clda.model: cLDA Model

In clda: Convolution-Based Linear Discriminant Analysis

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.

Author(s)

Grover E. Castro Guzman

André Fujita

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))
## obtain the model with the given data
clda.model(time_series_data,label_time_series)

clda documentation built on July 2, 2020, 2:11 a.m.