Help for package ipft

Type:

Package

Title:

Indoor Positioning Fingerprinting Toolset

Depends:

R (≥ 2.10)

Version:

0.7.2

Maintainer:

Emilio Sansano <esansano@uji.es>

Description:

Algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data sets, estimation of positions,comparison of the performance of different models, and graphical visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or probabilistic fingerprinting are implemented in this package to perform analysis and estimations over RSSI data sets.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

6.0.1

LinkingTo:

Rcpp

Imports:

Rcpp, methods, stats, apcluster, cluster, dplyr, ggplot2

NeedsCompilation:

yes

Packaged:

2018-01-03 15:51:29 UTC; emilio

Author:

Emilio Sansano [aut, cre], Raúl Montoliu [ctb]

Repository:

CRAN

Date/Publication:

2018-01-04 09:36:52 UTC

Creates clusters using the specified method

Description

Creates clusters using the the specified method and assigns a cluster id to each cluster

Usage

ipfCluster(data, method = "k-means", k = NULL, grid = NULL, ...)

Arguments

data

a data frame

method

the method to use to clusterize the data. Implemented methods are: 'k-means' for k-means algorithm. Requires parameter k. 'grid' for clustering based on grid partition. Requires parameter grid. 'AP' for affinity propagation algorithm.

k

parameter k

grid

a vector with the grid size for the 'grid' method

...

additional parameters for k-means, apcluster and apclusterK for 'k-means' method additional parameters see: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html for 'apcluster'AP' method additional parameters see: https://cran.r-project.org/web/packages/apcluster/index.html

Value

A list with: clusters -> a numeric vector with the ids of the clusters centers -> a data frame with the centers of the clusters

Examples


    clusters <- ipfCluster(head(ipftrain, 20)[, 169:170], k = 4)

    clusters <- ipfCluster(head(ipftrain[, grep('^wap', names(ipftrain))], 20),
    method = 'AP')$clusters

Distance function

Description

This function computes the distance from every observation in the test set to every observation in the train test

Usage

ipfDistance(train, test, method = "euclidean", subset = NULL, norm = 2,
  sd = 10, epsilon = 1e-30, alpha = 20, threshold = 20)

Arguments

train

a vector, matrix or data frame containing a set of training examples

test

a vector, matrix or data frame containing a set of test examples

method

The method to be used to calculate the distance. Implemented methods are: 'euclidean', 'manhattan', 'norm', 'LGD' and 'PLGD'

subset

columns to use to compute the distance.

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

Value

This function returns a matrix with dimensions: nrow(test) x nrow(train), containing the distances from test observations to train observations

Examples

    dist <- ipfDistance(ipftrain[,1:168], ipftest[,1:168])

    dist <- ipfDistance(ipftrain, ipftest, subset = c('X', 'Y'), method = 'manhattan')

Estimates the location of the test observations

Description

Estimates the location of the test observations

Usage

ipfEstimate(ipfmodel, test_fgp, test_pos = NULL)

Arguments

ipfmodel

an ipfModel

test_fgp

a matrix or a data frame containing the fingerprints of the test set

test_pos

a matrix or a data frame containing the position of the test set fingerprints

Value

An S3 object of class ipfEstimation, with the following properties: location -> a matrix with the predicted locations errors -> a numeric vector with the errors neighbors -> a matrix with k columns and nrow(test) rows, with the k most similar training observation for each test observation weights -> a matrix with k columns and nrow(test) rows, with the weights

Examples


    model <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170])
    estimation <- ipfEstimate(model, ipftest[, 1:168], ipftest[, 169:170])

## Not run: 
    model <- ipfProbabilistic(ipftrain[, 1:168], ipftrain[, 169:170], k = 9, delta = 10)
    estimation <- ipfEstimate(model, ipftest[, 1:170], ipftest[, 169:170])

## End(Not run)

Estimates the positions of the emitter beacons

Description

Estimates the positions of the emitter beacons

Usage

ipfEstimateBeaconPositions(fingerprints, positions, method = "wcentroid",
  rssirange = c(-100, 0), norssi = NA)

Arguments

fingerprints

a data frame or a matrix with the RSSI fingerprints

positions

a data frame or a matrix with the positions of the fingerprints

method

method to use to estimate the position of the access points: 'centroid', 'wcentroid' or 'wip'

rssirange

a numeric vector with the range of the RSSI data

norssi

value used in dataRSSI when a beacon is not detected

Examples


    wapp <- ipfEstimateBeaconPositions(ipftrain[, 1:168], ipftrain[, 169:170], method = 'wcentroid')

Creates groups based on the specified parameters

Description

This function groups the data based on the specified variables and assigns an id to each group

Usage

ipfGroup(data, ...)

