Hello, I have a question about using the malloc function
Something that is less common! I wanted to know if this does not create a problem for embedded systems?
https://www.embedded.com/use-malloc-why-not/

Hi, I really appreciate your work. I saw your ML benchmark results for different models and datasets and it is not clear for me how do you measure the execution time and accuracy. Is the execution time measured on target ? What about accuracy ? Is there any difference between online and offline computation in python ?

Hi there,

I am trying to make my own "on board" color classification following this example "https://github.com/eloquentarduino/EloquentMicroML/blob/master/examples/ColorClassificationTrainingExample/ColorClassificationTrainingExample.ino" and this instruction "https://eloquentarduino.github.io/2020/03/how-to-train-a-color-classification-machine-learning-classifier-directly-on-your-arduino-board/#comment-5040745624" but for some reason, the output label of my "model" doesn't seems to work out (attached image), could you please help me spotting the problem for this ?

Here is the code that I have on my board (Seeeduino LoRaWAN) and the color sensor I am using is a Grove_Color_TCS34725, I also highlighted the "int label" that you defined in the "predict" command part, may you explain why you defined this int ? Because it wasn't used at all in this function.

`
#include <EloquentSVMSMO.h>
#include "Adafruit_TCS34725.h"

#define MAX_TRAINING_SAMPLES 20
#define FEATURES_DIM 3

using namespace Eloquent::ML;

int numSamples;
float X_train[MAX_TRAINING_SAMPLES][FEATURES_DIM];
int y_train[MAX_TRAINING_SAMPLES];
SVMSMO<FEATURES_DIM> classifier(linearKernel);

Adafruit_TCS34725 tcs = Adafruit_TCS34725(TCS34725_INTEGRATIONTIME_50MS, TCS34725_GAIN_4X);

void setup() {
// Initizaling the board and color sensor
digitalWrite(38, HIGH);
Serial.begin(9600);
while (!Serial) {};
Serial.println("Color Sensor TCS34725 Connected!");

if (tcs.begin()) 
{
  Serial.println("Found sensor");
} 
else 
{
  Serial.println("No TCS34725 found ... check your connections");
  while (1); // halt!
}

classifier.setC(5);
classifier.setTol(1e-5);
classifier.setMaxIter(10000);

}

void loop() {
if (!Serial.available()) {
delay(100);
return;
}

String command = Serial.readStringUntil('\n');

if (command == "help") {
    Serial.println("Available commands:");
    Serial.println("\tfit: train the classifier on a new set of samples");
    Serial.println("\tpredict: classify a new sample");
    Serial.println("\tinspect: print X_train and y_train");
}
else if (command == "fit") {
    Serial.println("How many samples will you record? ");
    numSamples = readSerialNumber();

    for (int i = 0; i < numSamples; i++) {
        Serial.print(i + 1);
        Serial.print("/");
        Serial.print(numSamples);
        Serial.println(" Which class does the sample belongs to, 1 or -1?");
        y_train[i] = readSerialNumber() > 0 ? 1 : -1;
        Serial.print(y_train[i]);
        Serial.println(" was select");
        getFeatures(X_train[i]);
    }

    Serial.println("Training in progress ... ");
    classifier.fit(X_train, y_train, numSamples);
    Serial.println("Done");
}
else if (command == "predict") {
    int label;
    float x[FEATURES_DIM];

    getFeatures(x);
    Serial.print("Predicted label is ");
    Serial.println(classifier.predict(X_train, x));
}
else if (command == "inspect") {
    for (int i = 0; i < numSamples; i++) {
        Serial.print("[");
        Serial.print(y_train[i]);
        Serial.print("] ");

        for (int j = 0; j < FEATURES_DIM; j++) {
            Serial.print(X_train[i][j]);
            Serial.print(", ");
        }

        Serial.println();
    }
}

}

/**
*

• @return
  */
  int readSerialNumber() {
  while (!Serial.available()) delay(1);

  return Serial.readStringUntil('\n').toInt();
  }

/**

• Get features for new sample

• @param x
  */
  void getFeatures(float x[FEATURES_DIM])
  {
  readColorSensor(x);

  for (int i = 0; i < FEATURES_DIM; i++) {
  Serial.print(x[i]);
  Serial.print(", ");
  }

  Serial.println();
  }

void readColorSensor(float x[3])
{
// Define RGB float
float red, green, blue;
tcs.setInterrupt(false); // turn on LED
delay(60); // takes 60ms to read
tcs.getRGB(&red, &green, &blue);
tcs.setInterrupt(true); // turn off LED

x[0] = red;
x[1] = green;
x[2] = blue;

}

`

Used MicroMLgen to generate c file to convert a Wifi scan result into a feature vector for classification.
Came across the following error:

• Traceback (most recent call last):
  File "C:\Users\parth\Documents\Arduino\Wifi-Indoor positioning\feature-generator.py", line 10, in
  X, y, classmap, converter_code = port_wifi_indoor_positioning(samples)
  File "C:\Python37\lib\site-packages\micromlgen\wifiindoorpositioning.py", line 60, in port_wifi_indoor_positioning
  classmap = get_classmap(samples)
  File "C:\Python37\lib\site-packages\micromlgen\wifiindoorpositioning.py", line 21, in get_classmap
  locations = list(parse_samples(samples, parser))
  File "C:\Python37\lib\site-packages\micromlgen\wifiindoorpositioning.py", line 7, in parse_samples
  for line in samples.split('\n'):
  AttributeError: 'dict' object has no attribute 'split'

Hi there,

This error keeps displaying while generating C code

TemplateNotFound Traceback (most recent call last)
in
1 ocs = OneClassSVM(kernel='rbf', nu=0.5, gamma=0.1)
2 ocs.fit(df)
----> 3 print(port(ocs))

\lib\site-packages\jinja2\loaders.py in get_source(self, environment, template)
195
196 return contents, filename, uptodate
--> 197 raise TemplateNotFound(template)
198
199 def list_templates(self):

TemplateNotFound: svm/svm.jinja

Could you help me?

I tried to run the tasks in "The Ultimate Guide to Wifi..", but ended up with the following message when I came to the most interesting part of it (Generating the features converter):

C:\Users\adria\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/adria/PycharmProjects/IoT_localization/ArduinoLocalization.py
Traceback (most recent call last):
File "C:/Users/adria/PycharmProjects/IoT_localization/ArduinoLocalization.py", line 10, in
x, y, classmap, converter_code = port_wifi_indoor_positioning(samples)
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\micromlgen\wifiindoorpositioning.py", line 68, in port_wifi_indoor_positioning
'networkmap': networkmap
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\micromlgen\utils.py", line 49, in jinja
template = Environment(loader=loader).get_template(template_file)
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\jinja2\environment.py", line 883, in get_template
return self._load_template(name, self.make_globals(globals))
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\jinja2\environment.py", line 857, in _load_template
template = self.loader.load(self, name, globals)
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\jinja2\loaders.py", line 115, in load
source, filename, uptodate = self.get_source(environment, name)
File "C:\Users\adria\AppData\Local\Programs\Python\Python37-32\lib\site-packages\jinja2\loaders.py", line 197, in get_source
raise TemplateNotFound(template)
jinja2.exceptions.TemplateNotFound: wifiindoorpositioning/wifiindoorpositioning.jinja

Process finished with exit code 1

most likely, my installation has an issue… Thank you for an idea, what the reason of this problem could be. Best, Adrian