Ai|oS Documentation

# Clone the repository
git clone https://github.com/yourusername/aios.git
cd aios

# Install dependencies
pip install -r requirements.txt

# Verify installation
python -c "from aios.quantum_ml_algorithms import check_dependencies; check_dependencies()"

# Quantum algorithms only
pip install torch numpy scipy

# ML algorithms only (some require PyTorch, some only NumPy)
pip install torch numpy

# Autonomous discovery (requires all)
pip install torch numpy scipy

from aios.quantum_ml_algorithms import QuantumStateEngine

# Create quantum circuit
qc = QuantumStateEngine(num_qubits=5)

# Build superposition
for i in range(5):
    qc.hadamard(i)

# Apply entanglement
for i in range(4):
    qc.cnot(i, i + 1)

# Measure
results = qc.measure(shots=1000)
print(f"Results: {results}")

# Expectation value
energy = qc.expectation_value('Z0*Z1')
print(f"Energy: {energy}")

from aios.quantum_ml_algorithms import QuantumVQE

# Define Hamiltonian
def hamiltonian(qc):
    return qc.expectation_value('Z0') - 0.5 * qc.expectation_value('Z1')

# Initialize VQE
vqe = QuantumVQE(num_qubits=4, depth=3)

# Optimize
energy, params = vqe.optimize(hamiltonian, max_iter=100)
print(f"Ground state energy: {energy:.6f}")
print(f"Optimal parameters: {params}")

from aios.quantum_hhl_algorithm import hhl_linear_system_solver
import numpy as np

# Define linear system
A = np.array([[2.0, -0.5], [-0.5, 2.0]])
b = np.array([1.0, 0.0])

# Solve with quantum advantage
result = hhl_linear_system_solver(A, b)

print(f"Success probability: {result['success_probability']:.4f}")
print(f"Quantum advantage: {result['quantum_advantage']:.1f}x")
print(f"Condition number: {result['condition_number']:.2f}")

# Note: HHL outputs expectation values, not full solution vector
print(f"Solution expectation: {result['solution_expectation']}")

from aios.ml_algorithms import AdaptiveStateSpace
import torch

# Initialize Mamba layer
mamba = AdaptiveStateSpace(
    input_dim=512,
    state_dim=128,
    output_dim=512
)

# Process sequence
x = torch.randn(32, 1000, 512)  # (batch, length, features)
output, final_state = mamba(x)

print(f"Output shape: {output.shape}")  # (32, 1000, 512)
print(f"Final state: {final_state.shape}")  # (32, 128)

from aios.ml_algorithms import OptimalTransportFlowMatcher
import torch

# Initialize flow matcher
flow = OptimalTransportFlowMatcher(
    data_dim=784,  # e.g., 28x28 images
    time_dim=32
)

# Sample batch
samples = flow.sample(
    num_samples=16,
    num_steps=20  # Only 20 steps for high quality!
)

print(f"Generated samples: {samples.shape}")  # (16, 784)

from aios.ml_algorithms import NeuralGuidedMCTS

# Define game environment
def policy_fn(state):
    """Neural network policy"""
    return [(action, prob) for action, prob in action_probs]

def value_fn(state):
    """Neural network value estimate"""
    return estimated_value

# Initialize MCTS
mcts = NeuralGuidedMCTS(
    policy_fn=policy_fn,
    value_fn=value_fn,
    num_simulations=800,
    c_puct=1.0  # Exploration constant
)

# Search for best move
best_action = mcts.search(current_state)
print(f"Best action: {best_action}")

from aios.ml_algorithms import AdaptiveParticleFilter
import numpy as np

# Initialize filter
pf = AdaptiveParticleFilter(
    num_particles=1000,
    state_dim=4,  # e.g., [x, y, vx, vy]
    obs_dim=2     # e.g., [x, y] measurements
)

# Time update (prediction)
def transition_fn(x):
    """State transition model"""
    return x + 0.1 * np.random.randn(*x.shape)

pf.predict(transition_fn=transition_fn, process_noise=0.05)

# Measurement update
def likelihood_fn(x, obs):
    """Measurement likelihood"""
    diff = x[:2] - obs  # Compare position to observation
    return np.exp(-0.5 * np.sum(diff**2))

observation = np.array([1.0, 2.0])
pf.update(observation=observation, likelihood_fn=likelihood_fn)

# Get estimate
estimate = pf.estimate()
print(f"State estimate: {estimate}")

from aios.autonomous_discovery import AutonomousLLMAgent, AgentAutonomy

# Create Level 4 autonomous agent
agent = AutonomousLLMAgent(
    model_name="deepseek-r1",
    autonomy_level=AgentAutonomy.LEVEL_4
)

