Hykilpikonna
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
"""CSC110 Fall 2021 Assignment 3, Part 1: Text Generation, Uniformly Random Model (SOLUTIONS)
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Instructions (READ THIS FIRST!)
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===============================
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Implement each of the functions in this file. As usual, do not change any function headers
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or preconditions. You do NOT need to add doctests.
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You may create some additional helper functions to help break up your code into smaller parts.
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Copyright and Usage Information
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===============================
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This file is provided solely for the personal and private use of students
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taking CSC110 at the University of Toronto St. George campus. All forms of
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distribution of this code, whether as given or with any changes, are
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expressly prohibited. For more information on copyright for CSC110 materials,
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please consult our Course Syllabus.
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This file is Copyright (c) 2021 Mario Badr and Tom Fairgrieve.
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"""
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import random
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def generate_text_uniform(model: dict[str, int], n: int) -> str:
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"""Return a string of n randomly-generated words chosen from the given model.
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Each word in the returned string is separated by a single space.
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Preconditions:
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- n >= 0
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- model != {}
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"""
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# Unpack model into two lists whose indexes correspond to one another
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words = []
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word_frequencies = []
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for word in model:
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words.append(word)
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word_frequencies.append(model[word])
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new_words = random.choices(words, weights=word_frequencies, k=n)
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return str.join(' ', new_words)
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def create_model_uniform(text: str) -> dict[str, int]:
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"""Return a model of the words in text.
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The model is a mapping of words to the number of times the word occurs in text. A "word"
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contains no spaces.
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This function should return model that is valid input to generate_text_uniform.
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Preconditions:
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- text != ''
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IMPLEMENTATION NOTE: Use the str.split method to get a list of words.
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"""
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words = text.split()
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return {unique: words.count(unique) for unique in set(words)}
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def run_example(filename: str, num_words: int) -> str:
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"""Run an example to demonstrate random text generation with num_words words based on the data
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in filename.
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To call this function:
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- Make sure you see that the 'data' folder is in the same directory as this file
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- Use an argument for filename like: 'data/texts/sample_text_raw.txt'
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- Try out the other plaintext files in 'data/texts', too
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"""
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with open(filename) as f:
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file_text = f.read()
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stripped_text = str.strip(file_text) # str.strip removes leading/trailing whitespace
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model_from_file = create_model_uniform(stripped_text)
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generated_words = generate_text_uniform(model_from_file, num_words)
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return generated_words
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if __name__ == '__main__':
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import python_ta
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import python_ta.contracts
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python_ta.contracts.DEBUG_CONTRACTS = False
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python_ta.contracts.check_all_contracts()
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# When you are ready to check your work with python_ta, uncomment the following lines.
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# (Delete the "#" and space before each line.)
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# IMPORTANT: keep this code indented inside the "if __name__ == '__main__'" block
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python_ta.check_all(config={
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'allowed-io': ['run_example'],
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'extra-imports': ['python_ta.contracts', 'random'],
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'max-line-length': 100,
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'disable': ['R1705', 'C0200']
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})
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