OneNN: 1NN classification for a pair of train/test time series datasets.

Description

Given a specific distance measure, this function provides the 1NN classification values and the associated error for a specific train/test pair of time series databases.

Usage

OneNN(train, trainc, test, testc, distance, ...)

Arguments

train

Time series database saved in a numeric matrix, a list, an mts object, a zoo object or xts object.

trainc

Numerical vector which indicates the class of each of the series in the training set.

test

Time series database saved in a numeric matrix, a list, an mts object, a zoo object or xts object.

testc

Numerical vector which indicates the class of each of the series in the testing set.

distance

Distance measure to be used. It must be one of: "euclidean", "manhattan", "minkowski", "infnorm", "ccor", "sts", "dtw", "keogh_lb", "edr",

...

Additional parameters required by the chosen distance measure.

Value

classesNumerical vector providing the predicted class values for the series in the test set.
errorError obtained in the 1NN classification process.

Details

This function is useful to evaluate the performance of different distance measures in the task of classification of time series.

Examples

Run this code

# The example.database2 synthetic database is loaded
data(example.database2)

# Create train/test by dividing the dataset 70%-30%
set.seed(100)
trainindex <- sample(1:100, 70, replace=FALSE)
train <- example.database2[[1]][trainindex, ]
test <- example.database2[[1]][-trainindex, ]
trainclass <- example.database2[[2]][trainindex]
testclass <- example.database2[[2]][-trainindex]

# Apply the 1NN classifier for different distance measures

OneNN(train, trainclass, test, testclass, "euclidean")
OneNN(train, trainclass, test, testclass, "cid")
OneNN(train, trainclass, test, testclass, "pdc")