public void MulticlassTest1()
{
Accord.Math.Tools.SetupGenerator(0);
// Suppose we would like to teach a network to recognize
// the following input vectors into 3 possible classes:
//
double[][] inputs =
{
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] classes =
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// First we have to convert this problem into a way that the neural
// network can handle. The first step is to expand the classes into
// indicator vectors, where a 1 into a position signifies that this
// position indicates the class the sample belongs to.
//
double[][] outputs = Statistics.Tools.Expand(classes, -1, +1);
// Create an activation function for the net
var function = new BipolarSigmoidFunction();
// Create an activation network with the function and
// 4 inputs, 5 hidden neurons and 3 possible outputs:
var network = new ActivationNetwork(function, 4, 5, 3);
// Randomly initialize the network
new NguyenWidrow(network).Randomize();
// Teach the network using parallel Rprop:
var teacher = new ParallelResilientBackpropagationLearning(network);
double error = 1.0;
while (error > 1e-5)
error = teacher.RunEpoch(inputs, outputs);
// Checks if the network has learned
for (int i = 0; i < inputs.Length; i++)
{
double[] answer = network.Compute(inputs[i]);
int expected = classes[i];
int actual; answer.Max(out actual);
Assert.AreEqual(expected, actual, 0.01);
}
}
#endif
