JARIS

import random

class StatefulWeightedDomainGraph(WeightedDomainGraph):
    def __init__(self):
        super().__init__()
        self.node_states = {}
        self.activation_threshold = 2  # Threshold for state change
        self.decay_rate = 0.1  # Rate at which inactive nodes decay

    def add_node(self, node, domain=None, initial_state="inactive"):
        super().add_node(node, domain)
        self.node_states[node] = initial_state

    def update_node_state(self, node, new_state):
        if node in self.node_states:
            self.node_states[node] = new_state

    def simulate_feedback(self):
        new_states = self.node_states.copy()

        for node in self.graph:
            neighbors = self.get_neighbors(node)
            total_influence = 0

            for neighbor, weight in neighbors.items():
                if self.node_states[neighbor] == "active":
                    total_influence += weight

            # Thresholding: Node activates if influence exceeds threshold
            if total_influence > self.activation_threshold:
                new_states[node] = "active"
            else:
                # Decay: Node decays if inactive
                if self.node_states[node] == "inactive":
                    if random.random() < self.decay_rate:
                        new_states[node] = "very inactive"
                if self.node_states[node] == "very inactive":
                    new_states[node] = "inactive"

        self.node_states = new_states

    def get_node_state(self, node):
        return self.node_states.get(node, "inactive")

# Initialize the stateful graph
stateful_graph = StatefulWeightedDomainGraph()

# Add nodes and their domains with initial states
stateful_graph.add_node("VIN", ["Identity", "Medical", "Electrical", "Tracking"], initial_state="active")
stateful_graph.add_node("SLB", ["Stabilization", "Logistics", "Bio-Physical Systems"])
stateful_graph.add_node("CLARITY", ["Output", "Signal Processing", "Decision Layer"])
stateful_graph.add_node("YTIRALC", ["Feedback", "Error Correction", "Symbolic Inversion"])

# Add edges with weights
stateful_graph.add_edge("VIN", "SLB", 3)
stateful_graph.add_edge("SLB", "CLARITY", 1)
stateful_graph.add_edge("CLARITY", "YTIRALC", 2)
stateful_graph.add_edge("YTIRALC", "VIN", 4)

# Simulate feedback over multiple iterations
for _ in range(5):  # Simulate 5 time steps
    stateful_graph.simulate_feedback()
    print("States after simulation step:")
    print("VIN:", stateful_graph.get_node_state("VIN"))
    print("SLB:", stateful_graph.get_node_state("SLB"))
    print("CLARITY:", stateful_graph.get_node_state("CLARITY"))
    print("YTIRALC:", stateful_graph.get_node_state("YTIRALC"))