Source code for GPy.mappings.linear

# Copyright (c) 2013, 2014 GPy authors (see AUTHORS.txt).
# Copyright (c) 2015, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
from ..core.mapping import Mapping
from ..core.parameterization import Param

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