Are you getting started with Machine learning on Arduino boards? Do you want to run the model you trained in Python into any C++ project, be it Arduino, STM32, ESP32?

In this tutorial I'll show you how easy it is: we'll go from start to end in just 4 easy steps!

Step 1. Load the data

To train a classifier, we need some data. If you're starting from zero and don't have already a preferred folder structure, I suggest you to create a folder that will hold the data you collect.

Inside this folder, create a dedicated file (.csv) for each of the classes you want to classify, putting a sample on each line. If doing so, you can use the next function to load this data.

import numpy as np
from glob import glob
from os.path import basename

def load_features(folder):
    dataset = None
    classmap = {}
    for class_idx, filename in enumerate(glob('%s/*.csv' % folder)):
        class_name = basename(filename)[:-4]
        classmap[class_idx] = class_name
        samples = np.loadtxt(filename, dtype=float, delimiter=',')
        labels = np.ones((len(samples), 1)) * class_idx
        samples = np.hstack((samples, labels))
        dataset = samples if dataset is None else np.vstack((dataset, samples))

    return dataset, classmap

Step 2. Train the classifier

Once you have the data, it's time to train the classifier.

The micromlgen package (the package that can port Machine learning classifiers to plain C) supports the following classes:

I really like Random Forest, but you can swap it with any of the other ones without changing the rest of the code.

from sklearn.ensemble import RandomForestClassifier

def get_classifier(features):
    X, y = features[:, :-1], features[:, -1]

    return RandomForestClassifier(20, max_depth=10).fit(X, y)

Step 3. Export to plain C

Now you can convert the trained classifier to plain C code using the micromlgen package.

pip install micromlgen
from micromlgen import port

if __name__ == '__main__':
    features, classmap = load_features('your-data-folder')
    classifier = get_model(features)
    c_code = port(classifier, classmap=classmap)
    print(c_code)

This is the code you need to import in your Arduino project. To follow along with the tutorials on this blog, save it in a file called model.h.

Step 4. Use in your project

Now we have the code we need to run Machine learning directly on our microcontroller.

// put the code you got in Step 3 into this file
#include "model.h"

// this class will be different if you used another type of classifier, just check the model.h file
Eloquent::ML::Port::RandomForest classifier;

void classify() {
    float x_sample[] = { /* fill this vector with sample values */ };

    Serial.print("Predicted class: ");
    Serial.println(classifier.predictLabel(x_sample));
}

That's it: if everything went fine, your microcontroller is running Machine learning!

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