mxfusion.components.distributions.gp.kernels.rbf¶
Members¶
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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.StationaryKernelRadial 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 – a binary switch for Automatic Relevance Determination (ARD). If true, the squared distance is divided
by a lengthscale for individual dimensions. :type ARD: boolean :param variance: the initial value for the variance parameter (scalar), which scales the whole covariance matrix. :type variance: float or MXNet NDArray :param lengthscale: the initial value for the lengthscale parameter. :type lengthscale: float or MXNet NDArray :param name: the name of the kernel. The name is used to access kernel parameters. :type name: str :param active_dims: The dimensions of the inputs that are taken for the covariance matrix computation. (default: None, taking all the dimensions). :type active_dims: [int] or None :param dtype: the data type for float point numbers. :type dtype: numpy.float32 or numpy.float64 :param ctx: the mxnet context (default: None/current context). :type ctx: None or mxnet.cpu or mxnet.gpu
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broadcastable= True¶