(model.Tree, lmodel.Tree);
}
public void Save_And_Load_HouseDT()
{
var data = House.GetData();
var description = Descriptor.Create <House>();
var generator = new DecisionTreeGenerator {
Depth = 50
};
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize <DecisionTreeModel>();
Assert.Equal(model.Descriptor, lmodel.Descriptor);
Assert.Equal(model.Hint, lmodel.Hint);
Assert.Equal(model.Tree, lmodel.Tree);
}
public void Tennis_DT_and_Prediction()
{
var data = Tennis.GetData();
var description = Descriptor.Create <Tennis>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
Tennis t = new Tennis
{
Humidity = Humidity.Normal,
Outlook = Outlook.Overcast,
Temperature = Temperature.Cool,
Windy = true
};
model.Predict <Tennis>(t);
Assert.IsTrue(t.Play);
}
public void ArbitraryPrediction_Test_With_Feature_Value_Greater_Than_Trained_Instances()
{
var data = ArbitraryPrediction.GetData();
var description = Descriptor.Create<ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction predictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
//This value is greater than any of the trained instances
SecondTestFeature = 106.0m,
ThirdTestFeature = 1.2m
};
var expectedValue = model.Predict<ArbitraryPrediction>(predictionValue).OutcomeLabel;
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedValue);
}
public void ValueObject_Test_With_Yield_Enumerator()
{
var data = ValueObject.GetData();
var generator = new DecisionTreeGenerator()
{
Descriptor = Descriptor.Create <ValueObject>()
};
var model = generator.Generate(data);
var s = model.ToString();
var o = new ValueObject()
{
V1 = 1, V2 = 60
};
var os = model.Predict <ValueObject>(o).R;
Assert.AreEqual("l".Sanitize(), os);
}
public void Iris_DT_and_Prediction()
{
var data = Iris.Load();
var description = Descriptor.Create <Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
// should be Iris-Setosa
Iris iris = new Iris
{
PetalWidth = 0.5m,
PetalLength = 2.3m,
SepalLength = 2.1m,
SepalWidth = 2.1m
};
iris = model.Predict <Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}
public void ArbitraryPrediction_Test_With_Feature_Value_Greater_Than_Trained_Instances()
{
var data = ArbitraryPrediction.GetData();
var description = Descriptor.Create <ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction predictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
//This value is greater than any of the trained instances
SecondTestFeature = 106.0m,
ThirdTestFeature = 1.2m
};
var expectedValue = model.Predict <ArbitraryPrediction>(predictionValue).OutcomeLabel;
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedValue);
}
public void Save_And_Load_HouseDT_Json()
{
var data = House.GetData();
var description = Descriptor.Create <House>();
var generator = new DecisionTreeGenerator {
Depth = 50
};
var model = generator.Generate(description, data) as DecisionTreeModel;
var file = GetPath();
Register.Type <House>();
var lmodel = SaveAndLoadJson(model);
Assert.Equal(model.Descriptor, lmodel.Descriptor);
Assert.Equal(model.Hint, lmodel.Hint);
Assert.Equal(model.Tree, lmodel.Tree);
}
public void House_DT_and_Prediction()
{
var data = House.GetData();
var description = Descriptor.Create <House>();
var generator = new DecisionTreeGenerator {
Depth = 50
};
var model = generator.Generate(description, data);
House h = new House
{
District = District.Rural,
HouseType = HouseType.Detached,
Income = Income.High,
PreviousCustomer = false
};
var prediction = model.Predict(h);
Assert.IsTrue(prediction.Response);
}
public static void Main(string[] args)
{
Console.WriteLine("Iris Machine Learning Example");
// our data
var data = Iris.Load();
// descriptor
var description = Descriptor.Create <Iris>();
Console.WriteLine(description);
// generator makes model
var generator = new DecisionTreeGenerator();
var model = generator.Generate(description, data);
Console.WriteLine("Generated model:");
Console.WriteLine(model);
// use model
// model.Predict(...)
}
public static void Go()
{
// Start with our data
Tennis[] data = SampleData.GetTennisData();
// Create the corresponding descriptor
var descriptor = Descriptor.Create <Tennis>();
// Choose our generator
var generator = new DecisionTreeGenerator(descriptor);
generator.SetHint(false);
Console.WriteLine($"Using the {generator.GetType().Name}\n");
// Create the model by learning from the data using the generator
LearningModel learningModel = Learner.Learn(data, 0.80, 1000, generator);
Console.WriteLine(learningModel);
// Now we could predict using the learning info's Model.
var toPredict = new Tennis()
{
Outlook = Outlook.Rainy,
Temperature = Temperature.Low,
Windy = true,
// Play = ? - This is what we will predict
};
var prediction = learningModel.Model.Predict(toPredict);
// And we're spent...
Console.WriteLine($"ToPredict: {toPredict}");
Console.WriteLine($"Prediction: Play = {prediction.Play}\n");
Console.WriteLine("Press any key...\n");
Console.ReadKey();
}
/// <summary>
/// Do the <c>SimpleNumlWorkflow</c> using a <c>DecisionTree</c> generator/model.
