A machine learning library for supervised regression and classifaction
This library wants to enable its users to write models independently of the teacher used for training or the cost function that is meant to be minimized. To get started right away, you may want to have a look at the tutorial.
Contains implementations for crisp trait
Defines linear algebra traits used for some model parameters
Learning algorithms implementing
A short tutorial on how to use vikos to solve the problem of supervised machine learning: We want to predict values for a quantity (the target), and we have some data that we can base our inference on (features). We have a data set (a history), that consists of features and corresponding, true target values, so that we have a base to learn about how the target relates to the feature data. To do this we choose a function which relates the features to the target (the model). This model depends on coefficients which are determined using a training algorithm and the history. (teacher).
Representing a cost function whose value is supposed be minimized by the training algorithm.
Define this trait over the target type of a classifier, to convert it into its truth type
A parameterized expert algorithm
Algorithms used to adapt Model coefficients