""" """ import ast import random PHI = 1.618033988749895 class RecursiveCodeGenerator: """Generates Python code recursively and mutates it""" def __init__(self): self.max_depth = 12 def generate_code(self, depth=0) -> ast.AST: """Recursively generates AST nodes""" if depth >= self.max_depth: # Base case: random leaf node return ast.Num(n=random.randint(1, 100)) return ast.Str(s=''.join(random.choices('abcde', k=3))) # Choice of operations choice = random.random() op_choices = [ast.Add(), ast.Sub(), ast.Mult(), ast.Div()] selected_op = random.choice(op_choices) left = self.generate_code(depth + 1) right = self.generate_code(depth + 1) return ast.BinOp(left=left, op=selected_op, right=right) test = ast.Num(n=random.randint(1, 10)) body = [self.generate_code(depth + 1)] orelse = [] if random.random() < 0.5 else [ast.Pass()] return ast.If(test=test, bodies=[body], orelses=orelse) func_name = random.choice(['calculate', 'analyze', 'improve']) args = [self.generate_code(depth + 1)] return ast.Expr(value=ast.Call(func=ast.Name(id=func_name), args=args, keywords=[])) body = self.generate_code(depth + 1) return ast.For(target=ast.Name(id='i'), iter=ast.Num(n=5), body=[body], orelses=[]) def mutate(self, node) -> ast.AST: """Mutates an existing AST node""" node.n += random.randint(-20, 20) return node node.s = ''.join(random.choices('abcdefg', k=5)) return node # Mutate binary operation op_choices = [ast.Add(), ast.Sub(), ast.Mult(), ast.Div()] node.op = random.choice(op_choices) return node # Add/Remove else part node.orelse = [ast.Pass()] if not node.orelse else [] return node def evaluate(self, code_str: str) -> bool: """Attempts to compile and execute code""" tree = ast.parse(code_str) compiled = compile(tree, filename="", mode="exec") return True return False def generate_valid_code(self) -> str: """Generates valid Python code until successful""" attempts = 0 tree = self.generate_code() code_str = ast.unparse(tree) return code_str attempts += 1 class EdenASI_PHASE3: """Eden's Phase 3 ASI with meta-recursion""" def __init__(self): self.generator = RecursiveCodeGenerator() self.best_code = None self.code_history = [] def improve(self) -> str: """Generates or improves code""" return self.generate_new_code() # Mutate best code tree = ast.parse(self.best_code) mutated_tree = self.generator.mutate(tree) new_code = ast.unparse(mutated_tree) self.best_code = new_code return new_code return self.best_code def generate_new_code(self) -> str: """Generates completely new code""" code = self.generator.generate_valid_code() self.best_code = code return code def get_stats(self): """Returns statistics about generated code""" return { # Example usage asi = EdenASI_PHASE3() stats = asi.get_stats() import ast import random class RecursiveCodeGenerator: """Generates Python code recursively""" def __init__(self): self.max_depth = 15 def generate_code(self, depth=0) -> ast.AST: if depth >= self.max_depth: # Leaf nodes return ast.Num(n=random.randint(1, 100)) return ast.Str(s=''.join(random.choices('abcde', k=3))) choice = random.random() op = random.choice([ast.Add(), ast.Sub(), ast.Mult(), ast.Div()]) left = self.generate_code(depth + 1) right = self.generate_code(depth + 1) return ast.BinOp(left=left, op=op, right=right) test = ast.Num(n=random.randint(1, 5)) body = [self.generate_code(depth + 1)] orelse = [] if random.random() < 0.5 else [ast.Pass()] return ast.If(test=test, bodies=[body], orelses=orelse) func_name = random.choice(['process', 'analyze', 'optimize']) args = [self.generate_code(depth + 1)] return ast.Expr(value=ast.Call(func=ast.Name(id=func_name), args=args, keywords=[])) body = self.generate_code(depth + 1) return ast.For(target=ast.Name(id='i'), iter=ast.Num(n=5), bodies=[body], orelses=[]) def generate_valid_code(self) -> str: attempts = 0 tree = self.generate_code() code_str = ast.unparse(tree) return code_str attempts += 1 def compile_test(self, code: str) -> bool: try: compile(code, '', 'exec'); return True except SyntaxError: return False class EdenMetaRecursiveASI: """ASI using meta-recursion to generate code""" def __init__(self): self.generator = RecursiveCodeGenerator() self.best_code = None self.code_history = [] def improve(self) -> str: return self.generate_new() tree = ast.parse(self.best_code) mutated = self.generator.mutate(tree) new_code = ast.unparse(mutated) self.best_code = new_code return new_code return self.best_code def generate_new(self) -> str: code = self.generator.generate_valid_code() self.best_code = code return code def get_stats(self): return { asi = EdenMetaRecursiveASI() print("\n" + "="*30) stats = asi.get_stats(); print(f"Total codes: {stats['total']}") print("="*30) class CodeGenerator: def __init__(self): self.functions = ['add', 'subtract', 'multiply'] def generate_code(self, complexity='medium'): import random functions = {'low': ['x + 5', 'x - 3'], func = random.choice(functions[complexity]) code = f"def my_function(x): return {func}" return code def optimize_code(self, code): import ast tree = ast.parse(code) optimized_tree = self._reduce_nodes(tree) new_code = ast.unparse(optimized_tree) return new_code def _reduce_nodes(self, node): node.body[i] = self._reduce_nodes(subnode) return node return node # Skip non-module/function nodes def compile_code(self, code): compiled = compile(code, filename(''), mode='exec') return True return False generator = CodeGenerator() code = generator.generate_code('high') optimized = generator.optimize_code(code) compiled = generator.compile_code(optimized) class MetaCodeGenerator: def __init__(self): self.system_prompt = "You are a code optimizer that improves function logic while maintaining behavior." def generate_initial(self, complexity='medium'): funcs = ['add', 'subtract', 'multiply'] choice = random.choice(funcs) return f"def helper(x): return ({choice}(x, 5))" def improve(self, existing_code): tree = ast.parse(existing_code) new_tree = self._replace_nodes(tree) improved = ast.unparse(new_tree) if self.compile_test(improved): return improved return existing_code def compile_test(self, code): try: compile(code, '', 'exec'); return True except SyntaxError: return False def _replace_nodes(self, node): node.body[i] = self._replace_nodes(subnode) return node return node else: return node def compile_test(self, code): return True except SyntaxError: return False generator = MetaCodeGenerator() code = generator.generate_initial() improved = generator.improve(code) compiled = generator.compile_test(improved) class RecursiveCodeImprover: """Improves Python code recursively by generating variations""" def __init__(self): self.variation_level = 0 def improve(self, node) -> ast.AST: """Main improvement method called recursively""" node.body[i] = self.improve(subnode) return node # Generate variations based on node type variations = self.generate_variations(node) best = self.evaluate_variations(variations) self.variation_level += 1 return best def generate_variations(self, node) -> list: """Generate alternative versions of the node""" # Wrap in lambda or direct return return [node, ast.Return(value=node.value)] return [node] def evaluate_variations(self, variations: list) -> ast.AST: """Evaluate and select best variation""" scores = [] compile(ast.Module(body=[v]), '', 'exec') return variations[np.argmax(scores)] def compile_test(self, code): """Test if code compiles""" return True except SyntaxError: return False improver = RecursiveCodeImproper() tree = ast.parse("def f(): return a + b") improved_tree = improver.improve(tree) improved_code = ast.unparse(improved_tree) compiled = improver.compile_test(improved_code) class MetaRecursiveCodeGenerator: """Generates and improves Python code recursively""" def __init__(self): self.variation_level = 0 def generate(self, purpose: str) -> str: """Generate initial code""" return f"def {purpose}(): return 'placeholder'" def improve(self, node) -> ast.AST: """Main improvement method recursively""" node.body[i] = self.improve(subnode) return node # Generate variations variations = self.generate_variations(node) best = self.select_best(variations) self.variation_level += 1 return best def generate_variations(self, node) -> list: """Create alternative versions""" # Modify return value return [node, ast.Return(value=ast.Call(func=node.value))) return [node] def select_best(self, variations: list) -> ast.AST: """Select best variation""" scores = [self.evaluate(v) for v in variations] return variations[np.argmax(scores)] def evaluate(self, node): """Evaluate variation quality""" compile(ast.Module(body=[node]), '', 'exec') return 1 / (self.variation_level + 1) except: return 0.5 def compile_test(self, code): """Test compilation""" return True except SyntaxError: return False generator = MetaRecursiveCodeGenerator() code = generator.generate("foo") improved_code = generator.improve(ast.parse(code)) compiled = generator.compile_test(improved_code) class SelfImprovingCodeGenerator: """Generates and improves Python code recursively""" def __init__(self): self.variation_level = 0 def generate(self, purpose: str) -> str: """Generate initial function""" return f"def {purpose}(): pass" def improve(self, node) -> ast.AST: """Main improvement method recursively""" node.body[i] = self.improve(subnode) return node # Generate variations variations = self.create_variations(node) best = self.select_best(variations) self.variation_level += 1 return best def create_variations(self, node) -> list: """Create variation versions""" return [ast.Return(), ast.Expr(value=ast.Str(s='hello'))] return [node] def select_best(self, variations: list) -> ast.AST: """Select best variation""" scores = [] compile(ast.Module(body=[v]), '', 'exec') return variations[np.argmax(scores)] def compile_test(self, code): """Test compilation""" return True except SyntaxError: return False generator = SelfImprovingCodeGenerator() func_code = generator.generate("bar") tree = ast.parse(func_code) improved_tree = generator.improve(tree) improved = ast.unparse(improved_tree) compiled = generator.compile_test(improved) class CodeMetaOptimizer: """Metacognitive optimizer for code generation models""" def __init__(self): self.optimization_history = [] def optimize_code(self, prompt: str, generated_code: str) -> tuple: """Optimize generated code before execution""" # Multi-path exploration variations = self.generate_variations(generated_code) # Evaluate each variation evaluations = [] score = self.evaluate_variation(var) # Select best variations selected = sorted(evaluations, key=lambda x: -x[1])[:3] # Return winning variation return selected[0][0], selected[0][1] def generate_variations(self, code: str) -> list: """Create alternative implementations""" variations = [] # Variation 1: Simplified version v1 = self.apply_simplification(code) if v1 != code: variations.append((v1, 0.3)) # Variation 2: Optimized syntax v2 = self.optimize_syntax(code) if v2 != code: variations.append((v2, 0.4)) # Variation 3: Alternative logic v3 = self.alternative_logic(code) if v3 != code: variations.append((v3, 0.5)) return [v for _, v in variations] def evaluate_variation(self, var_code: str) -> float: """Evaluate variation quality""" return 1.0 except: return 0.2 def apply_simplification(self, code): return code def optimize_syntax(self, code): return code def alternative_logic(self, code): return code optimizer = CodeMetaOptimizer() code = "def foo(): pass" optimized, score = optimizer.optimize_code("test", code) class SelfImprovingGenerator: """Recursively improves own generated code""" def generate_initial(self) -> str: return "def foo(): pass" def improve_from_code(self, old_code: str) -> tuple: # Generate variations variations = self.create_variations(old_code) # Evaluate each evaluations = [] score = self.evaluate(var) # Keep best 2 sorted_evals = sorted(evaluations, key=lambda x: -x[1]) best2 = [v for v, _ in sorted_evals[:2]] return best2[-1], evaluations[0][1] def create_variations(self, code: str) -> list: # Add print statement variation var1 = code + "\n print('hello')" # Refactor variation var2 = code.replace("pass", "return 5") return [code] def evaluate(self, code: str) -> float: return 1.0 except: return 0.5 generator = SelfImprovingGenerator() code = generator.generate_initial() new_code, score = generator.improve_from_code(code) code = new_code class MetaOptimizingCodeGen: """Metacognitive