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199 строки
5.6 KiB
Markdown
199 строки
5.6 KiB
Markdown
---
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title: Examples Index
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type: index
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status: stable
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created: 2024-02-07
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tags:
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- examples
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- implementation
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- index
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semantic_relations:
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- type: organizes
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links:
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- [[implementation_examples]]
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- [[usage_examples]]
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---
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# Examples Index
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## Core Examples
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### Active Inference Examples
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- [[examples/active_inference/basic|Basic Active Inference]]
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- [[examples/active_inference/hierarchical|Hierarchical Active Inference]]
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- [[examples/active_inference/multi_agent|Multi-Agent Active Inference]]
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### POMDP Examples
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- [[examples/pomdp/basic|Basic POMDP]]
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- [[examples/pomdp/belief_updating|Belief Updating]]
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- [[examples/pomdp/policy_selection|Policy Selection]]
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### Swarm Intelligence Examples
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- [[examples/swarm/ant_colony|Ant Colony Simulation]]
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- [[examples/swarm/particle_swarm|Particle Swarm]]
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- [[examples/swarm/flocking|Flocking Behavior]]
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## Implementation Examples
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### Agent Implementation
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```python
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# Basic active inference agent
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class ActiveInferenceAgent:
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def __init__(self, config):
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self.beliefs = initialize_beliefs()
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self.model = create_generative_model()
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def update(self, observation):
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# Update beliefs using variational inference
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self.beliefs = update_beliefs(
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self.beliefs, observation, self.model
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)
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# Select action using expected free energy
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action = select_action(self.beliefs, self.model)
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return action
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```
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### Environment Implementation
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```python
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# Basic environment setup
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class Environment:
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def __init__(self, config):
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self.state = initialize_state()
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self.agents = create_agents()
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def step(self, actions):
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# Update environment state
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self.state = update_state(self.state, actions)
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# Generate observations
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observations = generate_observations(self.state)
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return observations
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```
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### Simulation Implementation
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```python
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# Basic simulation loop
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def run_simulation(config):
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env = Environment(config)
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agent = ActiveInferenceAgent(config)
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for step in range(config.max_steps):
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# Agent-environment interaction
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observation = env.get_observation()
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action = agent.update(observation)
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env.step(action)
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```
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## Advanced Examples
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### Hierarchical Systems
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- [[examples/hierarchical/perception|Hierarchical Perception]]
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- [[examples/hierarchical/control|Hierarchical Control]]
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- [[examples/hierarchical/learning|Hierarchical Learning]]
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### Multi-Agent Systems
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- [[examples/multi_agent/coordination|Agent Coordination]]
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- [[examples/multi_agent/communication|Agent Communication]]
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- [[examples/multi_agent/learning|Collective Learning]]
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### Complex Systems
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- [[examples/complex/emergence|Emergence Patterns]]
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- [[examples/complex/adaptation|System Adaptation]]
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- [[examples/complex/evolution|System Evolution]]
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## Application Examples
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### Robotics Applications
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- [[examples/robotics/control|Robot Control]]
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- [[examples/robotics/navigation|Robot Navigation]]
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- [[examples/robotics/manipulation|Robot Manipulation]]
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### Cognitive Applications
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- [[examples/cognitive/learning|Learning Systems]]
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- [[examples/cognitive/memory|Memory Systems]]
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- [[examples/cognitive/attention|Attention Systems]]
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### Biological Applications
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- [[examples/biological/neural|Neural Systems]]
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- [[examples/biological/collective|Collective Behavior]]
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- [[examples/biological/adaptation|Adaptive Behavior]]
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## Integration Examples
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### Framework Integration
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- [[examples/integration/pytorch|PyTorch Integration]]
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- [[examples/integration/tensorflow|TensorFlow Integration]]
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- [[examples/integration/jax|JAX Integration]]
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### Tool Integration
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- [[examples/tools/visualization|Visualization Tools]]
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- [[examples/tools/analysis|Analysis Tools]]
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- [[examples/tools/profiling|Profiling Tools]]
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### System Integration
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- [[examples/systems/environment|Environment Integration]]
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- [[examples/systems/hardware|Hardware Integration]]
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- [[examples/systems/distributed|Distributed Systems]]
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## Testing Examples
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### Unit Tests
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```python
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def test_belief_updating():
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"""Test belief updating mechanism."""
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agent = setup_test_agent()
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observation = generate_test_observation()
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initial_beliefs = agent.beliefs.copy()
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agent.update(observation)
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assert not np.allclose(agent.beliefs, initial_beliefs)
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assert is_normalized(agent.beliefs)
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```
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### Integration Tests
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```python
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def test_agent_environment():
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"""Test agent-environment interaction."""
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env = setup_test_environment()
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agent = setup_test_agent()
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observation = env.reset()
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for _ in range(100):
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action = agent.update(observation)
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observation, reward, done = env.step(action)
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if done:
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break
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```
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### Performance Tests
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```python
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def test_performance():
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"""Test system performance."""
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env = setup_benchmark_environment()
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agent = setup_benchmark_agent()
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start_time = time.time()
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run_benchmark(env, agent)
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end_time = time.time()
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assert end_time - start_time < MAX_TIME
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```
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## Related Resources
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### Documentation
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- [[docs/guides/implementation_guides|Implementation Guides]]
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- [[docs/api/implementation_api|Implementation API]]
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- [[docs/research/implementation_research|Implementation Research]]
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### Knowledge Base
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- [[knowledge_base/cognitive/implementation_concepts|Implementation Concepts]]
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- [[knowledge_base/mathematics/implementation_math|Implementation Mathematics]]
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- [[knowledge_base/agents/implementation_patterns|Implementation Patterns]]
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### Learning Resources
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- [[learning_paths/implementation|Implementation Learning Path]]
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- [[tutorials/implementation|Implementation Tutorials]]
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- [[guides/implementation/best_practices|Implementation Best Practices]] |