Arguments

data

A data frame

...

Variables to group by. All variables (columns) will be used if no parameter is provided.

Value

A numeric vector with the ids of the groups, in the same order as they appear in the data provided.

Examples


    group <- ipfGroup(mtcars, cyl)

    group <- ipfGroup(mtcars, gear, carb)

    group <- ipfGroup(ipftrain, X, Y)

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 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 and locations

Examples


    model <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170], k = 9, method = 'manhattan')

Plots the cumulative distribution function of the estimated error

Description

Plots the cumulative distribution function of the estimated error

Usage

ipfPlotEcdf(estimation, xlab = "error",
  ylab = "cumulative density of error",
  title = "Empirical cumulative density function")

Arguments

estimation

an ipfEstimation

xlab

x-axis label

ylab

y-axis label

title

plot title

Examples


    model <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170])
    estimation <- ipfEstimate(model, ipftest[, 1:168], ipftest[, 169:170])
    ipfPlotEcdf(estimation)

Plots the estimated locations

Description

Plots the estimated locations

Usage

ipfPlotEstimation(model, estimation, testpos = NULL, observations = c(1),
  reverseAxis = FALSE, showneighbors = FALSE, showLabels = FALSE,
  xlab = NULL, ylab = NULL, title = "")

Arguments

model

an ipfModel

estimation

an ipfEstimation

testpos

position of the test observations

observations

a numeric vector with the indices of estimations to plot

reverseAxis

swaps axis

showneighbors

plot the k selected neighbors

showLabels

shows labels

xlab

x-axis label

ylab

y-axis label

title

plot title

Examples


    model      <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170])
    estimation <- ipfEstimate(model, ipftest[, 1:168], ipftest[, 169:170])
    ipfPlotEstimation(model, estimation, ipftest[, 169:170],
                      observations = seq(7,10), showneighbors = TRUE,
                      reverseAxis = TRUE)

Plots the spatial location of the observations

Description

Plots the spatial location of the observations

Usage

ipfPlotLocation(positions, plabel = FALSE, reverseAxis = FALSE,
  xlab = NULL, ylab = NULL, title = "", pgrid = FALSE)

Arguments

positions

a data frame or matrix with the positions

plabel

if TRUE, adds labels to groups / observations

reverseAxis

swaps axis

xlab

x-axis label

ylab

y-axis label

title

plot title

pgrid

plot grid (boolean)

Examples


    ipfPlotLocation(ipftrain[, 169:170])

    ipfPlotLocation(ipftrain[, 169:170], plabel = TRUE, reverseAxis = TRUE,
                    title = 'Position of training set observations')

Plots the probability density function of the estimated error

Description

Plots the probability density function of the estimated error

Usage

ipfPlotPdf(estimation, xlab = "error", ylab = "density",
  title = "Probability density function")

Arguments

estimation

an ipfEstimation

xlab

x-axis label

ylab

y-axis label

title

plot title

Examples


    model <- ipfKnn(ipftrain[, 1:168], ipftrain[, 169:170])
    estimation <- ipfEstimate(model, ipftest[, 1:168], ipftest[, 169:170])
    ipfPlotPdf(estimation)

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


    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)

## End(Not run)

Estimates the position of the observations from its fingerprints and the access point location usins a logarithmic path loss model

Description

Estimates the position of the observations from its fingerprints and the access point location usins a logarithmic path loss model

Usage

ipfProximity(bpos, rssirange = c(-100, 0), norssi = NA, alpha = 5,
  wapPow1 = -30)

