/// <summary>
/// Train the model.
/// </summary>
/// <remarks>
/// Input is a list of Song objects which are the expected outputs.
/// This function will pull the bextract values and use the class's bextract subset for training.
/// </remarks>
/// <param name="expectedOutputs">List of Song objects to use for training.</param>
public void Train(List <Song> expectedOutputs)
{
//Pull out the expected outputs
string[] songPaths = new string[expectedOutputs.Count];
double[] posOutputs = new double[expectedOutputs.Count];
double[] energyOutputs = new double[expectedOutputs.Count];
for (int i = 0; i < expectedOutputs.Count; i++)
{
Song song = expectedOutputs[i];
songPaths[i] = song.title;
posOutputs[i] = song.positivity;
energyOutputs[i] = song.energy;
}
//Get bextract values
System.Console.WriteLine(System.DateTime.Now.ToString() + " Extracting features...");
List <SongDataDTO> songFeatures = getFeatures(songPaths);
//Stick them in double arrays
double[][] posInputs = new double[songFeatures.Count][];
double[][] energyInputs = new double[songFeatures.Count][];
for (int i = 0; i < songFeatures.Count; i++)
{
ConvertSongDataDtoToDoubleArrays(songFeatures[i], ref posInputs[i], ref energyInputs[i]);
}
//Train
System.Console.WriteLine(System.DateTime.Now.ToString() + " Training positivity.");
var learn = new SequentialMinimalOptimizationRegression()
{
Kernel = new Gaussian(0.25),
UseComplexityHeuristic = true
};
posSvm = learn.Learn(posInputs, posOutputs);
System.Console.WriteLine(System.DateTime.Now.ToString() + " Training energy.");
learn = new SequentialMinimalOptimizationRegression()
{
Kernel = new Gaussian(0.5),
UseComplexityHeuristic = true
};
energySvm = learn.Learn(energyInputs, energyOutputs);
}
/// <summary>
/// Load the models from files. These files should be generated by the SaveModels function.
/// </summary>
/// <param name="positivityPath">Path to positivity SVM.</param>
/// <param name="energyPath">Path to energy SVM.</param>
public override void LoadModels(string positivityPath, string energyPath)
{
posSvm = Serializer.Load <Accord.MachineLearning.VectorMachines.SupportVectorMachine <IKernel> >(positivityPath);
energySvm = Serializer.Load <Accord.MachineLearning.VectorMachines.SupportVectorMachine <IKernel> >(energyPath);
}
