{
  "author": "Maclain Hunter",
  "title": "MCB Server, Processor, AI, NN, Infinite Range Code",
  "identifier": "hunter-mcb-server-code",
  "sourceKind": "authored-foundational-record",
  "content": "MCB Server, Processor, AI, NN, Infinite Range Code:\n\n# Copyright 2024 Maclain Hunter All Rights Reserved\n\n# Define MCB Base (-1)\nBase = -1\nBase_Inv = 1 \n\n# Define a range of numbers\nnumber_range = range(-10, 11)\n\n# Define data lists corresponding to each numerical value\ndata_lists = []\nfor num in number_range:\n\n    data_lists.append([num, Base ** num, (Base/10) ** num])\n    if num == 0:\n        data_lists.append([num, 1, 1])\n        \n    else:\n        data_lists.append([num, Base ** num, (Base/10) ** num])\n\n# Example: Print the data corresponding to each numerical value\nfor data in data_lists:\n    print(data)\n\n# Map register positions to numerical values\nregister_mapping = {num: f\"Register_{num}\" for num in number_range}\n\n# Example: Print the register positions corresponding to each numerical value\nfor num, register_position in register_mapping.items():\n    print(f\"Numerical Value: {num}, Register Position: {register_position}\")\n    \nimport socket\nimport multiprocessing\n\ndef handle_client(client_socket):\n    try:\n        # Receive client request\n        request = client_socket.recv(1024).decode()\n\n        # Process the request\n        if request == \"GET_DATA\":\n            # Logic for retrieving data from the server\n            response = \"Here is the requested data\"\n        elif request == \"PERFORM_CALCULATION\":\n            # Logic for performing calculations\n            response = \"Here is the result of the calculation\"\n        elif request == \"EXECUTE_OPERATION\":\n            # Logic for executing specific operations\n            response = \"The operation was successfully executed\"\n        else:\n            response = \"Invalid request\"\n\n        # Send response back to the client\n        client_socket.send(response.encode())\n\n    except Exception as e:\n        print(f\"Error handling client: {e}\")\n\n    finally:\n        # Close the client connection\n        client_socket.close()\n\n# Map client_number to numerical values\nclient_mapping = {num: f\"Server_{num}\" for num in number_range}\n\ndef start_server(port, num_clients):\n    global server_socket\n\n    while True:\n        try:\n            # Create a TCP/IP socket\n            server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n            # Bind the socket to a specific address and port\n            server_address = ('', port)\n            server_socket.bind(server_address)\n\n            # Listen for incoming connections\n            server_socket.listen(num_clients)\n\n            print(f\"Server started on port {port}\")\n\n            # Initialize client counter\n            client_counter = 0\n\n            while True:\n                # Accept a client connection\n                connection, client_address = server_socket.accept()\n\n                # Assign a unique number to the client\n                client_counter += 1\n                client_number = client_counter\n\n                # Create a new process to handle the client\n                process = multiprocessing.Process(target=handle_client, args=(connection, client_number))\n                process.start()\n\n        except Exception as e:\n            print(f\"Error in start_server: {e}\")\n            server_socket.close()\n            continue\n\n# Define functions for each \"screen\" or \"view\"\ndef main_menu():\n    print(\"MCB Processor - Main Menu\")\n    print(\"1. MCB Processor Input\")\n    print(\"2. MCB Processor Output\")\n    print(\"3. MCB Neural Network\")\n    print(\"4. Quit\")\n\ndef option1_menu():\n    print(\"Input Menu\")\n    print(\"1. Input Source\")\n    print(\"2. Processing Settings\")\n    print(\"3. Back to Main Menu\")\n    \ndef inputsource_menu():\n    print(\"Input Source\")\n    print(\"1. Digital Input\")\n    print(\"2. Analog Input\")\n    print(\"3. Back to Input Menu\")\n    \ndef processingsettings_menu():\n    print(\"Processing Settings\")\n    print(\"1. Mappings\")\n    print(\"2. Processing Method\")\n    print(\"3. Back to Input Source\")\n\ndef option2_menu():\n    print(\"Output Menu\")\n    print(\"1. Output Destination\")\n    print(\"2. Output Settings\")\n    print(\"3. Back to Main Menu\")\n    \ndef outputdestination_menu(): \n    print(\"Output Destination\")\n    print(\"1. Hardware Default (Offline)\")\n    print(\"2. Serve Network (Online)\")\n    print(\"3. Back to Output Menu\")\n    \ndef outputsettings_menu():\n    print(\"Output Settings\")\n    print(\"1. Output Format\")\n    print(\"2. Encryption Settings\")\n    print(\"3. Back to Output Destination