mxfusion.modules.gp_modules.gp_regression

Members

class mxfusion.modules.gp_modules.gp_regression.GPRegressionLogPdf(model, posterior, observed, jitter=0.0)

Bases: mxfusion.inference.variational.VariationalInference

The method to compute the logarithm of the probability density function of a Gaussian process model with Gaussian likelihood.

compute(F, variables)

The abstract method for the computation of the inference 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.gp_regression.GPRegressionSampling(model, observed, num_samples=1, target_variables=None, rand_gen=None)

Bases: mxfusion.inference.inference_alg.SamplingAlgorithm

The method for drawing samples from the prior distribution of a Gaussian process regression model.

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.gp_regression.GPRegressionMeanVariancePrediction(model, posterior, observed, 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.gp_regression.GPRegressionSamplingPrediction(model, posterior, observed, rand_gen=None, noise_free=True, diagonal_variance=True, jitter=0.0)

Bases: mxfusion.inference.inference_alg.SamplingAlgorithm

The method for drawing samples from the posterior distribution of a Gaussian process regression model.

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.gp_regression.GPRegression(X, kernel, noise_var, mean=None, rand_gen=None, dtype=None, ctx=None)

Bases: mxfusion.modules.module.Module

Gaussian process regression module

This module contains a 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
  • mean (Variable) – the mean of Gaussian process.
  • rand_gen (RandomGenerator) – the random generator (default: MXNetRandomGenerator).
  • 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).
static define_variable(X, kernel, noise_var, shape=None, mean=None, rand_gen=None, dtype=None, ctx=None)

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

Parameters:X – the input variables on which the random variables are

conditioned. :type X: Variable :param kernel: the kernel of Gaussian process :type kernel: Kernel :param noise_var: the variance of the Gaussian likelihood :type noise_var: Variable :param 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 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.