/// <summary>
/// Constructs a Multinomial Logistic Regression Analysis.
/// </summary>
///
/// <param name="inputs">The input data for the analysis.</param>
/// <param name="outputs">The output data for the analysis.</param>
///
public MultinomialLogisticRegressionAnalysis(double[][] inputs, int[] outputs)
{
// Initial argument checking
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("The number of rows in the input array must match the number of given outputs.");
}
init(inputs, Categorical.OneHot(outputs));
}
/// <summary>
/// Gets the Deviance for the model.
/// </summary>
///
/// <remarks>
/// The deviance is defined as -2*Log-Likelihood.
/// </remarks>
///
/// <param name="inputs">A set of input data.</param>
/// <param name="classes">A set of output data.</param>
/// <returns>
/// The deviance (a measure of performance) of the model
/// calculated over the given data sets.
/// </returns>
///
public double GetLogLikelihood(double[][] inputs, int[] classes)
{
return(GetLogLikelihood(inputs, Categorical.OneHot(classes)));
}
/// <summary>
/// The likelihood ratio test of the overall model, also called the model chi-square test.
/// </summary>
///
/// <remarks>
/// <para>
/// The Chi-square test, also called the likelihood ratio test or the log-likelihood test
/// is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test
/// indicates whether there is evidence of the need to move from a simpler model to a more
/// complicated one (where the simpler model is nested within the complicated one).</para>
/// <para>
/// The difference between the log-likelihood ratios for the researcher's model and a
/// simpler model is often called the "model chi-square".</para>
/// </remarks>
///
public ChiSquareTest ChiSquare(double[][] input, int[] classes)
{
return(ChiSquare(input, Categorical.OneHot(classes)));
}
/// <summary>
/// Runs one iteration of the Lower-Bound Newton-Raphson iteration.
/// </summary>
/// <param name="inputs">The input data.</param>
/// <param name="classes">The outputs associated with each input vector.</param>
/// <returns>The maximum relative change in the parameters after the iteration.</returns>
///
public double Run(double[][] inputs, int[] classes)
{
return(run(inputs, Categorical.OneHot(classes)));
}
