def normalize_activations()

in neuron-explainer/neuron_explainer/activations/activation_records.py [0:0]

• 4 lines of code
• 4 McCabe index (conditional complexity)

def normalize_activations(activation_record: list[float], max_activation: float) -> list[int]:
    """Convert raw neuron activations to integers on the range [0, 10]."""
    if max_activation <= 0:
        return [0 for x in activation_record]
    # Relu is used to assume any values less than 0 are indicating the neuron is in the resting
    # state. This is a simplifying assumption that works with relu/gelu.
    return [min(10, math.floor(10 * relu(x) / max_activation)) for x in activation_record]