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assignments/a3/a3_part2.py

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+"""CSC110 Fall 2021 Assignment 3, Part 2: Text Generation, One-Word Context Model (SOLUTIONS)
+
+Instructions (READ THIS FIRST!)
+===============================
+Implement each of the functions in this file. As usual, do not change any function headers
+or preconditions. You do NOT need to add doctests.
+
+You may create some additional helper functions to help break up your code into smaller parts.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC110 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC110 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2021 Mario Badr and Tom Fairgrieve.
+"""
+import random
+
+
+###############################################################################
+# Question 2
+###############################################################################
+def update_follow_list(model: dict[str, list[str]], word: str, follow_word: str) -> None:
+    """Add follow_word and, when applicable, word to model.
+
+    If word is not already present in model, add it to the model with the follow list
+    [follow_word]. Otherwise, add follow_word to the follow list of word.
+    """
+
+
+def create_model_owc(text: str) -> tuple[int, dict[str, list[str]]]:
+    """Return a tuple of the number of words in text and one-word context model of the given text,
+    as described in the handout.
+
+    Your implementation MUST use the update_follow_list helper function. We recommend completing
+    that function first, as it's simpler and will get you thinking about how to use it here.
+
+    Preconditions:
+        - text != ''
+        - len(str.split(text)) > 1
+    """
+
+
+###############################################################################
+# Question 3
+###############################################################################
+def choose_from_keys(transitions: dict[str, list[str]]) -> str:
+    """Return a random key from transitions.
+
+    Preconditions:
+        - transitions != {}
+    """
+
+
+def choose_from_follow_list(key: str, transitions: dict[str, list[str]]) -> str:
+    """Return a random word from the follow list in transitions that is associated with key.
+
+    Also remove one occurrence of the random word from the follow list. If the follow list is then
+    empty, remove the key-value pair from transitions.
+
+    Preconditions:
+        - transitions != {}
+        - key in transitions
+        - transitions[key] != []
+    """
+
+
+def generate_text_owc(count: int, transitions: dict[str, list[str]]) -> str:
+    """Return a string containing (count - 1) randomly generated words based on the data in
+    transitions, which maps words to a list of words that follow it.
+
+    A randomly generated word is selected from the keys of transitions when:
+        - it is the first word; or
+        - the last randomly generated word is not a key in transitions.
+
+    A randomly generated word is selected from the follow list of a key in transitions when the
+    last randomly generated word is a key in transitions. In addition, one occurrence of the word
+    selected from the follow list is removed from the follow list (i.e., mutation). When there are
+    no words in the follow list for a key, the key-value pair is also removed from transitions
+    (i.e., mutation).
+
+    Your implementation MUST use the helper functions: choose_from_keys and choose_from_follow_list.
+    We recommend completing these functions first, as they simpler and will get you thinking about
+    how to use it here.
+
+    Preconditions:
+        - model is in the format described by the assignment handout
+    """
+    # ACCUMULATOR: a list of the randomly-generated words so far
+    words_so_far = []
+
+    # We've provided this template as a starting point; you may modify it as necessary.
+    current_word = ''
+    for _ in range(count - 1):
+        ...
+
+    return str.join(' ', words_so_far)
+
+
+def run_example(filename: str) -> str:
+    """Run an example to demonstrate random text generation based on the data in filename.
+
+    To call this function:
+        - Make sure you see that the 'data' folder is in the same directory as this file
+        - Use an argument for filename like: 'data/texts/sample_text_raw.txt'
+        - Try out the other plaintext files in  'data/texts', too
+    """
+    with open(filename) as f:
+        file_text = f.read()
+
+    stripped_text = str.strip(file_text)  # str.strip removes leading/trailing whitespace
+    word_count, transition_model = create_model_owc(stripped_text)
+    generated_words = generate_text_owc(word_count, transition_model)
+
+    return generated_words
+
+
+if __name__ == '__main__':
+    import python_ta
+    import python_ta.contracts
+    python_ta.contracts.DEBUG_CONTRACTS = False
+    python_ta.contracts.check_all_contracts()
+
+    # When you are ready to check your work with python_ta, uncomment the following lines.
+    # (Delete the "#" and space before each line.)
+    # IMPORTANT: keep this code indented inside the "if __name__ == '__main__'" block
+    # python_ta.check_all(config={
+    #     'allowed-io': ['run_example'],
+    #     'extra-imports': ['python_ta.contracts', 'random'],
+    #     'max-line-length': 100,
+    #     'max-nested-blocks': 4,
+    #     'disable': ['R1705', 'C0200']
+    # })