similarity_indices.py

# Generate random data
x = np.random.rand(5, 5)
y = np.random.rand(1, 5)

print("x:", "\n", x)
print("y:", "\n", y)

# Calculate cosine similarity in NumPy
results = np_cosine_similarity(x, y)

# Get the index with highest cosine similarity (argsort sorts asc)
indices = np.argsort(np.squeeze(-results), axis=0)[:1]

# Print cosine similarity, best result and score
print("np:", "\n", results)
print(x[indices], results[indices])

# Calculate cosine similarity in TensorFlow
results = tf_cosine_similarity(x, y)

# Get the index of the highest cosine similarity (top_k sorts desc)
indices = tf.math.top_k(tf.squeeze(results), k=1).indices

# Print cosine similarity, best result and score
print("tf:", "\n", results.numpy())
print(x[indices.numpy()], tf.gather(results, indices).numpy())