dbcsp-class | R Documentation |
An object of class dbcsp. 'dbcsp' stands for Distance-Based Common Spatial Patterns. The object includes the Common Spatial Patterns filter obtained with the input lists and using the distance method indicated.
If the lists of matrices X1
or X2
contain NA values, these are automatically interpolated by a linear interpolation
using na.approx
function. These new interpolated matrices are saved in the X1 and X2 slots of the object.
The supported distances for type
are these ones:
Included in TSdist
: infnorm, ccor, sts, lb.keogh, edr, erp, lcss, fourier, tquest, dissim, acf, pacf, ar.lpc.ceps, ar.mah,
ar.mah.statistic, ar.mah.pvalue, ar.pic, cdm, cid, cor, cort, int.per, per, mindist.sax, ncd, pred, spec.glk, spec.isd,
spec.llr, pdc, frechet, tam.
Included in parallelDist
: bhjattacharyya, bray, canberra, chord, divergence, dtw, euclidean, fJaccard, geodesic, hellinger,
kullback, mahalanobis, manhattan, maximum, minkowski, podani, soergel, wave, whittaker.
It is possible to use a custom distance. The name of the custom distance function is passed as character to the type
parameter.
In order to use the parallelDist
custom distance option, the custom function must be defined as
explained in "Details: User-defined distance functions
" part of parallelDist
documentation.
See Examples section below.
The additional parameters for the selected distance (see TSdist
, parallelDist
) can be passed
as parameters when creating the object, which will be saved in more
slot. See Examples section below.
The output is a list containing this information (object@out
):
vectors
The projection vectors obtained after applying CSP.
eig
The eigenvalues obtained after applying CSP.
proy
The variance values of the projected signals obtained after applying CSP.
And if training=TRUE
the following values are also saved:
acc
The mean accuracy value obtained for training data applying cross validation.
used_folds
List of the folds used in the cross validation.
folds_acc
Accuracy values for each of the folds of the cross validation.
model
The trained LDA classifier.
selected_q
The number of vectors used when training.
X1
list of matrices for data class 1.
X2
list of matrices for data class 2.
q
integer value indicating the number of vectors used in the projection, by default q=15
.
labels
vector of two strings indicating labels names, by default names of variables X1 and X2.
type
string which sets the type of distance to be considered, by default type='EUCL'
. See details section.
w
weight for the distances mixture D_mixture = w*D_euclidean + (1-w)*D_type, by default w=0.5
.
mixture
logical value indicating whether to use distances mixture or not (EUCL + other), by default mixture=FALSE
.
training
logical value indicating whether to perform the training or not.
fold
integer value, by default fold=10
. It controls the number of partitions when training.
If fold==1
a train/test split is performed, with p=0.2 for test indices.
seed
numeric value, by default seed=NULL
. Set a seed to ensure reproducible results.
eig.tol
numeric value, by default eig.tol=1e-06
, tolerance to convert distance matrix to be definite positive.
verbose
logical
more
list, additional parameters to be passed to the distance methods. See details section.
out
list containing the output.
dbcsp
, print
, summary
, train
, selectQ
, predict
, plot
, boxplot
# To create an instance of a class dbcsp given data from 2 classes x <- AR.data$come[1:20] y <- AR.data$five[1:20] mydbcsp <- new("dbcsp", X1 = x, X2 = y) # CUSTOM DISTANCE x <- AR.data$come[1:10] y <- AR.data$five[1:10] fn <- function(x, y, eps=1) mean(1 - cos(x - y))*eps mydbcsp <- new("dbcsp", X1 = x, X2 = y, type="fn", eps=0.9)
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