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)