def create_netcdf_target_classes():
execute_create_netcdf_target_classes_start_time = datetime.datetime.now()
letter = []
diacritics = []
searchCounter = 0
targetClass = []
beforeWhileLoop = datetime.datetime.now()
for eachItem in range(0, len(selected_letters_in_this_loop)):
yourLabel = selected_letters_in_this_loop[eachItem][7]
OneHotTargetClassNotFound = True
decomposed_letter = WordLetterProcessingHelperMethod.decompose_diac_char_into_char_and_diacritics(
yourLabel)
if len(decomposed_letter) == 2 and decomposed_letter[1] == u'ّ':
decomposed_letter[1] = u'َّ'
letter.append(decomposed_letter[0])
diacritics.append(decomposed_letter[1])
yourLabel = WordLetterProcessingHelperMethod.attach_diacritics_to_chars(
letter, diacritics)[0]
while OneHotTargetClassNotFound:
try:
if listOfDiacritizedCharacter[searchCounter][1] == yourLabel:
OneHotTargetClassNotFound = False
targetClass.append(
listOfDiacritizedCharacter[searchCounter][0])
searchCounter = 0
else:
searchCounter += 1
except:
x = 1
afterWhileLoop = datetime.datetime.now()
print "While Loop takes : ", afterWhileLoop - beforeWhileLoop
global purified_target_class
purified_target_class = []
purified_target_class = np.array(targetClass)
execute_create_netcdf_target_class_end_time = datetime.datetime.now()
print "createNetCDFTargetClasses takes : ", \
execute_create_netcdf_target_class_end_time - execute_create_netcdf_target_classes_start_time
for file_name, sentence_number in zip(result, list_of_sentence_numbers):
selected_sentence = DBHelperMethod.get_sentence_by(sentence_number)
rnn_output = ExcelHelperMethod.read_rnn_op_csv_file(path + file_name)
neurons_with_highest_probability = RNNOPProcessingHelperMethod.get_neurons_numbers_with_highest_output_value(
rnn_output)
list_of_available_diacritics = DBHelperMethod.get_all_diacritics()
RNN_Predicted_diacritics = RNNOPProcessingHelperMethod.\
deduce_from_rnn_op_predicted_chars(list_of_available_diacritics, neurons_with_highest_probability)
IP_Undiacritized_Chars = DBHelperMethod.get_un_diacritized_chars_by(
sentence_number, type)
RNN_Predicted_chars = WordLetterProcessingHelperMethod.attach_diacritics_to_chars(
IP_Undiacritized_Chars, RNN_Predicted_diacritics)
RNN_Predicted_Chars_Count = WordLetterProcessingHelperMethod.get_chars_count_for_each_word_in_this(
selected_sentence)
RNN_Predicted_Chars_And_Its_Location = WordLetterProcessingHelperMethod.get_location_of_each_char(
RNN_Predicted_chars, RNN_Predicted_Chars_Count)
# Post Processing
RNN_Predicted_Chars_After_Sukun = SukunCorrection.sukun_correction(
deepcopy(RNN_Predicted_Chars_And_Its_Location))
RNN_Predicted_Chars_After_Fatha = FathaCorrection.fatha_correction(
deepcopy(RNN_Predicted_Chars_After_Sukun))
RNN_Predicted_Chars_After_Dictionary = DictionaryCorrection.get_diac_version_with_smallest_dist(
deepcopy(RNN_Predicted_Chars_After_Fatha), sentence_number)
# Expected OP