public void KNearestNeighbor_CrossValidation()
{
// 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
};
// Create a new Cross-validation algorithm passing the data set size and the number of folds
var crossvalidation = new CrossValidation(size: inputs.Length, folds: 3);
// Define a fitting function using Support Vector Machines. The objective of this
// function is to learn a SVM in the subset of the data indicated by cross-validation.
crossvalidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
// The fitting function is passing the indices of the original set which
// should be considered training data and the indices of the original set
// which should be considered validation data.
// Lets now grab the training data:
var trainingInputs = inputs.Submatrix(indicesTrain);
var trainingOutputs = outputs.Submatrix(indicesTrain);
// And now the validation data:
var validationInputs = inputs.Submatrix(indicesValidation);
var validationOutputs = outputs.Submatrix(indicesValidation);
// 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 knn = new KNearestNeighbors(k: 4, classes: 3,
inputs: inputs, outputs: outputs);
// After the algorithm has been created, we can classify instances:
int[] train_predicted = trainingInputs.Apply(knn.Compute);
int[] test_predicted = validationInputs.Apply(knn.Compute);
// Compute classification error
var cmTrain = new ConfusionMatrix(train_predicted, trainingOutputs);
double trainingAcc = cmTrain.Accuracy;
// Now we can compute the validation error on the validation data:
var cmTest = new ConfusionMatrix(test_predicted, validationOutputs);
double validationAcc = cmTest.Accuracy;
// Return a new information structure containing the model and the errors achieved.
return new CrossValidationValues(knn, trainingAcc, validationAcc);
};
// Compute the cross-validation
var result = crossvalidation.Compute();
// Finally, access the measured performance.
double trainingAccs = result.Training.Mean;
double validationAccs = result.Validation.Mean;
Assert.AreEqual(1, trainingAccs);
Assert.AreEqual(1, validationAccs);
}
}
