# 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|Active Inference]]
      - [[knowledge_base/cognitive/free_energy_principle|Free Energy Principle]]
  - type: relates
    links:
      - [[docs/api/api_reference|API Reference]]
      - [[docs/concepts/theoretical_foundations|Theoretical Foundations]]
---

## Overview

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

## Core Implementation Patterns

### Active Inference Implementation
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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
```python
# @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|Code Organization Guide]]
- Use [[docs/guides/naming_conventions|Naming Conventions]]
- Implement [[docs/guides/documentation_standards|Documentation Standards]]

### 2. Performance Optimization
- Follow [[docs/guides/performance_optimization|Optimization Guide]]
- Use [[docs/guides/profiling_guide|Profiling Guide]]
- Monitor [[docs/guides/performance_metrics|Performance Metrics]]

### 3. Quality Assurance
- Implement [[docs/guides/testing_guide|Testing Guidelines]]
- Follow [[docs/guides/code_review|Code Review Process]]
- Use [[docs/guides/validation_framework|Validation Framework]]

## Related Documentation
- [[knowledge_base/cognitive/cognitive_science|Cognitive Science Theory]]
- [[docs/api/api_reference|API Reference]]
- [[docs/guides/system_integration|System Integration]]
- [[docs/guides/validation_guide|Validation Guide]]

## References
- [[knowledge_base/cognitive/active_inference|Active Inference]]
- [[knowledge_base/cognitive/free_energy_principle|Free Energy Principle]]
- [[knowledge_base/cognitive/predictive_processing|Predictive Processing]]
- [[docs/concepts/theoretical_foundations|Theoretical Foundations]]