Tammet: Uus lehekülg: '

 #!/usr/bin/python3  # https://medium.com/analytics-vidhya/basics-of-using-pre-trained-glove-vectors-in-python-d38905f356db   import numpy as np from scipy import spatial im...'

2025-03-11T13:37:36Z

Uus lehekülg: '<pre> #!/usr/bin/python3 # https://medium.com/analytics-vidhya/basics-of-using-pre-trained-glove-vectors-in-python-d38905f356db import numpy as np from scipy import spatial im...'

Uus lehekülg

<pre>
#!/usr/bin/python3

# https://medium.com/analytics-vidhya/basics-of-using-pre-trained-glove-vectors-in-python-d38905f356db

import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE

def find_closest_embeddings(embedding):
return sorted(embeddings_dict.keys(),
key=lambda word: spatial.distance.euclidean(embeddings_dict[word], embedding))

embeddings_dict = {}

with open("glove.6B.50d.txt", 'r', encoding="utf-8") as f:
for line in f:
values = line.split()
word = values[0]
vector = np.asarray(values[1:], "float32")
embeddings_dict[word] = vector

#print(find_closest_embeddings(
# embeddings_dict["twig"] - embeddings_dict["branch"] + embeddings_dict["hand"]
#)[:5])

print(find_closest_embeddings(
embeddings_dict["king"] - embeddings_dict["man"] + embeddings_dict["woman"]
)[:20])

#print(find_closest_embeddings(
# embeddings_dict["king"]
#)[:100])

"""

John sat on a bike and started ....
car and drove
house water I car

"""
</pre>

Katse Glove vektoritega - Redigeerimiste ajalugu

Tammet: Uus lehekülg: '
#!/usr/bin/python3 # https://medium.com/analytics-vidhya/basics-of-using-pre-trained-glove-vectors-in-python-d38905f356db import numpy as np from scipy import spatial im...'