mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-12 05:22:38 +02:00
exact gaussian inference now accepts a kern and X, not K
This commit is contained in:
James Hensman
parent
206ec5dfc1
commit
e94b16e0b3
4 changed files with 58 additions and 519 deletions
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@ -60,9 +60,7 @@ class GP(Model):
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self.parameters_changed()
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def parameters_changed(self):
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super(GP, self).parameters_changed()
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self.K = self.kern.K(self.X)
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self.posterior = self.inference_method.inference(self.K, self.likelihood, self.Y)
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self.posterior = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
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def log_likelihood(self):
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return self.posterior.log_marginal
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@ -592,463 +592,4 @@ class Parameterized(Constrainable, Pickleable, Observable):
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return '\n'.format(sep).join(to_print)
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pass
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#
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# class Parameterized_old(object):
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# def __init__(self):
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# """
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# This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
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# """
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# self.tied_indices = []
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# self.fixed_indices = []
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# self.fixed_values = []
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# self.constrained_indices = []
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# self.constraints = []
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#
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# def _get_params(self):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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# def _set_params(self, x):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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#
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# def _get_param_names(self):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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# #def _get_print_names(self):
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# # """ Override for which parameter_names to print out, when using print m """
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# # return self._get_param_names()
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#
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# def pickle(self, filename, protocol=None):
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# if protocol is None:
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# if self._has_get_set_state():
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# protocol = 0
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# else:
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# protocol = -1
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# with open(filename, 'w') as f:
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# cPickle.dump(self, f, protocol)
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#
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# def copy(self):
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# """Returns a (deep) copy of the current model """
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# return copy.deepcopy(self)
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#
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# def __getstate__(self):
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# if self._has_get_set_state():
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# return self.getstate()
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# return self.__dict__
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#
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# def __setstate__(self, state):
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# if self._has_get_set_state():
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# self.setstate(state) # set state
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# self._set_params(self._get_params()) # restore all values
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# return
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# self.__dict__ = state
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#
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# def _has_get_set_state(self):
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# return 'getstate' in vars(self.__class__) and 'setstate' in vars(self.__class__)
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#
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# def getstate(self):
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# """
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# Get the current state of the class,
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# here just all the indices, rest can get recomputed
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# For inheriting from Parameterized:
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#
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# Allways append the state of the inherited object
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# and call down to the inherited object in setstate!!
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# """
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# return [self.tied_indices,
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# self.fixed_indices,
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# self.fixed_values,
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# self.constrained_indices,
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# self.constraints]
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#
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# def setstate(self, state):
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# self.constraints = state.pop()
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# self.constrained_indices = state.pop()
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# self.fixed_values = state.pop()
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# self.fixed_indices = state.pop()
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# self.tied_indices = state.pop()
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#
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# def __getitem__(self, regexp, return_names=False):
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# """
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# Get a model param by name. The name is applied as a regular
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# expression and all parameters that match that regular expression are
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# returned.
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# """
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# matches = self.grep_param_names(regexp)
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# if len(matches):
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# if return_names:
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# return self._get_params()[matches], np.asarray(self._get_param_names())[matches].tolist()
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# else:
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# return self._get_params()[matches]
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# else:
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# raise AttributeError, "no param matches %s" % regexp
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#
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# def __setitem__(self, name, val):
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# """
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# Set model param(s) by name. The name is provided as a regular
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# expression. All parameters matching that regular expression are set to
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# the given value.
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# """
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# matches = self.grep_param_names(name)
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# if len(matches):
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# val = np.array(val)
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# assert (val.size == 1) or val.size == len(matches), "Shape mismatch: {}:({},)".format(val.size, len(matches))
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# x = self._get_params()
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# x[matches] = val
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# self._set_params(x)
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# else:
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# raise AttributeError, "no param matches %s" % name
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#
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# def tie_params(self, regexp):
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# """
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# Tie (all!) parameters matching the regular expression `regexp`.
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# """
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# matches = self.grep_param_names(regexp)
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# assert matches.size > 0, "need at least something to tie together"
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# if len(self.tied_indices):
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# assert not np.any(matches[:, None] == np.hstack(self.tied_indices)), "Some indices are already tied!"
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# self.tied_indices.append(matches)
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# # TODO only one of the priors will be evaluated. Give a warning message if the priors are not identical
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# if hasattr(self, 'prior'):
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# pass
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#
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# self._set_params_transformed(self._get_params_transformed()) # sets tied parameters to single value
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#
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# def untie_everything(self):
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# """Unties all parameters by setting tied_indices to an empty list."""
