GPy.mappings package¶
Submodules¶
GPy.mappings.additive module¶
-
class
Additive
(mapping1, mapping2)[source]¶ Bases:
GPy.core.mapping.Mapping
Mapping based on adding two existing mappings together.
\[f(\mathbf{x}*) = f_1(\mathbf{x}*) + f_2(\mathbf(x)*)\]Parameters: - mapping1 (GPy.mappings.Mapping) – first mapping to add together.
- mapping2 (GPy.mappings.Mapping) – second mapping to add together.
GPy.mappings.compound module¶
-
class
Compound
(mapping1, mapping2)[source]¶ Bases:
GPy.core.mapping.Mapping
Mapping based on passing one mapping through another
\[f(\mathbf{x}) = f_2(f_1(\mathbf{x}))\]Parameters: - mapping1 (GPy.mappings.Mapping) – first mapping
- mapping2 (GPy.mappings.Mapping) – second mapping
GPy.mappings.constant module¶
-
class
Constant
(input_dim, output_dim, value=0.0, name='constmap')[source]¶ Bases:
GPy.core.mapping.Mapping
A Linear mapping.
\[F(\mathbf{x}) = c\]Parameters: - input_dim (int) – dimension of input.
- output_dim (int) – dimension of output.
Param: value the value of this constant mapping
GPy.mappings.identity module¶
-
class
Identity
(input_dim, output_dim, name='identity')[source]¶ Bases:
GPy.core.mapping.Mapping
A mapping that does nothing!
GPy.mappings.kernel module¶
-
class
Kernel
(input_dim, output_dim, Z, kernel, name='kernmap')[source]¶ Bases:
GPy.core.mapping.Mapping
Mapping based on a kernel/covariance function.
\[f(\mathbf{x}) = \sum_i lpha_i k(\mathbf{z}_i, \mathbf{x})\]or for multple outputs
\[f_i(\mathbf{x}) = \sum_j lpha_{i,j} k(\mathbf{z}_i, \mathbf{x})\]Parameters: - input_dim (int) – dimension of input.
- output_dim (int) – dimension of output.
- Z (ndarray) – input observations containing \(\mathbf{Z}\)
- kernel (GPy.kern.kern) – a GPy kernel, defaults to GPy.kern.RBF
GPy.mappings.linear module¶
-
class
Linear
(input_dim, output_dim, name='linmap')[source]¶ Bases:
GPy.core.mapping.Mapping
A Linear mapping.
\[F(\mathbf{x}) = \mathbf{A} \mathbf{x})\]Parameters: - input_dim (int) – dimension of input.
- output_dim (int) – dimension of output.
- kernel (GPy.kern.kern) – a GPy kernel, defaults to GPy.kern.RBF
GPy.mappings.mlp module¶
GPy.mappings.mlpext module¶
-
class
MLPext
(input_dim=1, output_dim=1, hidden_dims=[3], prior=None, activation='tanh', name='mlpmap')[source]¶ Bases:
GPy.core.mapping.Mapping
Mapping based on a multi-layer perceptron neural network model, with multiple hidden layers. Activation function is applied to all hidden layers. The output is a linear combination of the last layer features, i.e. the last layer is linear.
Parameters: - input_dim – number of input dimensions
- output_dim – number of output dimensions
- hidden_dims – list of hidden sizes of hidden layers
- prior – variance of Gaussian prior on all variables. If None, no prior is used (default: None)
- activation – choose activation function. Allowed values are ‘tanh’ and ‘sigmoid’
- name –
GPy.mappings.piecewise_linear module¶
-
class
PiecewiseLinear
(input_dim, output_dim, values, breaks, name='piecewise_linear')[source]¶ Bases:
GPy.core.mapping.Mapping
A piecewise-linear mapping.
The parameters of this mapping are the positions and values of the function where it is broken (self.breaks, self.values).
Outside the range of the breaks, the function is assumed to have gradient 1