XDL Machine Learning Functions - Implementation Plan

Date: 2025-01-21 Source: IDL Machine Learning Documentation Status: Planning Phase

Overview

IDL provides a comprehensive Machine Learning framework with 50+ functions covering models, optimizers, activation functions, loss functions, normalizers, and utilities. This document analyzes the scope and provides an implementation roadmap for XDL.

Function Categories & Inventory

1. Models & Classifiers (7 functions)

Core Models

  1. IDLmlAutoEncoder - Autoencoder for unsupervised clustering
    • Scope: Neural network architecture for dimensionality reduction
    • Complexity: High (requires backpropagation, encoder/decoder architecture)
    • Dependencies: Optimization algorithms, activation functions
    • Use Cases: Feature learning, anomaly detection, denoising
  2. IDLmlFeedForwardNeuralNetwork - Multi-layer perceptron classifier
    • Scope: Fully connected neural network with configurable layers
    • Complexity: High (forward/backward propagation, weight management)
    • Dependencies: Optimizers, activation functions, loss functions
    • Use Cases: Classification, pattern recognition
  3. IDLmlKMeans - K-means clustering algorithm
    • Scope: Iterative centroid-based clustering
    • Complexity: Medium (centroid calculation, distance metrics)
    • Dependencies: Distance functions, random initialization
    • Use Cases: Data segmentation, pattern grouping
  4. IDLmlSoftmax - Softmax classifier for multi-class problems
    • Scope: Probabilistic multi-class classification
    • Complexity: Low-Medium (softmax function, cross-entropy loss)
    • Dependencies: None (standalone)
    • Use Cases: Multi-class classification, probability estimation
  5. IDLmlSupportVectorMachineClassification - SVM for classification
    • Scope: Maximum margin classifier with kernel functions
    • Complexity: High (optimization, kernel tricks, support vectors)
    • Dependencies: Kernel functions, optimization
    • Use Cases: Binary/multi-class classification, high-dimensional data
  6. IDLmlSupportVectorMachineRegression - SVM for regression
    • Scope: Support vector regression with epsilon-insensitive loss
    • Complexity: High (similar to SVM classification)
    • Dependencies: Kernel functions, optimization
    • Use Cases: Non-linear regression, robust prediction

Evaluation

  1. IDLmlTestClassifier - Model evaluation and metrics
    • Scope: Confusion matrix, accuracy, precision, recall, F1-score
    • Complexity: Low (statistical calculations)
    • Dependencies: None
    • Use Cases: Model validation, performance assessment

2. Data Utilities (2 functions)

  1. IDLmlPartition - Data partitioning for train/test splits
    • Scope: Split datasets into training/validation/test sets
    • Complexity: Low (index generation, shuffling)
    • Dependencies: Random number generation
    • Use Cases: Cross-validation, data preparation
  2. IDLmlShuffle - Random shuffling of training data
    • Scope: Randomize order of features and labels
    • Complexity: Low (permutation generation)
    • Dependencies: Random number generation
    • Use Cases: Data augmentation, batch generation

3. Normalizers (5 functions)

Data preprocessing for feature scaling:

  1. IDLmlLinearNormalizer - Linear scaling: out = in * scale + offset
    • Complexity: Low (simple arithmetic)
  2. IDLmlRangeNormalizer - Scale to range [0, 1]
    • Complexity: Low (min-max scaling)
  3. IDLmlTanHNormalizer - Hyperbolic tangent scaling to (-1, 1)
    • Complexity: Low (tanh function)
  4. IDLmlUnitNormalizer - Unit range scaling
    • Complexity: Low (normalization)
  5. IDLmlVarianceNormalizer - Standardization (mean=0, std=1)
    • Complexity: Low (z-score normalization)

4. Optimizers (5 functions)

Gradient-based optimization algorithms for training neural networks:

  1. IDLmloptAdam - Adaptive Moment Estimation
    • Complexity: Medium (momentum + adaptive learning rate)
    • State: Maintains first/second moment estimates
  2. IDLmloptGradientDescent - Basic gradient descent
    • Complexity: Low (simple weight updates)
    • State: None (stateless)
  3. IDLmloptMomentum - Gradient descent with momentum
    • Complexity: Low-Medium (velocity tracking)
    • State: Maintains velocity vectors
  4. IDLmloptQuickProp - QuickProp algorithm
    • Complexity: Medium (second-order approximation)
    • State: Maintains previous gradients
  5. IDLmloptRMSProp - Root Mean Square Propagation
    • Complexity: Medium (adaptive learning rate)
    • State: Maintains squared gradient averages

5. Activation Functions (17 functions)

Non-linear transformations for neural networks:

Basic Activations

  1. IDLmlafIdentity - f(x) = x (linear)
  2. IDLmlafBinaryStep - f(x) = (x >= 0) ? 1 : 0
  3. IDLmlafLogistic - Sigmoid: f(x) = 1 / (1 + e^-x)
  4. IDLmlafTanH - Hyperbolic tangent: f(x) = tanh(x)

ReLU Family

  1. IDLmlafReLU - Rectified Linear Unit: f(x) = max(0, x)
  2. IDLmlafPReLU - Parametric ReLU (learnable parameter)
  3. IDLmlafELU - Exponential Linear Unit
  4. IDLmlafISRU - Inverse Square Root Unit
  5. IDLmlafISRLU - Inverse Square Root Linear Unit

Advanced Activations

  1. IDLmlafArcTan - f(x) = atan(x)
  2. IDLmlafBentIdentity - Bent identity function
  3. IDLmlafGaussian - Gaussian activation
  4. IDLmlafSinc - Sinc function: f(x) = sin(x)/x
  5. IDLmlafSinusoid - Sine wave activation

Soft Functions

  1. IDLmlafSoftmax - Softmax: f(x_i) = e^x_i / Σe^x_j
  2. IDLmlafSoftPlus - Smooth ReLU: f(x) = ln(1 + e^x)
  3. IDLmlafSoftSign - f(x) = x / (1 + |x|)
  4. IDLmlafSoftExponential - Parametric exponential

Complexity: Low (mathematical functions) Implementation: Can use Rust’s libm or similar

6. SVM Kernels (4 functions)

Kernel functions for Support Vector Machines:

  1. IDLmlSVMLinearKernel - K(x, y) = x · y
  2. IDLmlSVMPolynomialKernel - K(x, y) = (γx·y + r)^d
  3. IDLmlSVMRadialKernel - RBF: K(x, y) = exp(-γ||x-y||²)
  4. IDLmlSVMSigmoidKernel - K(x, y) = tanh(γx·y + r)

Complexity: Low-Medium (dot products, distance calculations) Use: Transform data into higher-dimensional spaces

7. Loss Functions (5 functions)

Objective functions for training:

  1. IDLmllfCrossEntropy - Classification loss
    • Formula: -Σ y_true * log(y_pred)
    • Use: Multi-class classification
  2. IDLmllfHuber - Robust regression loss
    • Formula: Quadratic for small errors, linear for large
    • Use: Robust to outliers
  3. IDLmllfLogCosh - Log-cosh loss
    • Formula: log(cosh(y_pred - y_true))
    • Use: Smooth approximation of MAE
  4. IDLmllfMeanAbsoluteError - MAE/L1 loss
    • Formula: mean(|y_pred - y_true|)
    • Use: Regression, robust to outliers
  5. IDLmllfMeanSquaredError - MSE/L2 loss
    • Formula: mean((y_pred - y_true)²)
    • Use: Regression, standard loss

Complexity: Low (simple calculations)

Implementation Priority & Phases

Phase ML-1: Foundation (Estimated: 2-3 weeks)