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# self.tied_indices = []
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#
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# def grep_param_names(self, regexp, transformed=False, search=False):
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# """
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# :param regexp: regular expression to select param parameter_names
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# :type regexp: re | str | int
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# :rtype: the indices of self._get_param_names which match the regular expression.
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#
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# Note:-
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# Other objects are passed through - i.e. integers which weren't meant for grepping
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# """
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#
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# if transformed:
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# parameter_names = self._get_param_names_transformed()
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# else:
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# parameter_names = self._get_param_names()
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#
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# if type(regexp) in [str, np.string_, np.str]:
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# regexp = re.compile(regexp)
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# elif type(regexp) is re._pattern_type:
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# pass
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# else:
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# return regexp
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# if search:
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# return np.nonzero([regexp.search(name) for name in parameter_names])[0]
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# else:
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# return np.nonzero([regexp.match(name) for name in parameter_names])[0]
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#
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# def num_params_transformed(self):
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# removed = 0
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# for tie in self.tied_indices:
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# removed += tie.size - 1
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#
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# for fix in self.fixed_indices:
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# removed += fix.size
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#
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# return len(self._get_params()) - removed
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#
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# def unconstrain(self, regexp):
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# """Unconstrain matching parameters. Does not untie parameters"""
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# matches = self.grep_param_names(regexp)
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#
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# # tranformed contraints:
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# for match in matches:
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# self.constrained_indices = [i[i <> match] for i in self.constrained_indices]
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#
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# # remove empty constraints
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# tmp = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
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# if tmp:
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# self.constrained_indices, self.constraints = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
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# self.constrained_indices, self.constraints = list(self.constrained_indices), list(self.constraints)
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#
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# # fixed:
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# self.fixed_values = [np.delete(values, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices, values in zip(self.fixed_indices, self.fixed_values)]
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# self.fixed_indices = [np.delete(indices, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices in self.fixed_indices]
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#
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# # remove empty elements
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# tmp = [(i, v) for i, v in zip(self.fixed_indices, self.fixed_values) if len(i)]
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# if tmp:
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# self.fixed_indices, self.fixed_values = zip(*tmp)
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# self.fixed_indices, self.fixed_values = list(self.fixed_indices), list(self.fixed_values)
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# else:
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# self.fixed_indices, self.fixed_values = [], []
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#
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# def constrain_negative(self, regexp, warning=True):
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# """ Set negative constraints. """
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# self.constrain(regexp, transformations.NegativeLogexp(), warning)
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#
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# def constrain_positive(self, regexp, warning=True):
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# """ Set positive constraints. """
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# self.constrain(regexp, transformations.Logexp(), warning)
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#
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# def constrain_bounded(self, regexp, lower, upper, warning=True):
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# """ Set bounded constraints. """
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# self.constrain(regexp, transformations.Logistic(lower, upper), warning)
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#
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# def all_constrained_indices(self):
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# if len(self.constrained_indices) or len(self.fixed_indices):
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# return np.hstack(self.constrained_indices + self.fixed_indices)
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# else:
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# return np.empty(shape=(0,))
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#
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# def constrain(self, regexp, transform, warning=True):
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# assert isinstance(transform, transformations.Transformation)
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#
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# matches = self.grep_param_names(regexp)
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# overlap = set(matches).intersection(set(self.all_constrained_indices()))
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# if overlap:
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# self.unconstrain(np.asarray(list(overlap)))
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# if warning:
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# print 'Warning: re-constraining these parameters'
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# pn = self._get_param_names()
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# for i in overlap:
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# print pn[i]
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#
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# self.constrained_indices.append(matches)
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# self.constraints.append(transform)
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# x = self._get_params()
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# x[matches] = transform.initialize(x[matches])
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# self._set_params(x)
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#
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# def constrain_fixed(self, regexp, value=None, warning=True):
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# """
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#
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# :param regexp: which parameters need to be fixed.
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# :type regexp: ndarray(dtype=int) or regular expression object or string
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# :param value: the vlaue to fix the parameters to. If the value is not specified,
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# the param is fixed to the current value
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# :type value: float
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#
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# **Notes**
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#
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# Fixing a param which is tied to another, or constrained in some way will result in an error.
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#
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# To fix multiple parameters to the same value, simply pass a regular expression which matches both param parameter_names, or pass both of the indexes.
