def createLangDistribution(line, distribution=None):
if distribution is None:
flag = True
distribution = LangDistro()
else:
flag = False
for c in line:
if standardchar(c):
distribution.appenddist(c.lower())
else:
pass
if flag:
distribution.sort()
distribution.makePercentage()
return distribution
print("Loading Model")
model = keras.models.load_model('GRU.h5')
model.summary()
classifications = model.predict(testing_padded)
classifications = classifications.tolist()
sumtests = LangDistro() # ile testow z danego jezyka bylo
sumfails = LangDistro() # ile testow nasz algorytm zawalil
# Testujemy!
print("Runing tests...")
counter = 0
for x in range(len(testing_labels)):
# Zwiekszamy ilosc testow o 1
sumtests.appenddist(LANGS[testing_labels[x]])
# Przygotowywujemy licznik do testow oblanych z akrutalnego jezyka
if LANGS[testing_labels[x]] not in sumfails:
sumfails[LANGS[testing_labels[x]]] = 0
result = classifications[counter].index(max(classifications[counter]))
# Jezeli zly jezyk zwiekszamy liczne porazek
if LANGS[result] != LANGS[testing_labels[x]]:
sumfails.appenddist(LANGS[testing_labels[x]])
#if LANGS[testing_labels[x]] == "eng":
# print("Example:")
# print(x)
# print(x_test[counter])
# for i in range(len(LANGS)):
# print(LANGS[i] + ": " + str(classifications[counter][i]))