UnifiedObserverLib is a comprehensive Python library implementing the Unified Observer concept for advanced AI and machine learning applications. It provides tools for quantum-inspired neural networks, predictive modeling, ethical evaluation, and much more.
You can install UnifiedObserverLib using pip:
pip install unified-observer-lib- Unified Observer framework
- Quantum-inspired neural networks
- Self-observing optimization
- Reality wave function modeling
- Phase transition and catastrophe theory analysis
- Topological and multifractal analysis
- Entropy analysis
- Advanced visualization tools
- Ethical evaluation
- Distributed computing support
- Quantum machine learning integration
The central class that implements the Unified Observer concept.
from unified_observer_lib import UnifiedObserver
uo = UnifiedObserver(initial_q=0.5, initial_tau=0, use_gpu=False, precision=53)
uo.observe(data)
state = uo.get_state()Represents the wave function of reality in the Unified Observer framework.
from unified_observer_lib import RealityWaveFunction
rwf = RealityWaveFunction(unified_observer)
psi = rwf.psi_R(q, tau)
evolved_psi = rwf.evolve(delta_tau)A quantum-inspired neural network for advanced machine learning tasks.
from unified_observer_lib import QuantumNN
qnn = QuantumNN(layer_sizes=[64, 32, 1], activation='tanh', use_phase=True)
output = qnn(input_data)Analyzes phase transitions in the system's behavior.
from unified_observer_lib import PhaseTransitionAnalyzer
analyzer = PhaseTransitionAnalyzer(unified_observer)
transitions = analyzer.analyze_observer_transitions()Computes various entropy measures for the system.
from unified_observer_lib import EntropyAnalyzer
entropy_analyzer = EntropyAnalyzer(unified_observer)
entropy_results = entropy_analyzer.analyze_observer_entropy()Performs topological data analysis on the system's state.
from unified_observer_lib import TopologicalAnalyzer
topo_analyzer = TopologicalAnalyzer(max_dimension=2)
result = topo_analyzer.analyze_observer_topology(unified_observer)Provides advanced visualization tools for the Unified Observer system.
from unified_observer_lib import AdvancedVisualizer
visualizer = AdvancedVisualizer(unified_observer)
visualizer.plot_parameter_evolution(num_steps=1000)
visualizer.plot_wave_function(reality_wave_function, q_range=(0, 1), tau_range=(0, 10))
visualizer.plot_multidimensional_state()Here's a more comprehensive example that demonstrates how to use UnifiedObserverLib:
from unified_observer_lib import UnifiedObserver, PredictiveModel, PhaseTransitionAnalyzer, AdvancedVisualizer
import numpy as np
# Create a Unified Observer
uo = UnifiedObserver(initial_q=0.5, initial_tau=0)
# Create a predictive model
model = PredictiveModel(uo, nn_layer_sizes=[64, 32, 1])
# Generate some example data
X = np.random.randn(1000, 10)
y = np.sin(X[:, 0]) + 0.1 * np.random.randn(1000)
# Train the model
model.train(X, y, epochs=100, batch_size=32)
# Make predictions
X_test = np.random.randn(100, 10)
predictions = model.predict(X_test, future_tau=5)
# Analyze phase transitions
analyzer = PhaseTransitionAnalyzer(uo)
transitions = analyzer.analyze_observer_transitions()
# Visualize results
visualizer = AdvancedVisualizer(uo)
visualizer.plot_parameter_evolution(num_steps=1000)
visualizer.plot_multidimensional_state()
print(f"Predictions: {predictions[:5]}")
print(f"Detected {len(transitions)} phase transitions")Contributions to UnifiedObserverLib are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License. See the LICENSE file for details.