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starting to sort out likelihoods WARNING not working

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Max Zwiessele 2013-10-17 14:38:43 +01:00
parent bc765134c0
commit 055eb3b70b
10 changed files with 247 additions and 142 deletions
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11
GPy/core/gp.py
View file

@ -25,22 +25,18 @@ class GP(GPBase):
""" """
def __init__(self, X, likelihood, kernel, normalize_X=False): def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X) GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params()) #self._set_params(self._get_params())
def getstate(self): def getstate(self):
return GPBase.getstate(self) return GPBase.getstate(self)
def setstate(self, state): def setstate(self, state):
GPBase.setstate(self, state) GPBase.setstate(self, state)
self._set_params(self._get_params()) #self._set_params(self._get_params())
def _set_params(self, p):
self.kern._set_params_transformed(p[:self.kern.num_params_transformed()])
self.likelihood._set_params(p[self.kern.num_params_transformed():])
def parameters_changed(self):
self.K = self.kern.K(self.X) self.K = self.kern.K(self.X)
self.K += self.likelihood.covariance_matrix self.K += self.likelihood.covariance_matrix
self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K) self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
# the gradient of the likelihood wrt the covariance matrix # the gradient of the likelihood wrt the covariance matrix
@ -102,6 +98,7 @@ class GP(GPBase):
Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
""" """
#return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK)))) #return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
if not isinstance(self.likelihood,EP): if not isinstance(self.likelihood,EP):
tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK)))) tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
else: else:

7
GPy/core/gp_base.py
View file

@ -29,11 +29,18 @@ class GPBase(Model):
self._Xoffset = np.zeros((1, self.input_dim)) self._Xoffset = np.zeros((1, self.input_dim))
self._Xscale = np.ones((1, self.input_dim)) self._Xscale = np.ones((1, self.input_dim))
self.set_as_parameter(self.kern._parameters_)
self.set_as_parameter(self.likelihood._parameters_)
super(GPBase, self).__init__() super(GPBase, self).__init__()
# Model.__init__(self) # Model.__init__(self)
# All leaf nodes should call self._set_params(self._get_params()) at # All leaf nodes should call self._set_params(self._get_params()) at
# the end # the end
def parameters_changed(self):
self.kern.parameters_changed()
self.likelihood.parameters_changed()
def getstate(self): def getstate(self):
""" """
Get the current state of the class, here we return everything that is needed to recompute the model. Get the current state of the class, here we return everything that is needed to recompute the model.

