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21a0de8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | import os
import random
import torch
import torch.nn.functional as F
def load_vocab(vocab_path):
if not os.path.exists(vocab_path):
print(f"Error: vocab file not found at {vocab_path}")
return []
with open(vocab_path, 'r') as f:
vocab = [line.strip().lower() for line in f if line.strip()]
return vocab
def filter_words_by_hint(vocab, hint):
"""
Filters the vocabulary based on a hint pattern.
The hint uses underscores '_' for unknown characters.
Example: "__pl_" for a 5-letter word matching "apple"
"""
valid_words = []
for word in vocab:
if len(word) != len(hint):
continue
match = True
for i in range(len(word)):
if hint[i] != '_' and hint[i] != word[i]:
match = False
break
if match:
valid_words.append(word)
return valid_words
def simulate_prediction(vocab, hint, target_word=None):
print(f"\n{'='*50}")
print(f"Simulating Prediction for hint: '{hint}'")
print(f"{'='*50}")
# 1. Filter words
valid_words = filter_words_by_hint(vocab, hint)
print(f"Found {len(valid_words)} valid words matching the hint pattern.")
if not valid_words:
print("No valid words found!")
return
if len(valid_words) <= 10:
print(f"Valid words: {valid_words}")
else:
print(f"Valid words: {valid_words[:5]} ... (and {len(valid_words)-5} more)")
# 2. Mock Embeddings
# We will give the target word a slightly higher similarity, and the rest random.
# In reality, this comes from the model.
print("\nCalculating simulated probabilities...")
logits = []
# Make up a random embedding for the "image"
output_emb = torch.randn(300)
word_embeddings = {}
for word in valid_words:
# If this is the correct word, make its embedding closer to the output
if word == target_word:
word_emb = output_emb + torch.randn(300) * 0.5
else:
word_emb = torch.randn(300)
word_embeddings[word] = word_emb
sim = F.cosine_similarity(output_emb, word_emb, dim=0)
logits.append(sim)
logits_tensor = torch.stack(logits)
# Apply scaled softmax (temperature scaling, as in predictor.py)
probabilities = torch.softmax(logits_tensor * 10.0, dim=0).tolist()
similarities = [(valid_words[i], probabilities[i]) for i in range(len(valid_words))]
similarities.sort(key=lambda x: x[1], reverse=True)
print("\nTop 5 Predictions:")
for i, (word, prob) in enumerate(similarities[:5]):
print(f"{i+1}. {word} ({prob*100:.2f}%)")
def main():
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
vocab_path = os.path.join(base_dir, "data", "vocab.txt")
vocab = load_vocab(vocab_path)
if not vocab:
return
print(f"Loaded {len(vocab)} total vocabulary words.")
# Test Case 1: Only word length known (5 letters)
simulate_prediction(vocab, "_____", target_word="purse")
# Test Case 2: One letter revealed
simulate_prediction(vocab, "p____", target_word="purse")
# Test Case 3: More letters revealed
simulate_prediction(vocab, "p___e", target_word="purse")
if __name__ == "__main__":
main()
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