bestLearner, trainErrors, currentPenalties = resultsList[2]
meanCvGrid[methodInd, :] += trainErrors + currentPenalties[gammaInd, epsilonInd, :]
meanBetaPenalties += currentPenalties[gammaInd, epsilonInd, :]
predY = bestLearner.predict(testX)
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
#Compute ideal penalties and error on training data
meanIdealPenalities += learner.parallelPenaltyGrid(trainX, trainY, testX, testY, paramDict)
#Compute true error grid
methodInd = 4
cvGrid = learner.parallelSplitGrid(trainX, trainY, testX, testY, paramDict)
meanCvGrid[methodInd, :] += cvGrid
bestLearner = learner.getBestLearner(cvGrid, paramDict, trainX, trainY)
predY = bestLearner.predict(testX)
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
#Compute true error grid using only training data
methodInd = 5
cvGrid = learner.parallelSplitGrid(trainX, trainY, trainX, trainY, paramDict)
meanCvGrid[methodInd, :] += cvGrid
bestLearner = learner.getBestLearner(cvGrid, paramDict, trainX, trainY)
predY = bestLearner.predict(testX)
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
#Compute norms
tempMeanNorms = numpy.zeros(numParams)
tempMeanSVs = numpy.zeros(numParams)
