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)
for s, C in enumerate(Cs):
for trainInds, testInds in idx:
validX = trainX[trainInds, :]
validY = trainY[trainInds]
learner.setC(C)
learner.learnModel(validX, validY)
tempMeanNorms[s] += learner.weightNorm()
tempMeanSVs[s] += learner.model.support_.shape[0]
learner.learnModel(trainX, trainY)
testMeanNorms[s] = learner.weightNorm()
testMeanSVs[s] += learner.model.support_.shape[0]
tempMeanNorms /= float(folds)
meanNorms += tempMeanNorms
tempMeanSVs /= float(folds)
meanSVs+= tempMeanSVs
numRealisations = float(numRealisations)
meanCvGrid /= numRealisations
#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]))
for i, C in enumerate(Cs):
for j, gamma in enumerate(gammas):
learner.setEpsilon(epsilons[0])
learner.setC(C)
learner.setGamma(gamma)
learner.learnModel(validX, validY)
predY = learner.predict(testX)
errors[i, j] = Evaluator.meanAbsError(predY, testY)
norms[i, j] = learner.weightNorm()
for i in range(gammas.shape[0]):
plt.figure(i)
plt.plot(numpy.log(Cs), errors[:, i], label=str(sampleSize))
plt.legend(loc="upper left")
plt.xlabel("C")
plt.ylabel("Error")
plt.figure(i+gammas.shape[0])
plt.plot(norms[:, i], errors[:, i], label=str(sampleSize))
plt.legend(loc="upper left")
plt.xlabel("Norm")
plt.ylabel("Error")
