kusanagi.ghost.regression.GPRegressor.VSSGP Class Reference

Variational Sparse Spectral Gaussian Process Regression. More...

Inheritance diagram for kusanagi.ghost.regression.GPRegressor.VSSGP:

Collaboration diagram for kusanagi.ghost.regression.GPRegressor.VSSGP:

Public Member Functions
def	__init__
def	init_log_likelihood
Public Member Functions inherited from kusanagi.ghost.regression.GPRegressor.SSGP
def	__init__
def	set_state
def	get_state
def	init_log_likelihood
def	set_spectral_samples
def	get_params
def	loss_ss
def	train
def	predict_symbolic
Public Member Functions inherited from kusanagi.ghost.regression.GPRegressor.GP
def	__init__
def	set_dataset
def	append_dataset
def	init_loghyp
def	set_loghyp
def	get_params
def	set_params
def	init_log_likelihood
def	init_predict
def	predict_symbolic
def	predict
def	loss
def	train
def	load
def	save
def	set_state
def	get_state

Public Attributes
	n_basis
	A
	iA
	beta_ss
	w_mean_
	w_cov_
	z_
	b_bounds_
	fc_mean_
	fc_cov_
	w_mean
	w_cov
	z
	b_bounds
	fc_mean
	fc_cov
	nlml_ss
	dnlml_ss
Public Attributes inherited from kusanagi.ghost.regression.GPRegressor.SSGP
	w
	w_
	sr
	A
	Lmm
	beta_ss
	nlml_ss
	dnlml_ss
	n_basis
	should_recompile
	iA
Public Attributes inherited from kusanagi.ghost.regression.GPRegressor.GP
	profile
	compile_mode
	min_method
	state_changed
	should_recompile
	uncertain_inputs
	hyperparameter_gradients
	snr_penalty
	D
	E
	X_
	Y_
	loghyp_
	loghyp
	X
	Y
	K
	L
	beta
	nlml
	predict_
	predict_d_
	name
	filename
	ready
	N
	kernel_func
	dnlml

Detailed Description

Variational Sparse Spectral Gaussian Process Regression.

Constructor & Destructor Documentation

def kusanagi.ghost.regression.GPRegressor.VSSGP.__init__	(		self,
			X_dataset = None,
			Y_dataset = None,
			name = 'VSSGP',
			idims = None,
			odims = None,
			profile = False,
			n_basis = 100,
			uncertain_inputs = False,
			hyperparameter_gradients = False
	)

Member Function Documentation

def kusanagi.ghost.regression.GPRegressor.VSSGP.init_log_likelihood

(

self

)

Member Data Documentation

kusanagi.ghost.regression.GPRegressor.VSSGP.A

kusanagi.ghost.regression.GPRegressor.VSSGP.b_bounds

kusanagi.ghost.regression.GPRegressor.VSSGP.b_bounds_

kusanagi.ghost.regression.GPRegressor.VSSGP.beta_ss

kusanagi.ghost.regression.GPRegressor.VSSGP.dnlml_ss

kusanagi.ghost.regression.GPRegressor.VSSGP.fc_cov

kusanagi.ghost.regression.GPRegressor.VSSGP.fc_cov_

kusanagi.ghost.regression.GPRegressor.VSSGP.fc_mean

kusanagi.ghost.regression.GPRegressor.VSSGP.fc_mean_

kusanagi.ghost.regression.GPRegressor.VSSGP.iA

kusanagi.ghost.regression.GPRegressor.VSSGP.n_basis

kusanagi.ghost.regression.GPRegressor.VSSGP.nlml_ss

kusanagi.ghost.regression.GPRegressor.VSSGP.w_cov

kusanagi.ghost.regression.GPRegressor.VSSGP.w_cov_

kusanagi.ghost.regression.GPRegressor.VSSGP.w_mean

kusanagi.ghost.regression.GPRegressor.VSSGP.w_mean_

kusanagi.ghost.regression.GPRegressor.VSSGP.z

kusanagi.ghost.regression.GPRegressor.VSSGP.z_

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The documentation for this class was generated from the following file:

• ghost/regression/GPRegressor.py