ipft (version 0.2.2)

ipfKnn: This function implements the k-nearest neighbors algorithm

Description

This function implements the k-nearest neighbors algorithm

Usage

ipfKnn(train, test, k = 3, method = "euclidean", norm = 2, sd = 5,
  epsilon = 0.001, alpha = 1, threshold = 20, FUN = NULL, ...)

Arguments

train

a data frame containing the RSSI vectors of the training set

test

a data frame containing the RSSI vectors of the test set

k

the k parameter for knn algorithm (number of nearest neighbors)

method

the method to compute the distance between the RSSI vectors: 'euclidean', 'manhattan', 'norm', 'LGD' or 'PLGD'

norm

parameter for the 'norm' method

sd

parameter for 'LGD' and 'PLGD' methods

epsilon

parameter for 'LGD' and 'PLGD' methods

alpha

parameter for 'PLGD' method

threshold

parameter for 'PLGD' method

FUN

an alternative function provided to compute the distance. This function must return a matrix of dimensions: nrow(test) x nrow(train), containing the distances from test observations to train observations

...

additional parameters for provided function FUN

Value

An S4 class object of type ipfModel, with the following slots: neighbors -> a matrix with k columns and nrow(test) rows, with the k nearest neighbors for each test observation weights -> a matrix with k columns and nrow(test) rows, with the weight for each neighbour distances -> a matrix with k columns and nrow(test) rows, with the distances between test and each neighbour k -> k parameter groups -> the group index for each training observation

Examples

    model <- ipfKnn(ipftrain[, 1:168], ipftest[, 1:168])

    model <- ipfKnn(ipftrain[, 1:168], ipftest[, 1:168], k = 9, method = 'manhattan')