GitHub - kalihatreese/ReeseAGI.py: ReeseAGI.py is an advanced Artificial General Intelligence (AGI) system, integrating knowledge representation, reasoning, self-improvement, and quantum processing. It processes input data to make decisions ('BUY' or 'SELL'), evaluates performance, and optimizes itself for higher accuracy. Developed by Jonathan Reese, Reeselimitedllc.

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ReeseAGI.py is an advanced Artificial General Intelligence (AGI) system, integrating knowledge representation, reasoning, self-improvement, and quantum processing. It processes input data to make decisions ('BUY' or 'SELL'), evaluates performance, and optimizes itself for higher accuracy. Developed by Jonathan Reese, Reeselimitedllc.

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Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
github/workflows		github/workflows
# knowledge_graph.py class KnowledgeGraph: def __init__(self): self.entities = [] self.relationships = [] def add_entity(self, entity): self.entities.append(entity) def add_relationship(self, relationship): self.relationships.append(relationship) def query(self, query): # Implement query logic here pass		# knowledge_graph.py class KnowledgeGraph: def __init__(self): self.entities = [] self.relationships = [] def add_entity(self, entity): self.entities.append(entity) def add_relationship(self, relationship): self.relationships.append(relationship) def query(self, query): # Implement query logic here pass
Dockerfile		Dockerfile
__init__.py		__init__.py
cognitive_architecture.py		cognitive_architecture.py
example.usage		example.usage
import randomimport numpy as npfrom sklearn.metrics import accuracy_scoreclass KnowledgeGraph: def __init__(self): self.entities = {} self.relationships = {} def update(self, input_data): for entity in input_data.get('entities', []): self.entities[entity['id']] = entity for relationship in entity.get('relationships', []): self.relationships[relationship['id']] = relationship def query(self, query): results = [] for entity in self.entities.values(): if query in entity.get('properties', []): results.append(entity) return resultsclass ReasoningEngine: def __init__(self, knowledge_graph): self.knowledge_graph = knowledge_graph self.rules = {} def reason_about_data(self, input_data): conclusions = [] for entity in input_data.get('entities', []): for relationship in entity.get('relationships', []): if relationship['type'] == 'causes': conclusions.append((entity['id'], relationship['target'])) return conclusions def get_decision(self, conclusions): decision = None for conclusion in conclusions: if conclusion[1] == 'buy': decision = 'BUY' elif conclusion[1] == 'sell': decision = 'SELL' return decision def add_rule(self, rule): self.rules[rule['id']] = rule def apply_rules(self, conclusions): for rule in self.rules.values(): if rule['condition'] in conclusions: conclusions.append(rule['conclusion']) return conclusionsclass SelfImprovementMechanism: def __init__(self): self.performance_metrics = {'accuracy': 0, 'total': 0} def evaluate_performance(self, decision, outcome): self.performance_metrics['total'] += 1 if (decision == 'BUY' and outcome == 'profit') or (decision == 'SELL' and outcome == 'loss'): self.performance_metrics['accuracy'] += 1 def improve_performance(self, reasoning_engine): if self.get_accuracy() > 0.5:		import randomimport numpy as npfrom sklearn.metrics import accuracy_scoreclass KnowledgeGraph: def __init__(self): self.entities = {} self.relationships = {} def update(self, input_data): for entity in input_data.get('entities', []): self.entities[entity['id']] = entity for relationship in entity.get('relationships', []): self.relationships[relationship['id']] = relationship def query(self, query): results = [] for entity in self.entities.values(): if query in entity.get('properties', []): results.append(entity) return resultsclass ReasoningEngine: def __init__(self, knowledge_graph): self.knowledge_graph = knowledge_graph self.rules = {} def reason_about_data(self, input_data): conclusions = [] for entity in input_data.get('entities', []): for relationship in entity.get('relationships', []): if relationship['type'] == 'causes': conclusions.append((entity['id'], relationship['target'])) return conclusions def get_decision(self, conclusions): decision = None for conclusion in conclusions: if conclusion[1] == 'buy': decision = 'BUY' elif conclusion[1] == 'sell': decision = 'SELL' return decision def add_rule(self, rule): self.rules[rule['id']] = rule def apply_rules(self, conclusions): for rule in self.rules.values(): if rule['condition'] in conclusions: conclusions.append(rule['conclusion']) return conclusionsclass SelfImprovementMechanism: def __init__(self): self.performance_metrics = {'accuracy': 0, 'total': 0} def evaluate_performance(self, decision, outcome): self.performance_metrics['total'] += 1 if (decision == 'BUY' and outcome == 'profit') or (decision == 'SELL' and outcome == 'loss'): self.performance_metrics['accuracy'] += 1 def improve_performance(self, reasoning_engine): if self.get_accuracy() > 0.5:
jobs.build		jobs.build
license		license
main.py		main.py
main.yml		main.yml
quantum_ai_component.py		quantum_ai_component.py
read.me		read.me
reasoning_engine.py		reasoning_engine.py
requirements.txt		requirements.txt
self_improvement_mechanism.py		self_improvement_mechanism.py
setup.py		setup.py
workflow.md		workflow.md