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print_all function removed, print m works as before.

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This commit is contained in:
Ricardo 2013-09-16 12:24:37 +01:00
parent 4bb2ea9606
commit 1931e447f4
5 changed files with 53 additions and 43 deletions
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21
GPy/core/model.py
View file

@ -31,8 +31,8 @@ class Model(Parameterized):
def getstate(self):
"""
Get the current state of the class.
Inherited from Parameterized, so add those parameters to the state
:return: list of states from the model.
"""
@ -46,7 +46,7 @@ class Model(Parameterized):
call Parameterized with the rest of the state
:param state: the state of the model.
:type state: list as returned from getstate.
:type state: list as returned from getstate.
"""
self.preferred_optimizer = state.pop()
self.sampling_runs = state.pop()
@ -397,17 +397,20 @@ class Model(Parameterized):
return np.nan
return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
def __str__(self, names=None):
if names is None:
names = self._get_print_names()
s = Parameterized.__str__(self, names=names).split('\n')
def __str__(self):
s = Parameterized.__str__(self).split('\n')
#def __str__(self, names=None):
# if names is None:
# names = self._get_print_names()
#s = Parameterized.__str__(self, names=names).split('\n')
# add priors to the string
if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors]
else:
strs = [''] * len(self._get_param_names())
name_indices = self.grep_param_names("|".join(names))
strs = np.array(strs)[name_indices]
strs = [''] * len(self._get_params())
# strs = [''] * len(self._get_param_names())
# name_indices = self.grep_param_names("|".join(names))
# strs = np.array(strs)[name_indices]
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()

34
GPy/core/parameterized.py
View file

@ -27,9 +27,9 @@ class Parameterized(object):
def _get_param_names(self):
raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
def _get_print_names(self):
""" Override for which names to print out, when using print m """
return self._get_param_names()
#def _get_print_names(self):
# """ Override for which names to print out, when using print m """
# return self._get_param_names()
def pickle(self, filename, protocol=None):
if protocol is None:
@ -63,10 +63,10 @@ class Parameterized(object):
"""
Get the current state of the class,
here just all the indices, rest can get recomputed
For inheriting from Parameterized:
Allways append the state of the inherited object
and call down to the inherited object in setstate!!
Allways append the state of the inherited object
and call down to the inherited object in setstate!!
"""
return [self.tied_indices,
self.fixed_indices,
@ -336,26 +336,30 @@ class Parameterized(object):
n = [nn for i, nn in enumerate(n) if not i in remove]
return n
@property
def all(self):
return self.__str__(self._get_param_names())
#@property
#def all(self):
# return self.__str__(self._get_param_names())
def __str__(self, names=None, nw=30):
#def __str__(self, names=None, nw=30):
def __str__(self, nw=30):
"""
Return a string describing the parameter names and their ties and constraints
"""
if names is None:
names = self._get_print_names()
name_indices = self.grep_param_names("|".join(names))
names = self._get_param_names()
#if names is None:
# names = self._get_print_names()
#name_indices = self.grep_param_names("|".join(names))
N = len(names)
if not N:
return "This object has no free parameters."
header = ['Name', 'Value', 'Constraints', 'Ties']
values = self._get_params()[name_indices] # map(str,self._get_params())
values = self._get_params() # map(str,self._get_params())
#values = self._get_params()[name_indices] # map(str,self._get_params())
# sort out the constraints
constraints = [''] * len(self._get_param_names())
constraints = [''] * len(names)
#constraints = [''] * len(self._get_param_names())
for i, t in zip(self.constrained_indices, self.constraints):
for ii in i:
constraints[ii] = t.__str__()

4
GPy/core/sparse_gp.py
View file

@ -208,8 +208,8 @@ class SparseGP(GPBase):
return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], [])\
+ self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
def _get_print_names(self):
return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
#def _get_print_names(self):
# return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
def update_likelihood_approximation(self):
"""

4
GPy/models/bayesian_gplvm.py
View file

@ -66,8 +66,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self))
def _get_print_names(self):
return SparseGP._get_print_names(self)
#def _get_print_names(self):
# return SparseGP._get_print_names(self)
def _get_params(self):
"""

33
GPy/models/mrd.py
View file

@ -25,11 +25,11 @@ class MRD(Model):
:param input_dim: latent dimensionality
:type input_dim: int
:param initx: initialisation method for the latent space :
* 'concat' - PCA on concatenation of all datasets
* 'single' - Concatenation of PCA on datasets, respectively
* 'random' - Random draw from a normal
:type initx: ['concat'|'single'|'random']
:param initz: initialisation method for inducing inputs
:type initz: 'permute'|'random'
@ -163,28 +163,31 @@ class MRD(Model):
self._init_X(initx, self.likelihood_list)
self._init_Z(initz, self.X)
def _get_latent_param_names(self):
#def _get_latent_param_names(self):
def _get_param_names(self):
n1 = self.gref._get_param_names()
n1var = n1[:self.NQ * 2 + self.MQ]
return n1var
def _get_kernel_names(self):
# return n1var
#
#def _get_kernel_names(self):
map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
itertools.izip(ns,
itertools.repeat(name)))
kernel_names = (map_names(SparseGP._get_param_names(g)[self.MQ:], n) for g, n in zip(self.bgplvms, self.names))
return kernel_names
return list(itertools.chain(n1var, *(map_names(\
SparseGP._get_param_names(g)[self.MQ:], n) \
for g, n in zip(self.bgplvms, self.names))))
# kernel_names = (map_names(SparseGP._get_param_names(g)[self.MQ:], n) for g, n in zip(self.bgplvms, self.names))
# return kernel_names
def _get_param_names(self):
#def _get_param_names(self):
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
n1var = self._get_latent_param_names()
kernel_names = self._get_kernel_names()
return list(itertools.chain(n1var, *kernel_names))
# n1var = self._get_latent_param_names()
# kernel_names = self._get_kernel_names()
# return list(itertools.chain(n1var, *kernel_names))
def _get_print_names(self):
return list(itertools.chain(*self._get_kernel_names()))
#def _get_print_names(self):
# return list(itertools.chain(*self._get_kernel_names()))
def _get_params(self):
"""