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Hi, I’m new to c++, but wanted to ask you guys, for reference, if you could route this „neural network“ using c++.
Like using the UFL library with its if(read.uflInt() == 13){}, for example?

I’ve used the UFL library already and found the learning process to be pretty easy.

I’m not sure if I can help much with the actual construction of the neural network itself. I don’t know the jargon or what the „colours“ and „neurons“ mean. But I can help with the learning part.

You can have more than one layer of neurons. You can have 6 layers. You have a single input neuron, a hidden layer, and a single output neuron.

Next, you have to have a weight. For each neuron, you need a variable, which will hold the weight. Usually you just do a weight variable = getRandom(0,1) for each neuron. To find the weights, you use a set of „training“ data. You look at a training set of 50 data points, and you use them to find a weight that is close to 1. You save the weight. Then, you look at a data point, and you find what your output is. Based on the training data, the output should come out as close to the training data as possible. You add the right weight to the right variable, and you make sure that your output in the current data set is less than the output you get from the training data. You train. Then you test. If everything is working, the test data is close to the training data, and you’ve gotten closer to what you want. You repeat the process until you are satisfied. After you’ve trained, then you can feed in new data points. A working neural network can teach itself. It’s a pretty complex and precise thing.

I can’t remember where to find the example I used, but I know it was UFL related.

Since your data looks linearly separable, you could have five neurons, one for the first, second, third, fourth, and fifth dimensions. You could randomly generate the weights for those neurons, and this would be a five-neuron network. The data would be a vector of length 5. You input your data into the first and second

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