Menu\")\n    \ndef option3_menu(): \n    print(\"MCB Neural Network\")\n    print(\"1. Nueral Network I/O\")\n    print(\"2. Neural Network Settings\")\n    print(\"3. Back to Main Menu\")\n    \ndef nnio_menu():\n    print(\"Neural Network I/O\")\n    print(\"1. Neural Net Input\")\n    print(\"2. Neural Net Output\")\n    print(\"3. Back to Neural Network Menu\")\n    \ndef nnnetset_menu():\n    print(\"Neural Network Settings\")\n    print(\"1. Neural Net Processing/Server Settings\")\n    print(\"2. Neural Net Learning/AI Settings\")\n    print(\"3. Natural Language Chat Interface\")\n    print(\"4. Back to Neural Network Menu\")\n    \n# Main loop\nwhile True:\n    main_menu()\n    choice = input(\"Enter Command:\")\n\n    if choice == \"1\":\n        while True:\n            option1_menu()\n            sub_choice = input(\"Enter Command: \")\n            if sub_choice == \"1\":\n                print(\"You Selected: Input Source\")\n            elif sub_choice == \"2\":\n                print(\"You selected: processing settings\")\n            elif sub_choice == \"3\":\n                break  # Go back to Main Menu\n            \n            else:\n                print(\"Invalid Command.\")\n                \n# Sub loop input\n            while True:\n                option1_menu()\n                sub_choice = input(\"Enter Command:\")\n                    \n                if sub_choice == \"1\":\n                    while True:\n                        inputsource_menu()\n                        sub_choice = input(\"Enter Command: \")\n                        if sub_choice == \"1\":\n                            print(\"You Selected: Digital Input\")\n                        elif sub_choice == \"2\":\n                            print(\"You selected: Analog Input\")\n                        elif sub_choice == \"3\":\n                            break  # Go back to Input Menu\n                                    \n                        else:\n                            print(\"Invalid Command.\")\n                            \n                elif sub_choice == \"2\":\n                    while True:\n                        processingsettings_menu()\n                        sub_choice = input(\"Enter Command: \")\n                        if sub_choice == \"1\":\n                            print(\"You Selected: Mappings\")\n                        elif sub_choice == \"2\":\n                            print(\"You Selected: Processing Method\")\n                        elif sub_choice == \"3\":\n                            break # Go back to Input Source\n                        \n                        else:\n                            print(\"Invalid Command.\")\n                            \n                elif sub_choice == \"3\":\n                        break\n                    \n                else:\n                    print(\"Invalid Command.\")\n                            \n            \n                \n    elif choice == \"2\":\n        while True:\n            option2_menu()\n            sub_choice = input(\"Enter Command: \")\n            if sub_choice == \"1\":\n                print(\"Output Destination\")\n            elif sub_choice == \"2\":\n                print(\"Output Settings\")\n            elif sub_choice == \"3\":\n                break  # Go back to Main Menu \n            \n            else:\n                print(\"Invalid Command.\")\n                \n# Sub loop output\n            while True:\n                option2_menu()\n                sub_choice = input(\"Enter Command:\")\n                    \n                if sub_choice == \"1\":\n                    while True:\n                        outputdestination_menu()\n                        sub_choice = input(\"Enter Command: \")\n                        if sub_choice == \"1\":\n                            print(\"You Selected: Hardware Default (Offline)\")\n                        elif sub_choice == \"2\":\n                            print(\"You selected: Serve Network (Online)\")\n                        elif sub_choice == \"3\":\n                            break  # Go back to Output Menu\n            \n                        else:\n                            print(\"Invalid Command.\")\n            \n                elif sub_choice == \"2\":\n                    while True:\n                        outputsettings_menu()\n                        sub_choice = input(\"Enter Command: \")\n                        if sub_choice == \"1\":\n                            print(\"You Selected: Output Format\")\n                        elif sub_choice == \"2\":\n                            print(\"You Selected: Encryption Settings\")\n                        elif sub_choice == \"3\":\n                            break # Go back to Output Settings \n                       \n                        else:\n                            print(\"Invalid Command.