public void Save_Model_Test_Json() { Tennis t = new Tennis { Humidity = Humidity.Normal, Outlook = Outlook.Overcast, Temperature = Temperature.Cool, Windy = true }; var model = (NeuralNetworkModel)BaseSupervised.Prediction <Tennis>( new NeuralNetworkGenerator(), Tennis.GetData(), t, p => p.Play ); var file = GetPath(); Register.Type <Tennis>(); var model2 = SaveAndLoadJson(model); Assert.Equal(model.Descriptor, model2.Descriptor); AreEqual(model.Network, model2.Network); }
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 void Tennis_Naive_Bayes_Save_And_Load_Test() { var data = Tennis.GetData(); var description = Descriptor.Create <Tennis>(); var generator = new NaiveBayesGenerator(2); var model = generator.Generate(description, data); Serialize(model); var lmodel = Deserialize <NaiveBayesModel>(); }
public void Tennis_Naive_Bayes_Save_And_Load_Test_Json() { var data = Tennis.GetData(); var description = Descriptor.Create <Tennis>(); var generator = new NaiveBayesGenerator(2); var model = generator.Generate(description, data) as NaiveBayesModel; var file = GetPath(); Register.Type <Tennis>(); var lmodel = SaveAndLoadJson(model); Assert.Equal(model.Root, lmodel.Root); }
public void TennisPrediction(IGenerator generator) { Tennis t = new Tennis { Humidity = Humidity.Normal, Outlook = Outlook.Overcast, Temperature = Temperature.Cool, Windy = true }; Prediction <Tennis>( generator, // generator Tennis.GetData(), // training data t, // test object p => p.Play // should be true ); }
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 Main_Naive_Bayes_Test() { var data = Tennis.GetData(); var description = Descriptor.Create <Tennis>(); var generator = new NaiveBayesGenerator(2); 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 Save_Node_Test() { Tennis t = new Tennis { Humidity = Humidity.Normal, Outlook = Outlook.Overcast, Temperature = Temperature.Cool, Windy = true }; var model = (NeuralNetworkModel)BaseSupervised.Prediction <Tennis>( new NeuralNetworkGenerator(), Tennis.GetData(), t, p => p.Play ); var node = model.Network.In[0].Out[0].Target; Serialize(node); }
public void Save_Network_Test() { Tennis t = new Tennis { Humidity = Humidity.Normal, Outlook = Outlook.Overcast, Temperature = Temperature.Cool, Windy = true }; var model = (NeuralNetworkModel)BaseSupervised.Prediction <Tennis>( new NeuralNetworkGenerator(), Tennis.GetData(), t, p => p.Play ); var file = GetPath(); var network = SaveAndLoad(model.Network, file); AreEqual(model.Network, network); }
public void Save_Model_Test() { Tennis t = new Tennis { Humidity = Humidity.Normal, Outlook = Outlook.Overcast, Temperature = Temperature.Cool, Windy = true }; var model = (NeuralNetworkModel)BaseSupervised.Prediction <Tennis>( new NeuralNetworkGenerator(), Tennis.GetData(), t, p => p.Play ); Serialize(model); var model2 = Deserialize <NeuralNetworkModel>(); Assert.Equal(model.Descriptor, model2.Descriptor); AreEqual(model.Network, model2.Network); }