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.NativeKernelLinear 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 (boolean) – a binary switch for Automatic Relevance Determination (ARD). If true, the squared distance is divided by a lengthscale for individual dimensions.
- variances (float or MXNet NDArray) – the initial value for the variances parameter, which scales the input dimensions.
- 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).