alpha = 1.0
Cs = 2.0**numpy.arange(-10, 14, 2, dtype=numpy.float)
gammas = 2.0**numpy.arange(-10, 4, 2, dtype=numpy.float)
epsilons = numpy.array([2**-2])
paramDict = {}
paramDict["setC"] = Cs
paramDict["setGamma"] = gammas
paramDict["setEpsilon"] = epsilons
sampleMethod = Sampling.crossValidation
numProcesses = multiprocessing.cpu_count()
j = 0
trainX, trainY, testX, testY = ModelSelectUtils.loadRegressDataset(dataDir, datasetName, j)
learner = LibSVM(kernel='gaussian', type="Epsilon_SVR", processes=numProcesses)
for sampleSize in sampleSizes:
print("Sample size " +str(sampleSize))
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
#errors = learner.parallelPenaltyGrid(validX, validY, testX, testY, paramDict, computeTestError)
#errors = numpy.squeeze(errors)
errors = numpy.zeros((Cs.shape[0], gammas.shape[0]))
norms = numpy.zeros((Cs.shape[0], gammas.shape[0]))
