Authored proof knowledge base · The Logos Architecture
MCB Server, Processor, AI, NN, Infinite Range Code
Authored by Maclain Hunter · first-party-authored · authored-foundational-record
Stored 4P source diagnostic: unresolved · finding unverified_concern · admission held_for_format. This intake result is separate from the theorem checks of the exact propositional sequent.
MCB Server, Processor, AI, NN, Infinite Range Code:
# Copyright 2024 Maclain Hunter All Rights Reserved
# Define MCB Base (-1)
Base = -1
Base_Inv = 1
# Define a range of numbers
number_range = range(-10, 11)
# Define data lists corresponding to each numerical value
data_lists = []
for num in number_range:
data_lists.append([num, Base ** num, (Base/10) ** num])
if num == 0:
data_lists.append([num, 1, 1])
else:
data_lists.append([num, Base ** num, (Base/10) ** num])
# Example: Print the data corresponding to each numerical value
for data in data_lists:
print(data)
# Map register positions to numerical values
register_mapping = {num: f"Register_{num}" for num in number_range}
# Example: Print the register positions corresponding to each numerical value
for num, register_position in register_mapping.items():
print(f"Numerical Value: {num}, Register Position: {register_position}")
import socket
import multiprocessing
def handle_client(client_socket):
try:
# Receive client request
request = client_socket.recv(1024).decode()
# Process the request
if request == "GET_DATA":
# Logic for retrieving data from the server
response = "Here is the requested data"
elif request == "PERFORM_CALCULATION":
# Logic for performing calculations
response = "Here is the result of the calculation"
elif request == "EXECUTE_OPERATION":
# Logic for executing specific operations
response = "The operation was successfully executed"
else:
response = "Invalid request"
# Send response back to the client
client_socket.send(response.encode())
except Exception as e:
print(f"Error handling client: {e}")
finally:
# Close the client connection
client_socket.close()
# Map client_number to numerical values
client_mapping = {num: f"Server_{num}" for num in number_range}
def start_server(port, num_clients):
global server_socket
while True:
try:
# Create a TCP/IP socket
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
# Bind the socket to a specific address and port
server_address = ('', port)
server_socket.bind(server_address)
# Listen for incoming connections
server_socket.listen(num_clients)
print(f"Server started on port {port}")
# Initialize client counter
client_counter = 0
while True:
# Accept a client connection
connection, client_address = server_socket.accept()
# Assign a unique number to the client
client_counter += 1
client_number = client_counter
# Create a new process to handle the client
process = multiprocessing.Process(target=handle_client, args=(connection, client_number))
process.start()
except Exception as e:
print(f"Error in start_server: {e}")
server_socket.close()
continue
# Define functions for each "screen" or "view"
def main_menu():
print("MCB Processor - Main Menu")
print("1. MCB Processor Input")
print("2. MCB Processor Output")
print("3. MCB Neural Network")
print("4. Quit")
def option1_menu():
print("Input Menu")
print("1. Input Source")
print("2. Processing Settings")
print("3. Back to Main Menu")
def inputsource_menu():
print("Input Source")
print("1. Digital Input")
print("2. Analog Input")
print("3. Back to Input Menu")
def processingsettings_menu():
print("Processing Settings")
print("1. Mappings")
print("2. Processing Method")
print("3. Back to Input Source")
def option2_menu():
print("Output Menu")
print("1. Output Destination")
print("2. Output Settings")
print("3. Back to Main Menu")
def outputdestination_menu():
print("Output Destination")
print("1. Hardware Default (Offline)")
print("2. Serve Network (Online)")
print("3. Back to Output Menu")
def outputsettings_menu():
print("Output Settings")
print("1. Output Format")
print("2. Encryption Settings")
print("3. Back to Output Destination Menu")
def option3_menu():
print("MCB Neural Network")
print("1. Nueral Network I/O")
print("2. Neural Network Settings")
print("3. Back to Main Menu")
def nnio_menu():
print("Neural Network I/O")
print("1. Neural Net Input")
print("2. Neural Net Output")
print("3. Back to Neural Network Menu")
def nnnetset_menu():
print("Neural Network Settings")
print("1. Neural Net Processing/Server Settings")
print("2. Neural Net Learning/AI Settings")
print("3. Natural Language Chat Interface")
print("4. Back to Neural Network Menu")
# Main loop
while True:
main_menu()
choice = input("Enter Command:")
if choice == "1":
while True:
option1_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Input Source")
elif sub_choice == "2":
print("You selected: processing settings")
elif sub_choice == "3":
break # Go back to Main Menu
else:
print("Invalid Command.")
