def calculate_variance(scores: list[float]):
if not scores or len(scores) < 2:
return 0.0
mean = sum(scores) / len(scores)
squared_diffs = [(s - mean) ** 2 for s in scores]
return sum(squared_diffs) / len(scores)
def compute_dynamic_weights(
vector_scores: list[float],
keyword_scores: list[float],
base_vector_weight: float,
base_keyword_weight: float,
variance_threshold: float = 0.1,
min_weight_factor: float = 0.5,
):
vector_variance = calculate_variance(vector_scores) if vector_scores else 0.0
keyword_variance = calculate_variance(keyword_scores) if keyword_scores else 0.0
vector_factor = 1.0
keyword_factor = 1.0
if vector_variance < variance_threshold and vector_scores:
vector_factor = max(min_weight_factor, vector_variance / variance_threshold)
if keyword_variance < variance_threshold and keyword_scores:
keyword_factor = max(min_weight_factor, keyword_variance / variance_threshold)
adjusted_vector = base_vector_weight * vector_factor
adjusted_keyword = base_keyword_weight * keyword_factor
total = adjusted_vector + adjusted_keyword
if total > 0:
adjusted_vector = adjusted_vector * (base_vector_weight + base_keyword_weight) / total
adjusted_keyword = adjusted_keyword * (base_vector_weight + base_keyword_weight) / total
return adjusted_vector, adjusted_keyword
