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README.md

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+# Semi-Supervised Support Vector Machines (S3VM)
+
+Python implementation of semi-supervised SVM algorithms for binary classification using both labeled and unlabeled data.
+
+## Algorithms
+
+- **NewtonUTSVM**: Newton-based method using two parallel planes
+- **S3VM Constrained**: Constrained formulation with MIP
+- **S3VM Unconstrained**: Unconstrained smooth optimization
+- **MC_NDCC**: Synthetic dataset generator
+- **SRMSVM**: 1-norm SVM for sparse solutions
+
+## Requirements
+
+- Python 3.x
+- numpy
+- pandas
+- scipy
+- scikit-learn
+
+## Installation
+
+```bash
+pip install numpy pandas scipy scikit-learn
+```
+
+## Usage
+
+### Run Experiments
+
+```bash
+cd code
+python main.py
+```
+
+### Basic Example
+
+```python
+from NewtonUTSVM import NewtonUTSVM
+from utils import load_dataset, calculate_accuracy
+
+# Load dataset
+X, y, x_test, y_test, U = load_dataset("data/diabetes.csv")
+y = y.reshape(y.shape[0], 1)
+
+# Train
+C = [0.1, 0.1, 0.1, 0.1, 0.3, 0.3]
+model = NewtonUTSVM(X, y, U, C=C, eps=1e-4)
+model.fit()
+
+# Predict
+model.predict(x_test=x_test)
+predictions = model.get_preds()
+accuracy = calculate_accuracy(y_test, predictions)
+print(f"Accuracy: {accuracy}%")
+```
+
+## Datasets
+
+Real-world: diabetes, ionosphere, musk, sonar, gender, wpbc
+
+Synthetic: NDCC datasets (100_10, 100_100, 500_10, 1000_10)
+
+## Project Structure
+
+```
+code/
+├── main.py              # Main script
+├── NewtonUTSVM.py       # NewtonUTSVM
+├── S3VM_constrained.py  # Constrained S3VM
+├── S3VM_unconstrained.py # Unconstrained S3VM
+├── SRMSVM.py            # SRMSVM
+├── MC_NDCC.py           # Dataset generator
+├── utils.py             # Utilities
+└── data/                # Datasets
+```
+
+## Notes
+
+- Binary classification only (labels: +1, -1)
+- Datasets are automatically normalized
+- NewtonUTSVM requires 6 C parameters