mxfusion.modules.gp_modules.gp_regression¶
Members¶
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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.
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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
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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.
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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.gp_regression.
GPRegressionMeanVariancePrediction
(model, posterior, observed, 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.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.
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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.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).
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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
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replicate_self
(attribute_map=None)¶ The copy constructor for the function.