67
GPy/core/model.py
View file

@ -5,12 +5,13 @@
from .. import likelihoods from .. import likelihoods
from ..inference import optimization from ..inference import optimization
from ..util.linalg import jitchol from ..util.linalg import jitchol
from GPy.util.misc import opt_wrapper from ..util.misc import opt_wrapper
from parameterized import Parameterized from parameterized import Parameterized
import multiprocessing as mp import multiprocessing as mp
import numpy as np import numpy as np
from GPy.core.domains import _POSITIVE, _REAL from domains import _POSITIVE, _REAL
from numpy.linalg.linalg import LinAlgError from numpy.linalg.linalg import LinAlgError
from index_operations import ParameterIndexOperations
# import numdifftools as ndt # import numdifftools as ndt
class Model(Parameterized): class Model(Parameterized):
@ -18,7 +19,7 @@ class Model(Parameterized):
_allowed_failures = 10 # number of allowed failures _allowed_failures = 10 # number of allowed failures
def __init__(self): def __init__(self):
Parameterized.__init__(self) Parameterized.__init__(self)
self.priors = None self._priors = ParameterIndexOperations()
self.optimization_runs = [] self.optimization_runs = []
self.sampling_runs = [] self.sampling_runs = []
self.preferred_optimizer = 'scg' self.preferred_optimizer = 'scg'
@ -399,36 +400,36 @@ class Model(Parameterized):
return np.nan return np.nan
return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
def __str__(self): # def __str__(self):
s = Parameterized.__str__(self).split('\n') # s = Parameterized.__str__(self).split('\n')
#def __str__(self, names=None): # #def __str__(self, names=None):
# if names is None: # # if names is None:
# names = self._get_print_names() # # names = self._get_print_names()
#s = Parameterized.__str__(self, names=names).split('\n') # #s = Parameterized.__str__(self, names=names).split('\n')
# add priors to the string # # add priors to the string
if self.priors is not None: # if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors] # strs = [str(p) if p is not None else '' for p in self.priors]
else: # else:
strs = [''] * len(self._get_params()) # strs = [''] * len(self._get_params())
# strs = [''] * len(self._get_param_names()) # # strs = [''] * len(self._get_param_names())
# name_indices = self.grep_param_names("|".join(names)) # # name_indices = self.grep_param_names("|".join(names))
# strs = np.array(strs)[name_indices] # # strs = np.array(strs)[name_indices]
width = np.array(max([len(p) for p in strs] + [5])) + 4 # width = np.array(max([len(p) for p in strs] + [5])) + 4
#
log_like = self.log_likelihood() # log_like = self.log_likelihood()
log_prior = self.log_prior() # log_prior = self.log_prior()
obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like) # obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
if len(''.join(strs)) != 0: # if len(''.join(strs)) != 0:
obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior) # obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior)
obj_funct += '\n\n' # obj_funct += '\n\n'
s[0] = obj_funct + s[0] # s[0] = obj_funct + s[0]
s[0] += "|{h:^{col}}".format(h='prior', col=width) # s[0] += "|{h:^{col}}".format(h='prior', col=width)
s[1] += '-' * (width + 1) # s[1] += '-' * (width + 1)
#
for p in range(2, len(strs) + 2): # for p in range(2, len(strs) + 2):
s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width) # s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)
#
return '\n'.join(s) # return '\n'.join(s)
def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3): def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):

51
GPy/core/priors.py
View file

@ -8,9 +8,11 @@ from scipy.special import gammaln, digamma
from ..util.linalg import pdinv from ..util.linalg import pdinv
from GPy.core.domains import _REAL, _POSITIVE from GPy.core.domains import _REAL, _POSITIVE
import warnings import warnings
import weakref
class Prior: class Prior:
domain = None domain = None
def pdf(self, x): def pdf(self, x):
return np.exp(self.lnpdf(x)) return np.exp(self.lnpdf(x))
@ -33,6 +35,16 @@ class Gaussian(Prior):
""" """
domain = _REAL domain = _REAL
_instances = []
def __new__(cls, mu, sigma): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().mu == mu and instance().sigma == sigma:
return instance()
o = super(Prior, cls).__new__(cls, mu, sigma)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, mu, sigma): def __init__(self, mu, sigma):
self.mu = float(mu) self.mu = float(mu)
self.sigma = float(sigma) self.sigma = float(sigma)
@ -63,6 +75,16 @@ class LogGaussian(Prior):
""" """
domain = _POSITIVE domain = _POSITIVE
_instances = []
def __new__(cls, mu, sigma): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().mu == mu and instance().sigma == sigma:
return instance()
o = super(Prior, cls).__new__(cls, mu, sigma)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, mu, sigma): def __init__(self, mu, sigma):
self.mu = float(mu) self.mu = float(mu)
self.sigma = float(sigma) self.sigma = float(sigma)
@ -93,6 +115,16 @@ class MultivariateGaussian:
""" """
domain = _REAL domain = _REAL
_instances = []
def __new__(cls, mu, var): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if np.all(instance().mu == mu) and np.all(instance().var == var):
return instance()
o = super(Prior, cls).__new__(cls, mu, var)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, mu, var): def __init__(self, mu, var):
self.mu = np.array(mu).flatten() self.mu = np.array(mu).flatten()
self.var = np.array(var) self.var = np.array(var)
@ -148,6 +180,16 @@ class Gamma(Prior):
""" """
domain = _POSITIVE domain = _POSITIVE
_instances = []
def __new__(cls, a, b): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().a == a and instance().b == b:
return instance()
o = super(Prior, cls).__new__(cls, a, b)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, a, b): def __init__(self, a, b):
self.a = float(a) self.a = float(a)
self.b = float(b) self.b = float(b)
@ -199,6 +241,15 @@ class inverse_gamma(Prior):
""" """
domain = _POSITIVE domain = _POSITIVE
def __new__(cls, a, b): # Singleton:
if cls._instances:
cls._instances[:] = [instance for instance in cls._instances if instance()]
for instance in cls._instances:
if instance().a == a and instance().b == b:
return instance()
o = super(Prior, cls).__new__(cls, a, b)
cls._instances.append(weakref.ref(o))
return cls._instances[-1]()
def __init__(self, a, b): def __init__(self, a, b):
self.a = float(a) self.a = float(a)
self.b = float(b) self.b = float(b)

