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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
# Copyright (c) 2013,2014, GPy authors (see AUTHORS.txt).
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# Copyright (c) 2015, James Hensman
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import sys
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from .parameterization import Parameterized
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import numpy as np
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class Mapping(Parameterized):
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"""
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Base model for shared mapping behaviours
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"""
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def __init__(self, input_dim, output_dim, name='mapping'):
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self.input_dim = input_dim
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self.output_dim = output_dim
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super(Mapping, self).__init__(name=name)
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def f(self, X):
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raise NotImplementedError
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def gradients_X(self, dL_dF, X):
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raise NotImplementedError
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def update_gradients(self, dL_dF, X):
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raise NotImplementedError
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class Bijective_mapping(Mapping):
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"""
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This is a mapping that is bijective, i.e. you can go from X to f and
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also back from f to X. The inverse mapping is called g().
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"""
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def __init__(self, input_dim, output_dim, name='bijective_mapping'):
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super(Bijective_mapping, self).__init__(name=name)
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def g(self, f):
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"""Inverse mapping from output domain of the function to the inputs."""
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raise NotImplementedError
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