polynomial_example.py

#!/usr/bin/env python3
"""
KortexDL Wine Classification Example
=====================================

Multi-class classification using the Wine dataset.

Usage:
    python polynomial_example.py
"""

import numpy as np
import kortexdl as bd

try:
    from sklearn.datasets import load_wine
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import train_test_split
    SKLEARN_AVAILABLE = True
except ImportError:
    SKLEARN_AVAILABLE = False


def main():
    print("🎯 KortexDL Classification - Wine Dataset")
    print("=" * 60)
    
    if not SKLEARN_AVAILABLE:
        print("❌ sklearn is required. Install with: pip install scikit-learn")
        return 1
    
    # Load Wine dataset
    print("\n📁 Loading Wine dataset...")
    data = load_wine()
    X = data.data.astype(np.float32)
    y_raw = data.target
    
    # One-hot encode
    n_classes = 3
    y = np.zeros((len(y_raw), n_classes), dtype=np.float32)
    for i, label in enumerate(y_raw):
        y[i, label] = 1.0
    
    print(f"✅ Dataset: {len(X)} samples")
    print(f"✅ Features: {X.shape[1]} (alcohol, malic acid, ash, etc.)")
    print(f"✅ Classes: {n_classes} wine cultivars")
    
    # Normalize
    scaler = StandardScaler()
    X = scaler.fit_transform(X).astype(np.float32)
    
    # Split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    y_test_raw = np.argmax(y_test, axis=1)
    
    print(f"✅ Split: {len(X_train)} train, {len(X_test)} test")
    
    # Create network
    print("\n🧠 Creating classification network...")
    n_features = X.shape[1]
    net = bd.Network([n_features, 32, 16, n_classes], bd.ActivationType.Sigmoid)
    print(f"✅ Network: {n_features} -> 32 -> 16 -> {n_classes}")
    
    # Training
    print("\n🏋️ Training...")
    epochs = 300
    X_flat = X_train.flatten().tolist()
    y_flat = y_train.flatten().tolist()
    
    for epoch in range(epochs):
        loss = net.train_batch(X_flat, y_flat, bd.LossType.MSE, 0.1, len(X_train))
        if epoch % 50 == 0:
            print(f"  Epoch {epoch:3d}: Loss = {loss:.4f}")
    
    print("✅ Training complete!")
    
    # Evaluation
    print("\n📈 Evaluating...")
    correct = 0
    predictions = []
    
    for i in range(len(X_test)):
        output = net.forward(X_test[i].tolist(), 1, False)
        pred = np.argmax(output)
        true = y_test_raw[i]
        predictions.append(pred)
        if pred == true:
            correct += 1
    
    accuracy = correct / len(X_test) * 100
    print(f"✅ Test Accuracy: {accuracy:.1f}%")
    print(f"✅ Correct: {correct}/{len(X_test)}")
    
    # Sample predictions
    class_names = ['Class 0', 'Class 1', 'Class 2']
    print("\n📊 Sample Predictions:")
    for i in range(min(10, len(X_test))):
        pred = predictions[i]
        true = y_test_raw[i]
        status = "✓" if pred == true else "✗"
        print(f"  True: {class_names[true]}  Pred: {class_names[pred]}  {status}")
    
    print("\n" + "=" * 60)
    print("✅ Complete!")
    return 0


if __name__ == "__main__":
    exit(main())