Preparing the input features for the model inference
As we know, the model's input features are the scaled and quantized temperature and humidity of the last three hours. Using this data, the ML model can forecast whether it will snow.
In this recipe, we will see how to prepare the input data to feed into our ML model. In particular, this recipe will teach us how to acquire, scale, and quantize the sensor measurements and keep them in temporal order using a circular buffer.
The following Arduino sketch contains the code referred to in this recipe:
08_input_features.ino:
Getting ready
Our application will acquire the temperature and humidity every hour to get the necessary input features for the model. However, how can we keep the last three measurements in temporal order to feed the network the correct input?
In this recipe, we will use a...
