clda.classify: cLDA classify

In clda: Convolution-Based Linear Discriminant Analysis

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 clda.model.
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

clda.model

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)

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