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#
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# """
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# matches = self.grep_param_names(regexp)
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# overlap = set(matches).intersection(set(self.all_constrained_indices()))
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# if overlap:
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# self.unconstrain(np.asarray(list(overlap)))
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# if warning:
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# print 'Warning: re-constraining these parameters'
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# pn = self._get_param_names()
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# for i in overlap:
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# print pn[i]
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#
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# self.fixed_indices.append(matches)
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# if value != None:
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# self.fixed_values.append(value)
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# else:
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# self.fixed_values.append(self._get_params()[self.fixed_indices[-1]])
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#
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# # self.fixed_values.append(value)
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# self._set_params_transformed(self._get_params_transformed())
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#
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# def _get_params_transformed(self):
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# """use self._get_params to get the 'true' parameters of the model, which are then tied, constrained and fixed"""
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# x = self._get_params()
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# [np.put(x, i, t.finv(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
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#
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# to_remove = self.fixed_indices + [t[1:] for t in self.tied_indices]
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# if len(to_remove):
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# return np.delete(x, np.hstack(to_remove))
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# else:
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# return x
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#
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# def _set_params_transformed(self, x):
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# """ takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params"""
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# self._set_params(self._untransform_params(x))
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#
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# def _untransform_params(self, x):
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# """
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# The Transformation required for _set_params_transformed.
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#
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# This moves the vector x seen by the optimiser (unconstrained) to the
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# valid param vector seen by the model
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#
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# Note:
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# - This function is separate from _set_params_transformed for downstream flexibility
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# """
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# # work out how many places are fixed, and where they are. tricky logic!
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# fix_places = self.fixed_indices + [t[1:] for t in self.tied_indices]
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# if len(fix_places):
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# fix_places = np.hstack(fix_places)
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# Nfix_places = fix_places.size
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# else:
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# Nfix_places = 0
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#
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# free_places = np.setdiff1d(np.arange(Nfix_places + x.size, dtype=np.int), fix_places)
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#
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# # put the models values in the vector xx
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# xx = np.zeros(Nfix_places + free_places.size, dtype=np.float64)
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#
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# xx[free_places] = x
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# [np.put(xx, i, v) for i, v in zip(self.fixed_indices, self.fixed_values)]
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# [np.put(xx, i, v) for i, v in [(t[1:], xx[t[0]]) for t in self.tied_indices] ]
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#
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# [np.put(xx, i, t.f(xx[i])) for i, t in zip(self.constrained_indices, self.constraints)]
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# if hasattr(self, 'debug'):
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# stop # @UndefinedVariable
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#
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# return xx
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#
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# def _get_param_names_transformed(self):
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# """
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# Returns the param parameter_names as propagated after constraining,
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# tying or fixing, i.e. a list of the same length as _get_params_transformed()
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# """
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# n = self._get_param_names()
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#
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# # remove/concatenate the tied param parameter_names
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# if len(self.tied_indices):
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# for t in self.tied_indices:
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# n[t[0]] = "<tie>".join([n[tt] for tt in t])
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# remove = np.hstack([t[1:] for t in self.tied_indices])
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# else:
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# remove = np.empty(shape=(0,), dtype=np.int)
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#
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# # also remove the fixed params
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# if len(self.fixed_indices):
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# remove = np.hstack((remove, np.hstack(self.fixed_indices)))
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#
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# # add markers to show that some variables are constrained
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# for i, t in zip(self.constrained_indices, self.constraints):
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# for ii in i:
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# n[ii] = n[ii] + t.__str__()
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#
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# n = [nn for i, nn in enumerate(n) if not i in remove]
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# return n
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#
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# #@property
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# #def all(self):
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# # return self.__str__(self._get_param_names())
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#
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#
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# #def __str__(self, parameter_names=None, nw=30):
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# def __str__(self, nw=30):
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# """
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# Return a string describing the param parameter_names and their ties and constraints
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# """
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# parameter_names = self._get_param_names()
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# #if parameter_names is None:
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# # parameter_names = self._get_print_names()
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# #name_indices = self.grep_param_names("|".join(parameter_names))
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# N = len(parameter_names)
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#
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# if not N:
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# return "This object has no free parameters."
