trainX = trainX[trainInds,:] trainY = trainY[trainInds] idx = sampleMethod(folds, trainX.shape[0]) #Now try penalisation resultsList = learner.parallelPen(trainX, trainY, idx, paramDict, Cvs) bestLearner, trainErrors, currentPenalties = resultsList[0] meanPenalties[k] += currentPenalties meanTrainError += trainErrors predY = bestLearner.predict(testX) meanErrors[k] += bestLearner.getMetricMethod()(testY, predY) #Compute ideal penalties and error on training data meanIdealPenalities[k] += learner.parallelPenaltyGrid(trainX, trainY, testX, testY, paramDict) for i in range(len(paramDict["setGamma"])): allError = 0 learner.setGamma(paramDict["setGamma"][i]) for trainInds, testInds in idx: validX = trainX[trainInds, :] validY = trainY[trainInds] learner.learnModel(validX, validY) predY = learner.predict(trainX) allError += learner.getMetricMethod()(predY, trainY) meanAllErrors[i] += allError/float(len(idx)) k+= 1 numRealisations = float(numRealisations)