NexusMind

from typing import List, Optional from loguru import logger from src.asr_got_reimagined.domain.models.common import CertaintyScore, ConfidenceVector from src.asr_got_reimagined.domain.models.graph_elements import EdgeType, StatisticalPower def bayesian_update_confidence( prior_confidence: ConfidenceVector, evidence_strength: CertaintyScore, # A single score representing how strong this piece of evidence is evidence_supports_hypothesis: bool, # True if supportive, False if contradictory statistical_power: Optional[StatisticalPower] = None, # P1.26 edge_type: Optional[EdgeType] = None, # P1.10, P1.24, P1.25 for context ) -> ConfidenceVector: """ Simplified Bayesian-inspired update for a ConfidenceVector. P1.14: Apply Bayesian updates... considering evidence reliability (P1.26 power) and edge type. Args: prior_confidence: The current confidence vector of the hypothesis. evidence_strength: A score (0-1) indicating the strength/reliability of the new evidence. evidence_supports_hypothesis: Boolean indicating if evidence supports or contradicts. statistical_power: Statistical power of the evidence. edge_type: Type of edge connecting evidence to hypothesis, can influence update. Returns: A new ConfidenceVector with updated values. """ # This is a highly simplified placeholder. True Bayesian updates involve likelihoods, priors, etc. # and updating probability distributions, not just scores. # P1.14 mentions "probability distributions" for confidence components. # For now, we'll do a weighted adjustment. # Weight factor considers evidence strength and statistical power. power_multiplier = ( statistical_power.value if statistical_power else 0.5 ) # Default if no power info weight = evidence_strength * power_multiplier # Edge type influence (P1.14) - very simplified edge_type_factor = 1.0 if edge_type: if edge_type in [EdgeType.CAUSES, EdgeType.SUPPORTIVE]: edge_type_factor = 1.1 elif edge_type == EdgeType.CORRELATIVE: edge_type_factor = 0.9 elif edge_type == EdgeType.CONTRADICTORY: # This case should be handled by evidence_supports_hypothesis=False pass weight *= edge_type_factor weight = max(0, min(weight, 1.0)) # Clamp weight new_confidence_values = prior_confidence.to_list() target_value = 1.0 if evidence_supports_hypothesis else 0.0 # Adjust each component of the confidence vector # A more sophisticated approach would update each dimension based on how the evidence relates to it. # E.g., empirical evidence primarily boosts empirical_support. # This simple version boosts/reduces all components slightly. for i in range(len(new_confidence_values)): current_val = new_confidence_values[i] adjustment = weight * (target_value - current_val) new_confidence_values[i] = max(0.0, min(1.0, current_val + adjustment)) logger.debug( f"Bayesian update: Prior {prior_confidence.to_list()}, Evidence Strength {evidence_strength}, " f"Supports: {evidence_supports_hypothesis}, Power: {power_multiplier}, Edge: {edge_type} " f"-> New {new_confidence_values}" ) return ConfidenceVector.from_list(new_confidence_values) def calculate_information_gain( prior_distribution: List[float], posterior_distribution: List[float] ) -> float: """ Placeholder for calculating information gain (e.g., KL divergence reduction). P1.27. This would compare the uncertainty before and after evidence. """ # Simplified: sum of absolute changes in probability for now if len(prior_distribution) != len(posterior_distribution): return 0.0 # Or raise error gain = sum( abs(p - q) for p, q in zip(prior_distribution, posterior_distribution) ) / len(prior_distribution) logger.debug(f"Calculated simplified info gain: {gain}") return gain