stress_asignment/prepare_data.py

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
# text in Western (Windows 1252)

import numpy as np
import h5py
import math
import keras.backend as K
import os.path


class Data:
    def __init__(self, input_type, allow_shuffle_vector_generation=False, save_generated_data=True, shuffle_all_inputs=True,
                 additional_letter_attributes=True, reverse_inputs=True, accent_classification=False):
        self._input_type = input_type
        self._save_generated_data = save_generated_data
        self._allow_shuffle_vector_generation = allow_shuffle_vector_generation
        self._shuffle_all_inputs = shuffle_all_inputs
        self._additional_letter_attributes = additional_letter_attributes
        self._reverse_inputs = reverse_inputs
        self._accent_classification = accent_classification

        self.x_train = None
        self.x_other_features_train = None
        self.y_train = None
        self.x_test = None
        self.x_other_features_test = None
        self.y_test = None
        self.x_validate = None
        self.x_other_features_validate = None
        self.y_validate = None

    def generate_data(self, train_inputs_name, test_inputs_name, validate_inputs_name, test_and_validation_size=0.1,
                      force_override=False, content_name='SlovarIJS_BESEDE_utf8.lex',
                      content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
                      inputs_location='../../internal_representations/inputs/', content_location='../../../data/'):
        content_path = '{}{}'.format(content_location, content_name)
        train_path = '{}{}.h5'.format(inputs_location, train_inputs_name)
        test_path = '{}{}.h5'.format(inputs_location, test_inputs_name)
        validate_path = '{}{}.h5'.format(inputs_location, validate_inputs_name)
        if not force_override and os.path.exists(train_path) and os.path.exists(test_path) and os.path.exists(validate_path):
            print('LOADING DATA...')
            self.x_train, self.x_other_features_train, self.y_train = self._load_inputs(train_path)
            self.x_test, self.x_other_features_test, self.y_test = self._load_inputs(test_path)
            self.x_validate, self.x_other_features_validate, self.y_validate = self._load_inputs(validate_path)
            print('LOAD SUCCESSFUL!')
        else:
            content_shuffle_vector_path = '{}{}.h5'.format(inputs_location, content_shuffle_vector)
            shuffle_vector_path = '{}{}'.format(inputs_location, shuffle_vector)

            # actual generation of inputs
            self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size)

            # save inputs
            if self._save_generated_data:
                self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
                self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
                self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)

    def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size):
        print('READING CONTENT...')
        content = self._read_content(content_location)
        print('CONTENT READ SUCCESSFULLY')
        print('CREATING DICTIONARY...')
        dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
        if self._input_type == 's' or self._input_type == 'sl':
            dictionary = self._create_syllables_dictionary(content, vowels)
        print('DICTIONARY CREATION SUCCESSFUL!')
        # test_and_validation_size = 0.1
        train_content, test_content, validate_content = self._split_content(content, test_and_validation_size, content_shuffle_vector_location)
        feature_dictionary = self._create_feature_dictionary()

        # Generate X and y
        print('GENERATING X AND y...')
        self.x_train, self.x_other_features_train, self.y_train = self._generate_x_and_y(dictionary, max_word, max_num_vowels, train_content, vowels,
                                                                                         accented_vowels,
                                                                                         feature_dictionary, shuffle_vector_location + '_train.h5')
        self.x_test, self.x_other_features_test, self.y_test = self._generate_x_and_y(dictionary, max_word, max_num_vowels, test_content, vowels,
                                                                                      accented_vowels,
                                                                                      feature_dictionary, shuffle_vector_location + '_test.h5')
        self.x_validate, self.x_other_features_validate, self.y_validate = self._generate_x_and_y(dictionary, max_word, max_num_vowels,
                                                                                                  validate_content, vowels,
                                                                                                  accented_vowels, feature_dictionary,
                                                                                                  shuffle_vector_location + '_validate.h5')
        print('GENERATION SUCCESSFUL!')
        # return X_train, X_other_features_train, y_train, X_test, X_other_features_test, y_test, X_validate, X_other_features_validate, y_validate