51
GPy/kern/kern.py
View file

@ -53,9 +53,14 @@ class kern(Parameterized):
assert isinstance(p, Kernpart), "bad kernel part" assert isinstance(p, Kernpart), "bad kernel part"
self.compute_param_slices() self.compute_param_slices()
self._parameters_ = []
for p in self.parts:
self._parameters_.extend(p._parameters_)
Parameterized.__init__(self) Parameterized.__init__(self)
def parameters_changed(self):
[p.parameters_changed() for p in self.parts]
def getstate(self): def getstate(self):
""" """
Get the current state of the class, Get the current state of the class,
@ -303,20 +308,20 @@ class kern(Parameterized):
for i, t in zip(prev_constr_ind, prev_constr): for i, t in zip(prev_constr_ind, prev_constr):
self.constrain(np.where(index_param == i)[0], t) self.constrain(np.where(index_param == i)[0], t)
def _get_params(self): # def _get_params(self):
return np.hstack([p._get_params() for p in self.parts]) # return np.hstack([p._get_params() for p in self.parts])
#
def _set_params(self, x): # def _set_params(self, x):
[p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)] # [p._set_params(x[s]) for p, s in zip(self.parts, self.param_slices)]
#
def _get_param_names(self): # def _get_param_names(self):
# this is a bit nasty: we want to distinguish between parts with the same name by appending a count # # this is a bit nasty: we want to distinguish between parts with the same name by appending a count
part_names = np.array([k.name for k in self.parts], dtype=np.str) # part_names = np.array([k.name for k in self.parts], dtype=np.str)
counts = [np.sum(part_names == ni) for i, ni in enumerate(part_names)] # counts = [np.sum(part_names == ni) for i, ni in enumerate(part_names)]
cum_counts = [np.sum(part_names[i:] == ni) for i, ni in enumerate(part_names)] # cum_counts = [np.sum(part_names[i:] == ni) for i, ni in enumerate(part_names)]
names = [name + '_' + str(cum_count) if count > 1 else name for name, count, cum_count in zip(part_names, counts, cum_counts)] # names = [name + '_' + str(cum_count) if count > 1 else name for name, count, cum_count in zip(part_names, counts, cum_counts)]
#
return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], []) # return sum([[name + '_' + n for n in k._get_param_names()] for name, k in zip(names, self.parts)], [])
def K(self, X, X2=None, which_parts='all'): def K(self, X, X2=None, which_parts='all'):
""" """
@ -606,14 +611,14 @@ class Kern_check_model(Model):
else: else:
return True return True
def _get_params(self): # def _get_params(self):
return self.kernel._get_params() # return self.kernel._get_params()
#
def _get_param_names(self): # def _get_param_names(self):
return self.kernel._get_param_names() # return self.kernel._get_param_names()
#
def _set_params(self, x): # def _set_params(self, x):
self.kernel._set_params(x) # self.kernel._set_params(x)
def log_likelihood(self): def log_likelihood(self):
return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum() return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()

