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)
meanErrors /= numRealisations
meanPenalties /= numRealisations
meanIdealPenalities /= numRealisations
print(meanErrors)
plt.plot(sampleSizes, meanPenalties*numpy.sqrt(sampleSizes), label="Penalty")
plt.plot(sampleSizes, meanIdealPenalities*numpy.sqrt(sampleSizes), label="Ideal penalty")
plt.xlabel("Sample sizes")
