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534 строки
19 KiB
Python
534 строки
19 KiB
Python
"""
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Main BioFirm implementation.
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Integrates all components for bioregional evaluation and stewardship.
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"""
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from typing import Dict, List, Optional, Tuple, Union, Any
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import numpy as np
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import yaml
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from pathlib import Path
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import logging
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import time
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import copy
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import networkx as nx
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from collections import defaultdict
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from .core.state_spaces import StateSpace, BioregionalState
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from .core.observation import (
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ObservationModel, HierarchicalObservation, ObservationAggregator
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)
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from .core.transition import (
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TransitionModel, HierarchicalTransition, EcologicalConstraints
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)
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from .core.stewardship import (
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Intervention, StewardshipMetrics, StewardshipMode,
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AdaptiveComanagement, BioregionalStewardship
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)
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from .visualization.plotting import BioregionalVisualization
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logger = logging.getLogger(__name__)
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class BioFirm:
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"""Main BioFirm class for bioregional active inference."""
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def __init__(self, config_path: Union[str, Path]):
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"""
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Initialize BioFirm framework.
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Args:
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config_path: Path to configuration file
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"""
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self.config = self._load_config(config_path)
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self._setup_components()
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self._initialize_state()
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self.visualization = BioregionalVisualization()
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def _load_config(self, config_path: Union[str, Path]) -> Dict:
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"""Load configuration from file."""
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try:
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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self._validate_config(config)
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return config
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except Exception as e:
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logger.error(f"Error loading config: {str(e)}")
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raise
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def _validate_config(self, config: Dict) -> bool:
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"""Validate configuration structure."""
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required_sections = [
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'system',
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'state_spaces',
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'observation',
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'transition',
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'stewardship',
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'visualization'
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]
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for section in required_sections:
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if section not in config:
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raise ValueError(f"Missing required config section: {section}")
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return True
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def _setup_components(self):
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"""Set up all framework components."""
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# Set up state spaces
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self.state_spaces = self._setup_state_spaces()
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# Set up observation model
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self.observation_model = self._setup_observation_model()
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# Set up transition model
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self.transition_model = self._setup_transition_model()
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# Set up stewardship
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self.stewardship = self._setup_stewardship()
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def _setup_state_spaces(self) -> Dict[str, StateSpace]:
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"""Set up state spaces for each scale."""
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spaces = {}
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for scale, config in self.config['state_spaces'].items():
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spaces[scale] = StateSpace(
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dimensions=config['dimensions'],
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labels=config['labels'],
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mappings=config['mappings'],
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scale=scale
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)
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return spaces
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def _setup_observation_model(self) -> HierarchicalObservation:
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"""Set up hierarchical observation model."""
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models = {}
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for scale, config in self.config['observation'].items():
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models[scale] = ObservationModel(
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state_space=self.state_spaces[scale],
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observation_space=StateSpace(**config['observation_space']),
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likelihood_matrix=np.array(config['likelihood_matrix']),
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noise_model=config.get('noise_model', 'gaussian'),
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precision=config.get('precision', 1.0)
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)
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scale_couplings = {
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tuple(k.split('_to_')): v
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for k, v in self.config['observation']['couplings'].items()
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}
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return HierarchicalObservation(models, scale_couplings)
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def _setup_transition_model(self) -> HierarchicalTransition:
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"""Set up hierarchical transition model."""
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models = {}
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for scale, config in self.config['transition'].items():
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models[scale] = TransitionModel(
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state_space=self.state_spaces[scale],
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action_space=StateSpace(**config['action_space']),
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transition_matrices={
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action: np.array(matrix)
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for action, matrix in config['transition_matrices'].items()
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},
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temporal_horizon=config['temporal_horizon'],
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control_modes=config.get('control_modes', ['homeostatic'])
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)
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scale_couplings = {
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tuple(k.split('_to_')): v
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for k, v in self.config['transition']['couplings'].items()
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}
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return HierarchicalTransition(models, scale_couplings)
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def _setup_stewardship(self) -> BioregionalStewardship:
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"""Set up stewardship framework."""
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config = self.config['stewardship']
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# Create stewardship mode
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mode = AdaptiveComanagement(
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stakeholder_weights=config['stakeholder_weights'],
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learning_rate=config.get('learning_rate', 0.1)
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)
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return BioregionalStewardship(
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observation_model=self.observation_model,
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transition_model=self.transition_model,
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stewardship_mode=mode
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)
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def _initialize_state(self):
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"""Initialize system state."""
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self.current_states = {}
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self.target_states = {}
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for scale, config in self.config['system']['initial_states'].items():
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self.current_states[scale] = BioregionalState(**config)
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for scale, config in self.config['system']['target_states'].items():
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self.target_states[scale] = BioregionalState(**config)
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def evaluate_system(self) -> Dict[str, float]:
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"""Evaluate current system state."""
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return self.stewardship.evaluate_system(
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self.current_states,
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self.target_states
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)
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def plan_interventions(self,
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constraints: Optional[Dict[str, Any]] = None
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) -> Dict[str, List[Intervention]]:
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"""Plan interventions across scales."""