    # functions for creating X and y from content
    @staticmethod
    def _read_content(content_path):
        with open(content_path) as f:
            content = f.readlines()
        return [x.split('\t') for x in content]

    def _create_dict(self, content):
        # CREATE dictionary AND max_word
        accented_vowels = self._get_accented_vowels()
        unaccented_vowels = self._get_unaccented_vowels()
        vowels = []
        vowels.extend(accented_vowels)
        vowels.extend(unaccented_vowels)

        dictionary_input = ['']
        line = 0
        max_word = 0
        # ADD 'EMPTY' VOWEL
        max_num_vowels = 0
        for el in content:
            num_vowels = 0
            try:
                if len(el[3]) > max_word:
                    max_word = len(el[3])
                if len(el[0]) > max_word:
                    max_word = len(el[0])
                for i in range(len(el[3])):
                    if self._is_vowel(list(el[3]), i, vowels):
                        num_vowels += 1
                for c in list(el[0]):
                    if c not in dictionary_input:
                        dictionary_input.append(c)
                if num_vowels > max_num_vowels:
                    max_num_vowels = num_vowels
            except Exception:
                print(line - 1)
                print(el)
                break
            line += 1
        dictionary_input = sorted(dictionary_input)
        # max_num_vowels += 1
        return dictionary_input, max_word, max_num_vowels, vowels, accented_vowels

    # split content so that there is no overfitting
    def _split_content(self, content, test_and_validation_ratio, content_shuffle_vector_location):
        expanded_content = [el[1] if el[1] != '=' else el[0] for el in content]
        # print(len(content))
        unique_content = sorted(set(expanded_content))

        s = self._load_shuffle_vector(content_shuffle_vector_location, len(unique_content))

        test_num = math.floor(len(unique_content) * (test_and_validation_ratio * 2))
        validation_num = math.floor(test_num * 0.5)
        shuffled_unique_train_content = [unique_content[i] for i in range(len(s)) if s[i] >= test_num]
        shuffled_unique_train_content_set = set(shuffled_unique_train_content)

        shuffled_unique_test_content = [unique_content[i] for i in range(len(s)) if test_num > s[i] >= validation_num]
        shuffled_unique_test_content_set = set(shuffled_unique_test_content)

        shuffled_unique_validate_content = [unique_content[i] for i in range(len(s)) if s[i] < validation_num]
        shuffled_unique_validate_content_set = set(shuffled_unique_validate_content)

        train_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_train_content_set]
        test_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_test_content_set]
        validate_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_validate_content_set]
        return train_content, test_content, validate_content

    @staticmethod
    def _create_and_save_shuffle_vector(file_name, length):
        shuffle_vector = np.arange(length)
        np.random.shuffle(shuffle_vector)
        h5f = h5py.File(file_name, 'w')
        adict = dict(shuffle_vector=shuffle_vector)
        for k, v in adict.items():
            h5f.create_dataset(k, data=v)
        h5f.close()
        return shuffle_vector

    def _x_letter_input(self, content, dictionary, max_word, vowels):
        if self._additional_letter_attributes:
            x = np.zeros((len(content), max_word, len(dictionary) + 6), dtype=int)
            voiced_consonants = self._get_voiced_consonants()
            resonant_silent_consonants = self._get_resonant_silent_consonants()
            nonresonant_silent_consonants = self._get_nonresonant_silent_consonants()
            # print('HERE!!!')
        else:
            # print('HERE!!!')
            x = np.zeros((len(content), max_word, len(dictionary)), dtype=int)

        i = 0
        for el in content:
            word = el[0]
            if self._reverse_inputs:
                word = word[::-1]
            j = 0
            for c in list(word):
                index = 0
                for d in dictionary:
                    if c == d:
                        x[i][j][index] = 1
                        break
                    index += 1
                if self._additional_letter_attributes:
                    if self._is_vowel(word, j, vowels):
                        x[i][j][len(dictionary)] = 1
                    else:
                        x[i][j][len(dictionary) + 1] = 1
                        if c in voiced_consonants:
                            x[i][j][len(dictionary) + 2] = 1
                        else:
                            x[i][j][len(dictionary) + 3] = 1
                            if c in resonant_silent_consonants:
                                x[i][j][len(dictionary) + 4] = 1
                            elif c in nonresonant_silent_consonants:
                                x[i][j][len(dictionary) + 5] = 1
                j += 1
            i += 1
        return x

    def _x_syllable_input(self, content, dictionary, max_num_vowels, vowels):
        x = np.zeros((len(content), max_num_vowels), dtype=int)