\")\n                           \n                elif sub_choice == \"3\":\n                    break\n                \n                else:\n                    print(\"Invalid Command.\")\n                    \n    elif choice == \"3\": \n        while True:\n            option3_menu()\n            sub_choice = input(\"Enter Command:\")\n                    \n            if sub_choice == \"1\":\n                while True:\n            \n                    nnio_menu()\n                \n                    sub_choice = input(\"Enter Command: \")\n                    if sub_choice == \"1\":\n                                                 print(\"You Selected: Neural Network Input\")\n                    elif sub_choice == \"2\":\n                                                 print(\"You selected: Neural Network Output\")\n                    elif sub_choice == \"3\":\n                                             break  # Go back to Output Menu\n                        \n                    else:\n                        print(\"Invalid Command.\")\n                        \n            elif sub_choice == \"2\":\n                while True:\n                        \n                    nnnetset_menu()\n                    sub_choice = input(\"Enter Command: \")\n                                    \n                    if sub_choice == \"1\":\n                                        print(\"You Selected: Processing/Server Settings\")\n                    elif sub_choice == \"2\":\n                                        print(\"You Selected: Learning/AI Settings\")\n                    elif sub_choice == \"3\":\n                                        \n                        print(\"You Selected: Natural Language Chat Interface\")\n                                    \n                    elif sub_choice == \"4\":\n                                        break # Go back to Output Settings \n                                   \n                    else:\n                                        print(\"Invalid Command.\")\n                  \n    elif choice == \"4\":\n            print(\"Goodbye.\")\n            break  # Exit the program\n                    \n    else:\n        print(\"Invalid Command.\")\n        \ndef nn_integration():\n    global nn_output\n    while True:\n        nn_menu()\n        nn_choice = input(\"Enter Command:\")\n        if nn_choice == \"1\":\n            # Get data from the input source\n            data = get_data_from_input_source()\n            # Pass the data to the neural network\n            nn_output = neural_network.process(data)\n            # Display the output of the neural network\n            print(\"Neural Network Output:\", nn_output)\n            \n        elif nn_choice == \"2\":\n    # Get output destination from the user\n            output_destination = get_output_destination_from_user()\n    # Send the output of the neural network to the specified output destination\n            send_output_to_destination(nn_output, output_destination)\n\n        elif nn_choice == \"3\":\n    # Update the neural network with new data\n            new_data = get_new_data_from_user()\n            neural_network.update(new_data)\n\n        elif nn_choice == \"4\":\n    # Save the current state of the neural network\n            neural_network.save()\n\n        elif nn_choice == \"5\":\n    # Load a previously saved state of the neural network\n            neural_network.load()\n\n        elif nn_choice == \"6\":\n    # Display the current state of the neural network\n            neural_network.display()\n\n        elif nn_choice == \"7\":\n    # Quit the neural network integration\n            break\n\n        else:\n            print(\"Invalid Command.\")\n    \n    if choice == \"4\":\n    # Integrate the neural network in the main loop\n        nn_integration()\n        \nimport random\n \n# Define number of nodes per layer  \ninput_nodes = len(number_range)\nhidden_nodes = len(number_range)\noutput_nodes = 1\n\n# Create Node objects  \nclass Node:\n  def __init__(self, number_range):\n    self.inputs = [0] * num\n    self.output = 0\n\n  # Activation function\n  def sigmoid(self, x):\n    return 1 / (1 + np.exp(-x))\n\n# Create input and hidden nodes  \ninput_nodes = [Node(input_nodes) for data in data_lists] \nhidden_nodes = [Node(input_nodes) for num in range(0, 101)]\n\n# Output node\noutput_node = Node(hidden_nodes)  \n\n# Generate random weights\nweights_ih = [random.random() for num in range(len(number_range) * len(number_range))]\nweights_ho = [random.random() for num in range(len(number_range) * len(number_range))]\n\n# Generate random weights based