kusanagi.ghost.regression.GPRegressor.GP Class Reference

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

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

Public Member Functions
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
	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

Constructor & Destructor Documentation

def kusanagi.ghost.regression.GPRegressor.GP.__init__	(		self,
			X_dataset = None,
			Y_dataset = None,
			name = 'GP',
			idims = None,
			odims = None,
			profile = theano.config.profile,
			uncertain_inputs = False,
			hyperparameter_gradients = False,
			snr_penalty = SNRpenalty.SEard
	)

Member Function Documentation

def kusanagi.ghost.regression.GPRegressor.GP.append_dataset	(		self,
			X_dataset,
			Y_dataset
	)

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

def kusanagi.ghost.regression.GPRegressor.GP.get_state

(

self

)

def kusanagi.ghost.regression.GPRegressor.GP.init_log_likelihood

(

self

)

def kusanagi.ghost.regression.GPRegressor.GP.init_loghyp	(		self,
			reinit = False
	)

def kusanagi.ghost.regression.GPRegressor.GP.init_predict	(		self,
			derivs = False
	)

def kusanagi.ghost.regression.GPRegressor.GP.load

(

self

)

def kusanagi.ghost.regression.GPRegressor.GP.loss	(		self,
			loghyp
	)

def kusanagi.ghost.regression.GPRegressor.GP.predict	(		self,
			mx,
			Sx = None,
			derivs = False
	)

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

def kusanagi.ghost.regression.GPRegressor.GP.save

(

self

)

def kusanagi.ghost.regression.GPRegressor.GP.set_dataset	(		self,
			X_dataset,
			Y_dataset
	)

def kusanagi.ghost.regression.GPRegressor.GP.set_loghyp	(		self,
			loghyp
	)

def kusanagi.ghost.regression.GPRegressor.GP.set_params	(		self,
			params
	)

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

def kusanagi.ghost.regression.GPRegressor.GP.train

(

self

)

Member Data Documentation

kusanagi.ghost.regression.GPRegressor.GP.beta

kusanagi.ghost.regression.GPRegressor.GP.compile_mode

kusanagi.ghost.regression.GPRegressor.GP.D

kusanagi.ghost.regression.GPRegressor.GP.dnlml

kusanagi.ghost.regression.GPRegressor.GP.E

kusanagi.ghost.regression.GPRegressor.GP.filename

kusanagi.ghost.regression.GPRegressor.GP.hyperparameter_gradients

kusanagi.ghost.regression.GPRegressor.GP.K

kusanagi.ghost.regression.GPRegressor.GP.kernel_func

kusanagi.ghost.regression.GPRegressor.GP.L

kusanagi.ghost.regression.GPRegressor.GP.loghyp

kusanagi.ghost.regression.GPRegressor.GP.loghyp_

kusanagi.ghost.regression.GPRegressor.GP.min_method

kusanagi.ghost.regression.GPRegressor.GP.N

kusanagi.ghost.regression.GPRegressor.GP.name

kusanagi.ghost.regression.GPRegressor.GP.nlml

kusanagi.ghost.regression.GPRegressor.GP.predict_

kusanagi.ghost.regression.GPRegressor.GP.predict_d_

kusanagi.ghost.regression.GPRegressor.GP.profile

kusanagi.ghost.regression.GPRegressor.GP.ready

kusanagi.ghost.regression.GPRegressor.GP.should_recompile

kusanagi.ghost.regression.GPRegressor.GP.snr_penalty

kusanagi.ghost.regression.GPRegressor.GP.state_changed

kusanagi.ghost.regression.GPRegressor.GP.uncertain_inputs

kusanagi.ghost.regression.GPRegressor.GP.X

kusanagi.ghost.regression.GPRegressor.GP.X_

kusanagi.ghost.regression.GPRegressor.GP.Y

kusanagi.ghost.regression.GPRegressor.GP.Y_

---

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

• ghost/regression/GPRegressor.py