# Give it a mission and let it learn
agent.set_mission(
    "quantum computing machine learning applications",
    duration_hours=1.0
)

# Agent autonomously:
# - Decomposes mission into learning objectives
# - Balances exploration vs exploitation
# - Builds knowledge graph
# - Self-evaluates and adapts
await agent.pursue_autonomous_learning()

# Export discovered knowledge
knowledge = agent.export_knowledge_graph()
print(f"Discovered {knowledge['stats']['total_concepts']} concepts")
print(f"Average confidence: {knowledge['stats']['average_confidence']:.2f}")

from aios.autonomous_discovery import create_autonomous_discovery_action

# Security agent learns threat patterns
async def security_research(ctx):
    mission = "ransomware attack vectors cloud vulnerabilities"
    discovery = create_autonomous_discovery_action(
        mission,
        duration_hours=0.5
    )

    knowledge = await discovery()
    ctx.publish_metadata('security.threat_patterns', knowledge)

    return ActionResult(
        success=True,
        message=f"Discovered {knowledge['stats']['total_concepts']} threat patterns",
        payload=knowledge['stats']
    )

from aios.ml_algorithms import get_algorithm_catalog

catalog = get_algorithm_catalog()

for name, info in catalog.items():
    print(f"{name}:")
    print(f"  Category: {info['category']}")
    print(f"  Complexity: {info['complexity']}")
    print(f"  Available: {info['available']}")
    print()

from aios.quantum_ml_algorithms import QuantumVQE

# H2 molecule Hamiltonian (simplified)
def h2_hamiltonian(qc):
    # Coefficients from chemistry calculation
    c1 = -1.0523  # ZZ term
    c2 = 0.3979   # Z term

    zz = qc.expectation_value('Z0*Z1')
    z = qc.expectation_value('Z0')

    return c1 * zz + c2 * z

# Optimize
vqe = QuantumVQE(num_qubits=2, depth=2)
energy, params = vqe.optimize(h2_hamiltonian, max_iter=100)

print(f"Ground state energy: {energy:.6f} Hartree")
# Expected: ~-1.137 Hartree for H2 at equilibrium

from aios.ml_algorithms import AdaptiveParticleFilter
import numpy as np

# Track object with noisy GPS measurements
pf = AdaptiveParticleFilter(num_particles=500, state_dim=4, obs_dim=2)

# Simulate tracking over time
true_trajectory = []
estimated_trajectory = []

for t in range(100):
    # True state (unknown to filter)
    true_pos = np.array([t * 0.1, np.sin(t * 0.1)])
    true_trajectory.append(true_pos)

    # Prediction step
    def transition(x):
        # Constant velocity model
        x[:2] += x[2:] * 0.1  # Position update
        return x

    pf.predict(transition_fn=transition, process_noise=0.01)

    # Noisy measurement
    measurement = true_pos + np.random.randn(2) * 0.1

    # Update step
    def likelihood(x, obs):
        diff = x[:2] - obs
        return np.exp(-np.sum(diff**2) / (2 * 0.01))

    pf.update(observation=measurement, likelihood_fn=likelihood)

    # Estimate
    estimate = pf.estimate()
    estimated_trajectory.append(estimate[:2])

# Calculate tracking error
error = np.mean([np.linalg.norm(t - e)
                 for t, e in zip(true_trajectory, estimated_trajectory)])
print(f"Average tracking error: {error:.4f}")

from aios.autonomous_discovery import AutonomousLLMAgent, AgentAutonomy

async def main():
    # Create Level 4 agent
    agent = AutonomousLLMAgent(
        model_name="deepseek-r1",
        autonomy_level=AgentAutonomy.LEVEL_4
    )

    # Cycle 1: Learn about quantum computing
    agent.set_mission("quantum computing fundamentals", duration_hours=0.5)
    await agent.pursue_autonomous_learning()

    # Cycle 2: Agent autonomously identifies gaps and continues
    agent.set_mission("quantum error correction codes", duration_hours=0.5)
    await agent.pursue_autonomous_learning()

    # Export comprehensive knowledge
    knowledge = agent.export_knowledge_graph()

    print(f"Total concepts: {knowledge['stats']['total_concepts']}")
    print(f"High confidence (>0.9): {knowledge['stats']['high_confidence_count']}")

    # Find most important concepts
    sorted_concepts = sorted(
        knowledge['nodes'].items(),
        key=lambda x: x[1]['confidence'],
        reverse=True
    )

    print("\nTop 5 concepts:")
    for concept, data in sorted_concepts[:5]:
        print(f"  {concept}: {data['confidence']:.3f}")

import asyncio
asyncio.run(main())