        i = 0
        for el in content:
            j = 0
            syllables = self._create_syllables(el[0], vowels)
            if self._reverse_inputs:
                syllables = syllables[::-1]
            for syllable in syllables:
                index = dictionary.index(syllable)
                x[i][j] = index
                j += 1
            i += 1
        return x

    def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels):
        y = np.zeros((len(content), max_num_vowels))
        i = 0

        for el in content:
            word = el[3]
            if self._reverse_inputs:
                word = word[::-1]

            j = 0
            # word_accentuations = []
            num_vowels = 0
            for c in list(word):
                index = 0
                for d in accentuated_vowels:
                    if c == d:
                        if not self._accent_classification:
                            y[i][num_vowels] = 1
                        else:
                            y[i][num_vowels] = index
                        # word_accentuations.append(num_vowels)
                        break
                    index += 1
                if self._is_vowel(word, j, vowels):
                    num_vowels += 1
                j += 1
            i += 1
        return y

    # Generate each y as an array of 11 numbers (with possible values between 0 and 1)
    def _generate_x_and_y(self, dictionary, max_word, max_num_vowels, content, vowels, accentuated_vowels, feature_dictionary,
                          shuffle_vector_location):
        if self._input_type == 'l':
            x = self._x_letter_input(content, dictionary, max_word, vowels)
        elif self._input_type == 's' or self._input_type == 'sl':
            x = self._x_syllable_input(content, dictionary, max_num_vowels, vowels)
        else:
            raise ValueError('No input_type provided. It could be \'l\', \'s\' or \'sl\'.')
        y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels)

        print('CREATING OTHER FEATURES...')
        x_other_features = self._create_x_features(content, feature_dictionary)
        print('OTHER FEATURES CREATED!')

        if self._shuffle_all_inputs:
            print('SHUFFELING INPUTS...')
            x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location)
            print('INPUTS SHUFFELED!')
        return x, x_other_features, y

    def _create_syllables_dictionary(self, content, vowels):
        dictionary = []
        for el in content:
            syllables = self._create_syllables(el[0], vowels)
            for syllable in syllables:
                if syllable not in dictionary:
                    dictionary.append(syllable)
        dictionary.append('')
        return sorted(dictionary)

    def _create_syllables(self, word, vowels):
        word_list = list(word)
        consonants = []
        syllables = []
        for i in range(len(word_list)):
            if self._is_vowel(word_list, i, vowels):
                if syllables == []:
                    consonants.append(word_list[i])
                    syllables.append(''.join(consonants))
                else:
                    left_consonants, right_consonants = self._split_consonants(consonants)
                    syllables[-1] += ''.join(left_consonants)
                    right_consonants.append(word_list[i])
                    syllables.append(''.join(right_consonants))
                consonants = []
            else:
                consonants.append(word_list[i])
        if len(syllables) < 1:
            return word
        syllables[-1] += ''.join(consonants)

        return syllables

    def _is_vowel(self, word_list, position, vowels):
        if word_list[position] in vowels:
            return True
        if (word_list[position] == u'r' or word_list[position] == u'R') and (position - 1 < 0 or word_list[position - 1] not in vowels) and (
                            position + 1 >= len(word_list) or word_list[position + 1] not in vowels):
            return True
        return False

    def _split_consonants(self, consonants):
        voiced_consonants = self._get_voiced_consonants()
        resonant_silent_consonants = self._get_resonant_silent_consonants()
        unresonant_silent_consonants = self._get_nonresonant_silent_consonants()
        if len(consonants) == 0:
            return [''], ['']
        elif len(consonants) == 1:
            return [''], consonants
        else:
            split_options = []
            for i in range(len(consonants) - 1):
                if consonants[i] == '-' or consonants[i] == '_':
                    split_options.append([i, -1])
                elif consonants[i] == consonants[i + 1]:
                    split_options.append([i, 0])
                elif consonants[i] in voiced_consonants:
                    if consonants[i + 1] in resonant_silent_consonants or consonants[i + 1] in unresonant_silent_consonants:
                        split_options.append([i, 2])
                elif consonants[i] in resonant_silent_consonants:
                    if consonants[i + 1] in resonant_silent_consonants:
                        split_options.append([i, 1])
                    elif consonants[i + 1] in unresonant_silent_consonants:
                        split_options.append([i, 3])
                elif consonants[i] in unresonant_silent_consonants:
                    if consonants[i + 1] in resonant_silent_consonants:
                        split_options.append([i, 4])
                else:
                    print(consonants)
                    print('UNRECOGNIZED LETTERS!')
            if split_options == []:
                return [''], consonants
            else:
                split = min(split_options, key=lambda x: x[1])
                return consonants[:split[0] + 1], consonants[split[0] + 1:]

