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Revision 55 - (download) (as text) (annotate)
Wed Mar 2 14:48:07 2016 UTC (3 years, 1 month ago) by variani
File size: 2367 byte(s)
new Rd produced by roxygen2
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/support.lib.R, R/ChemosensorsClass.R, R/ChemosensorsClassMethods.R, R/package.R
\docType{package}
\name{loadUNIMANdata}
\alias{ConcUnitsNames}
\alias{chemosensors-package}
\alias{defaultConcUnits}
\alias{defaultConcUnitsInt}
\alias{defaultConcUnitsSorption}
\alias{defaultDataDistr}
\alias{defaultDataDriftNoiseModel}
\alias{defaultDataModel}
\alias{defaultDataPackage}
\alias{defaultDataSensorModel}
\alias{defaultDataSensorNoiseModel}
\alias{defaultDataSorptionModel}
\alias{defaultSet}
\alias{loadUNIMANdata}
\alias{subClasses}
\title{Function loadUNIMANdata.}
\usage{
loadUNIMANdata(dataset)

subClasses(class)

ConcUnitsNames()

defaultDataModel()

defaultConcUnitsInt()

defaultConcUnits()

defaultConcUnitsSorption()

defaultSet()

defaultDataPackage()

defaultDataSensorModel()

defaultDataDistr()

defaultDataSensorNoiseModel()

defaultDataSorptionModel()

defaultDataDriftNoiseModel()
}
\arguments{
\item{dataset}{Name of dataset to be loaded.}

\item{class}{Class name.}
}
\value{
Character vector of sub-classes.

Character vector of units names.
}
\description{
Function loadUNIMANdata.

Support function to get sub-classes.

Function to get available names for concentration units.

Function defaultDataModel.

Function defaultConcUnitsInt.

Function defaultConcUnits.

Function defaultConcUnitsSorption.

Function defaultSet

Function defaultDataPackage.

Function defaultDataSensorModel.

Function defaultDataDistr.

Function defaultDataSensorNoiseModel.

Function defaultDataSorptionModel.

Function defaultDataDriftNoiseModel.

A tool to set up synthetic experiments in machine olfaction.
}
\examples{

# concentration matrix of 3 gas classes: A, C and AC
conc <- matrix(0, 60, 3)
conc[1:10, 1] <- 0.01 #  A0.01
conc[11:20, 3] <- 0.1 # C0.1
conc[21:30, 1] <- 0.05 #  A0.05
conc[31:40, 3] <- 1 # C1
conc[41:50, 1] <- 0.01 # A0.01C0.1
conc[41:50, 3] <- 0.1 # A0.01C0.1
conc[51:60, 1] <- 0.05 # A0.05C1
conc[51:60, 3] <- 1 # A0.05C1

conc <- conc[sample(1:nrow(conc)), ]

# sensor array of 20 sensors with parametrized noise parameters
sa <- SensorArray(nsensors=20, csd=0.1, ssd=0.1, dsd=0.1)

# get information about the array
print(sa)
plot(sa)

# generate the data
sdata <- predict(sa, conc)

# plot the data
plot(sa, "prediction", conc=conc, sdata=sdata, leg="top")
}


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