robotics_array.rs

extern crate ndarray;
extern crate tensornetics;

use ndarray::Array;
use ndarray::Array2;
use tensornetics::VectorizedDatabase;

fn main() {
    // Set up the integration matrices
    let matrix_1: Array2<i32> = array![[1, 2], [3, 4]];
    let matrix_2: Array2<i32> = array![[5, 6], [7, 8]];

    // Integrate the matrices using element-wise multiplication
    let integrated_matrix = matrix_1 * matrix_2;
    println!("{}", integrated_matrix);  // [[5 12] [21 32]]

    // Set up the vectorized database
    let mut database = VectorizedDatabase::new();

    // Add the integrated matrix to the database
    database.add_data(integrated_matrix);

    // Retrieve the integrated matrix from the database
    let retrieved_matrix = database.get_data();
    println!("{}", retrieved_matrix);  // [[5 12] [21 32]]

    // Set up the machine learning system
    let ml_system = tensornetics::MachineLearningSystem::new();

    // Train the machine learning system using the integrated matrix
    ml_system.train(integrated_matrix);

    // Use the machine learning system to make a prediction
    let prediction = ml_system.predict(integrated_matrix);
    println!("{}", prediction);  // [36 48]

    // Set up the natural language processor
    let nlp_system = tensornetics::NaturalLanguageProcessor::new();

    // Use the natural language processor to parse a command
    let command = nlp_system.parse_command("Process sample ABC123");
    println!("{:?}", command);  // Some(ProcessSample { id: "ABC123" })

    // Set up the robotics library
    let robotics_lib = tensornetics::RoboticsLibrary::new();

    // Execute the command using the robotics library
    robotics_lib.execute_command(command);
}