def task_cv_single(t, modeLearn: ModeLearn, f:ActivFunct, theta:dict, errorFunct:ActivFunct, miniBatchDim = None):
(trSet, vlSet) = t
nn = NeuralNetwork(trSet, f, theta)
nn.learn(modeLearn, errorFunct, miniBatchDim)
vecErr = np.array([nn.getError(vlSet, i, 1/len(vlSet), errorFunct) for i in range(nn.hyp['OutputUnits'])])
return norm(vecErr,2)
def double_cross_validation(workers: int,
testFolder: int,
nFolder: int,
dataSet,
f: ActivFunct,
learnRate: list,
momRate: list,
regRate: list,
ValMax: list,
HiddenUnits: list,
OutputUnits: list,
MaxEpochs: list,
Tolerance: list,
startTime,
errorFunct=None,
modeLearn: ModeLearn = ModeLearn.BATCH,
miniBatchDim=None,
errorVlFunct=None,
hiddenF: ActivFunct = None):
if testFolder <= 1:
raise ValueError("Wrong value of num. folders inserted")
cp = dataSet.copy()
#Rimescolo il data set.
rnd.shuffle(cp)
#Costruisco la sottolista dei dati divisibile esattamente per testFolder.
h = len(cp) - len(cp) % testFolder
dataSetExact = cp[0:h]
#Creo la lista dei folders.
folderDim = int(len(dataSetExact) / testFolder)
folders = [
cp[i * folderDim:(i + 1) * folderDim] for i in range(testFolder)
]
#Inserisco gli elementi di avanzo.
for i in range(len(dataSet) - h):
folders[i].append(cp[i + h])
errList = list()
for i in range(len(folders)):
foldersCopy = folders.copy()
testSet = foldersCopy[i]
del (foldersCopy[i])
vlSet = list()
for j in range(len(foldersCopy)):
vlSet += foldersCopy[j]
e = cross_validation(workers,
nFolder,
modeLearn,
vlSet,
f,
errorFunct,
learnRate,
momRate,
regRate,
ValMax,
HiddenUnits,
OutputUnits,
MaxEpochs,
Tolerance,
startTime,
miniBatchDim=miniBatchDim,
errorVlFunct=errorVlFunct,
hiddenF=hiddenF)
theta = getBestResult(e)[0]
nn = NeuralNetwork(vlSet, f, new_hyp=theta, Hiddenf=hiddenF)
(_, testErr, _, _) = nn.learn(modeLearn, errorFunct, miniBatchDim,
testSet)
errList.append(testErr[-1])
return 1 / testFolder * sum(errList)
def k_fold_CV_single(k: int,
dataSet,
f: ActivFunct,
theta,
errorFunct=None,
modeLearn: ModeLearn = ModeLearn.BATCH,
miniBatchDim=None,
errorVlFunct=None,
hiddenF: ActivFunct = None):
if k <= 0:
raise ValueError("Wrong value of num. folders inserted")
cp = dataSet.copy()
#Rimescolo il data set.
rnd.shuffle(cp)
#Costruisco la sottolista dei dati divisibile esattamente per k.
h = len(cp) - len(cp) % k
dataSetExact = cp[0:h]
#Creo la lista dei folders.
folderDim = int(len(dataSetExact) / k)
folders = [cp[i * folderDim:(i + 1) * folderDim] for i in range(k)]
#Inserisco gli elementi di avanzo.
for i in range(len(cp) - h):
folders[i].append(cp[i + h])
errore = list()
#per stampare l'errore sul traininig set e sul validation set
trErrorPlot = list()
vlErrorPlot = list()
for i in range(len(folders)):
lcopy = folders.copy()
del (lcopy[i])
vlSet = folders[i]
trSet = list()
for j in range(len(lcopy)):
trSet += lcopy[j]
nn = NeuralNetwork(trSet, f, theta, Hiddenf=hiddenF)
(trErr, vlErr, trAcc, vlAcc) = nn.learn(modeLearn,
errorFunct,
miniBatchDim,
vlSet,
errorVlFunct=errorVlFunct)
trErrorPlot.append(trErr)
vlErrorPlot.append(vlErr)
errore.append(nn.getError(vlSet, 1 / len(vlSet), errorVlFunct))
err = sum(errore) / k
#controllo che tutti gli errorPlot abbiano la stessa lunghezza
maxLen = len(trErrorPlot[0])
for i in range(1, len(trErrorPlot)):
if len(trErrorPlot[i]) > maxLen:
maxLen = len(trErrorPlot[i])
for i in range(len(trErrorPlot)):
if len(trErrorPlot[i]) < maxLen:
for j in range(maxLen - len(trErrorPlot[i])):
trErrorPlot[i].append(trErrorPlot[i][-1])
vlErrorPlot[i].append(vlErrorPlot[i][-1])
trErrorArray = np.array(trErrorPlot[0])
vlErrorArray = np.array(vlErrorPlot[0])
for i in range(1, len(trErrorPlot)):
trErrorArray = trErrorArray + np.array(trErrorPlot[i])
trErrorArray = trErrorArray / k
for i in range(1, len(vlErrorPlot)):
vlErrorArray = vlErrorArray + np.array(vlErrorPlot[i])
vlErrorArray = vlErrorArray / k
return (err, trErrorArray, vlErrorArray)