-k-NN classifier: classifying using k-nearest neighbors algorithm. The nearest neighbors
-search method is euclidean distance
-Usage:
[predicted_labels,nn_index,accuracy] = KNN_(3,training,training_labels,testing,testing_labels)
predicted_labels = KNN_(3,training,training_labels,testing)
-Input:
- k: number of nearest neighbors
- data: (NxD) training data; N is the number of samples and D is the
dimensionality of each data point
- labels: training labels
- t_data: (MxD) testing data; M is the number of datapoints and D
is the dimensionality of each data point
- t_labels: testing labels (default = [])
-Output:
- predicted_labels: the predicted labels based on the k-NN
algorithm
- nn_index: the indices of the nearest training data point (Mx1).
- accuracy: if the testing labels are supported, the accuracy of
the classification is returned, otherwise it will be zero.