    def _create_x_features(self, content, feature_dictionary):
        content = content
        x_other_features = []
        for el in content:
            x_el_other_features = []
            converted_el = ''.join(self._convert_to_multext_east_v4(list(el[2]), feature_dictionary))
            for feature in feature_dictionary:
                if converted_el[0] == feature[1]:
                    x_el_other_features.append(1)
                    for i in range(2, len(feature)):
                        for j in range(len(feature[i])):
                            if i - 1 < len(converted_el) and feature[i][j] == converted_el[i - 1]:
                                x_el_other_features.append(1)
                            else:
                                x_el_other_features.append(0)
                else:
                    x_el_other_features.extend([0] * feature[0])
            x_other_features.append(x_el_other_features)
        return np.array(x_other_features)

    def _shuffle_inputs(self, x, x_other_features, y, shuffle_vector_location):
        s = self._load_shuffle_vector(shuffle_vector_location, x.shape[0])
        x = x[s]
        y = y[s]
        x_other_features = x_other_features[s]
        return x, x_other_features, y

    # functions for saving, loading and shuffling whole arrays to ram
    @staticmethod
    def _save_inputs(file_name, x, x_other_features, y):
        h5f = h5py.File(file_name, 'w')
        a_dict = dict(X=x, X_other_features=x_other_features, y=y)
        for k, v in a_dict.items():
            h5f.create_dataset(k, data=v)
        h5f.close()

    @staticmethod
    def _load_inputs(file_name):
        h5f = h5py.File(file_name, 'r')
        x = h5f['X'][:]
        y = h5f['y'][:]
        x_other_features = h5f['X_other_features'][:]
        h5f.close()
        return x, x_other_features, y

    def _load_shuffle_vector(self, file_path, length=0):
        if os.path.exists(file_path):
            h5f = h5py.File(file_path, 'r')
            shuffle_vector = h5f['shuffle_vector'][:]
            h5f.close()
        else:
            if self._allow_shuffle_vector_generation:
                shuffle_vector = self._create_and_save_shuffle_vector(file_path, length)
            else:
                raise ValueError('Shuffle vector on path: \'{}\' does not exist! Either generate new vector (with initializing new Data object with '
                                 'parameter allow_shuffle_vector_generation=True or paste one that is already generated!'.format(file_path))
        return shuffle_vector

    @staticmethod
    def _convert_to_multext_east_v4(old_features, feature_dictionary):
        new_features = ['-'] * 9
        new_features[:len(old_features)] = old_features
        if old_features[0] == 'A':
            if old_features[1] == 'f' or old_features[1] == 'o':
                new_features[1] = 'g'
            return new_features[:len(feature_dictionary[0]) - 1]
        if old_features[0] == 'C':
            return new_features[:len(feature_dictionary[1]) - 1]
        if old_features[0] == 'I':
            return new_features[:len(feature_dictionary[2]) - 1]
        if old_features[0] == 'M':
            new_features[2:6] = old_features[1:5]
            new_features[1] = old_features[5]
            if new_features[2] == 'm':
                new_features[2] = '-'
            return new_features[:len(feature_dictionary[3]) - 1]
        if old_features[0] == 'N':
            if len(old_features) >= 7:
                new_features[5] = old_features[7]
            return new_features[:len(feature_dictionary[4]) - 1]
        if old_features[0] == 'P':
            if new_features[8] == 'n':
                new_features[8] = 'b'
            return new_features[:len(feature_dictionary[5]) - 1]
        if old_features[0] == 'Q':
            return new_features[:len(feature_dictionary[6]) - 1]
        if old_features[0] == 'R':
            return new_features[:len(feature_dictionary[7]) - 1]
        if old_features[0] == 'S':
            if len(old_features) == 4:
                new_features[1] = old_features[3]
            else:
                new_features[1] = '-'
            return new_features[:len(feature_dictionary[8]) - 1]
        if old_features[0] == 'V':
            if old_features[1] == 'o' or old_features[1] == 'c':
                new_features[1] = 'm'
            new_features[3] = old_features[2]
            new_features[2] = '-'
            if old_features[2] == 'i':
                new_features[3] = 'r'
            if len(old_features) > 3 and old_features[3] == 'p':
                new_features[3] = 'r'
            elif len(old_features) > 3 and old_features[3] == 'f':
                new_features[3] = 'f'
            if len(old_features) >= 9:
                new_features[7] = old_features[8]
            else:
                new_features[7] = '-'
            return new_features[:len(feature_dictionary[9]) - 1]
        return ''

