predict.matchFeat: Match New Feature Vectors To Existing Clusters

In matchFeat: One-to-One Feature Matching

Match New Feature Vectors To Existing Clusters

Description

predict method for class "matchFeat"

Usage

## S3 method for class 'matchFeat'
predict(object, newdata, unit = NULL, ...)

Arguments

object	an object of class "matchFeat".
newdata	new dataset of feature vectors
unit	unit labels for new data. Only necessary if newdata is a matrix
...	for compatibility with the generic predict method; argument not currently used.

Details

The function predict.matchFeat finds the best matching for new feature vectors relative to an existing set of cluster/class centers. If codeobject results from a call to match.gaussmix, the same function is used for prediction (with fixed mean vectors and covariance matrices). In other cases, the function match.template is used for prediction.

Value

A list of class matchFeat with fields

sigma

best matching as set of permutations ((m,n) matrix)

cluster

best matching as cluster indicators ((m,n)-matrix)

objective

minimum objective value

mu

mean vector for each class/label ((p,m) matrix)

V

covariance matrix for each class/label ((p,p,m) array if equal.variance is FALSE, (p,p) matrix otherwise

call

function call

See Also

print.matchFeat, summary.matchFeat

Examples

data(optdigits)
train.result <- match.bca(optdigits$x[1:900,], optdigits$unit[1:900])  
test.result <- predict(train.result, optdigits$x[901:1000,], optdigits$unit[901:1000])
test.result

matchFeat documentation built on Dec. 16, 2022, 5:11 p.m.