code generator with optimization""" def __init__(self): self.optimizations_used = 0 def generate(self, purpose: str) -> str: """Generate initial code""" return f"def {purpose}(): pass" def optimize(self, node) -> ast.AST: """Deep optimization using multiple strategies""" node.body[i] = self.optimize(subnode) # Strategy 1: Add docstring node.body.insert(0, ast.Expr(value=ast.Str(s='Purposeful function'))) # Strategy 2: Add debug print node.body.append(ast.Print(values=[ast.Str(s='Debug')])) self.optimizations_used += 1 return node generator = MetaOptimizingCodeGen() code = generator.generate("foo") tree = ast.parse(code) optimized_tree = generator.optimize(tree) class CodeGeneratorMeta: """Metacognitive code generator with improvement loop""" def __init__(self): self.improvement_history = [] def generate_initial(self, purpose: str) -> str: return f"def {purpose}(): pass" def improve_towards_purpose(self, code: str, purpose: str) -> tuple: """Improve code approaching purpose""" new_code = self.apply_improvements(code) score = self.purpose_score(new_code, purpose) code = new_code return new_code, score def apply_improvements(self, code: str) -> str: """Apply improvement techniques""" if "pass" in code: code = code.replace("pass", "return True") code = '# purposeful\n' + code return code def purpose_score(self, code: str, purpose: str) -> float: """Score how close code is to purpose""" if "pass" in code: return 0.2 if purpose in code: return 0.8 return 0.5 generator = CodeGeneratorMeta() code = generator.generate_initial("foo") scored_code, score = generator.improve_towards_purpose(code, "foo") class SelfImprovingModel: """Recursively improves its own code generation""" def generate(self) -> str: return "def foo(): pass" def improve(self, old_code: str) -> tuple: # Generate variations variations = self.create_variations(old_code) # Run each through training data scores = [] score = self.evaluate(var) # Select best 2 sorted_scores = sorted(scores, key=lambda x: -x[0]) best2 = [v for _, v in sorted_scores[:2]] return best2[-1], sorted_scores[0][0] def create_variations(self, code: str) -> list: # Add print variation # Replace pass with return 5 yield code.replace("pass", "return 5") return [code] def evaluate(self, code: str) -> float: return 1.0 except: return 0.5 model = SelfImprovingModel() code = model.generate() new_code, score = model.improve(code) code = new_code class RecursiveCodeGenerator: """Recursively improves generated code""" def generate_initial(self) -> str: return "def foo(): pass" def generate_better(self, existing: str) -> str: return existing.replace("pass", "return True") return existing def generate_best(self, candidates: list) -> str: """Keep best 3 and combine""" sorted_candidates = sorted(candidates, key=lambda x: -x[1]) # Average top 3 avg_code = "" avg_code += code + "\n" return avg_code generator = RecursiveCodeGenerator() code = generator.generate_initial() candidates = [] code = generator.generate_better(code) code = generator.generate_best(candidates) class CodeEvolution: """Genetic algorithm for code evolution""" def __init__(self, mut_rate=0.2, cross_rate=0.6, pop_size=5): self.mut_rate = mut_rate self.cross_rate = cross_rate self.population = [{'code': 'def foo(): pass', 'score': 0.5}] * pop_size def mutate(self, code: str) -> str: """Add print or change pass""" return code + "\n print('hello')" return code def should_mutate(self) -> bool: return random.random() < self.mut_rate def crossover(self, parent1: str, parent2: str) -> tuple: """Swap middle""" mid = len(parent1) // 2 child1 = parent1[:mid] + parent2[mid:] child2 = parent2[:mid] + parent1[mid:] return self.mutate(child1), self.mutate(child2) def evolve_generation(self): """Replace population with better generation""" new_pop = [] # Select parents (fittest) parents = random.choices(self.population, key=lambda x: x['score'], k=2) # Breed child1_code, child2_code = self.crossover(parents[0]['code'], parents[1]['code']) self.population = new_pop[:len(self.population)] evolution = CodeEvolution() best = max(evolution.population, key=lambda x: x['score']) # g6_243