An SVM classifier class.
SVM is trained using either libsvm, or using a PyML SMO implementation
based on libsvm
|
|
|
|
|
|
|
|
|
modelDispatcher(self,
data,
svID,
alpha,
b,
**args) |
source code
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cv(classifier,
data,
numFolds=5,
**args)
perform k-fold cross validation |
source code
|
|
|
|
|
|
|
|
|
nCV(classifier,
data,
**args)
runs CV n times, returning a 'ResultsList' object. |
source code
|
|
|
project(self,
data)
project a test dataset to the training data features. |
source code
|
|
|
stratifiedCV(classifier,
data,
numFolds=5,
**args)
perform k-fold stratified cross-validation; in each fold the number of
patterns from each class is proportional to the relative fraction of the
class in the dataset |
source code
|
|
|
test(classifier,
data,
**args)
test a classifier on a given dataset |
source code
|
|
|
|
|
trainTest(classifierTemplate,
data,
trainingPatterns,
testingPatterns,
**args)
Train a classifier on the list of training patterns, and test it
on the test patterns |
source code
|
|
|
|