LowerBoundNewtonRaphson Class

public class LowerBoundNewtonRaphson : ISupervisedLearning<MultinomialLogisticRegression, double[], int>, 
	ISupervisedLearning<MultinomialLogisticRegression, double[], int[]>, ISupervisedLearning<MultinomialLogisticRegression, double[], double[]>, 
	IConvergenceLearning

Public Class LowerBoundNewtonRaphson
	Implements ISupervisedLearning(Of MultinomialLogisticRegression, Double(), Integer), 
	ISupervisedLearning(Of MultinomialLogisticRegression, Double(), Integer()), 
	ISupervisedLearning(Of MultinomialLogisticRegression, Double(), Double()), IConvergenceLearning

// Declare a very simple classification/regression
// problem with only 2 input variables (x and y):
double[][] inputs =
{
    new[] { 3.0, 1.0 },
    new[] { 7.0, 1.0 },
    new[] { 3.0, 1.1 },
    new[] { 3.0, 2.0 },
    new[] { 6.0, 1.0 },
};

// Class labels for each of the inputs
int[] outputs =
{
    0, 2, 0, 1, 2
};

// Create a estimation algorithm to estimate the regression
LowerBoundNewtonRaphson lbnr = new LowerBoundNewtonRaphson()
{
    MaxIterations = 100,
    Tolerance = 1e-6
};

// Now, we will iteratively estimate our model:
MultinomialLogisticRegression mlr = lbnr.Learn(inputs, outputs);

// We can compute the model answers
int[] answers = mlr.Decide(inputs);

// And also the probability of each of the answers
double[][] probabilities = mlr.Probabilities(inputs);

// Now we can check how good our model is at predicting
double error = new ZeroOneLoss(outputs).Loss(answers);

// We can also verify the classes with highest 
// probability are the ones being decided for:
int[] argmax = probabilities.ArgMax(dimension: 1); // should be same as 'answers