1.
Introduction
❱
1.1.
Cargo 101
1.2.
Digests
❱
1.2.1.
New project
1.2.2.
Functions
1.2.3.
Structures and traits
1.2.4.
Enums
1.2.5.
Option and Result
1.2.6.
Lint, check, test
2.
1D Ridge regression 🦀
❱
2.1.
Functional
❱
2.1.1.
Loss function
2.1.2.
Closed-form solution
2.1.3.
Gradient descent
2.1.4.
Putting things together
2.1.5.
Exposing API
2.2.
Structured
❱
2.2.1.
Closed-form solution
2.2.2.
Gradient descent
2.2.3.
Trait Ridge model
2.3.
Generics
❱
2.3.1.
Generics & trait bounds
2.3.2.
Closed-form solution
2.4.
Option, errors, ndarray
❱
2.4.1.
Improved constructor
2.4.2.
Fit function
2.4.3.
Predict function
2.4.4.
Error handling
2.4.5.
Implementing tests
3.
Simple optimizers 🦀🦀
❱
3.1.
Trait-based
❱
3.1.1.
Gradient descent
3.1.2.
GD with momentum
3.1.3.
API and usage
3.1.4.
Tests
3.2.
Enum-based
❱
3.2.1.
Constructors
3.2.2.
Step functions
3.2.3.
API and usage
3.2.4.
Tests
3.3.
Using ndarray
❱
3.3.1.
Gradient descent
3.3.2.
Momentum GD
3.3.3.
Nesterov AG
3.3.4.
API and usage
3.3.5.
Tests
4.
Kernel Ridge regression 🦀🦀
❱
4.1.
Kernel module
4.2.
Gram matrix
4.3.
KRR model
4.4.
Fit function
4.5.
Predict function
4.6.
Hyperparameter tuning
5.
2D Poisson FEM solver 🦀🦀
❱
5.1.
User interface
5.2.
Mesh module
5.3.
Element module
5.4.
Quadrature module
5.5.
Solver module
6.
Kalman filter 🦀🦀
❱
6.1.
KalmanFilter struct
6.2.
Constructor
6.3.
Predict and update
6.4.
Putting things together