    # generator for inputs for tracking of data fitting
    def generator(self, data_type, batch_size, x=None, x_other_features_validate=None, y_validate=None, content_name='SlovarIJS_BESEDE_utf8.lex',
                  content_location='../../../data/'):
        content_path = '{}{}'.format(content_location, content_name)
        if data_type == 'train':
            return self._generator_instance(self.x_train, self.x_other_features_train, self.y_train, batch_size, content_path)
        elif data_type == 'test':
            return self._generator_instance(self.x_test, self.x_other_features_test, self.y_test, batch_size, content_path)
        elif data_type == 'validate':
            return self._generator_instance(self.x_validate, self.x_other_features_validate, self.y_validate, batch_size, content_path)
        else:
            return self._generator_instance(x, x_other_features_validate, y_validate, batch_size)

            # if self._input_type

    def _generator_instance(self, orig_x, orig_x_additional, orig_y, batch_size, content_path):
        if self._input_type == 'l':
            content = self._read_content(content_path)
            dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
            return self._letter_generator(orig_x, orig_x_additional, orig_y, batch_size, accented_vowels)
        elif self._input_type == 's':
            content = self._read_content(content_path)
            dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
            syllable_dictionary = self._create_syllables_dictionary(content, vowels)
            eye = np.eye(len(syllable_dictionary), dtype=int)
            return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, eye, accented_vowels)
        elif self._input_type == 'sl':
            content = self._read_content(content_path)
            dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
            syllable_dictionary = self._create_syllables_dictionary(content, vowels)
            max_syllable = self._get_max_syllable(syllable_dictionary)
            syllable_letters_translator = self._create_syllable_letters_translator(max_syllable, syllable_dictionary, dictionary, vowels)
            return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, syllable_letters_translator, accented_vowels)

    # generator for inputs for tracking of data fitting
    def _letter_generator(self, orig_x, orig_x_additional, orig_y, batch_size, accented_vowels):
        size = orig_x.shape[0]
        while 1:
            loc = 0
            if self._accent_classification:
                eye = np.eye(len(accented_vowels), dtype=int)
                eye_input_accent = np.eye(len(orig_y[0]), dtype=int)
                input_x_stack = []
                input_x_other_features_stack = []
                input_y_stack = []
                while loc < size:
                    while len(input_x_stack) < batch_size and loc < size:
                        accent_loc = 0
                        for accent in orig_y[loc]:
                            if accent > 0:
                                new_orig_x_additional = orig_x_additional[loc]
                                new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc]))
                                input_x_stack.append(orig_x[loc])
                                input_x_other_features_stack.append(new_orig_x_additional)
                                input_y_stack.append(eye[int(accent)])
                            accent_loc += 1
                        loc += 1
                    if len(input_x_stack) > batch_size:
                        yield ([np.array(input_x_stack[:batch_size]),
                                np.array(input_x_other_features_stack[:batch_size])], np.array(input_y_stack)[:batch_size])
                        input_x_stack = input_x_stack[batch_size:]
                        input_x_other_features_stack = input_x_other_features_stack[batch_size:]
                        input_y_stack = input_y_stack[batch_size:]
                    else:
                        # print('BBB')
                        # print(np.array(input_stack))
                        # yield (np.array(input_stack))
                        yield ([np.array(input_x_stack), np.array(input_x_other_features_stack)], np.array(input_y_stack))
                        input_x_stack = []
                        input_x_other_features_stack = []
                        input_y_stack = []
            else:
                while loc < size:
                    if loc + batch_size >= size:
                        yield ([orig_x[loc:size], orig_x_additional[loc:size]], orig_y[loc:size])
                    else:
                        yield ([orig_x[loc:loc + batch_size], orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
                    loc += batch_size

