// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (This is completely fictional data).
double[][] input =
{
    new double[] { 55, 0 }, // 0 - no cancer
    new double[] { 28, 0 }, // 0
    new double[] { 65, 1 }, // 0
    new double[] { 46, 0 }, // 1 - have cancer
    new double[] { 86, 1 }, // 1
    new double[] { 56, 1 }, // 1
    new double[] { 85, 0 }, // 0
    new double[] { 33, 0 }, // 0
    new double[] { 21, 1 }, // 0
    new double[] { 42, 1 }, // 1
};

// We also know if they have had lung cancer or not, and 
// we would like to know whether smoking has any connection
// with lung cancer (This is completely fictional data).
double[] output =
{
    0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};


// To verify this hypothesis, we are going to create a GLM
// regression model for those two inputs (age and smoking).
var regression = new GeneralizedLinearRegression(new ProbitLinkFunction(), inputs: 2);

// Next, we are going to estimate this model. For this, we
// will use the Iteratively Reweighted Least Squares method.
var teacher = new IterativeReweightedLeastSquares(regression);

// Now, we will iteratively estimate our model. The Run method returns
// the maximum relative change in the model parameters and we will use
// it as the convergence criteria.

double delta = 0;
do
{
    // Perform an iteration
    delta = teacher.Run(input, output);

} while (delta > 0.001);

//Test cases
double[][] samples =
{
    new double[] { 1, 0 },
    new double[] { 1, 1 },
    new double[] { 20, 0 },
    new double[] { 20, 1 },
    new double[] { 40, 0 },
    new double[] { 40, 1 },
    new double[] { 60, 0 },
    new double[] { 60, 1 },
    new double[] { 80, 0 },
    new double[] { 80, 1 },
};
for (int i = 0; i < samples.Length; i++)
{
    Console.WriteLine("Input({0},{1}) => Predicted label {2}",
        samples[i][0], samples[i][1], regression.Transform(samples[i]));
}