core_framework.py

import numpy as np
import random

def initialize_network(num_neurons, connectivity=0.1, weight_scale=1.0):
    network = np.random.rand(num_neurons, num_neurons) * weight_scale
    mask = np.random.rand(num_neurons, num_neurons) < connectivity
    return network * mask

def create_new_neurons(num_neurons):
    return np.random.rand(num_neurons, num_neurons)

def integrate_neurons(network, new_neurons):
    size = len(network)
    new_size = size + len(new_neurons)
    new_network = np.zeros((new_size, new_size))
    new_network[:size, :size] = network
    new_network[size:, size:] = new_neurons
    new_network[:size, size:] = np.random.rand(size, len(new_neurons))
    new_network[size:, :size] = np.random.rand(len(new_neurons), size)
    return new_network

def apply_synaptic_plasticity(network, alpha=0.02, beta=0.02):
    pre_synaptic_activity = np.random.rand(len(network))
    post_synaptic_activity = np.random.rand(len(network))

    for i in range(len(network)):
        for j in range(len(network)):
            if pre_synaptic_activity[i] > 0.5 and post_synaptic_activity[j] > 0.5:
                network[i, j] += alpha * (1 - network[i, j])
            else:
                network[i, j] -= beta * network[i, j]
    return np.clip(network, 0, 1)

class RLAgent:
    def __init__(self):
        self.threshold = 0.7
        self.num_neurons_to_add = 5
        self.reward_history = []
    
    def get_reward(self, network):
        return np.mean(network)
    
    def update_policy(self, network, reward):
        self.reward_history.append(reward)
        avg_reward = np.mean(self.reward_history[-100:])
        if reward > self.threshold:
            self.threshold += 0.01
        else:
            self.threshold -= 0.01
        if len(self.reward_history) > 1000:
            self.reward_history = self.reward_history[-1000:]

class MultimodalSNN:
    def __init__(self, initial_neurons):
        self.network = initialize_network(initial_neurons)
        self.rl_agent = RLAgent()
        self.nlu_module = None
        self.tts_module = None
        self.servo_control_module = None
        
    def set_modules(self, nlu_module, tts_module, servo_control_module):
        self.nlu_module = nlu_module
        self.tts_module = tts_module
        self.servo_control_module = servo_control_module
        
    def process_nlu_input(self, text):
        return self.nlu_module.process(text)
    
    def process_tts_input(self, text):
        return self.tts_module.convert(text)
    
    def process_servo_input(self, commands):
        return self.servo_control_module.execute(commands)
    
    def evaluate_performance(self):
        return self.rl_agent.get_reward(self.network)
    
    def add_neurons(self, num_neurons):
        new_neurons = create_new_neurons(num_neurons)
        self.network = integrate_neurons(self.network, new_neurons)
        self.network = self.update_synaptic_weights()
    
    def update_synaptic_weights(self):
        self.network = apply_synaptic_plasticity(self.network)
    
    def train(self, iterations):
        for _ in range(iterations):
            reward = self.evaluate_performance()
            if reward < self.rl_agent.threshold:
                self.add_neurons(self.rl_agent.num_neurons_to_add)
            self.rl_agent.update_policy(self.network, reward)
    
    def handle_input(self, input_type, data, **kwargs):
        if input_type == 'nlu':
            processed_data = self.process_nlu_input(data, **kwargs)
        elif input_type == 'tts':
            processed_data = self.process_tts_input(data, **kwargs)
        elif input_type == 'servo':
            processed_data = self.process_servo_input(data)
        return f"Processed data: {processed_data}"