Arguments

bpos

a matrix or a data frame containing the position of the beacons, in the same order as they appear in fingerprints

rssirange

range of the RSSI data

norssi

value used to represent a not detected AP

alpha

path loss exponent

wapPow1

detected RSSI at one meter range

Value

An S3 object of class ipfEstimation, with the following properties: location -> a matrix with the predicted locations errors -> a numeric vector with the errors, if loctest has been provided neighbors -> NULL weights -> NULL

Examples


    ipfEst <- ipfProximity(ipftrain[1:10, 1:168], ipfpwap, ipftrain[1:10, 169:170], alpha = 4)

Estimates the inherent difficulty of the radio map

Description

Estimates the inherent difficulty of the radio map

Usage

ipfRMID(fingerprints, positions, rangeRSSI = c(-100, 0), noRSSI = NA,
  gridSize = 5)

Arguments

fingerprints

a matrix or a data frame containing the RSSI data (fingerprints) of the observations

positions

a matrix or a data frame containing the positions of the fingerprints

rangeRSSI

range of the RSSI data

noRSSI

value used to represent a not detected AP

gridSize

size of the grid to consider

Value

a numeric value representing the RMID value (Radio Map Inherent Difficulty)

Examples

## Not run: 
    rmid <- ipfRMID(ipftrain[, 1:168], ipftrain[, 169:170], noRSSI = NA)

## End(Not run)

Transform function

Description

Transforms the RSSI (Received Signal Strength Intensity) data to positive or exponential values

Usage

ipfTransform(data, outRange = c(0, 1), outNoRSSI = 0, inRange = NULL,
  inNoRSSI = 0, trans = "scale", base = exp(1), alpha = 24)

Arguments

data

a vector, matrix or data frame containing the RSSI vectors

outRange

the desired range for the output RSSI data.

outNoRSSI

value desired in the RSSI output data to represent a not detected AP.

inRange

a vector containing the range of the RSSI value from the initial data

inNoRSSI

value used in the RSSI data to represent a not detected AP.

trans

the transformation to perform, 'scale' or 'exponential'

base

base for the 'exponential' transformation

alpha

alpha parameter for the 'exponential' transformation

Value

This function returns a vector, matrix or data frame containing the transformed data

Examples

    trainRSSI <- ipftrain[,1:168]
    ipfTransform(trainRSSI, inRange = c(-100, 0), outRange = c(1, 100),
                 inNoRSSI = NA, outNoRSSI = 0)

Indoor localization data set with the positions of the wireless access points present in the ipftrain and ipftest data sets. Unknown locations are stored as NAs. Data from the positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Description

Indoor localization data set with the positions of the wireless access points present in the ipftrain and ipftest data sets. Unknown locations are stored as NAs. Data from the positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Usage

ipfpwap

Format

A data frame with columns:

X: X coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From -61 to 50
Y: Y coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From 8.5 to 117.5

Source

UJI - Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071, Castellón, Spain. http://www.init.uji.es/

Examples

## Not run: 
 ipfpwap

## End(Not run)

Indoor localization test data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Description

Indoor localization test data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Usage

ipftest

Format

A data frame with columns:

