/// <summary>
/// Multinomial Logistic Regression generalises Logistic Regression to multi-class classification
/// </summary>
/// <param name="data">The training data</param>
/// <param name="lap">Linear algebra provider</param>
/// <param name="trainingIterations">Number of training iterations</param>
/// <param name="trainingRate">Training rate</param>
/// <param name="lambda">L2 regularisation</param>
/// <param name="costCallback">Optional callback that is called after each iteration with the current cost</param>
/// <returns></returns>
public static MultinomialLogisticRegression TrainMultinomialLogisticRegression(
this IDataTable data, ILinearAlgebraProvider lap, int trainingIterations, float trainingRate,
float lambda = 0.1f, Func <float, bool> costCallback = null)
{
return(MultinomialLogisticRegressionTrainner.Train(data, lap, trainingIterations,
trainingRate, lambda, costCallback));
}
/// <summary>
/// Multinomial Logistic Regression generalises Logistic Regression to multi-class classification
/// </summary>
/// <param name="data">The training data</param>
/// <param name="lap">Linear algebra provider</param>
/// <param name="trainingIterations">Number of training iterations</param>
/// <param name="trainingRate">Training rate</param>
/// <param name="lambda">L2 regularisation</param>
/// <returns></returns>
public static MultinomialLogisticRegression TrainMultinomialLogisticRegression(this IDataTable data, ILinearAlgebraProvider lap, int trainingIterations, float trainingRate, float lambda = 0.1f)
{
return(MultinomialLogisticRegressionTrainner.Train(data, lap, trainingIterations, trainingRate, lambda));
}
