knn

knn(Y, X, type, nNeighbor, [power=2])

Y is a vector with the same length as the number of rows of X. Each element is a label corresponding to each row in X.

X is a table. Each row is an observation and each column is a feature.

type is a string. It can be either "regressor" or "classifier".

nNeighbor is a positive integer indicating the number of nearest neighbors in training.

power is a positive integer indicating the parameter of Minkowski distance used in training. The default value is 2 indicating Euclidean distance. If power=1, it means Manhattan distance is used in training.

Implement the k-nearest neighbors (k-NN) algorithm with a brute-force search for classification and regression. Return a dictionary with the following keys:

• nNeighbor: the number of nearest neighbors in training.

• modelName: string "KNN".

• model: the model to be saved.

• power: the parameter of Minkowski distance used in training.

• type: "regressor" or "classifier".

height = 158 158 158 160 160 163 163 160 163 165 165 165 168 168 168 170 170 170
weight = 58 59 63 59 60 60 61 64 64 61 62 65 62 63 66 63 64 68
t=table(height, weight)
labels=take(1,7) join take(2,11)
model = knn(labels,t,"classifier", 5);