(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
   b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance
"""
}

{
    "file": "/Eden/CORE/awareness/resonance_metrics.py",
    "function_to_modify": "def measure_resonance(state_history: List[dict]) -> float:",
    "new_code": """
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.)

def measure_resonance(state_history: List[dict]) -> float:
    if len(state_history) < 2:
        return 0.5
    
    current = state_history[-1]
    prev = state_history[-2]
    
    current_vec = np.array([current['integration']])
    prev_vec = np.array([prev['integration']])
    
    sim = cosine_similarity(current_vec, prev_vec)
    resonance = (sim + 1) / 2
    return resonance