ipft (version 0.7.2)

ipfProbabilistic: This function implements a probabilistic algorithm

Description

This function implements a probabilistic algorithm

Usage

ipfProbabilistic(train_fgp, train_pos, group_cols = NULL, groups = NULL,
  k = 3, FUN = sum, delta = 1, ...)

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

group_cols

a character vector with the names of the columns to be used as the criteria to group the fingerprints. By default the groups will be created using all the columns available in the train_pos data frame.

groups

a numeric vector of length = nrow(train) containing the group index for the training vectors

k

the k parameter for the algorithm (number of similar neighbors)

FUN

function to compute the similarity measurement. Default is 'sum'

delta

parameter delta

...

additional parameters for provided function FUN

Value

An S3 object of class ipfModel, with the following properties: params -> a list with the parameters passed to the function data -> a list with the fingerprints probabilistic parameters (means and standard deviations) and its locations

Examples

# NOT RUN {
    groups <- ipfGroup(ipftrain, X, Y)
    model <- ipfProbabilistic(ipftrain[, 1:168], ipftrain[, 169:170], groups = groups)

# }
# NOT RUN {
    model <- ipfProbabilistic(ipftrain[, 1:168], ipftrain[, 169:170], k = 9, delta = 10)
# }
# NOT RUN {
# }