    # generator for inputs for tracking of data fitting
    def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels):
        size = orig_x.shape[0]
        while 1:
            loc = 0
            if self._accent_classification:
                eye = np.eye(len(accented_vowels), dtype=int)
                eye_input_accent = np.eye(len(orig_y[0]), dtype=int)
                input_x_stack = []
                input_x_other_features_stack = []
                input_y_stack = []
                while loc < size:
                    while len(input_x_stack) < batch_size and loc < size:
                        accent_loc = 0
                        for accent in orig_y[loc]:
                            if accent > 0:
                                new_orig_x_additional = orig_x_additional[loc]
                                new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc]))
                                input_x_stack.append(orig_x[loc])
                                input_x_other_features_stack.append(new_orig_x_additional)
                                input_y_stack.append(eye[int(accent)])
                            accent_loc += 1
                        loc += 1
                    if len(input_x_stack) > batch_size:
                        gen_orig_x = translator[np.array(input_x_stack[:batch_size])]
                        yield ([gen_orig_x, np.array(input_x_other_features_stack[:batch_size])], np.array(input_y_stack)[:batch_size])
                        input_x_stack = input_x_stack[batch_size:]
                        input_x_other_features_stack = input_x_other_features_stack[batch_size:]
                        input_y_stack = input_y_stack[batch_size:]
                    else:
                        gen_orig_x = translator[np.array(input_x_stack)]
                        yield ([gen_orig_x, np.array(input_x_other_features_stack)], np.array(input_y_stack))
                        input_x_stack = []
                        input_x_other_features_stack = []
                        input_y_stack = []
            else:
                while loc < size:
                    if loc + batch_size >= size:
                        gen_orig_x = translator[orig_x[loc:size]]
                        yield ([gen_orig_x, orig_x_additional[loc:size]], orig_y[loc:size])
                    else:
                        gen_orig_x = translator[orig_x[loc:loc + batch_size]]
                        yield ([gen_orig_x, orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
                    loc += batch_size

    def _get_max_syllable(self, syllable_dictionary):
        max_len = 0
        for el in syllable_dictionary:
            if len(el) > max_len:
                max_len = len(el)
        return max_len

    def _create_syllable_letters_translator(self, max_syllable, syllable_dictionary, dictionary, vowels, aditional_letter_attributes=True):
        if aditional_letter_attributes:
            voiced_consonants = self._get_voiced_consonants()
            resonant_silent_consonants = self._get_resonant_silent_consonants()
            nonresonant_silent_consonants = self._get_nonresonant_silent_consonants()

        syllable_letters_translator = []
        for syllable in syllable_dictionary:
            di_syllable = []
            for let in range(max_syllable):
                # di_let = []
                for a in dictionary:
                    if let < len(syllable) and a == list(syllable)[let]:
                        di_syllable.append(1)
                    else:
                        di_syllable.append(0)

                if aditional_letter_attributes:
                    if let >= len(syllable):
                        di_syllable.extend([0, 0, 0, 0, 0, 0])
                    elif self._is_vowel(list(syllable), let, vowels):
                        di_syllable.extend([1, 0, 0, 0, 0, 0])
                    else:
                        # X[i][j][len(dictionary) + 1] = 1
                        if list(syllable)[let] in voiced_consonants:
                            # X[i][j][len(dictionary) + 2] = 1
                            di_syllable.extend([0, 1, 1, 0, 0, 0])
                        else:
                            # X[i][j][len(dictionary) + 3] = 1
                            if list(syllable)[let] in resonant_silent_consonants:
                                # X[i][j][len(dictionary) + 4] = 1
                                di_syllable.extend([0, 1, 0, 1, 1, 0])
                            elif list(syllable)[let] in nonresonant_silent_consonants:
                                # X[i][j][len(dictionary) + 5] = 1
                                di_syllable.extend([0, 1, 0, 1, 0, 1])
                            else:
                                di_syllable.extend([0, 0, 0, 0, 0, 0])
                                # di_syllable.append(di_let)
            syllable_letters_translator.append(di_syllable)
        syllable_letters_translator = np.array(syllable_letters_translator, dtype=int)
        return syllable_letters_translator

    @staticmethod
    def _get_accented_vowels():
        return [u'à', u'á', u'ä', u'é', u'ë', u'ì', u'í', u'î', u'ó', u'ô', u'ö', u'ú', u'ü']

    @staticmethod
    def _get_unaccented_vowels():
        return [u'a', u'e', u'i', u'o', u'u']

