Implementing the intelligent model
The implementation of this project will basically require all the knowledge from the previous chapters. We will make use of custom types, entities, functions, collections, iterations, and multiple entrypoint functions. If you have not covered these topics yet, I strongly suggest you take a step back and learn the required concepts first. However, if you are familiar with them, it is finally time to utilize all the learned concepts. Having said that, let's not waste more time and write some code.
Creating the basic functionality
First things first, let's define an entity called Perceptron, which will represent our perceptrons:
entity Perceptron {
field weights: List<Float64>;
field alpha: Float64 = 1.0f;
}
Every perceptron is going to have the weights vector (represented by List<Float64>) and the learning rate alpha equal to 1.0. The alpha value has been picked arbitrarily...