Stonito Lotto makes use of two different types of neural networks:
Number Prediction, network used for calculating probabilities of each number appearing in a next rounds, with predefined input values.
Winning Pattern, network used to calculate similarity of any combination with previous winning combinations. Input is a combination, and output is a probability (0-1).
Number prediction tab
This tab page is for setting up the networks used for getting number prediction. Result of those network are the probabilities (0-1) for each number to appear in the next draw.
This is tab page for setting Winning Pattern Networks. They have the same settings as the Number Prediction Networks, except for the last (at the bottom) setting. The result of it is a similarity (0-1) of any combination to the previous winning combinations.
Error changes on each epoch. The training will stop if the error representation in defined number of decimal places doesn't change in this count of epochs.
Used only for winning pattern network. It defines how many non-winning combinations are included in training set. For example, if you have 1000 combination in history draws, factor 2.0 means that the training set will include 2000 non-winning random combinations and 1000 winning. The factor 2.5 would make for 2500 non-winning combinations.
This tab page is for setting up the networks used for getting number prediction. Result of those network are the probabilities (0-1) for each number to appear in the next draw.
This is tab page for setting Winning Pattern Networks. They have the same settings as the Number Prediction Networks, except for the last (at the bottom) setting. The result of it is a similarity (0-1) of any combination to the previous winning combinations.