42
GPy/kern/parts/exponential.py
View file

@ -22,15 +22,17 @@ class Exponential(Kernpart):
:type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter) :type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension. :param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
:type ARD: Boolean :type ARD: Boolean
:param name: the name of the kernel
:rtype: kernel object :rtype: kernel object
""" """
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False): def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='exp'):
self.input_dim = input_dim self.input_dim = input_dim
self.ARD = ARD self.ARD = ARD
self.variance = variance
self.name = name
if ARD == False: if ARD == False:
self.num_params = 2 self.num_params = 2
self.name = 'exp'
if lengthscale is not None: if lengthscale is not None:
lengthscale = np.asarray(lengthscale) lengthscale = np.asarray(lengthscale)
assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel" assert lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
@ -38,30 +40,30 @@ class Exponential(Kernpart):
lengthscale = np.ones(1) lengthscale = np.ones(1)
else: else:
self.num_params = self.input_dim + 1 self.num_params = self.input_dim + 1
self.name = 'exp'
if lengthscale is not None: if lengthscale is not None:
lengthscale = np.asarray(lengthscale) lengthscale = np.asarray(lengthscale)
assert lengthscale.size == self.input_dim, "bad number of lengthscales" assert lengthscale.size == self.input_dim, "bad number of lengthscales"
else: else:
lengthscale = np.ones(self.input_dim) lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance, lengthscale.flatten()))) #self._set_params(np.hstack((variance, lengthscale.flatten())))
self.set_as_parameter('variance', 'lengthscale')
def _get_params(self): # def _get_params(self):
"""return the value of the parameters.""" # """return the value of the parameters."""
return np.hstack((self.variance, self.lengthscale)) # return np.hstack((self.variance, self.lengthscale))
#
def _set_params(self, x): # def _set_params(self, x):
"""set the value of the parameters.""" # """set the value of the parameters."""
assert x.size == self.num_params # assert x.size == self.num_params
self.variance = x[0] # self.variance = x[0]
self.lengthscale = x[1:] # self.lengthscale = x[1:]
#
def _get_param_names(self): # def _get_param_names(self):
"""return parameter names.""" # """return parameter names."""
if self.num_params == 2: # if self.num_params == 2:
return ['variance', 'lengthscale'] # return ['variance', 'lengthscale']
else: # else:
return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)] # return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def K(self, X, X2, target): def K(self, X, X2, target):
"""Compute the covariance matrix between X and X2.""" """Compute the covariance matrix between X and X2."""