# Sub loop input
while True:
option1_menu()
sub_choice = input("Enter Command:")
if sub_choice == "1":
while True:
inputsource_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Digital Input")
elif sub_choice == "2":
print("You selected: Analog Input")
elif sub_choice == "3":
break # Go back to Input Menu
else:
print("Invalid Command.")
elif sub_choice == "2":
while True:
processingsettings_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Mappings")
elif sub_choice == "2":
print("You Selected: Processing Method")
elif sub_choice == "3":
break # Go back to Input Source
else:
print("Invalid Command.")
elif sub_choice == "3":
break
else:
print("Invalid Command.")
elif choice == "2":
while True:
option2_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("Output Destination")
elif sub_choice == "2":
print("Output Settings")
elif sub_choice == "3":
break # Go back to Main Menu
else:
print("Invalid Command.")
# Sub loop output
while True:
option2_menu()
sub_choice = input("Enter Command:")
if sub_choice == "1":
while True:
outputdestination_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Hardware Default (Offline)")
elif sub_choice == "2":
print("You selected: Serve Network (Online)")
elif sub_choice == "3":
break # Go back to Output Menu
else:
print("Invalid Command.")
elif sub_choice == "2":
while True:
outputsettings_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Output Format")
elif sub_choice == "2":
print("You Selected: Encryption Settings")
elif sub_choice == "3":
break # Go back to Output Settings
else:
print("Invalid Command.")
elif sub_choice == "3":
break
else:
print("Invalid Command.")
elif choice == "3":
while True:
option3_menu()
sub_choice = input("Enter Command:")
if sub_choice == "1":
while True:
nnio_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Neural Network Input")
elif sub_choice == "2":
print("You selected: Neural Network Output")
elif sub_choice == "3":
break # Go back to Output Menu
else:
print("Invalid Command.")
elif sub_choice == "2":
while True:
nnnetset_menu()
sub_choice = input("Enter Command: ")
if sub_choice == "1":
print("You Selected: Processing/Server Settings")
elif sub_choice == "2":
print("You Selected: Learning/AI Settings")
elif sub_choice == "3":
print("You Selected: Natural Language Chat Interface")
elif sub_choice == "4":
break # Go back to Output Settings
else:
print("Invalid Command.")
elif choice == "4":
print("Goodbye.")
break # Exit the program
else:
print("Invalid Command.")
def nn_integration():
global nn_output
while True:
nn_menu()
nn_choice = input("Enter Command:")
if nn_choice == "1":
# Get data from the input source
data = get_data_from_input_source()
# Pass the data to the neural network
nn_output = neural_network.process(data)
# Display the output of the neural network
print("Neural Network Output:", nn_output)
elif nn_choice == "2":
# Get output destination from the user
output_destination = get_output_destination_from_user()
# Send the output of the neural network to the specified output destination
send_output_to_destination(nn_output, output_destination)
elif nn_choice == "3":
# Update the neural network with new data
new_data = get_new_data_from_user()
neural_network.update(new_data)
elif nn_choice == "4":
# Save the current state of the neural network
neural_network.save()
elif nn_choice == "5":
# Load a previously saved state of the neural network
neural_network.load()
elif nn_choice == "6":
# Display the current state of the neural network
neural_network.display()
elif nn_choice == "7":
# Quit the neural network integration
break
else:
print("Invalid Command.")
