Installation Guide

This guide provides comprehensive instructions for installing GeneExpressionProgramming.jl and its dependencies across different platforms and environments.

Prerequisites

Before installing GeneExpressionProgramming.jl, ensure you have the following prerequisites:

Julia Requirements

  • Julia 1.6 or later: The package requires Julia version 1.6 or higher. You can download Julia from the official website.
  • Package Manager: Julia's built-in package manager (Pkg) is required for installation.

System Requirements

The package has been tested on the following platforms:

  • Linux (Ubuntu 18.04+, CentOS 7+)
  • macOS (10.14+)
  • Windows 10/11

Memory and Performance Considerations

For optimal performance, we recommend:

  • Minimum RAM: 4 GB (8 GB recommended for large datasets)
  • CPU: Multi-core processor recommended for parallel operations
  • Storage: At least 1 GB free space for package dependencies

Installation Methods

Method 1: Direct Installation from GitHub (Recommended)

The most straightforward way to install GeneExpressionProgramming.jl is directly from the GitHub repository:

using Pkg
Pkg.add(url="https://github.com/maxreiss123/GeneExpressionProgramming.jl.git")

This method ensures you get the latest version with all recent updates and bug fixes.

Method 2: Development Installation

If you plan to contribute to the package or need to modify the source code, you can install it in development mode:

using Pkg
Pkg.develop(url="https://github.com/maxreiss123/GeneExpressionProgramming.jl.git")

This creates a local copy of the repository in your Julia development directory, allowing you to make changes and test them immediately.

Method 3: Local Installation

If you have downloaded the source code locally:

using Pkg
Pkg.add(path="/path/to/GeneExpressionProgramming.jl")

Replace /path/to/GeneExpressionProgramming.jl with the actual path to your local copy.

Dependency Installation

GeneExpressionProgramming.jl automatically installs its dependencies during the installation process. The main dependencies include:

Core Dependencies

  • DynamicExpressions.jl: For fast symbolic expression evaluation
  • Flux.jl: For tensor operations and neural network backends
  • Random: For random number generation and seeding
  • Statistics: For statistical operations
  • LinearAlgebra: For matrix operations

Optional Dependencies

For enhanced functionality, you may want to install additional packages:

using Pkg
Pkg.add(["Plots", "CSV", "DataFrames", "Tensors"])
  • Plots.jl: For visualization and plotting results
  • CSV.jl: For reading CSV data files
  • DataFrames.jl: For data manipulation and analysis
  • Tensors.jl: For advanced tensor operations

Verification

After installation, verify that the package works correctly:

using GeneExpressionProgramming

# Test basic functionality
println("GeneExpressionProgramming.jl installed successfully!")

# Create a simple regressor to test
regressor = GepRegressor(2)
println("Basic regressor created: ", typeof(regressor))

If the installation was successful, you should see output confirming the package is working.

Troubleshooting

Common Installation Issues

Issue 1: Package Not Found

ERROR: The following package names could not be resolved:
 * GeneExpressionProgramming (not found in project, manifest or registry)

Solution: Ensure you're using the correct URL and that you have internet connectivity:

Pkg.add(url="https://github.com/maxreiss123/GeneExpressionProgramming.jl.git")

Issue 2: Dependency Conflicts

ERROR: Unsatisfiable requirements detected for package...

Solution: Update your Julia packages and try again:

using Pkg
Pkg.update()
Pkg.add(url="https://github.com/maxreiss123/GeneExpressionProgramming.jl.git")

Issue 3: Compilation Errors

If you encounter compilation errors during installation:

  1. Update Julia: Ensure you're using Julia 1.6 or later
  2. Clear package cache: 
    using Pkg
Pkg.gc()
  3. Reinstall dependencies:
    Pkg.instantiate()

Issue 4: Permission Errors (Linux/macOS)

If you encounter permission errors:

  1. Check Julia installation: Ensure Julia is properly installed with appropriate permissions
  2. Use local package directory: Consider installing packages in a local directory
  3. Avoid sudo: Never use sudo with Julia package operations

Performance Optimization

Julia Startup Optimization

To improve Julia startup time with GeneExpressionProgramming.jl:

  1. Precompile packages:

    using Pkg
Pkg.precompile()

  2. Use PackageCompiler.jl for creating system images:

    using Pkg
Pkg.add("PackageCompiler")
using PackageCompiler
create_sysimage(["GeneExpressionProgramming"]; sysimage_path="gep_sysimage.so")

Memory Management

For large-scale problems:

  1. Increase Julia heap size:

    julia --heap-size-hint=8G

  2. Monitor memory usage:

    using Profile
@profile your_gep_code()
Profile.print()

Environment Setup

Jupyter Notebook Integration

To use GeneExpressionProgramming.jl in Jupyter notebooks:

using Pkg
Pkg.add("IJulia")
using IJulia
notebook()

VS Code Integration

For development with VS Code:

  1. Install the Julia extension for VS Code
  2. Configure the Julia executable path
  3. Use the integrated REPL for interactive development

Docker Environment

For containerized environments, use the official Julia Docker image:

FROM julia:1.8

RUN julia -e 'using Pkg; Pkg.add(url="https://github.com/maxreiss123/GeneExpressionProgramming.jl.git")'

WORKDIR /app
COPY . .

CMD ["julia", "your_script.jl"]

Next Steps

After successful installation, proceed to:

  1. Getting Started Guide - Learn basic usage patterns
  2. Core Concepts - Understand the theoretical foundations
  3. Examples - Explore practical applications

For additional help, consult GitHub repository.