graph LR
Geometry_Core["Geometry Core"]
Learning_Algorithms["Learning Algorithms"]
Numerical_Solvers["Numerical Solvers"]
Backend_Abstraction_Layer["Backend Abstraction Layer"]
Data_Management["Data Management"]
Geometric_Distributions["Geometric Distributions"]
Visualization_Engine["Visualization Engine"]
Geometry_Core -- "Defines Structures For" --> Learning_Algorithms
Geometry_Core -- "Defines Problems For" --> Numerical_Solvers
Geometry_Core -- "Provides Manifolds For" --> Geometric_Distributions
Geometry_Core -- "Provides Objects For" --> Visualization_Engine
Geometry_Core -- "Utilizes" --> Backend_Abstraction_Layer
Learning_Algorithms -- "Processes Data From" --> Data_Management
Learning_Algorithms -- "Uses" --> Geometry_Core
Learning_Algorithms -- "Leverages" --> Numerical_Solvers
Learning_Algorithms -- "Outputs Results To" --> Visualization_Engine
Learning_Algorithms -- "Utilizes" --> Backend_Abstraction_Layer
Numerical_Solvers -- "Solves Problems From" --> Geometry_Core
Numerical_Solvers -- "Supports" --> Learning_Algorithms
Numerical_Solvers -- "Utilizes" --> Backend_Abstraction_Layer
Backend_Abstraction_Layer -- "Provides Primitives To" --> Geometry_Core
Backend_Abstraction_Layer -- "Provides Primitives To" --> Learning_Algorithms
Backend_Abstraction_Layer -- "Provides Primitives To" --> Numerical_Solvers
Backend_Abstraction_Layer -- "Provides Primitives To" --> Data_Management
Backend_Abstraction_Layer -- "Provides Primitives To" --> Geometric_Distributions
Backend_Abstraction_Layer -- "Provides Primitives To" --> Visualization_Engine
Data_Management -- "Feeds Data To" --> Learning_Algorithms
Data_Management -- "Provides Data For" --> Visualization_Engine
Data_Management -- "Utilizes" --> Backend_Abstraction_Layer
Geometric_Distributions -- "Defines Distributions On" --> Geometry_Core
Geometric_Distributions -- "Generates Samples For" --> Learning_Algorithms
Geometric_Distributions -- "Utilizes" --> Backend_Abstraction_Layer
Visualization_Engine -- "Renders" --> Geometry_Core
Visualization_Engine -- "Renders" --> Learning_Algorithms
Visualization_Engine -- "Renders" --> Data_Management
Visualization_Engine -- "Utilizes" --> Backend_Abstraction_Layer
click Geometry_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Geometry_Core.md" "Details"
click Learning_Algorithms href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Learning_Algorithms.md" "Details"
click Numerical_Solvers href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Numerical_Solvers.md" "Details"
click Backend_Abstraction_Layer href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Backend_Abstraction_Layer.md" "Details"
click Data_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Data_Management.md" "Details"
click Geometric_Distributions href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/geomstats/Geometric_Distributions.md" "Details"
The geomstats architecture is centered around a robust Geometry Core that defines the fundamental mathematical structures of various manifolds. This core provides the necessary context and operations for Learning Algorithms to perform machine learning tasks directly on geometric data, and for Numerical Solvers to tackle complex mathematical problems inherent to these spaces. Data for these processes is handled by Data Management, and probabilistic modeling is supported by Geometric Distributions. A crucial Backend Abstraction Layer underpins the entire system, providing a unified interface for numerical computations across different libraries, ensuring flexibility and performance. Finally, the Visualization Engine offers essential tools for understanding and presenting the geometric objects, data, and results generated throughout the geomstats workflow. This modular design, with its clear component boundaries and backend flexibility, makes geomstats a powerful and extensible toolkit for geometric statistics and machine learning.
Geometry Core [Expand]
Defines mathematical structures of manifolds and their intrinsic properties.
Related Classes/Methods:
Learning Algorithms [Expand]
Implements machine learning algorithms adapted for geometric spaces.
Related Classes/Methods:
Numerical Solvers [Expand]
Provides computational methods for solving geometric problems (e.g., geodesics, optimization).
Related Classes/Methods:
Backend Abstraction Layer [Expand]
Offers a unified API for numerical operations across different backends (NumPy, Autograd, PyTorch).
Related Classes/Methods:
Data Management [Expand]
Handles loading, preparation, and management of geometric datasets.
Related Classes/Methods:
Geometric Distributions [Expand]
Defines probability distributions on geometric manifolds.
Related Classes/Methods:
Provides tools for graphically representing geometric objects and computational results.
Related Classes/Methods: