kusanagi.ghost.regression.GPRegressor.SSGP Class Reference

Sparse Spectral Gaussian Process Regression. More...

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

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

Public Member Functions
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
	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

Sparse Spectral Gaussian Process Regression.

Constructor & Destructor Documentation

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

Member Function Documentation

def kusanagi.ghost.regression.GPRegressor.SSGP.get_params	(		self,
			symbolic = True
	)

def kusanagi.ghost.regression.GPRegressor.SSGP.get_state

(

self

)

def kusanagi.ghost.regression.GPRegressor.SSGP.init_log_likelihood

(

self

)

def kusanagi.ghost.regression.GPRegressor.SSGP.loss_ss	(		self,
			params,
			parameter_shapes
	)

def kusanagi.ghost.regression.GPRegressor.SSGP.predict_symbolic	(		self,
			mx,
			Sx
	)

def kusanagi.ghost.regression.GPRegressor.SSGP.set_spectral_samples	(		self,
			w = None
	)

def kusanagi.ghost.regression.GPRegressor.SSGP.set_state	(		self,
			state
	)

def kusanagi.ghost.regression.GPRegressor.SSGP.train	(		self,
			pretrain_full = True
	)

Member Data Documentation

kusanagi.ghost.regression.GPRegressor.SSGP.A

kusanagi.ghost.regression.GPRegressor.SSGP.beta_ss

kusanagi.ghost.regression.GPRegressor.SSGP.dnlml_ss

kusanagi.ghost.regression.GPRegressor.SSGP.iA

kusanagi.ghost.regression.GPRegressor.SSGP.Lmm

kusanagi.ghost.regression.GPRegressor.SSGP.n_basis

kusanagi.ghost.regression.GPRegressor.SSGP.nlml_ss

kusanagi.ghost.regression.GPRegressor.SSGP.should_recompile

kusanagi.ghost.regression.GPRegressor.SSGP.sr

kusanagi.ghost.regression.GPRegressor.SSGP.w

kusanagi.ghost.regression.GPRegressor.SSGP.w_

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

• ghost/regression/GPRegressor.py