clda.classify {clda} | R Documentation |
cLDA classify
Description
Classify the time series and obtain the distances between the time series and the centroids of each class.
Usage
clda.classify(model, Data)
Arguments
model |
An object returned by the function |
Data |
Matrix of time series on the rows. |
Value
A list containing the predicted labels of the time series
and a matrix of distances between the time series and the centroids after applying
the filters obtained by clda.model
.
Author(s)
Grover E. Castro Guzman
André Fujita
See Also
Examples
## Generating 200 time series of length 100 with label 1
time_series_signal_1 = sin(matrix(runif(200*100),nrow = 200,ncol = 100))
time_series_error_1 = matrix(rnorm(200*100),nrow = 200,ncol = 100)
time_series_w_label_1 = time_series_signal_1 + time_series_error_1
## Generating another 200 time series of length 100 with label 2
time_series_signal_2 = cos(matrix(runif(200*100),nrow = 200,ncol = 100))
time_series_error_2 = matrix(rnorm(200*100),nrow = 200,ncol = 100)
time_series_w_label_2 = time_series_signal_2 + time_series_error_2
## Join the time series data in one matrix
time_series_data = rbind(time_series_w_label_1,time_series_w_label_2)
label_time_series = c(rep(1,200),rep(2,200))
clda_model <- clda.model(time_series_data,label_time_series)
## Create a test set
## data with label 1
Data_test_label_1 = sin(matrix(runif(50*100),nrow = 50,ncol = 100))
## data with label 2
Data_test_label_2 = cos(matrix(runif(50*100),nrow = 50,ncol = 100))
## join data into a single matrix
Data_test = rbind(Data_test_label_1,Data_test_label_2)
## obtain the labels and distances of each time series
clda.classify(clda_model,Data_test)
[Package clda version 0.1 Index]