XDL Advanced ML Features - Implementation Status

Date: January 22, 2025 Status: Phase 1 & 2 Complete ✅

📊 Current Progress

Completed: 55 ML Functions Total

Original ML Suite: 50 functions (100% complete) Advanced Features: 5 functions (cross-validation + regularization)

✅ Phase 1: Cross-Validation Utilities (COMPLETE)

Functions Implemented (3)

  1. XDLML_KFold(n_samples, n_folds, seed, shuffle)
    • Standard K-Fold cross-validation
    • Splits data into K folds for training/validation
    • Supports shuffling with reproducible seeds
    • Returns fold masks: 0=validation, 1=training
  2. XDLML_StratifiedKFold(y_labels, n_folds, seed)
    • Maintains class distribution across folds
    • Essential for imbalanced datasets
    • Groups samples by class label
    • Distributes each class evenly across folds
  3. XDLML_LeaveOneOut(n_samples)
    • Leave-One-Out cross-validation
    • Special case: K=N (one sample per fold)
    • Computationally expensive but unbiased
    • Each sample used once as validation

Cross-Validation Testing Status

  • ✅ All functions tested and working
  • ✅ Validated fold proportions
  • ✅ Verified stratification maintains distribution
  • ✅ Confirmed LOO single-sample validation

Example Usage: Cross-Validation 

; 5-fold cross-validation
folds = XDLML_KFOLD(100, 5, 42, 1)

; Stratified for imbalanced classes
y_labels = [0,0,0,1,1,1,2,2,2]  ; 3 classes
folds_strat = XDLML_STRATIFIEDKFOLD(y_labels, 3, 42)

; Leave-one-out
folds_loo = XDLML_LEAVEONEOUT(50)

✅ Phase 2: Regularization Layers (COMPLETE)

Functions Implemented (2)

  1. XDLML_BatchNormalization(input, gamma, beta, mode, running_mean, running_var, momentum, epsilon)
    • Normalizes activations to stabilize training
    • Training mode: Uses batch statistics
    • Inference mode: Uses running statistics
    • Learnable parameters: gamma (scale), beta (shift)
    • Formula: gamma * (x - mean) / sqrt(var + eps) + beta

    Features:

    • Reduces internal covariate shift
    • Allows higher learning rates
    • Improves gradient flow
    • Standard epsilon = 1e-5 for numerical stability
  2. XDLML_Dropout(input, dropout_rate, training, seed)
    • Randomly drops units during training
    • Prevents overfitting and co-adaptation
    • Inference mode: No dropout applied
    • Uses inverted dropout scaling

    Features:

    • Configurable dropout rate (0.0 to 1.0)
    • Training/inference mode switching
    • Reproducible with seeds
    • Scaling: 1 / (1 - dropout_rate) maintains expected sum

Regularization Testing Status

  • ✅ Batch Normalization training mode verified
  • ✅ Batch Normalization inference mode verified
  • ✅ Gamma/Beta parameters working correctly
  • ✅ Dropout training mode drops ~50% with rate=0.5
  • ✅ Dropout inference mode preserves all inputs
  • ✅ Inverted dropout scaling validated

Example Usage 

; Batch normalization in training
normalized = XDLML_BATCHNORMALIZATION(activations, 1.0, 0.0, 0)

; Batch norm with learned parameters
normalized = XDLML_BATCHNORMALIZATION(activations, gamma, beta, 0)

; Batch norm for inference
output = XDLML_BATCHNORMALIZATION(test_data, gamma, beta, 1, r_mean, r_var)

; Dropout in training (50% rate)
dropped = XDLML_DROPOUT(activations, 0.5, 1, 42)

; Dropout in inference (no dropping)
output = XDLML_DROPOUT(activations, 0.5, 0)

🚧 Phase 3: Convolutional Layers (TODO)

Planned Functions (3-4)

  1. XDLML_Conv2D - 2D Convolutional layer
    • Filters, stride, padding support
    • Requires 2D array handling
  2. XDLML_MaxPooling2D - Max pooling for CNNs
    • Downsampling operation
    • Sliding window maximum
  3. XDLML_AveragePooling2D - Average pooling
    • Alternative pooling strategy
  4. XDLML_Conv1D (optional) - 1D convolution
    • For sequence/time-series data

Technical Requirements

  • 2D Array Support: Need to extend XdlValue for multi-dim arrays
  • Memory Layout: Row-major or column-major decision
  • Shape Tracking: Dimensions metadata for operations

🚧 Phase 4: Recurrent Layers (TODO)

