/// <summary>
/// Do the <c>SimpleNumlWorkflow</c> using a <c>Neural Network</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="maxIterations"></param>
/// <param name="learningRate"></param>
static void DoSimple_NeuralNetwork(bool simpleData = true, int maxIterations = 10000,
double learningRate = 0.9d)
{
SimpleNumlWorkflowImpl(
getData: () => SampleData.GetTennisData(simpleData),
getDescriptor: () => Descriptor.Create <Tennis>(),
// This is where we choose the actual algorithm
getGenerator: (descriptor) =>
{
var generator = new numl.Supervised.NeuralNetwork.NeuralNetworkGenerator()
{
Descriptor = descriptor,
MaxIterations = maxIterations,
LearningRate = learningRate
};
return(generator);
},
getToPredict: () =>
{
return(new Tennis()
{
Outlook = Outlook.Rainy,
Temperature = Temperature.Low,
Windy = true
});
},
getPredictionDesc: (t) => "Play: " + t.Play.ToString(),
trainingPercentage: 0.8d,
repeat: 50
);
}
/// <summary>
/// Do the <see cref="SimpleNumlWorkflow" /> using a <c>Neural Network</c> generator/model.
/// </summary>
static void DoSimple_NeuralNetwork(Outlook outlook = Outlook.Sunny, double tempF = 15.0d, bool windy = true,
int maxIterations = 10000, double learningRate = 0.9d, int repeat = 500)
{
SimpleNumlWorkflow.SimpleNumlWorkflowImpl(
getData: () => SampleData_ComplexType.GetTennisData_ComplexType(predetermined: false),
getDescriptor: () => Descriptor.Create<Tennis_Complex>(),
// This is where we choose the actual algorithm
getGenerator: (descriptor) =>
{
var generator = new numl.Supervised.NeuralNetwork.NeuralNetworkGenerator()
{
Descriptor = descriptor,
MaxIterations = maxIterations,
LearningRate = learningRate
};
return generator;
},
getToPredict: () =>
{
return new Tennis_Complex()
{
WeatherConditions = new WeatherConditions()
{
Outlook = outlook,
Temperature = tempF,
Windy = windy,
}
};
},
getPredictionDesc: (t) => "Play: " + t.Play,
trainingPercentage: 0.8d,
repeat: repeat
);
}
