knn

swordfish.function.knn()

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

参数:

• Y (Constant) -- 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 (Constant) -- A table. Each row is an observation and each column is a feature.

• type (Constant) -- A string. It can be either "regressor" or "classifier".

• nNeighbor (Constant) -- A positive integer indicating the number of nearest neighbors in training.

• power (Constant) -- 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.

返回:

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".

返回类型:

Constant