clda (version 0.1)

clda.model: cLDA Model

Description

Obtains a set of filters for labeled time series data so that the between-class distances are maximized, and the within-class distances are minimized.

Usage

clda.model(Data, Labels)

Arguments

Data

Matrix of time series on the rows.

Labels

Label of each time series.

Value

A list containing the filters and their respective importance (g and eig_val), the class means (Means), the average of the class means (Mean), and the labels of each class mean (classes). The filters are the columns of the matrix g.

Examples

# NOT RUN {
## 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))
## obtain the model with the given data
clda.model(time_series_data,label_time_series)
# }