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cplockport
2026-03-02 12:29:18 -05:00
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.envrc Normal file
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source .venv/bin/activate

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.gitignore vendored Normal file
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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
.venv

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how much wood would a wood chuck chuck if a wood chuck could chuck wood
a wood chuck would chuck all the wood it could chuck if a wood chuck could chuck wood

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matrices.md Normal file
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# Matrix multiplication
Here are the matrices from the lab. They are written as lists of lists,
which is a good way to write your answers too.
a = [[1, 2, 3]]
B = [[6, 1],
[1, 4]]
C = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
D = [[1, 1],
[2, 2],
[3, 3]]
E = [[1, 2],
[1, 2],
[1, 2]]
## Exercises
Perform each of the following matrix multiplications if it is allowed.
If it's not allowed, explain why not.
1. BE
2. EB
3. BB
4. DD
5. CD
6. aC

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matrices.py Normal file
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# This script defines the matrices from the lab using numpy
# You should run it by calling `python -i matrices.py`
import numpy as np
a = np.array([[1, 2, 3]])
B = np.array([[6, 1],[1, 4]])
C = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
D = np.array([[1, 1], [2, 2], [3, 3]])
E = np.array([[1, 2], [1, 2], [1, 2]])
print("The following matrices are defined:")
print('a')
print(a)
print('B')
print(B)
print('C')
print(C)
print('D')
print(D)
print('E')
print(E)

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pyproject.toml Normal file
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[project]
name = "lab-embedding"
version = "0.1.0"
description = "Add your description here"
requires-python = ">=3.13"
dependencies = [
"click>=8.3.1",
"nltk>=3.9.3",
"numpy",
"tabulate>=0.9.0",
"tqdm>=4.67.3",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project.scripts]
tlm = "tlm.cli:cli"
[tool.hatch.build.targets.wheel]
packages = ["tlm"]

0
tlm/__init__.py Normal file
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tlm/cli.py Normal file
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import click
from .model import TinyLanguageModel
from .helpers import read_mail_text, join
from .tokenization import tokenize_text, tokenize_words
@click.group()
def cli():
"""TinyLM - A simple n-gram language model."""
pass
@cli.command()
@click.option('-l', "--length", default=50, help="Number of tokens to generate.")
@click.option('-n', "--context-window-words", default=2, help="Number of words in the context window.")
@click.option('-f', "--filepath", type=click.Path(exists=True), multiple=True, help="Text file(s) to use as training corpus. Can be specified multiple times.")
@click.option('-g', "--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.")
@click.option('-G', "--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
@click.option('-m', "--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
@click.option('-p', "--prompt", help="Prompt to start generation.")
@click.option('-i', "--interact", is_flag=True, help="Drop into interactive shell after generating.")
@click.option('-t', "--tokenize", 'tokenize_opts', multiple=True, type=click.Choice(['lower', 'char', 'alpha']), help="Preprocessing option (can be specified multiple times). 'lower': lowercase all input text. 'char': use characters as tokens instead of words.")
@click.option('-v', "--verbose", is_flag=True, help="Display step-by-step generation as a table.")
def generate(length, context_window_words, filepath, gutenberg, list_gutenberg, mbox, prompt, interact, tokenize_opts, verbose):
"""Generate text using the language model."""
import nltk
# Handle --list-gutenberg: list available keys
if list_gutenberg:
nltk.download("gutenberg", quiet=True)
from nltk.corpus import gutenberg as gutenberg_corpus
click.echo("Available Gutenberg corpus keys:")
for key in gutenberg_corpus.fileids():
click.echo(f" {key}")
return
# Determine training corpus
corpus = []
if filepath:
for fp in filepath:
with open(fp, "r") as f:
corpus.extend(tokenize_text(f.read(), tokenize_opts))
if gutenberg:
nltk.download("gutenberg", quiet=True)
from nltk.corpus import gutenberg as gutenberg_corpus
for key in gutenberg:
corpus.extend(tokenize_words(gutenberg_corpus.words(key), tokenize_opts))
if mbox:
mail_text = read_mail_text(mbox)
corpus.extend(tokenize_text(mail_text, tokenize_opts))
if not corpus:
raise click.UsageError("No training data provided. Must specify at least one of --filepath, --gutenberg, or --mbox.")
# Train and generate
model = TinyLanguageModel(n=context_window_words)
model.train(corpus)
if prompt:
prompt_tokens = tokenize_text(prompt, tokenize_opts)
else:
prompt_tokens = None
join_fn = ''.join if 'char' in tokenize_opts else None
display_join = join_fn or join
if verbose:
from tabulate import tabulate
rows = []
import textwrap
def step_callback(pattern, options, chosen):
opts = textwrap.fill(', '.join(sorted(set(options))), width=60)
rows.append([display_join(list(pattern)), opts, chosen])
output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn, step_callback=step_callback)
click.echo(tabulate(rows, headers=["Context", "Options", "Selected"], tablefmt="simple"))
click.echo()
else:
output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn)
click.echo(output)
if interact:
import code
code.interact(local=locals(), banner="Entering interactive shell. 'model' and 'output' are available.")
if __name__ == "__main__":
cli()