if choice == "4":
# Integrate the neural network in the main loop
nn_integration()
import random
# Define number of nodes per layer
input_nodes = len(number_range)
hidden_nodes = len(number_range)
output_nodes = 1
# Create Node objects
class Node:
def __init__(self, number_range):
self.inputs = [0] * num
self.output = 0
# Activation function
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
# Create input and hidden nodes
input_nodes = [Node(input_nodes) for data in data_lists]
hidden_nodes = [Node(input_nodes) for num in range(0, 101)]
# Output node
output_node = Node(hidden_nodes)
# Generate random weights
weights_ih = [random.random() for num in range(len(number_range) * len(number_range))]
weights_ho = [random.random() for num in range(len(number_range) * len(number_range))]
# Generate random weights based on processor math
weights_ih = [num / 10 ** Base for num in number_range]
weights_ho = [num / 10 ** Base for num in number_range]
def feedforward():
# Input layer
for i in range(len(input_nodes)):
input_nodes[i].output = data_lists[i]
# Hidden layer
for j in range(len(hidden_nodes)):
sum = 0
for i in range(len(input_nodes)):
sum += input_nodes[i].output * weights_ih[i * len(number_range) + j]
hidden_nodes[j].output = sum ** Base
print(f"Hidden Node {j}: Sum = {sum}, Output = {hidden_nodes[j].output}")
# Output layer
sum = 0
for j in range(len(hidden_nodes)):
sum += hidden_nodes[j].output * weights_ho[j * len(number_range)]
output_node.output = sum ** Base
print(f"Output Node: Sum = {sum}, Output = {output_node.output}")
def create_mcb_data(dataset):
mcb_data = []
for data in data_lists:
input_values = [val[0] for val in data_lists]
output_values = [val[1] for val in data_lists]
mcb_input = [(-1) ** input_value for input_value in input_values]
mcb_output = [1 if output_value == 'A' else -1 for output_value in output_values]
mcb_data.append((mcb_input, mcb_output))
return mcb_data
def initialize_mcb_neural_network(input_nodes, hidden_nodes, output_nodes):
input_layer = [Node(input_nodes) for num in number_range]
hidden_layer = [Node(input_nodes) for num in number_range]
output_layer = [Node(hidden_nodes) for num in number_range]
return input_layer, hidden_layer, output_layer
def mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho):
for i in range(len(input_layer)):
input_layer[i].output = data_lists[i]
for j in range(len(hidden_layer)):
sum = 0
for i in range(len(input_layer)):
sum += input_layer[i].output * weights_ih[i * len(input_layer) + j]
hidden_layer[j] = sum ** Base
sum = 0
for j in range(len(hidden_layer)):
sum += hidden_layer[j].output * weights_ho[j]
output_layer[0].output = sum ** Base
return output_layer[0].output
def train_mcb_neural_network(mcb_data, input_layer, hidden_layer, output_layer, learning_rate, num_epochs):
weights_ih = [random.random() for _ in range(len(input_layer) * len(hidden_layer))]
weights_ho = [random.random() for _ in range(len(hidden_layer) * len(output_layer))]
for epoch in range(num_epochs):
for data_point in mcb_data:
inputs, outputs = data_point
# Feedforward operation
output = mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho)
# Backpropagation
error = output - outputs[0]
hidden_layer_error = error * output_layer[0].sigmoid_derivative()
input_layer_error = [hidden_layer_error[j] * hidden_layer[j].output for j in range(len(hidden_layer))]
# Weight update
weights_ho = [weights_ho[i] + learning_rate * error * hidden_layer[i].output for i in range(len(hidden_layer))]
weights_ih = [weights_ih[i] + learning_rate * input_layer_error[j] * input_layer[j].output for i in range(len(input_layer), len(hidden_layer) + len(input_layer)) for j in range(len(input_layer))]
return weights_ih, weights_ho
dataset = [
([1, 2, 3], 'A'),
([4, 5, 6], 'B'),
([7, 8, 9], 'A'),
([10, 11, 12], 'B')
]
mcb_data = create_mcb_data(dataset)
input_layer, hidden_layer, output_layer = initialize_mcb_neural_network(len(number_range), len(number_range), 1)
learning_rate = 0.1
num_epochs = 1000
weights_ih, weights_ho = train_mcb_neural_network(mcb_data, input_layer, hidden_layer, output_layer, learning_rate, num_epochs)
# Testing
test_data = [
([1, 2, 3], 'A'),
([4, 5, 6], 'B'),
([7, 8, 9], 'A'),
([10, 11, 12], 'B'),
([13, 14, 15], 'A')
]
for data_point in test_data:
inputs, outputs = data_point
# Feedforward operation
output = mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho)
if output > 0:
print(f"Predicted: A, Actual: {outputs[0]}")
else:
print(f"Predicted: B, Actual: {outputs[0]}")
Lean 4 verification and timestamps
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Logic-centered continuity
This authored proof is connected to Logic, Logos, God, and the self-evident Ground of Reality, and to the distinct systems 4P Engine, MCB Core, 4P-MCB, and the source-first Canon. Formal proof, empirical evidence, and system-generated records remain separately typed.