Description Usage Arguments Details Value Author(s) References Examples
classif.com
trains a covariance operator based
functional data classifier that makes use of concentration inequalities.
predict.classif.com
uses the previously trained classifier
to classify new observations.
1 2 3 4 5 | classif.com(datGrp, dat)
## S3 method for class 'classif.com'
predict(object, dat, SOFT = FALSE, LOADING = FALSE,
...)
|
datGrp |
A vector of group labels. |
dat |
(n X m) data matrix of n samples of m long vectors. |
object |
A concentration-of-measure classifier object
of class inheriting from |
SOFT |
Boolean flag, which if TRUE, returns soft classification for each observation. |
LOADING |
Boolean flag, which if TRUE, prints a loading bar. |
... |
additional arguments affecting the predictions produced. |
These functions are used to train a functional data classifier and to predict the labels for a new set of observations. This method classifies based on the distances between each groups' sample covariance operator. A simplified version of Talagrand's concentration inequality is used to achieve this.
If the flag SOFT is set to TRUE, then soft classification occurs. In this case, given k different labels, a k-long probability vector is returned for each observation whose entries correspond to the probabilities that the observed function belongs to each specific label.
classif.com
returns a functional data classifier
object. predict.classif.com
returns a vector
of n labels ( or an array of n probability vectors if
SOFT=TRUE )
Adam B Kashlak kashlak@ualberta.ca
Kashlak, Adam B, John AD Aston, and Richard Nickl (2016). "Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities", (in review)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
library(fds);
# Setup training data
dat1 = rbind(
t(aa$y[,1:100]), t(ao$y[,1:100]), t(dcl$y[,1:100]),
t(iy$y[,1:100]), t(sh$y[,1:100])
);
# Setup testing data
dat2 = rbind(
t(aa$y[,101:400]), t(ao$y[,101:400]), t(dcl$y[,101:400]),
t(iy$y[,101:400]), t(sh$y[,101:400])
);
datgrp = gl(5,100);
clCom = classif.com( datgrp, dat1 );
grp = predict( clCom, dat2, LOADING=TRUE );
acc = c(
sum( grp[1:300]==1 ), sum( grp[301:600]==2 ), sum( grp[601:900]==3 ),
sum( grp[901:1200]==4 ), sum( grp[1201:1500]==5 )
)/300;
print(rbind(gl(5,1),signif(acc,3)));
## End(Not run)
|
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