current_sentence_counter = 0
counter = 0
start_range = 0
end_range = 0
for sentence_number in list_of_sentence_numbers:
#sentence_number = 4514
print("we start in sentence # ", sentence_number)
selected_sentence = DBHelperMethod.get_sentence_by(sentence_number)
undiac_words = get_undiac_words_for_selected_sentence(
list_of_all_words_and_sent_num, sentence_number)
dic_words_for_selected_sent = get_dic_words_for_selected_sentence(
dictionary, undiac_words)
rnn_input = DBHelperMethod.get_un_diacritized_chars_by(
sentence_number, 'testing')
num_of_chars_in_selected_sent = len(rnn_input)
end_range = num_of_chars_in_selected_sent + start_range
nn_op_letters = dp.concatenate_char_and_diacritization(
ip_letters[start_range:end_range:1],
nn_labels[start_range:end_range:1])
expected_letters = expected_op_letters[start_range:end_range:1]
location = loc[start_range:end_range:1]
# Post Processing
RNN_Predicted_Chars_And_Its_Location = dp.create_letter_location_object(
if len(list_of_sentence_numbers) != len(result):
raise Exception('Mismatch In Number Of Sentences')
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)