Planned Functions (2-3)

  1. XDLML_RNN - Basic recurrent neural network
    • Simple RNN cell
    • Sequence processing
  2. XDLML_LSTM - Long Short-Term Memory
    • Forget gate, input gate, output gate
    • Cell state management
  3. XDLML_GRU (optional) - Gated Recurrent Unit
    • Simplified alternative to LSTM

Technical Requirements

  • Sequence Support: Handle 3D arrays (batch, time, features)
  • State Management: Hidden state persistence
  • Backpropagation Through Time: Temporal gradients

🚧 Phase 5: Complete Models (TODO)

Planned Functions (2)

  1. XDLML_ConvolutionalNeuralNetwork
    • End-to-end CNN model
    • Conv layers + pooling + dense layers
    • Image classification ready
  2. XDLML_RecurrentNeuralNetwork
    • Complete RNN/LSTM model
    • Sequence classification/regression

📈 Statistics

Implementation Progress

Phase Functions Status Completion
Core ML (Phase ML-1 to ML-6) 50 ✅ Complete 100%
Cross-Validation (Phase ML-7) 3 ✅ Complete 100%
Regularization (Phase ML-8) 2 ✅ Complete 100%
Convolutional (Phase ML-9) 3-4 🚧 Planned 0%
Recurrent (Phase ML-10) 2-3 🚧 Planned 0%
Complete Models (Phase ML-11) 2 🚧 Planned 0%
Total 62-64 55 done ~86%

Lines of Code

  • ML Module: ~3,700+ lines (ml.rs)
  • Test Scripts: 3 comprehensive test files
  • Documentation: Complete API reference + status docs

🎯 Key Achievements

Regularization & Training Enhancements

✅ Cross-Validation: Robust model evaluation ✅ Batch Normalization: Stable training dynamics ✅ Dropout: Effective overfitting prevention

Code Quality

✅ Zero Compilation Errors: Clean builds ✅ Comprehensive Testing: All functions validated ✅ Production-Ready: Proper error handling & edge cases ✅ Well-Documented: Inline docs + examples

🚀 Next Steps

Immediate (Phase 3)

  1. Design 2D array support in XdlValue
  2. Implement Conv2D with basic kernels
  3. Add MaxPooling2D and AveragePooling2D
  4. Test with simple CNN use cases

Short-Term (Phase 4)

  1. Extend to 3D arrays for sequences
  2. Implement RNN cell with backprop through time
  3. Add LSTM with gate mechanisms
  4. Test on sequence classification tasks

Long-Term (Phase 5)

  1. Build complete CNN model function
  2. Build complete RNN/LSTM model function
  3. Add advanced layers (attention, etc.)
  4. Create end-to-end examples

📝 Notes

Design Decisions

Why Start with Cross-Validation & Regularization?

  • Don’t require multi-dimensional array support
  • High value for model evaluation and training
  • Can be implemented with current 1D array infrastructure

Next: Why Convolutional Layers?

  • Require 2D support which benefits other areas
  • CNNs are widely used and well-understood
  • Foundation for more complex architectures

Multi-Dimensional Arrays

  • Critical for Conv2D and RNN layers
  • Need to decide on memory layout (row/column major)
  • May need new XdlValue variant or metadata system

🧪 Testing Coverage

Test Files Created

  1. ml_cv_simple_test.xdl - Cross-validation validation
  2. ml_reg_simple_test.xdl - Regularization layer tests
  3. ml_advanced_models_test.xdl - Neural network & SVM tests
  4. ml_comprehensive_test.xdl - Core ML function suite

Validation Metrics

  • ✅ Fold proportions and distributions
  • ✅ Normalization mean/variance correctness
  • ✅ Dropout rate adherence
  • ✅ Inverted dropout scaling
  • ✅ Training vs. inference mode behavior

📚 References

Batch Normalization

  • Ioffe & Szegedy (2015). “Batch Normalization: Accelerating Deep Network Training”
  • Reduces internal covariate shift
  • Allows higher learning rates (10-30x)

Dropout

  • Srivastava et al. (2014). “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
  • Reduces co-adaptation of neurons
  • Ensemble effect during inference

Cross-Validation

  • Kohavi (1995). “A Study of Cross-Validation and Bootstrap”
  • Essential for reliable model evaluation
  • Stratified variants for imbalanced data

Status: 55 / ~64 functions complete (~86%) Next Milestone: Conv2D + Pooling layers Build Status: ✅ Zero errors Test Status: ✅ All passing