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tlm/helpers.py Normal file
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import mailbox
import email
from email.policy import default
from tqdm import tqdm
def rolling_window(iterable, n):
"""Passes a rolling window over the iterable, yielding each n-length tuple.
rolling_window(range(5), 3) -> (0, 1, 2), (1, 2, 3), (2, 3, 4)
"""
it = iter(iterable)
try:
window = [next(it) for _ in range(n)]
while True:
yield tuple(window)
window = window[1:] + [next(it)]
except StopIteration:
return
def read_mail_text(mbox_path):
"""
Extract and concatenate all plaintext content from an mbox file.
"""
texts = []
mbox = mailbox.mbox(
mbox_path,
factory=lambda f: email.message_from_binary_file(f, policy=default)
)
for msg in tqdm(mbox):
if msg.is_multipart():
for part in msg.walk():
if part.get_content_type() == "text/plain":
try:
text = part.get_content()
if text:
texts.append(text.strip())
except Exception:
pass
else:
if msg.get_content_type() == "text/plain":
try:
text = msg.get_content()
if text:
texts.append(text.strip())
except Exception:
pass
return "\n\n".join(texts)
def clean_corpus(corpus, max_length=10, remove_numbers=False, exclude=None):
result = []
for word in corpus:
if max_length and len(word) > max_length:
continue
if remove_numbers and word.isnumeric():
continue
if exclude and word in exclude:
continue
result.append(word)
return result
def join(tokens, punctuation=".,?!:;'\""):
"Joins text, but does not give extra space for punctuation"
tokens = [t if t in punctuation else ' ' + t for t in tokens]
return ''.join(tokens).strip()

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tlm/model.py Normal file
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import json
import random
import numpy as np
from .helpers import rolling_window, join
class TinyLanguageModel:
"""
A simple language model that predicts the next word based on the last n words.
The model stores everything it has learned in a matrix W with shape:
(vocabulary size) x (number of context windows seen)
Each row of W corresponds to one word in the vocabulary.
Each column of W corresponds to one context window (e.g. the words "the cat").
W[i, j] counts how many times word i was observed following context j.
To predict the next word, the model:
1. Represents the current context as a one-hot column vector x.
2. Computes Wx to get the counts for each word.
3. Divides by the total count to get a probability distribution.
4. Samples the next word from those probabilities.
"""
def __init__(self, n=2):
"Create a new model that looks at n words at a time."
self.n = n
self.vocab = None
self.word_to_idx = None
self.context_to_idx = None
self.W = None
def train(self, words):
"Learn word patterns from a list of words."
self.vocab, self.contexts = self.get_unique_contexts_and_words(words)
self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)}
self.context_to_idx = {ctx: idx for idx, ctx in enumerate(self.contexts)}
self.W = np.zeros((len(self.vocab), len(self.contexts)))
self.count_contexts_and_words(words)
def get_unique_contexts_and_words(self, words):
"Scan words and return the set of unique words and unique context windows."
unique_words = set()
unique_contexts = set()
for window in rolling_window(words, self.n + 1):
context, word = window[:-1], window[-1]
unique_words.add(word)
unique_contexts.add(context)
return sorted(unique_words), sorted(unique_contexts)
def count_contexts_and_words(self, words):
"Fill W by counting how often each word follows each context."
for window in rolling_window(words, self.n + 1):
context, word = window[:-1], window[-1]
self.W[self.word_to_idx[word], self.context_to_idx[context]] += 1
def generate(self, length, prompt=None, join_fn=None, step_callback=None):
"Create new text based on what the model learned."
if self.W is None:
raise Exception("The model has not been trained")
output = list(prompt or self.get_random_pattern())
while len(output) < length:
context = tuple(output[-self.n:])
if context not in self.context_to_idx:
break
context_col = self.context_to_idx[context]
one_hot = np.zeros(len(self.context_to_idx))
one_hot[context_col] = 1
counts = self.W @ one_hot
probs = counts / counts.sum()
chosen_idx = np.random.choice(len(self.vocab), p=probs)
chosen_word = self.vocab[chosen_idx]
if step_callback:
possible_next_words = [
self.vocab[j] for j in range(len(self.vocab)) if counts[j] > 0
]
step_callback(context, possible_next_words, chosen_word)
output.append(chosen_word)
return (join_fn or join)(output)
def get_random_pattern(self):
"Randomly chooses one of the observed context windows."
return random.choice(list(self.context_to_idx.keys()))
def save(self, filepath):
"Save the model to a file."
ordered_contexts = sorted(self.context_to_idx, key=self.context_to_idx.get)
model_data = {
"n": self.n,
"vocab": self.vocab,
"contexts": [list(ctx) for ctx in ordered_contexts],
"W": self.W.tolist(),
}
with open(filepath, "w") as f:
json.dump(model_data, f)
def load(self, filepath):
"Load a model from a file."
with open(filepath, "r") as f:
data = json.load(f)
self.n = data["n"]
self.vocab = data["vocab"]
self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)}
contexts = [tuple(ctx) for ctx in data["contexts"]]
self.context_to_idx = {ctx: idx for idx, ctx in enumerate(contexts)}
self.W = np.array(data["W"])

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def compress_whitespace(text):
"""Collapse sequences of whitespace into a single space."""
import re
return re.sub(r'\s+', ' ', text).strip()
def _is_alpha_token(token):
return all(c.isalpha() or c in " '" for c in token)
def tokenize_text(text, options):
"""Tokenize a raw text string according to the given options."""
if 'lower' in options:
text = text.lower()
words = text.split()
if 'alpha' in options:
words = [w for w in words if _is_alpha_token(w)]
if 'char' in options:
return list(compress_whitespace(' '.join(words)))
return words
def tokenize_words(words, options):
"""Apply tokenization options to an already word-tokenized sequence."""
if 'lower' in options:
words = [w.lower() for w in words]
if 'alpha' in options:
words = [w for w in words if _is_alpha_token(w)]
if 'char' in options:
return list(compress_whitespace(' '.join(words)))
return list(words)

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