mltoarduino 0.0.2

See readMe.ma

Convert a Python Machine Learning Model to Arduino Code (C++)

Introduction

Motivation

What ?
This project demonstrates the conversion of Python machine learning (ML) models to Arduino C++ code.
We will use some ML models purely as examples; the goal is not to find the best model or achieve minimal error.
Why ?
In certain applications, such as embedded systems, small microcontrollers with limited memory and computing resources are used. The idea is to train a machine learning model in a Python environment and then convert the trained model to C++ for deployment on a microcontroller.
In this project, we will use the Arduino Uno as an example, but the approach can be applied to other microcontrollers as well.
How ?
Follow the step-by-step guide below, or go directly to the PyPi package mltoarduino

Hardware

In this project, the Arduino Uno was used, but you can use other boards like Arduino Nano or Micro, Miga 2560, ESP32...
below a comparaison of some Arduino boards:

Feature	Arduino Uno	Arduino Nano	Arduino Micro	Arduino Mega 2560	ESP32
Microcontroller	ATmega328P	ATmega328P	ATmega32U4	ATmega2560	Tensilica Xtensa LX6
Operating Voltage	5V	5V	5V	5V	3.3V
Input Voltage	7-12V	7-12V	7-12V	7-12V	5V via USB or 7-12V
Digital I/O Pins	14 (6 PWM)	14 (6 PWM)	20 (7 PWM)	54 (15 PWM)	34
Analog Input Pins	6	8	12	16	18
Flash Memory	32 KB	32 KB	32 KB	256 KB	Up to 16 MB
SRAM	2 KB	2 KB	2.5 KB	8 KB	520 KB
EEPROM	1 KB	1 KB	1 KB	4 KB	None
Clock Speed	16 MHz	16 MHz	16 MHz	16 MHz	240 MHz (dual-core)
Connectivity	UART, I2C, SPI	UART, I2C, SPI	UART, I2C, SPI	UART, I2C, SPI	Wi-Fi, Bluetooth
USB Interface	USB-B	Mini USB	Micro USB	USB-B	Micro USB
Dimensions	68.6 x 53.4 mm	45 x 18 mm	48 x 18 mm	101.52 x 53.3 mm	51 x 25.5 mm
Power Consumption	~50 mA	~50 mA	~50 mA	~70 mA	Varies (~80-240 mA)
Special Features	Simple and robust	Compact	USB HID support	High I/O count	Wi-Fi and BLE
Price Range	Low	Low	Medium	Medium	Medium-High

How to use the package

!pip install mltoarduino