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UnifiedObserverLib

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.

Installation

You can install UnifiedObserverLib using pip:

pip install unified-observer-lib

Features

  • 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

Core Components

UnifiedObserver

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()

RealityWaveFunction

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)

QuantumNN

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)

Analysis Tools

PhaseTransitionAnalyzer

Analyzes phase transitions in the system's behavior.

from unified_observer_lib import PhaseTransitionAnalyzer

analyzer = PhaseTransitionAnalyzer(unified_observer)
transitions = analyzer.analyze_observer_transitions()

EntropyAnalyzer

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()

TopologicalAnalyzer

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)

Visualization

AdvancedVisualizer

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()

Example Usage

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")

Contributing

Contributions to UnifiedObserverLib are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.