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# header = ['Name', 'Value', 'Constraints', 'Ties']
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# values = self._get_params() # map(str,self._get_params())
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# #values = self._get_params()[name_indices] # map(str,self._get_params())
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# # sort out the constraints
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# constraints = [''] * len(parameter_names)
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# #constraints = [''] * len(self._get_param_names())
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# for i, t in zip(self.constrained_indices, self.constraints):
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# for ii in i:
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# constraints[ii] = t.__str__()
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# for i in self.fixed_indices:
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# for ii in i:
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# constraints[ii] = 'Fixed'
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# # sort out the ties
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# ties = [''] * len(parameter_names)
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# for i, tie in enumerate(self.tied_indices):
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# for j in tie:
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# ties[j] = '(' + str(i) + ')'
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#
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# if values.size == 1:
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# values = ['%.4f' %float(values)]
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# else:
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# values = ['%.4f' % float(v) for v in values]
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# max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
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# max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
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# max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
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# max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
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# cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
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# # columns = cols.sum()
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#
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# header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
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# header_string = map(lambda x: '|'.join(x), [header_string])
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# separator = '-' * len(header_string[0])
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# param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
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#
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#
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# return ('\n'.join([header_string[0], separator] + param_string)) + '\n'
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#
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# def grep_model(self,regexp):
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# regexp_indices = self.grep_param_names(regexp)
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# all_names = self._get_param_names()
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#
|
||||
# parameter_names = [all_names[pj] for pj in regexp_indices]
|
||||
# N = len(parameter_names)
|
||||
#
|
||||
# if not N:
|
||||
# return "Match not found."
|
||||
#
|
||||
# header = ['Name', 'Value', 'Constraints', 'Ties']
|
||||
# all_values = self._get_params()
|
||||
# values = np.array([all_values[pj] for pj in regexp_indices])
|
||||
# constraints = [''] * len(parameter_names)
|
||||
#
|
||||
# _constrained_indices,aux = self._pick_elements(regexp_indices,self.constrained_indices)
|
||||
# _constraints_ = [self.constraints[pj] for pj in aux]
|
||||
#
|
||||
# for i, t in zip(_constrained_indices, _constraints_):
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# constraints[iii] = t.__str__()
|
||||
#
|
||||
# _fixed_indices,aux = self._pick_elements(regexp_indices,self.fixed_indices)
|
||||
# for i in _fixed_indices:
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# constraints[ii] = 'Fixed'
|
||||
#
|
||||
# _tied_indices,aux = self._pick_elements(regexp_indices,self.tied_indices)
|
||||
# ties = [''] * len(parameter_names)
|
||||
# for i,ti in zip(_tied_indices,aux):
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# ties[iii] = '(' + str(ti) + ')'
|
||||
#
|
||||
# if values.size == 1:
|
||||
# values = ['%.4f' %float(values)]
|
||||
# else:
|
||||
# values = ['%.4f' % float(v) for v in values]
|
||||
#
|
||||
# max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
|
||||
# max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
|
||||
# max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
|
||||
# max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
|
||||
# cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
|
||||
#
|
||||
# header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
|
||||
# header_string = map(lambda x: '|'.join(x), [header_string])
|
||||
# separator = '-' * len(header_string[0])
|
||||
# param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
|
||||
#
|
||||
# print header_string[0]
|
||||
# print separator
|
||||
# for string in param_string:
|
||||
# print string
|
||||
#
|
||||
# def _pick_elements(self,regexp_ind,array_list):
|
||||
# """Removes from array_list the elements different from regexp_ind"""
|
||||
# new_array_list = [] #New list with elements matching regexp_ind
|
||||
# array_indices = [] #Indices that matches the arrays in new_array_list and array_list
|
||||
#
|
||||
# array_index = 0
|
||||
# for array in array_list:
|
||||
# _new = []
|
||||
# for ai in array:
|
||||
# if ai in regexp_ind:
|
||||
# _new.append(ai)
|
||||
# if len(_new):
|
||||
# new_array_list.append(np.array(_new))
|
||||
# array_indices.append(array_index)
|
||||
# array_index += 1
|
||||
# return new_array_list, array_indices
|
||||
|
||||
|
|
|
|||
|
|
@ -22,15 +22,15 @@ class ExactGaussianInference(object):
|
|||
|
||||
def get_YYTfactor(self, Y):
|
||||
"""
|
||||
find a matrix L which satisfies LLT = YYT.
|
||||
find a matrix L which satisfies LL^T = YY^T.
|
||||
|
||||
Note that L may have fewer columns than Y.
|
||||
Note that L may have fewer columns than Y, else L=Y.
|
||||
"""
|
||||
N, D = Y.shape
|
||||
if (N>D):
|
||||
return Y
|
||||
else:
|
||||
#if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
|
||||
#if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
|
||||
raise NotImplementedError, 'TODO' #TODO
|
||||
|
||||
def inference(self, K, likelihood, Y, Y_metadata=None):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue