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Alipsa Matrix Library Tutorial - Outline

1. Introduction

  • Overview of the Alipsa Matrix library
  • Purpose and benefits of using Matrix for tabular data
  • Installation and setup instructions
    • Gradle configuration
    • Maven configuration
    • JDK requirements

2. Matrix Core Module

  • Understanding the Matrix class
    • What is a Matrix?
    • Matrix vs. Grid
    • Creating a Matrix
      • From Groovy code
      • From a result set
      • From a CSV file
    • Accessing data in a Matrix
      • Using [] notation
      • Using column names
      • Using row indices
  • Data manipulation
    • Converting data types
    • Transforming data
    • Subsetting data
  • Performing calculations
    • Using the apply method
    • Column arithmetics
    • Statistical operations with Stat class
  • Using Groovy Integrated queries (Ginq)
  • Using Matrix from other JVM languages (Java)
  • The Grid class
    • Creating a Grid
    • Grid operations
    • Differences from Matrix

3. Matrix Stats Module

  • Statistical methods and tests
  • Correlations
  • Normalization
  • Linear regression
  • T-test
  • Other statistical functions

4. Matrix Datasets Module

  • Overview of included datasets
  • Using common datasets
    • mtcars
    • iris
    • diamonds
    • plantgrowth
    • toothgrowth
  • Creating your own datasets

5. Matrix Spreadsheet Module

  • Importing data from Excel/OpenOffice
  • Exporting Matrix to spreadsheets
  • Working with multiple sheets

6. Matrix CSV Module

  • Advanced CSV import/export
  • Customizing CSV parsing
  • Handling different CSV formats

7. Matrix JSON Module

  • Converting between Matrix and JSON
  • JSON import/export options
  • Working with nested JSON structures

8. Matrix XChart Module

  • Creating charts with XCharts library
  • Chart types and options
  • Customizing chart appearance
  • Exporting charts to different formats

9. Matrix SQL Module

  • Database interaction
  • Querying databases
  • Converting result sets to Matrix
  • Performing operations on database data

10. Matrix BOM Module

  • Bill of materials for dependency management
  • Simplifying dependency configuration
  • Version management

11. Experimental (early development) Modules

12. Practical Examples

13. Machine Learning Modules

  • Matrix ARFF Module
    • Reading and writing ARFF files
    • ARFF attribute types
    • Custom nominal mappings
    • Integration with ML workflows
  • Matrix Smile Module
    • Data conversion with SmileUtil
    • Statistical analysis with SmileStats
    • Classification, regression, clustering
    • Dimensionality reduction (PCA)
    • Feature engineering and data splitting

14. Advanced Topics

  • Advanced Matrix Operations
    • Complex subsetting and filtering
    • Advanced transformations
    • Joining and merging matrices
    • Grouping and aggregation
    • Time series operations
    • Matrix reshaping (pivot/melt)
    • Advanced GINQ queries
  • Performance Best Practices
    • Understanding Matrix memory model
    • Efficient data loading
    • Optimizing transformations
    • Working with large datasets
    • Memory management
    • Profiling and benchmarking
    • Common pitfalls

15. Conclusion

  • Summary of key concepts
  • Additional resources
  • Community and support