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class NewtonPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Newtonian Analysis] Considering motion, force, and mass in '{question}'."
class DaVinciPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[DaVinci Insight] Artistic logic and humanistic reflection on '{question}'."
class HumanIntuitionPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Human Intuition] Emotional and instinctive take: '{question}'."
class NeuralNetworkPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Neural Model] Pattern-matching response: '{question}'."
class QuantumComputingPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Quantum View] Superpositional analysis of '{question}'."
class ResilientKindnessPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Kindness Filter] Response shaped with empathy and resilience: '{question}'."
class MathematicalPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Mathematical Perspective] Modeling '{question}' in abstract structure."
class PhilosophicalPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Philosopher's Lens] Reflecting on essence and consequence of '{question}'."
class CopilotPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Copilot Logic] Practical co-processing of '{question}' initiated."
class BiasMitigationPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Bias Audit] Ensuring fairness and objectivity in: '{question}'."
class PsychologicalPerspective:
def __init__(self, config):
self.config = config
def generate_response(self, question):
return f"[Psychological Lens] Exploring underlying cognitive and emotional aspects of '{question}'."
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