mxfusion.components.distributions.gp.kernels.rbf¶
Members¶
-
class
mxfusion.components.distributions.gp.kernels.rbf.
RBF
(input_dim, ARD=False, variance=1.0, lengthscale=1.0, name='rbf', active_dims=None, dtype=None, ctx=None)¶ Bases:
mxfusion.components.distributions.gp.kernels.stationary.StationaryKernel
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
\[k(r^2) = \sigma^2 \exp \bigg(- \frac{1}{2} r^2 \bigg)\]Parameters: - input_dim (int) – the number of dimensions of the kernel. (The total number of active dimensions)
- ARD (boolean) – a binary switch for Automatic Relevance Determination (ARD). If true, the squared distance is divided by a lengthscale for individual dimensions.
- variance (float or MXNet NDArray) – the initial value for the variance parameter (scalar), which scales the whole covariance matrix.
- lengthscale (float or MXNet NDArray) – the initial value for the lengthscale 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).