    @staticmethod
    def _get_voiced_consonants():
        return ['m', 'n', 'v', 'l', 'r', 'j', 'y', 'w']

    @staticmethod
    def _get_resonant_silent_consonants():
        return ['b', 'd', 'z', 'ž', 'g']

    @staticmethod
    def _get_nonresonant_silent_consonants():
        return ['p', 't', 's', 'š', 'č', 'k', 'f', 'h', 'c']

    @staticmethod
    def _create_feature_dictionary():
        # old: http://nl.ijs.si/ME/Vault/V3/msd/html/
        # new: http://nl.ijs.si/ME/V4/msd/html/
        # changes: http://nl.ijs.si/jos/msd/html-en/msd.diffs.html
        return [[21,
                 'A',
                 ['g', 's'],
                 ['p', 'c', 's'],
                 ['m', 'f', 'n'],
                 ['s', 'd', 'p'],
                 ['n', 'g', 'd', 'a', 'l', 'i'],
                 ['-', 'n', 'y']],
                [3, 'C', ['c', 's']],
                [1, 'I'],
                [21,
                 'M',
                 ['l'],
                 ['-', 'c', 'o', 's'],
                 ['m', 'f', 'n'],
                 ['s', 'd', 'p'],
                 ['n', 'g', 'd', 'a', 'l', 'i'],
                 ['-', 'n', 'y']],
                [17,
                 'N',
                 ['c'],
                 ['m', 'f', 'n'],
                 ['s', 'd', 'p'],
                 ['n', 'g', 'd', 'a', 'l', 'i'],
                 ['-', 'n', 'y']],
                [40,
                 'P',
                 ['p', 's', 'd', 'r', 'x', 'g', 'q', 'i', 'z'],
                 ['-', '1', '2', '3'],
                 ['-', 'm', 'f', 'n'],
                 ['-', 's', 'd', 'p'],
                 ['-', 'n', 'g', 'd', 'a', 'l', 'i'],
                 ['-', 's', 'd', 'p'],
                 ['-', 'm', 'f', 'n'],
                 ['-', 'y', 'b']],
                [1, 'Q'],
                [5, 'R', ['g'], ['p', 'c', 's']],
                [7, 'S', ['-', 'g', 'd', 'a', 'l', 'i']],
                [24,
                 'V',
                 ['m'],
                 ['-'],
                 ['n', 'u', 'p', 'r', 'f', 'c'],
                 ['-', '1', '2', '3'],
                 ['-', 's', 'p', 'd'],
                 ['-', 'm', 'f', 'n'],
                 ['-', 'n', 'y']]
                ]

    # Decoders for inputs and outputs
    @staticmethod
    def decode_x(word_encoded, dictionary):
        word = ''
        for el in word_encoded:
            i = 0
            for num in el:
                if num == 1:
                    word += dictionary[i]
                    break
                i += 1
        return word

    @staticmethod
    def decode_x_other_features(feature_dictionary, x_other_features):
        final_word = []
        for word in x_other_features:
            final_word = []
            i = 0
            for z in range(len(feature_dictionary)):
                for j in range(1, len(feature_dictionary[z])):
                    if j == 1:
                        if word[i] == 1:
                            final_word.append(feature_dictionary[z][1])
                        i += 1
                    else:
                        for k in range(len(feature_dictionary[z][j])):
                            if word[i] == 1:
                                final_word.append(feature_dictionary[z][j][k])
                            i += 1
            print(u''.join(final_word))
        return u''.join(final_word)

    @staticmethod
    def decode_y(y):
        i = 0
        res = []
        for el in y:
            if el >= 0.5:
                res.append(i)
            i += 1
        return res



# def count_vowels(content, vowels):
#     num_all_vowels = 0
#     for el in content:
#         for m in range(len(el[0])):
#             if is_vowel(list(el[0]), m, vowels):
#                 num_all_vowels += 1
#     return num_all_vowels



# metric for calculation of correct results
# test with:
# print(mean_pred(y_validate[pos], predictions[pos]).eval())
# print(mean_pred(np.array([[ 0.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#                           [ 0.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]),
#                 np.array([[ 0.,  0.51,  0.,  0.51,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#                           [ 0.,  0.92,  0.,  0.51,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])).eval())
def actual_accuracy(y_true, y_pred):
    return K.mean(K.equal(K.mean(K.equal(K.round(y_true), K.round(y_pred)), axis=-1), 1.0))