53
GPy/kern/parts/kernpart.py
View file

@ -1,11 +1,13 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt). # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
from ...core.parameter import Param
#from ...core.parameterized.Parameterized import set_as_parameter
class Kernpart(object): class Kernpart(object):
def __init__(self,input_dim): def __init__(self,input_dim):
""" """
The base class for a kernpart: a positive definite function which forms part of a covariance function (kernel). The base class for a kernpart: a positive definite function
which forms part of a covariance function (kernel).
:param input_dim: the number of input dimensions to the function :param input_dim: the number of input dimensions to the function
:type input_dim: int :type input_dim: int
@ -18,13 +20,42 @@ class Kernpart(object):
self.num_params = 1 self.num_params = 1
# the name of the covariance function. # the name of the covariance function.
self.name = 'unnamed' self.name = 'unnamed'
# link to parameterized objects
self._parameters_ = []
def set_as_parameter_named(self, name, gradient, index=None, *args, **kwargs):
"""
:param names: name of parameter to set as parameter
:param gradient: gradient method to get the gradient of this parameter
:param index: index of where to place parameter in printing
:param args, kwargs: additional arguments to gradient
def _get_params(self): Convenience method to connect Kernpart parameters:
raise NotImplementedError parameter with name (attribute of this Kernpart) will be set as parameter with following name:
def _set_params(self,x):
raise NotImplementedError kernel_name + _ + parameter_name
def _get_param_names(self):
raise NotImplementedError To add the kernels name to the parameter name use this method to
add parameters.
"""
self.set_as_parameter(name, getattr(self, name), gradient, index, *args, **kwargs)
def set_as_parameter(self, name, array, gradient, index=None, *args, **kwargs):
"""
See :py:func:`GPy.core.parameterized.Parameterized.set_as_parameter`
Note: this method adds the kernels name in front of the parameter.
"""
p = Param(self.name+"_"+name, array, gradient, *args, **kwargs)
if index is None:
self._parameters_.append(p)
else:
self._parameters_.insert(index, p)
#set_as_parameter.__doc__ += set_as_parameter.__doc__ # @UndefinedVariable
# def _get_params(self):
# raise NotImplementedError
# def _set_params(self,x):
# raise NotImplementedError
# def _get_param_names(self):
# raise NotImplementedError
def K(self,X,X2,target): def K(self,X,X2,target):
raise NotImplementedError raise NotImplementedError
def Kdiag(self,X,target): def Kdiag(self,X,target):
@ -87,13 +118,13 @@ class Kernpart_stationary(Kernpart):
# initialize cache # initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2, self._params = np.empty(shape=(3, 1)) self._X, self._X2, self._parameters_ = np.empty(shape=(3, 1))
def _set_params(self, x): def _set_params(self, x):
self.lengthscale = x self.lengthscale = x
self.lengthscale2 = np.square(self.lengthscale) self.lengthscale2 = np.square(self.lengthscale)
# reset cached results # reset cached results
self._X, self._X2, self._params = np.empty(shape=(3, 1)) self._X, self._X2, self._parameters_ = np.empty(shape=(3, 1))
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
@ -120,4 +151,4 @@ class Kernpart_inner(Kernpart):
# initialize cache # initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2, self._params = np.empty(shape=(3, 1)) self._X, self._X2, self._parameters_ = np.empty(shape=(3, 1))

53
GPy/kern/parts/rbf.py
View file

@ -7,6 +7,7 @@ import numpy as np
from scipy import weave from scipy import weave
from ...util.linalg import tdot from ...util.linalg import tdot
from ...util.misc import fast_array_equal from ...util.misc import fast_array_equal
from GPy.core.parameter import Param
class RBF(Kernpart): class RBF(Kernpart):
""" """
@ -31,10 +32,11 @@ class RBF(Kernpart):
.. Note: this object implements both the ARD and 'spherical' version of the function .. Note: this object implements both the ARD and 'spherical' version of the function
""" """
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False): def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='rbf'):
self.input_dim = input_dim self.input_dim = input_dim
self.name = 'rbf' self.name = name
self.ARD = ARD self.ARD = ARD
self.variance = variance
if not ARD: if not ARD:
self.num_params = 2 self.num_params = 2
if lengthscale is not None: if lengthscale is not None:
@ -50,36 +52,43 @@ class RBF(Kernpart):
else: else:
lengthscale = np.ones(self.input_dim) lengthscale = np.ones(self.input_dim)
self._set_params(np.hstack((variance, lengthscale.flatten()))) #self._set_params(np.hstack((variance, lengthscale.flatten())))
self.set_as_parameter_named('variance', )
# initialize cache # initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2, self._params = np.empty(shape=(3, 1)) self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
# a set of optional args to pass to weave # a set of optional args to pass to weave
self.weave_options = {'headers' : ['<omp.h>'], self.weave_options = {'headers' : ['<omp.h>'],
'extra_compile_args': ['-fopenmp -O3'], # -march=native'], 'extra_compile_args': ['-fopenmp -O3'], # -march=native'],
'extra_link_args' : ['-lgomp']} 'extra_link_args' : ['-lgomp']}
def parameters_changed(self):
def _get_params(self):
return np.hstack((self.variance, self.lengthscale))
def _set_params(self, x):
assert x.size == (self.num_params)
self.variance = x[0]
self.lengthscale = x[1:]
self.lengthscale2 = np.square(self.lengthscale) self.lengthscale2 = np.square(self.lengthscale)
# reset cached results # reset cached results
self._X, self._X2, self._params = np.empty(shape=(3, 1)) self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
def _get_param_names(self): # def _get_params(self):
if self.num_params == 2: # return np.hstack((self.variance, self.lengthscale))
return ['variance', 'lengthscale'] #
else: # def _set_params(self, x):
return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)] # assert x.size == (self.num_params)
# self.variance = x[0]
# self.lengthscale = x[1:]
# self.lengthscale2 = np.square(self.lengthscale)
# # reset cached results
# self._X, self._X2, self._params_save = np.empty(shape=(3, 1))
# self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
#
# def _get_param_names(self):
# if self.num_params == 2:
# return ['variance', 'lengthscale']
# else:
# return ['variance'] + ['lengthscale_%i' % i for i in range(self.lengthscale.size)]
def _dK_dvariance(self, model):
pass
def K(self, X, X2, target): def K(self, X, X2, target):
self._K_computations(X, X2) self._K_computations(X, X2)
@ -225,9 +234,9 @@ class RBF(Kernpart):
def _K_computations(self, X, X2): def _K_computations(self, X, X2):
params = self._get_params() params = self._get_params()
if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2) and fast_array_equal(self._params , params)): if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2) and fast_array_equal(self._params_save , params)):
self._X = X.copy() self._X = X.copy()
self._params = params.copy() self._params_save = params.copy()
if X2 is None: if X2 is None:
self._X2 = None self._X2 = None
X = X / self.lengthscale X = X / self.lengthscale

