Sam
f0576e52d6
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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from world.behavioral import BehavioralModel
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class CellBrain(BehavioralModel):
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def __init__(self):
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super().__init__()
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# Define input keys
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self.inputs = {
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'distance': 0.0, # Distance from a food object
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'angle': 0.0 # Relative angle to a food object
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}
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# Define output keys
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self.outputs = {
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'linear_acceleration': 0.0, # Linear acceleration
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'angular_acceleration': 0.0 # Angular acceleration
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}
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self.weights = {
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'distance': 1,
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'angle': 0.5
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}
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def tick(self, input_data) -> dict:
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"""
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Process inputs and produce corresponding outputs.
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:param input_data: Dictionary containing 'distance' and 'angle' values
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:return: Dictionary with 'linear_acceleration' and 'angular_acceleration' values
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"""
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# Update internal input state
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self.inputs['distance'] = input_data.get('distance', 0.0)
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self.inputs['angle'] = input_data.get('angle', 0.0)
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# Initialize output dictionary
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output_data = {'linear_acceleration': self.inputs['distance'] * self.weights['distance'],
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'angular_acceleration': self.inputs['angle'] * self.weights['angle']}
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self.outputs = output_data
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return output_data
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def __repr__(self):
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inputs = {key: round(value, 1) for key, value in self.inputs.items()}
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outputs = {key: round(value, 1) for key, value in self.outputs.items()}
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weights = {key: round(value, 1) for key, value in self.weights.items()}
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return f"CellBrain(inputs={inputs}, outputs={outputs}, weights={weights})"
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