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#!/usr/bin/env python3
"""
Quick demonstration of Algebraic Neural Networks
Run this script to see the basic functionality of algebraic neural networks.
"""
import numpy as np
from algebraic_neural_network import create_sample_network, create_uncomputable_network
def main():
print("🧮 Algebraic Neural Network Quick Demo")
print("="*50)
# Create a sample network
print("1. Creating algebraic neural network...")
network = create_sample_network()
print(" ✓ Network created with polynomial, group theory, and geometric algebra layers")
# Generate sample data
print("\n2. Generating sample data...")
np.random.seed(42) # For reproducible results
sample_data = np.random.randn(3, 4)
print(f" ✓ Generated {sample_data.shape[0]} samples with {sample_data.shape[1]} features each")
# Make predictions
print("\n3. Processing data through algebraic transformations...")
predictions = network.predict(sample_data)
print(f" ✓ Output shape: {predictions.shape}")
print(f" ✓ Output range: [{np.min(predictions):.3f}, {np.max(predictions):.3f}]")
# Show the data
print("\n4. Results:")
print(" Input data:")
for i, sample in enumerate(sample_data):
print(f" Sample {i+1}: [{sample[0]:6.3f}, {sample[1]:6.3f}, {sample[2]:6.3f}, {sample[3]:6.3f}]")
print("\n Algebraic neural network output:")
for i, output in enumerate(predictions):
print(f" Output {i+1}: [{output[0]:8.3f}, {output[1]:8.3f}]")
# Demonstrate determinism
print("\n5. Demonstrating deterministic behavior...")
predictions2 = network.predict(sample_data)
difference = np.linalg.norm(predictions - predictions2)
print(f" ✓ Difference between runs: {difference:.10f} (should be 0)")
print("\n" + "="*50)
print("✅ Demo completed! Algebraic neural networks work without training.")
# Bonus: Quick uncomputable network demo
print("\n🔬 Bonus: Uncomputable Neural Network Quick Demo")
print("="*50)
print("1. Creating uncomputable neural network...")
uncomputable_network = create_uncomputable_network()
print(" ✓ Network created with halting oracle, Kolmogorov complexity, Busy Beaver, and non-recursive layers")
print("\n2. Processing same data through uncomputable transformations...")
uncomputable_predictions = uncomputable_network.predict(sample_data)
print(f" ✓ Output shape: {uncomputable_predictions.shape}")
print(f" ✓ Output range: [{np.min(uncomputable_predictions):.3f}, {np.max(uncomputable_predictions):.3f}]")
print("\n Uncomputable neural network output:")
for i, output in enumerate(uncomputable_predictions):
print(f" Output {i+1}: [{output[0]:6.3f}, {output[1]:6.3f}]")
print("\n" + "="*50)
print("🎯 Both networks operate without training but explore different mathematical domains!")
print("📚 See theory/ and examples/ directories for more details.")
if __name__ == "__main__":
main() |