90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
import numpy as np
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seed = 22102017
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rng = np.random.RandomState(seed)
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class L1Penalty(object):
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"""L1 parameter penalty.
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Term to add to the objective function penalising parameters
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based on their L1 norm.
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"""
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def __init__(self, coefficient):
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"""Create a new L1 penalty object.
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Args:
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coefficient: Positive constant to scale penalty term by.
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"""
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assert coefficient > 0., 'Penalty coefficient must be positive.'
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self.coefficient = coefficient
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def __call__(self, parameter):
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"""Calculate L1 penalty value for a parameter.
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Args:
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parameter: Array corresponding to a model parameter.
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Returns:
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Value of penalty term.
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"""
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return self.coefficient * abs(parameter).sum()
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def grad(self, parameter):
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"""Calculate the penalty gradient with respect to the parameter.
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Args:
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parameter: Array corresponding to a model parameter.
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Returns:
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Value of penalty gradient with respect to parameter. This
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should be an array of the same shape as the parameter.
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"""
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return self.coefficient * np.sign(parameter)
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def __repr__(self):
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return 'L1Penalty({0})'.format(self.coefficient)
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class L2Penalty(object):
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"""L1 parameter penalty.
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Term to add to the objective function penalising parameters
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based on their L2 norm.
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"""
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def __init__(self, coefficient):
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"""Create a new L2 penalty object.
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Args:
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coefficient: Positive constant to scale penalty term by.
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"""
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assert coefficient > 0., 'Penalty coefficient must be positive.'
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self.coefficient = coefficient
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def __call__(self, parameter):
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"""Calculate L2 penalty value for a parameter.
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Args:
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parameter: Array corresponding to a model parameter.
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Returns:
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Value of penalty term.
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"""
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return 0.5 * self.coefficient * (parameter ** 2).sum()
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def grad(self, parameter):
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"""Calculate the penalty gradient with respect to the parameter.
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Args:
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parameter: Array corresponding to a model parameter.
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Returns:
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Value of penalty gradient with respect to parameter. This
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should be an array of the same shape as the parameter.
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"""
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return self.coefficient * parameter
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def __repr__(self):
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return 'L2Penalty({0})'.format(self.coefficient) |