ipft (version 0.6)

ipfKnn: Implements the k-nearest neighbors algorithm

Description

Implements the k-nearest neighbors algorithm

Usage

ipfKnn(train_fgp, train_pos, k = 3, method = "euclidean",
  weights = "distance", norm = 2, sd = 5, epsilon = 0.001, alpha = 1,
  threshold = 20, FUN = NULL, ...)

Arguments

train_fgp

a data frame containing the fingerprint vectors of the training set

train_pos

a data frame containing the positions of the training set observations

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'

weights

the algorithm to compute the weights: 'distance' or 'uniform'

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. The two first parameters taken by the function must be train and test

...

additional parameters for provided function FUN

Value

An S4 class object of type ipfModel, with the following slots: params -> a list with the parameters passed to the function data -> a list with the fingerprints and locations

Examples

# NOT RUN {
    model <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170], k = 9, method = 'manhattan')

# }