knn
Purpose
K-nearest neighbor classifier.
Synopsis
pclass = knn(xref,xtest,k,options); %make prediction
without model
pclass = knn(xref,xtest,options); %use default k
model = knn(xref,k,options) %create model
pclass = knn(xref,xtest,k,options) %apply model to xtest
pclass = knn(xtest,model,options)
Description
Performs kNN classification where the "k" closest
samples in a reference set vote on the class of an unknown sample based on
distance to the reference samples. If no majority is found, the unknown is
assigned the class of the closest sample (see input options for other
no-majority behaviors).
INPUTS:
xref = a DataSet object of reference data,
xtest = a DataSet object or Double containing the unknown test
data.
OPTIONAL INPUTS:
model = an optional standard KNN model
structure which can be passed instead of xref (note order of inputs:
(xtest,model) ) to apply model to test data.
k
= number of components {default = rank of X-block}.
OUTPUTS:
pclass = an optional number of neighbors to use in vote for
class of unknown {default = 3}. If k=1, only the nearest sample will define the
class of the unknown.
model = if no test data (xtest) is supplied, a standard model
structure is returned which can be used with test data in the future to perform
a prediction.
Options
options =
structure array with the following fields :
display: [
'off' | {'on'} ] governs level of
display to screen.
preprocessing: {
[ ] } A cell containing a preprocessing structure or keyword (see PREPROCESS).
Use {'autoscale'} to perform autoscaling on reference and test data.
nomajority: [
'error' | {'closest'} | class_number ] Behavior when no majority is found in
the votes. 'closest' = return class of closest sample. 'error' = give error
message. class_number (i.e. any numerical value) = return this value for
no-majority votes (e.g. use 0 to return zero for all no-majority votes)
See Also
analysis, cluster, plsda, simca