on processor math\nweights_ih = [num / 10 ** Base for num in number_range]\nweights_ho = [num / 10 ** Base for num in number_range]\n  \ndef feedforward():\n                  # Input layer\n                  for i in range(len(input_nodes)):\n                      input_nodes[i].output = data_lists[i]\n              \n                  # Hidden layer\n                  for j in range(len(hidden_nodes)):\n                      sum = 0\n                      for i in range(len(input_nodes)):\n                          sum += input_nodes[i].output * weights_ih[i * len(number_range) + j]\n                      hidden_nodes[j].output = sum ** Base\n                      print(f\"Hidden Node {j}: Sum = {sum}, Output = {hidden_nodes[j].output}\")\n              \n                  # Output layer\n                  sum = 0\n                  for j in range(len(hidden_nodes)):\n                      sum += hidden_nodes[j].output * weights_ho[j * len(number_range)]\n                  output_node.output = sum ** Base\n                  print(f\"Output Node: Sum = {sum}, Output = {output_node.output}\")\n\ndef create_mcb_data(dataset):\n    mcb_data = []\n    for data in data_lists:\n        input_values = [val[0] for val in data_lists]\n        output_values = [val[1] for val in data_lists]\n\n        mcb_input = [(-1) ** input_value for input_value in input_values]\n        mcb_output = [1 if output_value == 'A' else -1 for output_value in output_values]\n\n        mcb_data.append((mcb_input, mcb_output))\n\n    return mcb_data\n\ndef initialize_mcb_neural_network(input_nodes, hidden_nodes, output_nodes):\n    input_layer = [Node(input_nodes) for num in number_range]\n    hidden_layer = [Node(input_nodes) for num in number_range]\n    output_layer = [Node(hidden_nodes) for num in number_range]\n\n    return input_layer, hidden_layer, output_layer\n\ndef mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho):\n    for i in range(len(input_layer)):\n        input_layer[i].output = data_lists[i]\n\n    for j in range(len(hidden_layer)):\n        sum = 0\n        for i in range(len(input_layer)):\n            sum += input_layer[i].output * weights_ih[i * len(input_layer) + j]\n        hidden_layer[j] = sum ** Base\n\n    sum = 0\n    for j in range(len(hidden_layer)):\n        sum += hidden_layer[j].output * weights_ho[j]\n    output_layer[0].output = sum ** Base\n\n    return output_layer[0].output\n\ndef train_mcb_neural_network(mcb_data, input_layer, hidden_layer, output_layer, learning_rate, num_epochs):\n    weights_ih = [random.random() for _ in range(len(input_layer) * len(hidden_layer))]\n    weights_ho = [random.random() for _ in range(len(hidden_layer) * len(output_layer))]\n\n    for epoch in range(num_epochs):\n        for data_point in mcb_data:\n            inputs, outputs = data_point\n\n            # Feedforward operation\n            output = mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho)\n\n            # Backpropagation\n            error = output - outputs[0]\n            hidden_layer_error = error * output_layer[0].sigmoid_derivative()\n            input_layer_error = [hidden_layer_error[j] * hidden_layer[j].output for j in range(len(hidden_layer))]\n\n            # Weight update\n            weights_ho = [weights_ho[i] + learning_rate * error * hidden_layer[i].output for i in range(len(hidden_layer))]\n            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))]\n\n    return weights_ih, weights_ho\n\ndataset = [\n    ([1, 2, 3], 'A'),\n    ([4, 5, 6], 'B'),\n    ([7, 8, 9], 'A'),\n    ([10, 11, 12], 'B')\n]\n\nmcb_data = create_mcb_data(dataset)\ninput_layer, hidden_layer, output_layer = initialize_mcb_neural_network(len(number_range), len(number_range), 1)\n\nlearning_rate = 0.1\nnum_epochs = 1000\n\nweights_ih, weights_ho = train_mcb_neural_network(mcb_data, input_layer, hidden_layer, output_layer, learning_rate, num_epochs)\n\n# Testing\ntest_data = [\n    ([1, 2, 3], 'A'),\n    ([4, 5, 6], 'B'),\n    ([7, 8, 9], 'A'),\n    ([10, 11, 12], 'B'),\n    ([13, 14, 15], 'A')\n]\n\nfor data_point in test_data:\n    inputs, outputs = data_point\n\n    # Feedforward operation\n    output = mcb_feedforward(input_layer, hidden_layer, output_layer, weights_ih, weights_ho)\n\n    if output > 0:\n        print(f\"Predicted: A, Actual: {outputs[0]}\")\n    else:\n        print(f\"Predicted: B, Actual: {outputs[0]}\")\n        \n        ",
  "formal": null,
  "structuredAuthoredProof": null,
  "authoredSteps": null,
  "recordedAt": "2026-10-02T21:22:46.012Z",
  "originalAuthoredAt": null,
  "propositionMeaningBoundary": "Atomic identifiers retain their meanings in the exact source. No arbitrary truth assignment establishes an ontological or empirical premise; the formal witness checks the written inference."
}