Implementations which are in an early stage of development. Interface and implementation are subject to change.
Modules | |
| Conjugate Gradient | |
| Finds the solution to the function \( \boldsymbol Ax = \boldsymbol b \), where \(A\) is a symmetric, positive-definite matrix and \(x\) and \( \boldsymbol b \) are vectors. | |
| k-Nearest Neighbors | |
| Finds k nearest data points to the given data point and outputs majority vote value of output classes for classification, and average value of target values for regression. | |
| Naive Bayes Classification | |
| Constructs a classification model from a dataset where each attribute independently contributes to the probability that a data point belongs to a category. | |
| Random Sampling | |
| Provides utility functions for sampling operations. | |