Description Usage Arguments Details Value Author(s) References Examples

Implement the K nearest neighbor classification algorithm to predict the label of a new input using a training data set.

1 | ```
myknn(train, test, K)
``` |

`train` |
Matrix of training data sets. An n by (d+1) matrix, where n is the sample size and d is the dimension. The last column is the class label. |

`test` |
Vector of a test point. It also admits a matrix input with each row representing a new test point. |

`K` |
Number of nearest neighbors considered. |

The tuning parameter K can be tuned via cross-validation, see cv.tune function for the tuning procedure.

It returns the predicted class label of the new test point. If input is a matrix, it returns a vector which contains the predicted class labels of all the new test points.

Wei Sun, Xingye Qiao, and Guang Cheng

Fix, E. and Hodges, J. L., Jr. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties. Randolph Field, Texas, Project 21-49-004, Report No.4.

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