mxfusion.modules.gp_modules.sparsegp_regression¶
Members¶
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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.
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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
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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
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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
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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
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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
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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: 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
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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: 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
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replicate_self
(attribute_map=None)¶ The copy constructor for the function.
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static