ml_svm 0.1.0

ml_svm: ^0.1.0 copied to clipboard

example/ml_svm_example.dart

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

void main() {
  // Sample dataset: [height, weight] and labels
  final features = <Float64List>[
    Float64List.fromList([160.0, 50.0]),
    Float64List.fromList([170.0, 65.0]),
    Float64List.fromList([180.0, 80.0]),
    Float64List.fromList([155.0, 48.0]),
    Float64List.fromList([175.0, 75.0]),
    Float64List.fromList([165.0, 52.0]),
  ];

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

  // Normalize the data
  final normalized = normalize(features);

  // Shuffle (optional)
  shuffleInPlace(normalized, labels, seed: 42);

  // Create and train model
  final model = SVMModel(
    learningRate: 0.01,
    regularizationC: 1.0,
    maxIterations: 500,
    tolerance: 1e-4,
  );

  model.fit(normalized, labels);

  // Test prediction
  final testSample = Float64List.fromList([172.0, 70.0]);
  final testNorm = normalize([testSample]).first;

  final predicted = model.predict(testNorm);
  final decision = model.decisionFunction(testNorm);

  print('Predicted class: $predicted');
  print('Decision scores: $decision');
}