Test Functional DD-Classifier
Trains functional DD-classifier on the learning sequence of the data and tests it on the testing sequence.
ddalphaf.test(learn, learnlabels, test, testlabels, disc.type = c("LS", "comp"), ...)learn |
list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively |
learnlabels |
list of output labels of the functional observations |
test |
the testing sequence. Has the same format as |
disc.type |
type of the used discretization scheme. "LS" for |
testlabels |
list of output labels of the functinal observations |
... |
additional parameters passed to |
error |
the part of incorrectly classified data |
correct |
the number of correctly classified objects |
incorrect |
the number of incorrectly classified objects |
total |
the number of classified objects |
ignored |
the number of ignored objects (outside the convex hull of the learning data) |
n |
the number of objects in the testing sequence |
time |
training time |
ddalphaf.train to train the functional DDα-classifier,
ddalphaf.classify for classification using functonal DDα-classifier,
ddalphaf.getErrorRateCV and ddalphaf.getErrorRatePart to get error rate of the functional DD-classifier on particular data.
# load the fdata
df = dataf.growth()
samp = c(35:70)
ddalphaf.test(learn = df$dataf[-samp], learnlabels = df$labels[-samp],
test = df$dataf[samp], testlabels = df$labels[samp],
adc.args = list(instance = "avr",
numFcn = 2,
numDer = 2))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.