Regression

Presentation

Data can be use to regression. Regression is used when you want specific output values. The outputs can have any values but it is better to normalize the output to 0 and 1 and use a sigmoid as the output enable function. (Or a tanh for values between -1 and 1)

Declaration

Data(problem:regression,
     std::vector<std::vector<float>>& trainingInputs,
     std::vector<std::vector<float>>& trainingLabels,
     std::vector<std::vector<float>>& testingInputs,
     std::vector<std::vector<float>>& testingLabels,
     nature typeOfTemporal = nature::nonTemporal,
     int numberOfRecurrences = 0);

Arguments

• trainingInputs: 2D vector of all the data inputs use to train the neural network. Each vector<float> represents an input for the neural network.
• trainingLabels: 2D vector of all the expected outputs use to train the neural network. Each vector<float> represents the expected output by the neural network for the corresponding input.
• testingInputs: 2D vector of all the data inputs use to evaluate the neural network. Each vector<float> represents an input for the neural network.
• testingLabels: 2D vector of all the expected outputs use to evaluate the neural network. Each vector<float> represents the expected output by the neural network for the corresponding input.
• typeOfTemporal: An enum corresponding to the temporal nature of problem associated with the data. There are 3 types of temporal nature nonTemporal, sequential and timeSeries.
• numberOfRecurrences: Size of sequence used for train neural network. Only used for timeSeries otherwise leave the value at 0.