🧠 ml_svm – Native Dart Support Vector Machine (SVM) #

A fully Dart-native implementation of the Support Vector Machine (SVM) algorithm.
This library supports binary and multi-class classification using hinge loss + L2 regularization, optimized with Float64List vectors and zero dependencies.

⚡ No Python, no bindings — just pure Dart for real machine learning on Flutter, CLI, or embedded apps.

📘 What is SVM? #

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression.
It works by finding the hyperplane that best separates different classes in a high-dimensional space.

This package implements:

• Linear SVM
• Hinge Loss
• L2 Regularization
• Gradient Descent Optimization
• One-vs-Rest (OvR) for multi-class classification

🚀 Features #

✅ Linear classification
✅ Binary and multi-class (OvR) support
✅ Optimized for Dart's Float64List
✅ Gradient descent training
✅ Intercept/bias term
✅ Utility functions: normalization, shuffling
✅ MIT License – Free for commercial or academic use

📦 Installation #

dart pub add ml_svm

⚡ Quick Example #

import 'dart:typed_data';
import 'package:ml_svm/ml_svm.dart';

void main() {
  // Training dataset: [height, weight]
  final features = <Float64List>[
    Float64List.fromList([160, 50]),
    Float64List.fromList([170, 65]),
    Float64List.fromList([180, 80]),
    Float64List.fromList([155, 48]),
  ];

  final labels = ['underweight', 'normal', 'overweight', 'underweight'];

  // Normalize input features
  final normalized = normalize(features);

  // Train the SVM model
  final model = SVMModel(
    learningRate: 0.01,
    regularizationC: 1.0,
    maxIterations: 500,
  );

  model.fit(normalized, labels);

  // Predict a new sample
  final testSample = normalize([
    Float64List.fromList([172, 70])
  ]).first;

  print('Prediction: ${model.predict(testSample)}');
  print('Scores: ${model.decisionFunction(testSample)}');
}

📌 Expected Output:

Prediction: normal
Scores: {underweight: -0.12, normal: 0.53, overweight: 0.42}

📚 API Documentation #

SVMModel Constructor #

SVMModel({
  double learningRate = 0.01,
  double regularizationC = 1.0,
  int maxIterations = 1000,
  double tolerance = 1e-4,
});

Main Methods #

🧰 Utility Functions #

• normalize(List<Float64List> X) → Min-max scaling to [0, 1]
• shuffleInPlace(List data, List labels, {int? seed}) → Random shuffle with labels synced

🔬 Performance Notes #

• Designed for small to medium-sized datasets
• Optimized for embedded & offline ML
• Uses CPU-friendly gradient descent (not SMO or QP)
• Not suitable for kernel methods or large-scale vector spaces

📌 Use Cases #

• 🎓 Teaching & academic learning in Dart
• 📱 Lightweight ML inside Flutter apps
• 🔐 Edge/IoT classification without TensorFlow
• ⚙️ Dart-native AI pipelines and decision systems

🧪 Testing #

Run all tests:

dart test

Test coverage includes:

• Model training and prediction
• Class scores with decisionFunction
• Label consistency & structure

🔗 Author & Contributions #

Created with ❤️ by Mehmet Çelik
If you found this useful, consider ⭐ starring the project and following for more!

Contributions are welcome! Open issues, suggest features, or submit PRs.

📄 License #

This project is licensed under the MIT License.
See the LICENSE file for details.