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137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
from parse_trees import load_trees_from_json
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from medialab import crear_base_datos, paso
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from random import shuffle, random
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# creating Markov Chain in text & trees
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def path(word, words_tree, words_path, trees):
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# Collects a list of trees to visit
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tree_index = {}
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itinerary = []
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current_step = word.capitalize() + ' '
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previous_steps = ''
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markov_decision_traces = [ ( word, 0, [word]) ]
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posibilities, dice, next_word = paso(word, words_tree, words_path)
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while len(itinerary) < 50 and next_word not in '.!?':
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if next_word in ',:;\)':
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current_step = current_step[:-1]
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current_step += ' '
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breath = random()
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if breath < 0.1:
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separator = '\n'
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else:
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separator = ' '
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current_step += (next_word + separator)
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markov_decision_traces.append(( next_word, dice, posibilities ))
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if next_word in words_tree:
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# Current word is a tree word, this step in the itinerary is 'complete'
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# Word is not yet in the index, add a tree for this word
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if next_word not in tree_index:
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# Add tree to index and remove from list of available trees
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tree_index[next_word] = trees.pop(0)
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# Retreive tree linked to this word from the index
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tree = tree_index[next_word]
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# Get a next word from the database
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word = next_word
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posibilities, dice, next_word = paso(word, words_tree, words_path)
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# Try to look ahead to the next word, if the next word
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# is interpunction, add it to the current step
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# but first remove trailing space
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if next_word in '.,:;!?\)':
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current_step = current_step[:-1] + next_word + ' '
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# Request a new next word to continue generation
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markov_decision_traces.append(( next_word, dice, posibilities ))
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# Test whether the next word marks the end of a sentence,
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# thus the end of the itinerary. Then don't touch it so the
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# while will break.
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if next_word not in '.!?':
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word = next_word
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posibilities, dice, next_word = paso(word, words_tree, words_path)
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# Add the current step, and the tree to the itinerary
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itinerary.append((
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current_step,
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previous_steps,
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tree,
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markov_decision_traces
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))
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previous_steps += current_step
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# Clear the current step
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current_step = ''
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markov_decision_traces = []
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else:
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word = next_word
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posibilities, dice, next_word = paso(word, words_tree, words_path)
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return itinerary
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# Genera un camino a partir de un texto y una palabra del texto
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def crear_camino(nombre_archivo, palabra_inicial):
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trees = load_trees_from_json()
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shuffle(trees)
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#print("Starting to read text")
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(palabras_arboles, palabras_camino) = crear_base_datos(nombre_archivo)
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#print("Amount of tree words: ", len(palabras_arboles))
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return path(palabra_inicial, palabras_arboles, palabras_camino, trees)
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if __name__ == '__main__':
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import os.path
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basepath = os.path.dirname(__file__)
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#EJECUCIÓN__________________________________________________________________
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print('Puedes elegir una novela para crear tu Paseo por árboles de Madrid.')
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print('Opción 1: La novela "La madre naturaleza" de la escritora feminista Emilia Pardo Bazán \
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fue publicada en 1887. Usa en esta obra una prosa poética y descriptiva, y en sus páginas se \
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siente el amor que profesa al paisaje gallego, con un conocimiento de la botánica y de \
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las costumbres rurales muy superior al de sus contemporáneos.')
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print('Opción 2: La novela "Miau" del escritor Benito Pérez Galdós fue publicada en 1888. \
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Enmarcada en el género realista, satiriza el Madrid burocrático de finales del siglo XIX \
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a partir de las vicisitudes vitales de su protagonista, Ramón Villaamil, \
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un competente exempleado del Ministerio de Hacienda, al que una serie de intrigas \
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han dejado cesante.')
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novel = input('Por favor, marca 1 o 2: ')
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first_word = 'un'
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if novel == '1':
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novel = os.path.join(basepath, '../data/emilia_prueba.txt')
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author = 'Emilia Pardo Bazán'
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title = 'La Madre Naturaleza'
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else:
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novel = os.path.join(basepath, '../data/prueba.txt')
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author = 'Benito Pérez Gáldos'
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title = 'Miau'
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# Create title/subtitle
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print('\nPaseo por los árboles de Madrid con', author, 'y', title, '\n')
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print('-------------------------------------------------------------------------------------------\n')
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# Create chapters
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path = crear_camino(novel, first_word)
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sentences = []
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for sentence, concatenated_steps, tree, traces in path:
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for word, dice, options in traces:
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print('Dice rolled - {} -'.format(dice))
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print('New word - {} - chosen from {}'.format(word, options))
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print('')
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sentences.append(sentence)
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print('Itinerary:\n{} \n'.format(''.join(sentences)))
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print('Tree linked to last word :', tree['properties']['NOMBRE_COMUN'], ' en ', tree['properties']['MINTDIRECCIONAUX'], '\n')
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print('\n')
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