public void KNearestNeighborGenericConstructorTest2()
{
// Create some sample learning data. In this data,
// the first two instances belong to a class, the
// four next belong to another class and the last
// three to yet another.
double[][] inputs =
{
// The first two are from class 0
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
// The next four are from class 1
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
// The last three are from class 2
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
int[] outputs =
{
0, 0, // First two from class 0
1, 1, 1, 1, // Next four from class 1
2, 2, 2 // Last three from class 2
};
// Now we will create the K-Nearest Neighbors algorithm. For this
// example, we will be choosing k = 4. This means that, for a given
// instance, its nearest 4 neighbors will be used to cast a decision.
KNearestNeighbors<double[]> knn = new KNearestNeighbors<double[]>(k: 4, classes: 3,
inputs: inputs, outputs: outputs, distance: new SquareEuclidean());
// After the algorithm has been created, we can classify a new instance:
int answer = knn.Compute(new double[] { 11, 5, 4 }); // answer will be 2.
Assert.AreEqual(2, answer);
}
