print(trainX.shape)
#logging.debug("Training set size: " + str(trainX.shape))
methodInd = 0
idx = sampleMethod(folds, trainX.shape[0])
bestLearner, cvGrid = learner.parallelModelSelect(trainX, trainY, idx, paramDict)
predY = bestLearner.predict(testX)
meanCvGrid[methodInd, :] += cvGrid
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
Cvs = [-5, (folds-1)*alpha, beta[j, sampleSizeInd, :]]
#Now try penalisation
methodInd = 1
resultsList = learner.parallelPen(trainX, trainY, idx, paramDict, Cvs)
bestLearner, trainErrors, currentPenalties = resultsList[1]
meanCvGrid[methodInd, :] += trainErrors + currentPenalties
meanPenalties += currentPenalties
meanTrainError += trainErrors
predY = bestLearner.predict(testX)
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
#Corrected penalisation
methodInd = 2
bestLearner, trainErrors, currentPenalties = resultsList[0]
meanCvGrid[methodInd, :] += trainErrors + currentPenalties
meanCorrectedPenalties += currentPenalties
predY = bestLearner.predict(testX)
meanErrors[methodInd] += bestLearner.getMetricMethod()(testY, predY)
