cognitive/docs/guides/implementation_patterns.md

Implementation Patterns Guide

---

title: Implementation Patterns Guide type: guide status: stable created: 2024-02-06 tags:

• implementation
• patterns
• design
• architecture semantic_relations:
• type: implements links:
  • knowledge_base/cognitive/active_inference
  • knowledge_base/cognitive/free_energy_principle
• type: relates links:
  • docs/api/api_reference
  • docs/concepts/theoretical_foundations

---

Overview

This guide outlines implementation patterns for translating cognitive science theory into practical code. While the knowledge_base/cognitive/cognitive_science provides theoretical foundations, this guide focuses on concrete implementation strategies.

Core Implementation Patterns

Active Inference Implementation

# @active_inference_pattern
class ActiveInferencePattern:
    """
    Core implementation pattern for Active Inference.
    
    Theory: [[knowledge_base/cognitive/active_inference|Active Inference]]
    API: [[docs/api/api_reference#active-inference-framework|API Reference]]
    """
    def __init__(self):
        self.belief_updater = BeliefUpdater()  # [[belief_updating]]
        self.policy_selector = PolicySelector()  # [[action_selection]]
        self.state_estimator = StateEstimator()  # [[state_estimation]]
    
    def cycle(self, observation: Observation) -> Action:
        """
        Single cycle of perception-action.
        
        Theory: [[knowledge_base/cognitive/active_inference#perception-action-cycle|Perception-Action Cycle]]
        """
        # Update beliefs
        beliefs = self.belief_updater.update(observation)
        
        # Select policy
        policy = self.policy_selector.select(beliefs)
        
        # Execute action
        action = policy.get_action()
        
        return action

Belief Updating Pattern

# @belief_updating_pattern
class BeliefUpdatePattern:
    """
    Implementation pattern for belief updating.
    
    Theory:
        - [[knowledge_base/cognitive/predictive_processing#belief-updating|Belief Updating]]
        - [[knowledge_base/cognitive/free_energy_principle#variational-inference|Variational Inference]]
    Mathematics:
        - [[knowledge_base/mathematics/variational_methods|Variational Methods]]
        - [[knowledge_base/mathematics/information_geometry|Information Geometry]]
    API: [[docs/api/api_reference#belief-updating-system|API Reference]]
    """
    def update_beliefs(self, 
                      prior: Distribution,
                      likelihood: Distribution) -> Distribution:
        """
        Update beliefs using variational inference.
        
        Theory:
            - [[knowledge_base/cognitive/free_energy_principle#variational-inference|Variational Inference]]
            - [[knowledge_base/mathematics/kullback_leibler|KL Divergence]]
        Implementation:
            - [[docs/api/matrix_operations#gradient-descent|Gradient Descent]]
            - [[docs/api/optimization_methods#natural-gradients|Natural Gradients]]
        """
        # Compute posterior using variational methods
        posterior = self._compute_posterior(prior, likelihood)
        
        # Minimize free energy using natural gradients
        optimized = self._minimize_free_energy(posterior)
        
        return optimized
    
    def _compute_posterior(self, prior: Distribution, likelihood: Distribution) -> Distribution:
        """
        Compute posterior distribution.
        
        Mathematics:
            - [[knowledge_base/mathematics/bayes_theorem|Bayes Theorem]]
            - [[knowledge_base/mathematics/exponential_families|Exponential Families]]
        Implementation:
            - [[docs/api/probability_utils#distribution-ops|Distribution Operations]]
        """
        pass
    
    def _minimize_free_energy(self, posterior: Distribution) -> Distribution:
        """
        Minimize variational free energy.
        
