mxfusion.components.distributions.gp.kernels.linear

Members

class mxfusion.components.distributions.gp.kernels.linear.Linear(input_dim, ARD=False, variances=1.0, name='linear', active_dims=None, dtype=None, ctx=None)

Bases: mxfusion.components.distributions.gp.kernels.kernel.NativeKernel

Linear kernel

\[k(x,y) = \sum_{i=1}^{\text{input_dim}} \sigma^2_i x_iy_i\]
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 variances: the initial value for the variances parameter, which scales the input dimensions. :type variances: 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

broadcastable = True
replicate_self(attribute_map=None)

The copy constructor for a kernel.