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if constraints is None:
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constraints = self.config['stewardship']['default_constraints']
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return self.stewardship.plan_interventions(
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self.current_states,
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constraints
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)
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def apply_intervention(self,
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intervention: Intervention,
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scale: str) -> BioregionalState:
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"""Apply intervention and update state."""
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# Get current state
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current_state = self.current_states[scale]
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# Predict next state
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next_state = self.transition_model.models[scale].predict_next_state(
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current_state,
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intervention.type
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)
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# Apply ecological constraints
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constraints = EcologicalConstraints(
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self.config['system']['ecological_constraints']
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)
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constrained_state = constraints.apply_constraints(next_state)
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# Update current state
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self.current_states[scale] = constrained_state
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# Update metrics
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self.stewardship.update_metrics(
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self.current_states,
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{scale: [intervention]}
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)
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return constrained_state
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def visualize_state(self,
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time_series: Optional[np.ndarray] = None,
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scale: Optional[str] = None):
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"""Visualize system state."""
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if scale is None:
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scale = list(self.current_states.keys())[0]
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return self.visualization.plot_system_state(
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self.current_states[scale],
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time_series
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)
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def visualize_intervention(self,
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before_state: BioregionalState,
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after_state: BioregionalState,
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intervention: Intervention):
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"""Visualize intervention impacts."""
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return self.visualization.plot_intervention_impacts(
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before_state,
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after_state,
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intervention.__dict__
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)
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def visualize_cross_scale(self,
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states: Optional[Dict[str, np.ndarray]] = None):
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"""Visualize cross-scale dynamics."""
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if states is None:
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states = {
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scale: np.array([state.to_vector()])
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for scale, state in self.current_states.items()
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}
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scales = list(states.keys())
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interactions = np.array([
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[self.transition_model.scale_couplings.get(
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(scale1, scale2), 0.0
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) for scale2 in scales]
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for scale1 in scales
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])
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return self.visualization.plot_cross_scale_dynamics(
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states, scales, interactions
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)
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class HierarchicalProcessor:
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"""Implements hierarchical processing across scales"""
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def __init__(self, scales: List[str]):
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self.scales = scales
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self.processors = {
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scale: ScaleProcessor(scale) for scale in scales
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}
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self.couplings = self._initialize_couplings()
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def _initialize_couplings(self) -> Dict[Tuple[str, str], float]:
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"""Initialize cross-scale coupling strengths"""
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return {
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(scale1, scale2): self._compute_coupling(scale1, scale2)
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for scale1 in self.scales
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for scale2 in self.scales
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if scale1 != scale2
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}
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def _compute_coupling(self, scale1: str, scale2: str) -> float:
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"""Compute coupling strength between scales"""
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# Implementation based on scale relationship
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pass
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def _get_messages(self,
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scale: str,
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beliefs: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
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"""Get messages from other scales"""
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messages = {}
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for other_scale in self.scales:
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if other_scale != scale:
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messages[other_scale] = self._compute_message(
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scale, other_scale, beliefs.get(other_scale)
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)
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return messages
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def process_hierarchy(self,
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observations: Dict[str, np.ndarray]
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) -> Dict[str, np.ndarray]:
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"""Process observations across hierarchical levels"""
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beliefs = {}
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for scale in self.scales:
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beliefs[scale] = self.processors[scale].update_beliefs(
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observations[scale],
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self._get_messages(scale, beliefs)
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)
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return beliefs
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class BeliefPropagator:
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"""Implements belief propagation across network"""
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def __init__(self, network: nx.Graph):
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self.network = network
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self.messages = defaultdict(dict)
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self.convergence_threshold = 1e-6
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def _update_messages(self):
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"""Update messages between nodes"""
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new_messages = defaultdict(dict)
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for node in self.network.nodes():
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for neighbor in self.network.neighbors(node):
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new_messages[node][neighbor] = self._compute_message(
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node, neighbor
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)
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self.messages = new_messages
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def _update_beliefs(self):
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"""Update node beliefs based on messages"""
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for node in self.network.nodes():
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self.network.nodes[node]['belief'] = self._compute_belief(node)
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def _check_convergence(self) -> bool:
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"""Check if belief propagation has converged"""
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# Implementation of convergence check
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pass
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def propagate_beliefs(self,
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initial_beliefs: Dict[str, np.ndarray],
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max_iterations: int = 100):
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"""Propagate beliefs through network"""
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self._initialize_beliefs(initial_beliefs)
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for iteration in range(max_iterations):
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self._update_messages()
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self._update_beliefs()
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if self._check_convergence():
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logger.info(f"Converged after {iteration + 1} iterations")
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break
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return self._get_final_beliefs()
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class AdaptiveController:
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"""Implements adaptive control mechanisms"""
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def __init__(self,
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control_params: Dict[str, Any],
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learning_rate: float = 0.01):
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self.params = control_params