        Mathematics:
            - [[knowledge_base/mathematics/variational_calculus|Variational Calculus]]
            - [[knowledge_base/mathematics/optimization_theory|Optimization Theory]]
        Implementation:
            - [[docs/api/optimization_methods#variational-optimization|Variational Optimization]]
        """
        pass

Policy Selection Pattern

# @policy_selection_pattern
class PolicySelectionPattern:
    """
    Implementation pattern for policy selection.
    
    Theory:
        - [[knowledge_base/cognitive/active_inference#policy-selection|Policy Selection]]
        - [[knowledge_base/cognitive/free_energy_principle#expected-free-energy|Expected Free Energy]]
    Mathematics:
        - [[knowledge_base/mathematics/information_theory|Information Theory]]
        - [[knowledge_base/mathematics/decision_theory|Decision Theory]]
    API: [[docs/api/api_reference#policy-selection|Policy Selection API]]
    """
    def select_policy(self,
                     beliefs: BeliefState,
                     policies: List[Policy]) -> Policy:
        """
        Select optimal policy using expected free energy.
        
        Theory:
            - [[knowledge_base/cognitive/free_energy_principle#expected-free-energy|Expected Free Energy]]
            - [[knowledge_base/cognitive/active_inference#action-selection|Action Selection]]
        Mathematics:
            - [[knowledge_base/mathematics/expected_utility|Expected Utility]]
            - [[knowledge_base/mathematics/information_gain|Information Gain]]
        Implementation:
            - [[docs/api/optimization_methods#policy-optimization|Policy Optimization]]
        """
        # Generate and evaluate policies
        policies = self._generate_policies()
        policy_values = self._evaluate_policies(policies, beliefs)
        
        # Select optimal policy using softmax
        optimal_policy = self._select_optimal_policy(policy_values)
        
        return optimal_policy
    
    def _evaluate_policies(self, 
                          policies: List[Policy], 
                          beliefs: BeliefState) -> np.ndarray:
        """
        Evaluate policies using expected free energy.
        
        Mathematics:
            - [[knowledge_base/mathematics/expected_free_energy|Expected Free Energy]]
            - [[knowledge_base/mathematics/path_integral|Path Integral]]
        Implementation:
            - [[docs/api/matrix_operations#policy-evaluation|Policy Evaluation]]
        """
        pass
    
    def _select_optimal_policy(self, 
                             policy_values: np.ndarray) -> Policy:
        """
        Select optimal policy using softmax.
        
        Mathematics:
            - [[knowledge_base/mathematics/softmax|Softmax Function]]
            - [[knowledge_base/mathematics/temperature_scaling|Temperature Scaling]]
        Implementation:
            - [[docs/api/probability_utils#softmax|Softmax Implementation]]
        """
        pass

Perception Implementation Pattern

# @perception_pattern
class PerceptionPattern:
    """
    Implementation pattern for perceptual processing.
    
    Theory:
        - [[knowledge_base/cognitive/predictive_processing#perception|Predictive Processing]]
        - [[knowledge_base/cognitive/free_energy_principle#perceptual-inference|Perceptual Inference]]
    Mathematics:
        - [[knowledge_base/mathematics/hierarchical_models|Hierarchical Models]]
        - [[knowledge_base/mathematics/message_passing|Message Passing]]
    API: [[docs/api/api_reference#perception-system|Perception API]]
    """
    def process_input(self, 
                     sensory_input: Input,
                     prior_beliefs: BeliefState) -> Observation:
        """
        Process sensory input using predictive processing.
        
        Theory:
            - [[knowledge_base/cognitive/predictive_processing#sensory-processing|Sensory Processing]]
            - [[knowledge_base/cognitive/active_inference#perception|Active Inference Perception]]
        Mathematics:
            - [[knowledge_base/mathematics/prediction_error|Prediction Error]]
            - [[knowledge_base/mathematics/precision_weighting|Precision Weighting]]
        Implementation:
            - [[docs/api/matrix_operations#error-computation|Error Computation]]
            - [[docs/api/probability_utils#precision-scaling|Precision Scaling]]
        """
        # Compute prediction errors
        prediction_errors = self._compute_prediction_errors(
            sensory_input, prior_beliefs
        )
        
        # Weight by precision
        weighted_errors = self._weight_by_precision(prediction_errors)
        
        # Update beliefs
        updated_beliefs = self._update_beliefs(weighted_errors)
        
        return updated_beliefs
    
    def _compute_prediction_errors(self,
                                 sensory_input: Input,
                                 predictions: Predictions) -> PredictionErrors:
        """
        Compute hierarchical prediction errors.
        
