public void ComputeTest3()
{
#region doc_multiclass
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
int[][] inputs =
{
// input output
new int[] { 0, 1, 1, 0 }, // 0
new int[] { 0, 1, 0, 0 }, // 0
new int[] { 0, 0, 1, 0 }, // 0
new int[] { 0, 1, 1, 0 }, // 0
new int[] { 0, 1, 0, 0 }, // 0
new int[] { 1, 0, 0, 0 }, // 1
new int[] { 1, 0, 0, 0 }, // 1
new int[] { 1, 0, 0, 1 }, // 1
new int[] { 0, 0, 0, 1 }, // 1
new int[] { 0, 0, 0, 1 }, // 1
new int[] { 1, 1, 1, 1 }, // 2
new int[] { 1, 0, 1, 1 }, // 2
new int[] { 1, 1, 0, 1 }, // 2
new int[] { 0, 1, 1, 1 }, // 2
new int[] { 1, 1, 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Let us create a learning algorithm
var learner = new NaiveBayesLearning();
// and teach a model on the data examples
NaiveBayes nb = learner.Learn(inputs, outputs);
// Now, let's test the model output for the first input sample:
int answer = nb.Decide(new int[] { 0, 1, 1, 0 }); // should be 1
#endregion
double error = new ZeroOneLoss(outputs).Loss(nb.Decide(inputs));
Assert.AreEqual(0, error);
for (int i = 0; i < inputs.Length; i++)
{
error = nb.Compute(inputs[i]);
double expected = outputs[i];
Assert.AreEqual(expected, error);
}
}