32
GPy/likelihoods/gaussian.py
View file

@ -31,8 +31,10 @@ class Gaussian(likelihood):
self.set_data(data) self.set_data(data)
self._variance = np.asarray(variance) + 1. self._variance = np.asarray(variance) + 1
self._set_params(np.asarray(variance)) self.variance = np.asarray(variance)
self.set_as_parameter(noise_variance=self.variance)
# self._set_params(np.asarray(variance))
super(Gaussian, self).__init__() super(Gaussian, self).__init__()
@ -50,24 +52,24 @@ class Gaussian(likelihood):
self.trYYT = np.sum(np.square(self.Y)) self.trYYT = np.sum(np.square(self.Y))
self.YYT_factor = self.Y self.YYT_factor = self.Y
def _get_params(self): # def _get_params(self):
return np.asarray(self._variance) # return np.asarray(self._variance)
#
def _get_param_names(self): # def _get_param_names(self):
return ["noise_variance"] # return ["noise_variance"]
#
def _set_params(self, x): # def _set_params(self, x):
x = np.float64(x) def parameters_changed(self):
if np.all(self._variance != x): if np.any(self._variance != self.variance):
if x == 0.:#special case of zero noise if np.all(self.variance == 0.):#special case of zero noise
self.precision = np.inf self.precision = np.inf
self.V = None self.V = None
else: else:
self.precision = 1. / x self.precision = 1. / self.variance
self.V = (self.precision) * self.Y self.V = (self.precision) * self.Y
self.VVT_factor = self.precision * self.YYT_factor self.VVT_factor = self.precision * self.YYT_factor
self.covariance_matrix = np.eye(self.N) * x self.covariance_matrix = np.eye(self.N) * self.variance
self._variance = x self._variance = self.variance
def predictive_values(self, mu, var, full_cov): def predictive_values(self, mu, var, full_cov):
""" """

16
GPy/likelihoods/likelihood.py
View file

@ -24,14 +24,14 @@ class likelihood(Parameterized):
def __init__(self): def __init__(self):
Parameterized.__init__(self) Parameterized.__init__(self)
def _get_params(self): # def _get_params(self):
raise NotImplementedError # raise NotImplementedError
#
def _get_param_names(self): # def _get_param_names(self):
raise NotImplementedError # raise NotImplementedError
#
def _set_params(self, x): # def _set_params(self, x):
raise NotImplementedError # raise NotImplementedError
def fit(self): def fit(self):
raise NotImplementedError raise NotImplementedError