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65 lines
1.9 KiB
Python
65 lines
1.9 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 numpy as np
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from ..core.mapping import Mapping
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from ..core.parameterization import Param
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class Linear(Mapping):
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"""
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A Linear mapping.
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.. math::
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F(\mathbf{x}) = \mathbf{A} \mathbf{x})
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:param input_dim: dimension of input.
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:type input_dim: int
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:param output_dim: dimension of output.
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:type output_dim: int
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:param kernel: a GPy kernel, defaults to GPy.kern.RBF
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:type kernel: GPy.kern.kern
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"""
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def __init__(self, input_dim, output_dim, name='linmap'):
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super(Linear, self).__init__(input_dim=input_dim, output_dim=output_dim, name=name)
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self.A = Param('A', np.random.randn(self.input_dim, self.output_dim))
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self.link_parameter(self.A)
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def f(self, X):
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return np.dot(X, self.A)
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def update_gradients(self, dL_dF, X):
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self.A.gradient = np.dot(X.T, dL_dF)
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def gradients_X(self, dL_dF, X):
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return np.dot(dL_dF, self.A.T)
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def to_dict(self):
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"""
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Convert the object into a json serializable dictionary.
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Note: It uses the private method _save_to_input_dict of the parent.
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:return dict: json serializable dictionary containing the needed information to instantiate the object
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"""
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input_dict = super(Linear, self)._save_to_input_dict()
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input_dict["class"] = "GPy.mappings.Linear"
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input_dict["A"] = self.A.values.tolist()
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return input_dict
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@staticmethod
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def _build_from_input_dict(mapping_class, input_dict):
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import copy
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input_dict = copy.deepcopy(input_dict)
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A = np.array(input_dict.pop('A'))
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l = Linear(**input_dict)
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l.unlink_parameter(l.A)
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l.update_model(False)
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l.A = Param('A', A)
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l.link_parameter(l.A)
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return l
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