Crate easy_ml

If this is your first time using Easy ML you should check out some of the examples to get an overview of how to use matrices or tensors then check out the Matrix type or Tensor type for what you need.

Matrix is a straightforward 2 dimensional matrix with APIs built around the notion of rows and columns; Tensor is a named tensor with full API support for 0 to 6 dimensions. Naturally, a 2 dimensional tensor is also a matrix, but the APIs are more general so may be less familiar or ergonomic if all you need is 2 dimensional data.

Examples

• Linear Regression
• k-means Clustering
• Logistic Regression
• Naïve Bayes
• Neural Network XOR Problem

API Modules

• Matrices
• Named tensors
• Linear Algebra
• Distributions
• (Automatic) Differentiation
• Numerical type definitions

Miscellaneous

• Web Assembly
• SARSA and Q-learning using a Matrix for a grid world
• Using custom numeric types

Modules

differentiation

(Automatic) Differentiation helpers

distributions

Models of distributions that samples can be drawn from.

interop

Interopability APIs between Matrix/MatrixView and Tensor/TensorView.

k_means

K-means example

linear_algebra

Linear algebra algorithms on numbers and matrices

linear_regression

Linear regression examples

logistic_regression

Logistic regression example

matrices

Generic matrix type.

naive_bayes

Naïve Bayes examples

neural_networks

Neural Network training examples

numeric

Numerical type definitions.

sarsa

SARSA and Q-learning using a Matrix for a grid world.

tensors

Generic N dimensional named tensors.

using_custom_types

Using custom numeric types examples.

web_assembly

Web Assembly examples