Test Functional DD-Classifier
Performs a cross-validation procedure over the given data.
On each step every numchunks observation is removed from the data, the functional DD-classifier is trained on these data and tested on the removed observations.
ddalphaf.getErrorRateCV (dataf, labels, numchunks = 10, disc.type = c("LS", "comp"), ...)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 |
numchunks |
number of subsets of testing data. Equals to the number of times the classifier is trained. |
disc.type |
type of the used discretization scheme. "LS" for |
... |
additional parameters passed to |
errors |
the part of incorrectly classified data |
time |
the mean training time |
time_sd |
the standard deviation of training time |
ddalphaf.train to train the functional DDα-classifier,
ddalphaf.classify for classification using functional DDα-classifier,
ddalphaf.test to test the functional DD-classifier on particular learning and testing data,
ddalphaf.getErrorRatePart to perform a benchmark study of the functional DD-classifier on particular data.
# load the fdata
df = dataf.growth()
stat <- ddalphaf.getErrorRateCV(dataf = df$dataf, labels = df$labels,
numchunks = 5,
adc.args = list(instance = "avr",
numFcn = 2,
numDer = 2))
cat("Classification error rate: ", stat$errors, ".\n", sep = "")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.