similarities

def calculate_accuracy(vectors, target_vector): # Calculate the similarity between the target vector and each vector in the database similarities = np.dot(vectors, target_vector) / (np.linalg.norm(vectors, axis=1) * np.linalg.norm(t…

# Check if the target accuracy is met if accuracy >= target_accuracy: print("Target accuracy achieved!") else: print("Target accuracy not achieved. Consider adjusting parameters or increasing the dataset size.") ``` ### Explanation…

# Define the vector database class VectorDatabase: def __init__(self): self.vectors = [] def add_vector(self, vector): self.vectors.append(vector) def search(self, query_vector, top_k=10): # Calculate t…

closest_synonyms.extend([synonyms[i] for i in np.argsort(similarities)[-2:]]) # Take top 2 closest synonyms return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonym…

term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…

- **Analyze Existing Code**: Review the proof of concept that achieved 91% intent accuracy with 1,500 queries. - **Identify Similarities and Differences**: Compare the existing code with the remaining 70% of the reformulation logic to…

[Session date: 2023/05/22 (Mon) 17:22] User: I'm looking for some new podcast recommendations. I've been listening to a lot of true crime and self-improvement stuff. I enjoy listening to then during my commute, but I want to branch out into…