mxfusion.components.distributions.gp.kernels.static¶
Members¶
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class
mxfusion.components.distributions.gp.kernels.static.
Bias
(input_dim, variance=1.0, name='bias', active_dims=None, dtype=None, ctx=None)¶ Bases:
mxfusion.components.distributions.gp.kernels.kernel.NativeKernel
Bias kernel, which produces a constant value for every entries of the covariance matrix.
\[k(x,y) = \sigma^2\]Parameters: - input_dim (int) – the number of dimensions of the kernel. (The total number of active dimensions).
- variance (float or MXNet NDArray) – the initial value for the variance parameter.
- name (str) – the name of the kernel. The name is used to access kernel parameters.
- active_dims ([int] or None) – The dimensions of the inputs that are taken for the covariance matrix computation. (default: None, taking all the dimensions).
- dtype (numpy.float32 or numpy.float64) – the data type for float point numbers.
- ctx (None or mxnet.cpu or mxnet.gpu) – the mxnet context (default: None/current context).
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class
mxfusion.components.distributions.gp.kernels.static.
White
(input_dim, variance=1.0, name='white', active_dims=None, dtype=None, ctx=None)¶ Bases:
mxfusion.components.distributions.gp.kernels.kernel.NativeKernel
White kernel, which produces a constant value for the diagonal of the covariance matrix.
\[K = \sigma^2 I\]Parameters: - input_dim (int) – the number of dimensions of the kernel. (The total number of active dimensions).
- variance (float or MXNet NDArray) – the initial value for the variance parameter.
- name (str) – the name of the kernel. The name is used to access kernel parameters.
- active_dims ([int] or None) – The dimensions of the inputs that are taken for the covariance matrix computation. (default: None, taking all the dimensions).
- dtype (numpy.float32 or numpy.float64) – the data type for float point numbers.
- ctx (None or mxnet.cpu or mxnet.gpu) – the mxnet context (default: None/current context).