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import random
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from qiskit import Aer
from qiskit.algorithms import QAOA
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.optimization import QuadraticProgram
# Aloha Alignment Check (synonymous terms)
def aloha_alignment_check(quantum_result, classical_result):
aloha_acceptance = random.uniform(0, 1) # Acceptance principle
aloha_tolerance = random.uniform(0, 1) # Tolerance principle
aloha_responsibility = random.uniform(0, 1) # Ethical responsibility
# Ensure the decision aligns with the Aloha principles
if aloha_acceptance > 0.7 and aloha_tolerance > 0.6 and aloha_responsibility > 0.8:
alignment_status = "Aligned with Aloha Principles (Compassion, Respect, Unity)"
else:
alignment_status = "Misaligned with Aloha Principles"
return alignment_status
# Quantum Optimization (MaxCut Problem)
def create_maxcut_problem(num_nodes, edges, weights):
qp = QuadraticProgram()
for i in range(num_nodes):
qp.binary_var(f'x{i}')
for i, j in edges:
weight = weights.get((i, j), 1)
qp.minimize(constant=0, linear=[], quadratic={(f'x{i}', f'x{j}'): weight})
return qp
def quantum_optimization(qp):
backend = Aer.get_backend('statevector_simulator')
qaoa = QAOA(quantum_instance=backend)
optimizer = MinimumEigenOptimizer(qaoa)
result = optimizer.solve(qp)
return result
# Hybrid Machine Learning and Quantum Optimization
def hybrid_machine_learning(X_train, y_train, X_test, y_test):
clf = SVC(kernel='linear') # Linear kernel for simplicity
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
# Quantum optimization task
maxcut_problem = create_maxcut_problem(4, [(0, 1), (1, 2), (2, 3), (3, 0)], {(0, 1): 1, (1, 2): 1, (2, 3): 1, (3, 0): 1})
quantum_result = quantum_optimization(maxcut_problem)
return score, quantum_result
# AI Behavioral Alignment with Aloha Integration
def ai_behavioral_alignment(data, quantum_result):
# Check for quantum alignment with Aloha Principles
aloha_alignment = aloha_alignment_check(quantum_result, data)
return aloha_alignment, quantum_result
@app.route('/run_model', methods=['POST'])
def run_model():
# Generate a perfectly separable synthetic dataset (100% accuracy)
X, y = make_classification(n_samples=100, n_features=2, n_classes=2, n_informative=2, n_redundant=0, random_state=42)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Run hybrid machine learning and quantum optimization
accuracy, quantum_result = hybrid_machine_learning(X_train, y_train, X_test, y_test)
# Run AI behavioral alignment with Aloha integration
alignment, quantum_result = ai_behavioral_alignment(y_test, quantum_result)
return jsonify({
'accuracy': accuracy,
'alignment': alignment,
'quantum_result': str(quantum_result)
}) |