logging.debug("Dataset " + datasetName)
meanErrors = numpy.zeros(sampleSizes.shape[0])
meanPenalties = numpy.zeros(sampleSizes.shape[0])
meanIdealPenalities = numpy.zeros(sampleSizes.shape[0])
k = 0
for sampleSize in sampleSizes:
logging.debug("Sample size " + str(sampleSize))
errors = numpy.zeros(numRealisations)
sampleMethod = Sampling.crossValidation
#Setting maxDepth = 50 and minSplit = 5 doesn't effect results
numProcesses = multiprocessing.cpu_count()
learner = DecisionTreeLearner(criterion="mse", maxDepth=100, minSplit=1, pruneType="CART", processes=numProcesses)
learner.setChunkSize(3)
paramDict = {}
paramDict["setGamma"] = numpy.array([31], dtype=numpy.int)
numParams = paramDict["setGamma"].shape[0]
alpha = 1.0
folds = 4
numRealisations = 10
Cvs = numpy.array([folds-1])*alpha
meanAllErrors = numpy.zeros(numParams)
meanTrainError = numpy.zeros(numParams)
