Module easy_ml::linear_algebra

Linear algebra algorithms on numbers and matrices

Note that many of these functions are also exposed as corresponding methods on the Matrix type, but in depth documentation is only presented here.

It is recommended to favor the corresponding methods on the Matrix type as the Rust compiler can get confused with the generics on these functions if you use these methods without turbofish syntax.

Functions

cholesky_decomposition	Computes the cholesky decomposition of a matrix. This yields a matrix L such that for the provided matrix A, L * L^T = A. L will always be lower triangular, ie all entries above the diagonal will be 0. Hence cholesky decomposition can be interpreted as a generalised square root function.
covariance_column_features	Computes the covariance matrix for an NxM feature matrix, in which each N'th row has M features to find the covariance and variance of.
covariance_row_features	Computes the covariance matrix for an NxM feature matrix, in which each M'th column has N features to find the covariance and variance of.
determinant	Computes the determinant of a square matrix. For a 2 x 2 matrix this is given by ad - bc for:
f1_score	Computes the F-1 score of the Precision and Recall
inverse	Computes the inverse of a matrix provided that it exists. To have an inverse a matrix must be square (same number of rows and columns) and it must also have a non zero determinant.
mean	Computes the mean of the values in an iterator, consuming the iterator.
variance	Computes the variance of the values in an iterator, consuming the iterator.