mxfusion.modules.gp_modules.sparsegp_regression

Members

class mxfusion.modules.gp_modules.sparsegp_regression.SparseGPRegressionLogPdf(model, posterior, observed, jitter=0.0)

Bases: mxfusion.inference.variational.VariationalInference

The inference algorithm for computing the variational lower bound of variational sparse Gaussian process.

compute(F, variables)

The method for the computation of the sampling algorithm

Parameters:
  • F (Python module) – the execution context (mxnet.ndarray or mxnet.symbol)
  • variables – the set of MXNet arrays that holds the values of

variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :returns: the outcome of the inference algorithm :rtype: mxnet.ndarray.ndarray.NDArray or mxnet.symbol.symbol.Symbol

class mxfusion.modules.gp_modules.sparsegp_regression.SparseGPRegressionMeanVariancePrediction(model, posterior, observed, target_variables=None, noise_free=True, diagonal_variance=True)

Bases: mxfusion.inference.inference_alg.SamplingAlgorithm

compute(F, variables)

The method for the computation of the sampling algorithm

Parameters:
  • F (Python module) – the execution context (mxnet.ndarray or mxnet.symbol)
  • variables – the set of MXNet arrays that holds the values of

variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :returns: the outcome of the inference algorithm :rtype: mxnet.ndarray.ndarray.NDArray or mxnet.symbol.symbol.Symbol

class mxfusion.modules.gp_modules.sparsegp_regression.SparseGPRegressionSamplingPrediction(model, posterior, observed, rand_gen=None, noise_free=True, diagonal_variance=True, jitter=0.0)

Bases: mxfusion.inference.inference_alg.SamplingAlgorithm

compute(F, variables)

The method for the computation of the sampling algorithm

Parameters:
  • F (Python module) – the execution context (mxnet.ndarray or mxnet.symbol)
  • variables – the set of MXNet arrays that holds the values of

variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :returns: the outcome of the inference algorithm :rtype: mxnet.ndarray.ndarray.NDArray or mxnet.symbol.symbol.Symbol

class mxfusion.modules.gp_modules.sparsegp_regression.SparseGPRegression(X, kernel, noise_var, inducing_inputs=None, num_inducing=10, mean=None, rand_gen=None, dtype=None, ctx=None)

Bases: mxfusion.modules.module.Module

Variational sparse Gaussian process regression module

This module contains a variational sparse Gaussian process with Gaussian likelihood.

Parameters:
  • X (Variable) – the input variables on which the random variables are conditioned.
  • kernel (Kernel) – the kernel of Gaussian process.
  • noise_var (Variable) – the variance of the Gaussian likelihood
  • inducing_inputs – the inducing inputs of the sparse GP (optional). This variable will be auto-generated

if not specified. :type inducing_inputs: Variable :param num_inducing: the number of inducing points of sparse GP (default: 10) :type num_inducing: int :param mean: the mean of Gaussian process. :type mean: Variable :param rand_gen: the random generator (default: MXNetRandomGenerator). :type rand_gen: RandomGenerator :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

static define_variable(X, kernel, noise_var, shape=None, inducing_inputs=None, num_inducing=10, mean=None, rand_gen=None, dtype=None, ctx=None)

Creates and returns a variable drawn from a sparse Gaussian process regression.

Parameters:
  • X (Variable) – the input variables on which the random variables are conditioned.
  • kernel (Kernel) – the kernel of Gaussian process.
  • noise_var (Variable) – the variance of the Gaussian likelihood
  • shape – the shape of the random variable(s) (the default shape is

the same shape as X but the last dimension is changed to one.) :type shape: tuple or [tuple] :param inducing_inputs: the inducing inputs of the sparse GP (optional). This variable will be auto-generated if not specified. :type inducing_inputs: Variable :param num_inducing: the number of inducing points of sparse GP (default: 10) :type num_inducing: int :param mean: the mean of Gaussian process. :type mean: Variable :param rand_gen: the random generator (default: MXNetRandomGenerator). :type rand_gen: RandomGenerator :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

replicate_self(attribute_map=None)

The copy constructor for the function.