/// </summary>
/// <param name="simpleData">If true, uses a preset small sample data set; else generates a larger, pseudo-random one.</param>
/// <param name="depth"></param>
/// <param name="width"></param>
static void DoSimple_DecisionTree(bool simpleData = true, int depth = 5,
int width = 2)
{
SimpleNumlWorkflowImpl(
getData: () => SampleData.GetTennisData(simpleData),
getDescriptor: () => Descriptor.Create <Tennis>(),
getGenerator: (descriptor) =>
{
var generator = new DecisionTreeGenerator(descriptor)
{
Depth = depth,
Width = width
};
generator.SetHint(false);
return(generator);
},
getToPredict: () =>
{
return(new Tennis()
{
Outlook = Outlook.Rainy,
Temperature = Temperature.Low,
Windy = true
});
},
getPredictionDesc: (t) => "Play: " + t.Play,
trainingPercentage: 0.8d,
repeat: 1000
);
}
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);
}
public void ValueObject_Test_With_Yield_Enumerator()
{
var data = ValueObject.GetData();
var generator = new DecisionTreeGenerator()
{
Descriptor = Descriptor.Create<ValueObject>()
};
var model = generator.Generate(data);
var s = model.ToString();
var o = new ValueObject() { V1 = 1, V2 = 60 };
var os = model.Predict<ValueObject>(o).R;
Assert.AreEqual("l".Sanitize(), os);
}
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);
}
private void btnOgren_Click(object sender, EventArgs e)
{
try
{
GuvenlikOzellikleri go = GuvenlikOzellikleri.YOK;
IcDonanim id = IcDonanim.YOK;
if (cbAbs.Checked)
{
go = go | GuvenlikOzellikleri.Seviye1;
}
else if (cbAsr.Checked)
{
go = go | GuvenlikOzellikleri.Seviye2;
}
else if (cbEsp.Checked)
{
go = go | GuvenlikOzellikleri.Seviye3;
}
else if (cbKumasKoltuk.Checked)
{
id = id | IcDonanim.Seviye1;
}
else if (cbKlima.Checked)
{
id = id | IcDonanim.Seviye2;
}
else if (cbDeriKoltuk.Checked)
{
id = id | IcDonanim.Seviye3;
}
else if (cbYolBilgsayarı.Checked)
{
id = id | IcDonanim.Seviye4;
}
Otomobiller.Add(new Otomobil(int.Parse(txtFiyat.Text), int.Parse(txtYıl.Text), int.Parse(txtKm.Text), cbOtomatik.Checked ? Vites.Otomatik : Vites.Manuel, cbDizel.Checked ? Yakit.Dizel : Yakit.Benzin, int.Parse(txtMotorHacmi.Text), int.Parse(txtMotorGucu.Text), cbGaranti.Checked, cbTakas.Checked, cbSıfır.Checked ? Durum.Sıfır : Durum.IkinciEl, go, id, int.Parse(txtBoyalıSayısı.Text), int.Parse(txtDegisenSayısı.Text), cbAlınır.Checked));
if (Otomobiller.Count < 2)
{
MessageBox.Show("Öğrenme Yapılabilmesi için en az 2 kayıt olması gerek");
lblDurum.ForeColor = Color.Green;
lblDurum.Text = "Kayıt eklendi.";
return;
}
var d = Descriptor.Create <Otomobil>();
var g = new DecisionTreeGenerator(d);
g.SetHint(true);
Model = Learner.Learn(Otomobiller, 0.80, 1000, g);
lblDurum.ForeColor = Color.Green;
lblDurum.Text = "Öğrenme Yapıldı : Doğruluk:" + Model.Accuracy;
btnOgren.Enabled = false;
linkLabel1.Visible = true;
}
catch (Exception)
{
lblDurum.ForeColor = Color.DarkRed;
lblDurum.Text = "Otomobil eklenemedi.";
linkLabel1.Visible = false;
}
}
public void House_DT_and_Prediction()
{
var data = House.GetData();
var description = Descriptor.Create<House>();
var generator = new DecisionTreeGenerator { Depth = 50 };
var model = generator.Generate(description, data);
House h = new House
{
District = District.Rural,
HouseType = HouseType.Detached,
Income = Income.High,
PreviousCustomer = false
};
var prediction = model.Predict(h);
Assert.IsTrue(prediction.Response);
}
public void Tennis_DT_and_Prediction()
{
var data = Tennis.GetData();
var description = Descriptor.Create<Tennis>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
Tennis t = new Tennis
{
Humidity = Humidity.Normal,
Outlook = Outlook.Overcast,
Temperature = Temperature.Cool,
Windy = true
};
model.Predict<Tennis>(t);
Assert.IsTrue(t.Play);
}
public void Iris_DT_and_Prediction()
{
var data = Iris.Load();
var description = Descriptor.Create<Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
// should be Iris-Setosa
Iris iris = new Iris
{
PetalWidth = 0.5m,
PetalLength = 2.3m,
SepalLength = 2.1m,
SepalWidth = 2.1m
};
iris = model.Predict<Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}