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self.learning_rate = learning_rate
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self.history = []
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def _compute_gradient(self,
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performance: PerformanceMetrics) -> Dict[str, np.ndarray]:
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"""Compute gradient for parameter updates"""
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gradients = {}
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for param_name, param_value in self.params.items():
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gradients[param_name] = self._estimate_gradient(
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param_name, param_value, performance
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)
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return gradients
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def _update_params(self, gradient: Dict[str, np.ndarray]):
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"""Update control parameters using gradient"""
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for param_name, grad in gradient.items():
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self.params[param_name] -= self.learning_rate * grad
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def _store_adaptation(self, context: SystemContext):
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"""Store adaptation history"""
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self.history.append({
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'timestamp': context.timestamp,
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'params': copy.deepcopy(self.params),
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'context': context.to_dict()
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})
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def adapt_control(self,
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performance: PerformanceMetrics,
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context: SystemContext):
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"""Adapt control parameters based on performance"""
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gradient = self._compute_gradient(performance)
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self._update_params(gradient)
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self._store_adaptation(context)
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return self.params
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class MetaLearner:
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"""Implements meta-learning capabilities"""
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def __init__(self,
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base_learners: List[BaseLearner],
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meta_params: Dict[str, Any]):
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self.learners = base_learners
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self.meta_params = meta_params
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self.meta_model = self._initialize_meta_model()
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def _compute_meta_gradient(self,
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experience: Experience) -> Dict[str, np.ndarray]:
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"""Compute meta-learning gradients"""
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# Implementation of meta-gradient computation
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pass
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def _update_meta_params(self,
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gradient: Dict[str, np.ndarray]):
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"""Update meta-learning parameters"""
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# Implementation of meta-parameter updates
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pass
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def _adapt_learners(self,
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performance: Dict[str, float]):
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"""Adapt base learners using meta-knowledge"""
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# Implementation of learner adaptation
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pass
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def meta_update(self,
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experience: Experience,
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performance: Dict[str, float]):
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"""Update meta-learning parameters"""
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meta_gradient = self._compute_meta_gradient(experience)
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self._update_meta_params(meta_gradient)
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self._adapt_learners(performance)
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return self.meta_params
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class StateManager:
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"""Manages bioregional state transitions"""
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def __init__(self,
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state_config: Dict[str, Any],
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constraints: List[Constraint]):
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self.config = state_config
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self.constraints = constraints
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self.state_history = []
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def _compute_next_state(self,
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current_state: BioregionalState,
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action: Action) -> BioregionalState:
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"""Compute next state based on current state and action"""
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# Implementation of state transition
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pass
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def _apply_constraints(self,
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proposed_state: BioregionalState) -> BioregionalState:
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"""Apply constraints to proposed state"""
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valid_state = proposed_state
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for constraint in self.constraints:
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valid_state = constraint.apply(valid_state)
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return valid_state
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def _record_transition(self,
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current_state: BioregionalState,
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action: Action,
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next_state: BioregionalState):
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"""Record state transition"""
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self.state_history.append({
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'timestamp': time.time(),
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'current_state': current_state.to_dict(),
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'action': action.to_dict(),
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'next_state': next_state.to_dict()
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})
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def transition_state(self,
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current_state: BioregionalState,
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action: Action) -> BioregionalState:
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"""Execute state transition with constraints"""
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proposed_state = self._compute_next_state(current_state, action)
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valid_state = self._apply_constraints(proposed_state)
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self._record_transition(current_state, action, valid_state)
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return valid_state
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class ObservationProcessor:
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"""Processes multi-scale observations"""
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def __init__(self,
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observation_models: Dict[str, ObservationModel],
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aggregator: ObservationAggregator):
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self.models = observation_models
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self.aggregator = aggregator
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def process_observations(self,
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raw_observations: Dict[str, np.ndarray],
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uncertainty: Dict[str, float]) -> Dict[str, np.ndarray]:
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"""Process and aggregate observations"""
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processed = {
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scale: model.process(obs, uncertainty[scale])
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for scale, (obs, model) in zip(
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raw_observations.items(),
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self.models.items()
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)
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}
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return self.aggregator.aggregate(processed)
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|
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class InterventionPlanner:
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"""Plans multi-scale interventions"""
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def __init__(self,
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planning_horizon: int,
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objective_weights: Dict[str, float]):
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self.horizon = planning_horizon
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self.weights = objective_weights
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self.planner = self._initialize_planner()
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def _compute_objectives(self,
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current_state: BioregionalState) -> Dict[str, float]:
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"""Compute objective values for current state"""
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# Implementation of objective computation
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pass
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def _validate_plan(self,
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plan: List[Intervention]) -> List[Intervention]:
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"""Validate and adjust intervention plan"""
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# Implementation of plan validation
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pass
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def plan_interventions(self,
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current_state: BioregionalState,
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constraints: Dict[str, Any]) -> List[Intervention]:
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"""Generate intervention plan"""
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objectives = self._compute_objectives(current_state)
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plan = self.planner.optimize(objectives, constraints)
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return self._validate_plan(plan) |