Implementation Example - The Codex of Systems

Let's dive deeper into the implementation of the Exponential Recursive Prism Matrix (ERPM). We'll explore a more detailed example and provide an interactive demonstration to help visualize its functionality.

Enhanced ERPM Implementation

class ERPMatrix:
    def __init__(self, dimensions, depth):
        self.dimensions = dimensions
        self.depth = depth
        self.nodes = self.generate_nodes()
        self.connections = self.generate_connections()
    
    def generate_nodes(self):
        def recursive_generator(current_depth):
            if current_depth == 0:
                return [Node()]
            else:
                return [Node(children=recursive_generator(current_depth - 1)) 
                        for _ in range(self.dimensions)]
        return recursive_generator(self.depth)
    
    def generate_connections(self):
        # Complex algorithm to create connections between nodes
        pass
    
    def analyze(self, input_data):
        result = []
        for dimension in range(self.dimensions):
            layer_result = self.process_layer(input_data, dimension)
            result.append(layer_result)
        return result
    
    def process_layer(self, data, dimension):
        # Process data through a specific dimension/layer
        pass
    
    def synthesize(self, output_requirements):
        solution = []
        for requirement in output_requirements:
            partial_solution = self.traverse_matrix(requirement)
            solution.append(partial_solution)
        return solution
    
    def traverse_matrix(self, requirement):
        # Traverse the matrix to find optimal solution path
        pass

class Node:
    def __init__(self, children=None):
        self.children = children or []
        self.value = self.generate_value()
    
    def generate_value(self):
        # Generate a value for this node
        pass

# Usage
matrix = ERPMatrix(dimensions=5, depth=10)
input_data = [1, 2, 3, 4, 5]
output_requirements = ["A", "B", "C"]

analysis_result = matrix.analyze(input_data)
synthesis_solution = matrix.synthesize(output_requirements)
    

Interactive Demonstration