ddalpha (version 1.3.16)

ddalphaf.getErrorRatePart: Test Functional DD-Classifier

Description

Performs a benchmark procedure by partitioning the given data. On each of times steps size observations are removed from the data, the functional DD-classifier is trained on these data and tested on the removed observations.

Usage

ddalphaf.getErrorRatePart(dataf, labels, size = 0.3, times = 10, 
                          disc.type = c("LS", "comp"),  ...)

Value

errors

the part of incorrectly classified data (mean)

errors_sd

the standard deviation of errors

errors_vec

vector of errors

time

the mean training time

time_sd

the standard deviation of training time

Arguments

dataf

list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively

labels

list of output labels of the functional observations

size

the excluded sequences size. Either an integer between \(1\) and \(n\), or a fraction of data between \(0\) and \(1\).

times

the number of times the classifier is trained.

disc.type

type of the used discretization scheme. "LS" for ddalphaf.train, "comp" for for compclassf.train

...

additional parameters passed to ddalphaf.train

See Also

ddalphaf.train to train the functional DD\(\alpha\)-classifier, ddalphaf.classify for classification using functional DD\(\alpha\)-classifier, ddalphaf.test to test the functional DD-classifier on particular learning and testing data, ddalphaf.getErrorRateCV to get error rate of the functional DD-classifier on particular data.

Examples

# load the fdata
df = dataf.growth()

stat <- ddalphaf.getErrorRatePart(dataf = df$dataf, labels = df$labels, 
                          size = 0.3, times = 5,
                          adc.args = list(instance = "avr", 
                                         numFcn = 2, 
                                         numDer = 2))

cat("Classification error rate: ", stat$errors, ".\n", sep = "")