MultinomialLogisticRegression Class

[SerializableAttribute]
public class MultinomialLogisticRegression : MulticlassLikelihoodClassifierBase<double[]>, 
	ICloneable

<SerializableAttribute>
Public Class MultinomialLogisticRegression
	Inherits MulticlassLikelihoodClassifierBase(Of Double())
	Implements ICloneable

// 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

// Declare a simple classification/regression
// problem with 5 input variables (a,b,c,d,e):
double[][] inputs =
{
    new double[] { 1, 4, 2, 0, 1 },
    new double[] { 1, 3, 2, 0, 1 },
    new double[] { 3, 0, 1, 1, 1 },
    new double[] { 3, 0, 1, 0, 1 },
    new double[] { 0, 5, 5, 5, 5 },
    new double[] { 1, 5, 5, 5, 5 },
    new double[] { 1, 0, 0, 0, 0 },
    new double[] { 1, 0, 0, 0, 0 },
    new double[] { 2, 4, 2, 0, 1 },
    new double[] { 2, 4, 2, 0, 1 },
    new double[] { 2, 6, 2, 0, 1 },
    new double[] { 2, 7, 5, 0, 1 },
};

// Class labels for each of the inputs
int[] outputs =
{
    0, 0, 1, 1, 2, 2, 3, 3, 0, 0, 0, 0
};

// Create a Conjugate Gradient algorithm to estimate the regression
var mcg = new MultinomialLogisticLearning<ConjugateGradient>();

// Now, we can estimate our model using Conjugate Gradient
MultinomialLogisticRegression mlr = mcg.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);

// Create a Conjugate Gradient algorithm to estimate the regression
var mgd = new MultinomialLogisticLearning<GradientDescent>();

// Now, we can estimate our model using Gradient Descent
MultinomialLogisticRegression mlr = mgd.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);

// Create a Conjugate Gradient algorithm to estimate the regression
var mlbfgs = new MultinomialLogisticLearning<BroydenFletcherGoldfarbShanno>();

// Now, we can estimate our model using BFGS
MultinomialLogisticRegression mlr = mlbfgs.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);