As input, i have 7 floating points, such as these:
"2.0", "0.23", "239.10","3.5", "12,0", "10.6", "0.62".
To simplify the output matrix, i've converted it in a array with the 144 elements of the matrix. So far i'm using MLPRegressor from Scikit.The problem is, there's absolutely no pattern in the predicted results.The predicted results include negative numbers, numbers really big and no "pattern" for the indexes whatsoever. Is there a way to adjust these things in the model or the problem is on my dataset? Thank you very much!
I wasn't very clear about the problem, so i'll try to explain it better.
About the problem: Predict water distribution in irrigation systems. I already have a program to simulate this, and works with rather good precision. The challenge now was to make an AI model to simulate this, instead of the old methods.
About the inputs: Inputs are one float representing the pressure of the sprinkler used to distribute water, two floats representing the speed and angle of the wind at the time data was collected. The 4 remaining are numbers representing some configuration of the sprinker (these numbers change very little between instances, but they do have impact in the result, which is why i decided to mantain them in the dataset).
About the output: Output is an array of 144 elements, representing the 12*12 matrix. Each element of the matrix contain a number representing the amount of water that was collected in that point. These collectors where evenly spread around the sprinkler. So the position of the matrix matter a lot, since in most cases the first and last lines and columns will have 0 or a close to 0 (but positive) number - this may vary depending especially on the speed and angle of the wind, but also on the sprinkler.
About the dataset: I have available 75 instances. They are all stored in a CSV file, where the 7 inputs and the 144 outputs are, each in one line.