OneNN {TSdist} | R Documentation |
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 |
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 |
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: |
... |
Additional parameters required by the chosen distance measure. |
Details
This function is useful to evaluate the performance of different distance measures in the task of classification of time series.
Value
classes |
Numerical vector providing the predicted class values for the series in the test set. |
error |
Error obtained in the 1NN classification process. |
Author(s)
Usue Mori, Alexander Mendiburu, Jose A. Lozano.
See Also
To calculate the distance matrices of time series databases the TSDatabaseDistances
is used.
Examples
# 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, "pdc")