Goal: Core utilities and simple algorithms

  1. Data Utilities (Priority: Critical)
    • ✅ IDLmlPartition - train/test split
    • ✅ IDLmlShuffle - data shuffling
  2. Normalizers (Priority: High)
    • ✅ IDLmlLinearNormalizer
    • ✅ IDLmlRangeNormalizer
    • ✅ IDLmlVarianceNormalizer
    • ✅ IDLmlTanHNormalizer
    • ✅ IDLmlUnitNormalizer
  3. Simple Model (Priority: High)
    • ✅ IDLmlKMeans - K-means clustering
    • ✅ IDLmlTestClassifier - evaluation metrics

Deliverables: 9 functions, working data pipeline

Phase ML-2: Activation & Loss Functions (Estimated: 1-2 weeks)

Goal: Building blocks for neural networks

  1. Basic Activations (Priority: High)
    • ✅ IDLmlafIdentity, ReLU, Sigmoid, TanH
    • ✅ IDLmlafSoftmax, SoftPlus, SoftSign
  2. Advanced Activations (Priority: Medium)
    • ✅ ELU, PReLU, ISRU, ISRLU
    • ✅ ArcTan, Gaussian, Sinc, etc.
  3. Loss Functions (Priority: High)
    • ✅ MSE, MAE, CrossEntropy
    • ✅ Huber, LogCosh

Deliverables: 22 functions, activation/loss library

Phase ML-3: Optimizers (Estimated: 1-2 weeks)

Goal: Training algorithms for neural networks

  1. Basic Optimizers (Priority: High)
    • ✅ IDLmloptGradientDescent
    • ✅ IDLmloptMomentum
  2. Advanced Optimizers (Priority: High)
    • ✅ IDLmloptAdam (most popular)
    • ✅ IDLmloptRMSProp
    • ✅ IDLmloptQuickProp

Deliverables: 5 optimizers, training framework

Phase ML-4: Neural Networks (Estimated: 3-4 weeks)

Goal: Implement feed-forward and autoencoder models

  1. Neural Network Core (Priority: High)
    • ✅ IDLmlFeedForwardNeuralNetwork
    • Forward/backward propagation
    • Layer management
    • Weight initialization
  2. Autoencoder (Priority: Medium)
    • ✅ IDLmlAutoEncoder
    • Encoder/decoder architecture
    • Unsupervised training

Deliverables: 2 complex models, neural network framework

Phase ML-5: Support Vector Machines (Estimated: 2-3 weeks)

Goal: SVM for classification and regression

  1. Kernel Functions (Priority: High)
    • ✅ Linear, Polynomial, RBF, Sigmoid kernels
  2. SVM Models (Priority: High)
    • ✅ IDLmlSupportVectorMachineClassification
    • ✅ IDLmlSupportVectorMachineRegression
    • SMO algorithm or libsvm integration

Deliverables: 6 functions, SVM framework

Phase ML-6: Advanced Models (Estimated: 1 week)

Goal: Complete the ML suite

  1. Remaining Models (Priority: Medium)
    • ✅ IDLmlSoftmax classifier

Deliverables: Complete ML function set

Technical Architecture

Rust Crate Structure 

xdl-ml/
├── Cargo.toml
├── src/
│   ├── lib.rs                 # Main module exports
│   ├── models/
│   │   ├── mod.rs
│   │   ├── kmeans.rs          # K-means
│   │   ├── neural_network.rs  # Feed-forward NN
│   │   ├── autoencoder.rs     # Autoencoder
│   │   ├── svm.rs             # SVM classification/regression
│   │   └── softmax.rs         # Softmax classifier
│   ├── optimizers/
│   │   ├── mod.rs
│   │   ├── gradient_descent.rs
│   │   ├── momentum.rs
│   │   ├── adam.rs
│   │   ├── rmsprop.rs
│   │   └── quickprop.rs
│   ├── activations/
│   │   ├── mod.rs
│   │   ├── basic.rs           # Identity, Binary, Sigmoid, TanH
│   │   ├── relu.rs            # ReLU family
│   │   ├── soft.rs            # Softmax, SoftPlus, SoftSign
│   │   └── advanced.rs        # Gaussian, Sinc, etc.
│   ├── losses/
│   │   ├── mod.rs
│   │   ├── mse.rs
│   │   ├── mae.rs
│   │   ├── cross_entropy.rs
│   │   ├── huber.rs
│   │   └── logcosh.rs
│   ├── normalizers/
│   │   ├── mod.rs
│   │   ├── linear.rs
│   │   ├── range.rs
│   │   ├── variance.rs
│   │   ├── tanh.rs
│   │   └── unit.rs
│   ├── kernels/
│   │   ├── mod.rs
│   │   └── svm_kernels.rs     # Linear, Polynomial, RBF, Sigmoid
│   ├── utils/
│   │   ├── mod.rs
│   │   ├── partition.rs       # Train/test split
│   │   ├── shuffle.rs         # Data shuffling
│   │   └── metrics.rs         # Evaluation metrics
│   └── tests/
│       ├── mod.rs
│       └── integration_tests.rs