wap1, wap2, wap3, wap4, wap5, wap6, wap7, wap8, wap9, wap10, wap11, wap12, wap13, wap14, wap15, wap16, wap17, wap18, wap19, wap20, wap21, wap22, wap23, wap24, wap25, wap26, wap27, wap28, wap29, wap30, wap31, wap32, wap33, wap34, wap35, wap36, wap37, wap38, wap39, wap40, wap41, wap42, wap43, wap44, wap45, wap46, wap47, wap48, wap49, wap50, wap51, wap52, wap53, wap54, wap55, wap56, wap57, wap58, wap59, wap60, wap61, wap62, wap63, wap64, wap65, wap66, wap67, wap68, wap69, wap70, wap71, wap72, wap73, wap74, wap75, wap76, wap77, wap78, wap79, wap80, wap81, wap82, wap83, wap84, wap85, wap86, wap87, wap88, wap89, wap90, wap91, wap92, wap93, wap94, wap95, wap96, wap97, wap98, wap99, wap100, wap101, wap102, wap103, wap104, wap105, wap106, wap107, wap108, wap109, wap110, wap111, wap112, wap113, wap114, wap115, wap116, wap117, wap118, wap119, wap120, wap121, wap122, wap123, wap124, wap125, wap126, wap127, wap128, wap129, wap130, wap131, wap132, wap133, wap134, wap135, wap136, wap137, wap138, wap139, wap140, wap141, wap142, wap143, wap144, wap145, wap146, wap147, wap148, wap149, wap150, wap151, wap152, wap153, wap154, wap155, wap156, wap157, wap158, wap159, wap160, wap161, wap162, wap163, wap164, wap165, wap166, wap167, wap168: Intensity value for WAPs. Negative integer values from -99 to 0. NA is used if WAP was not detected.
X: X coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From -0.60 to 4.39
Y: Y coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From 0.00 to 30.42
FLOOR: All the records of this dataset have been captured in the same floor. Therefore, the floor attribute is 0 to all the records.
BUILDINGID: All the records of this dataset have been captured in the same building. Therefore, the building attribute is 0 to all the records.
SPACEID: Internal ID number to identify the position at where the capture was taken.
USERID: User identifier. Students created the train dataset (UserID from 1 to 8), and professors the test one (UserID is 0 in this case).
PHONEID: All the records have 0 in this attribute. This attribute is not used in this dataset.
TIMESTAMP: UNIX Time when the capture was taken.

Source

UJI - Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071, Castellón, Spain. http://www.init.uji.es/

Examples

## Not run: 
 ipftest

## End(Not run)

Indoor localization training data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Description

Indoor localization training data set to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).

Usage

ipftrain

Format

A data frame with columns:

wap1, wap2, wap3, wap4, wap5, wap6, wap7, wap8, wap9, wap10, wap11, wap12, wap13, wap14, wap15, wap16, wap17, wap18, wap19, wap20, wap21, wap22, wap23, wap24, wap25, wap26, wap27, wap28, wap29, wap30, wap31, wap32, wap33, wap34, wap35, wap36, wap37, wap38, wap39, wap40, wap41, wap42, wap43, wap44, wap45, wap46, wap47, wap48, wap49, wap50, wap51, wap52, wap53, wap54, wap55, wap56, wap57, wap58, wap59, wap60, wap61, wap62, wap63, wap64, wap65, wap66, wap67, wap68, wap69, wap70, wap71, wap72, wap73, wap74, wap75, wap76, wap77, wap78, wap79, wap80, wap81, wap82, wap83, wap84, wap85, wap86, wap87, wap88, wap89, wap90, wap91, wap92, wap93, wap94, wap95, wap96, wap97, wap98, wap99, wap100, wap101, wap102, wap103, wap104, wap105, wap106, wap107, wap108, wap109, wap110, wap111, wap112, wap113, wap114, wap115, wap116, wap117, wap118, wap119, wap120, wap121, wap122, wap123, wap124, wap125, wap126, wap127, wap128, wap129, wap130, wap131, wap132, wap133, wap134, wap135, wap136, wap137, wap138, wap139, wap140, wap141, wap142, wap143, wap144, wap145, wap146, wap147, wap148, wap149, wap150, wap151, wap152, wap153, wap154, wap155, wap156, wap157, wap158, wap159, wap160, wap161, wap162, wap163, wap164, wap165, wap166, wap167, wap168: Intensity value for WAPs. Negative integer values from -99 to 0. NA is used if WAP was not detected.
X: X coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From -0.60 to 4.39
Y: Y coordinate in meters relative to the origin of a predefined Cartesian coordinate system. From 0.00 to 30.42
FLOOR: All the records of this dataset have been captured in the same floor. Therefore, the floor attribute is 0 to all the records.
BUILDINGID: All the records of this dataset have been captured in the same building. Therefore, the building attribute is 0 to all the records.
SPACEID: Internal ID number to identify the position at where the capture was taken.
USERID: User identifier. Students created the train dataset (UserID from 1 to 8), and professors the test one (UserID is 0 in this case).
PHONEID: All the records have 0 in this attribute. This attribute is not used in this dataset.
TIMESTAMP: UNIX Time when the capture was taken.

Source

UJI - Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071, Castellón, Spain. http://www.init.uji.es/

Examples

## Not run: 
 ipftrain

## End(Not run)