        Mathematics:
            - [[knowledge_base/mathematics/error_propagation|Error Propagation]]
            - [[knowledge_base/mathematics/hierarchical_inference|Hierarchical Inference]]
        Implementation:
            - [[docs/api/matrix_operations#error-propagation|Error Propagation]]
        """
        pass
    
    def _weight_by_precision(self,
                           prediction_errors: PredictionErrors) -> WeightedErrors:
        """
        Weight prediction errors by precision.
        
        Mathematics:
            - [[knowledge_base/mathematics/precision_matrices|Precision Matrices]]
            - [[knowledge_base/mathematics/uncertainty_propagation|Uncertainty Propagation]]
        Implementation:
            - [[docs/api/matrix_operations#precision-weighting|Precision Weighting]]
        """
        pass

Design Patterns

1. Model-View-Controller

# @mvc_pattern
class ModelPattern:
    """
    MVC pattern for cognitive models.
    
    Theory: [[knowledge_base/cognitive/cognitive_phenomena#model-architecture|Model Architecture]]
    """
    def __init__(self):
        self.model = GenerativeModel()  # Internal model
        self.view = ModelView()         # Visualization
        self.controller = ModelController()  # Control logic

2. Observer Pattern

# @observer_pattern
class BeliefObserver:
    """
    Observer pattern for belief monitoring.
    
    Theory: [[knowledge_base/cognitive/predictive_processing#belief-monitoring|Belief Monitoring]]
    """
    def update(self, beliefs: BeliefState):
        """Update on belief changes."""
        self._log_beliefs(beliefs)
        self._visualize_beliefs(beliefs)

Integration Patterns

1. Component Integration

# @integration_pattern
class IntegrationPattern:
    """
    Pattern for component integration.
    
    See [[docs/guides/system_integration|System Integration]]
    """
    def __init__(self):
        self.belief_component = BeliefComponent()
        self.action_component = ActionComponent()
        self.perception_component = PerceptionComponent()

2. Pipeline Pattern

# @pipeline_pattern
class ProcessingPipeline:
    """
    Pattern for information processing pipeline.
    
    Theory: [[knowledge_base/cognitive/predictive_processing#processing-hierarchy|Processing Hierarchy]]
    """
    def process(self, input_data: Data) -> Result:
        """Process data through pipeline stages."""
        preprocessed = self._preprocess(input_data)
        processed = self._process(preprocessed)
        postprocessed = self._postprocess(processed)
        return postprocessed

Validation Patterns

1. Model Validation

# @validation_pattern
class ValidationPattern:
    """
    Pattern for model validation.
    
    See [[docs/guides/validation_guide|Validation Guide]]
    """
    def validate_model(self, model: Model) -> ValidationResult:
        """Validate model components and behavior."""
        structure_valid = self._validate_structure(model)
        behavior_valid = self._validate_behavior(model)
        performance_valid = self._validate_performance(model)
        return ValidationResult(structure_valid, behavior_valid, performance_valid)

2. Testing Pattern

# @testing_pattern
class TestingPattern:
    """
    Pattern for comprehensive testing.
    
    See [[docs/guides/testing_guide|Testing Guide]]
    """
    def test_suite(self) -> TestResults:
        """Run complete test suite."""
        unit_results = self._run_unit_tests()
        integration_results = self._run_integration_tests()
        system_results = self._run_system_tests()
        return TestResults(unit_results, integration_results, system_results)

Best Practices

1. Code Organization

• Follow docs/guides/code_organization
• Use docs/guides/naming_conventions
• Implement docs/guides/documentation_standards

2. Performance Optimization

• Follow docs/guides/performance_optimization
• Use docs/guides/profiling_guide
• Monitor docs/guides/performance_metrics

3. Quality Assurance

• Implement docs/guides/testing_guide
• Follow docs/guides/code_review
• Use docs/guides/validation_framework

Related Documentation

• knowledge_base/cognitive/cognitive_science
• docs/api/api_reference
• docs/guides/system_integration
• docs/guides/validation_guide

References

• knowledge_base/cognitive/active_inference
• knowledge_base/cognitive/free_energy_principle
• knowledge_base/cognitive/predictive_processing
• docs/concepts/theoretical_foundations