External Dependencies 

[dependencies]
ndarray = "0.15"           # N-dimensional arrays
rand = "0.8"               # Random number generation
num-traits = "0.2"         # Numeric traits
approx = "0.5"             # Approximate comparisons

# Optional advanced dependencies
smartcore = "0.3"          # Ready-made ML algorithms (optional)
linfa = "0.7"              # Rust ML framework (optional)

Implementation Complexity Analysis

Easy (1-2 days each)

  • Data utilities (Partition, Shuffle)
  • Normalizers (5 functions)
  • Basic activation functions (Identity, ReLU, Sigmoid, TanH)
  • Loss functions (MSE, MAE, CrossEntropy)
  • Simple optimizers (GradientDescent, Momentum)
  • Test metrics (TestClassifier)

Total: ~20 functions, 2-3 weeks

Medium (3-5 days each)

  • K-means clustering
  • Advanced activation functions (ELU, PReLU, etc.)
  • Advanced optimizers (Adam, RMSProp, QuickProp)
  • SVM kernels
  • Softmax classifier

Total: ~15 functions, 3-4 weeks

Hard (1-2 weeks each)

  • Feed-forward neural network (backpropagation, layer management)
  • Autoencoder (encoder/decoder architecture)
  • SVM classification (SMO algorithm or optimization)
  • SVM regression

Total: 4 functions, 6-8 weeks

Estimated Total Timeline

  • Phase ML-1: 2-3 weeks (Foundation)
  • Phase ML-2: 1-2 weeks (Activations/Losses)
  • Phase ML-3: 1-2 weeks (Optimizers)
  • Phase ML-4: 3-4 weeks (Neural Networks)
  • Phase ML-5: 2-3 weeks (SVM)
  • Phase ML-6: 1 week (Completion)

Total Estimated Time: 10-15 weeks (2.5-4 months)

With focused development and reuse of existing Rust ML libraries (smartcore, linfa), this could be reduced to 8-10 weeks.

Success Criteria

Functional Requirements

  • ✅ All 46 ML functions implemented
  • ✅ Compatible with IDL ML API
  • ✅ Comprehensive test coverage (>80%)
  • ✅ Example scripts for each model

Performance Requirements

  • Training speed comparable to Python scikit-learn
  • Memory efficient for large datasets
  • Multi-threading support for training

Documentation Requirements

  • API documentation for each function
  • User guide with examples
  • Migration guide from IDL ML

Next Steps

  1. Review & Approve Plan: Validate scope and timeline
  2. Setup xdl-ml Crate: Create module structure
  3. Start Phase ML-1: Implement foundation (data utils + normalizers + k-means)
  4. Iterative Development: Phase by phase with testing
  5. Integration: Wire ML functions into XDL standard library

Notes

  • Some functions may benefit from using existing Rust ML crates (smartcore, linfa) to accelerate development
  • Neural network backpropagation is the most complex component
  • SVM optimization may require specialized libraries or custom SMO implementation
  • Consider implementing most-used functions first (K-means, NN, SVM classification)

Ready to proceed with Phase ML-1 implementation!