R: Unified Simulation of Isomorphisms Between Ant Colony Intelligence and Machine Learning

# Unified Simulation of Isomorphisms Between Ant Colony Intelligence and Machine Learning

## Documentation for package ‘AntsNet’ version 1.0.0

- DESCRIPTION file
- Package README

## Help Pages

AntsNet-package | AntsNet: Unified Simulation of Ant Colony / Machine Learning Isomorphisms
acar | Ant Colony Adaptive Recruitment (ACAR)
adaboost | AdaBoost with Decision Stumps
AntsNet | AntsNet: Unified Simulation of Ant Colony / Machine Learning Isomorphisms
calculate_margins | Calculate Boosting Margins
calculate_quorum_margin | Calculate Quorum Margin
colony_variance_experiment | Experiment 2: Ant Colony Variance Decomposition
convergence_experiment_boost | Convergence Rate Experiment
create_isomorphism_schematic | Figure 1: Isomorphism Schematic
find_best_stump | Find the Best Decision Stump
gacl | Generational Ant Colony Learning (GACL)
generate_all_figures | Generate All Manuscript Figures
generate_classification_data | Generate Synthetic Classification Data
generate_regression_data | Generate Synthetic Regression Data
generate_synthetic_data | Generate Synthetic Classification Data
isomorphism_test | Experiment 3: Direct Isomorphism Test
launch_app | Launch an Interactive Shiny App
noise_experiment_boost | Noise Robustness Experiment
optimal_decorrelation_experiment | Experiment 4: Optimal Decorrelation
plot_colony_accuracy | Supplementary: Colony Accuracy vs Size
plot_convergence_boost | Plot Figure 4: Convergence Rates
plot_convergence_complexity | Plot Convergence Across Complexity (Figure 6)
plot_correlation_decay | Figure 3: Correlation Decay Comparison
plot_gradient_dynamics | Plot Gradient Dynamics (Figure 7)
plot_isomorphism | Plot the Gradient Descent Isomorphism (Figure 1)
plot_learning_curves | Plot Learning Curves with Replicates (Figure 2)
plot_learning_rate_sensitivity | Plot Learning Rate Sensitivity (Figure 4)
plot_margin_quorum | Plot Figure 3: Margin vs Quorum
plot_noise_robustness_boost | Plot Figure 5: Noise Robustness
plot_noise_robustness_nn | Plot Noise Robustness (Figure 5)
plot_optimal_decorrelation | Figure 4: Optimal Decorrelation
plot_pheromone_weight | Plot Pheromone vs Weight Evolution (Figure 3)
plot_plasticity | Plot Plasticity and Adaptation (Figure 8)
plot_sensitivity_heatmap | Figure 5: Sensitivity Heat-map
plot_variance_decomposition | Figure 2: Variance Decomposition
plot_weak_learnability | Plot Figure 1: Weak Learnability Theorem
plot_weight_pheromone | Plot Figure 2: Weight vs Pheromone Evolution
predict_adaboost | Predict with an AdaBoost Ensemble
predict_stump | Predict with a Decision Stump
sensitivity_analysis | Experiment 5: Sensitivity Analysis
simple_neural_network | Simple Neural Network with Stochastic Gradient Descent
simulate_ant_colony | Simulate an Ant Colony Decision Process
sim_boost_recruitment | Boosting and Adaptive Recruitment Simulation
sim_colony_convergence | Colony Convergence Simulation
sim_decorrelation | Decorrelation Parameter Sweep
sim_gradient_colony | Gradient Descent and Generational Colony Learning
sim_margin_analysis | Margin Analysis
sim_plasticity | Plasticity and Environmental Adaptation
sim_variance_decomp | Variance Decomposition Simulation
test_isomorphism | Test Isomorphism Between Two Learning Curves
track_weights | Track AdaBoost Weight Evolution
variance_decomposition_experiment | Experiment 1: Random Forest Variance Decomposition
weak_learnability_experiment | Weak Learnability Experiment
within_colony_correlation | Compute Within-Colony Ant Correlation