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observable pattern through and thorugh

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This commit is contained in:
Max Zwiessele 2014-02-26 15:46:14 +00:00
parent 26aeb5e1db
commit 65fd6dd24e
11 changed files with 64 additions and 80 deletions
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3
GPy/core/parameterization/array_core.py
View file

@ -59,10 +59,9 @@ class ObservableArray(np.ndarray, Observable):
else:
return s.size != 0
def __setitem__(self, s, val, update=True):
def __setitem__(self, s, val):
if self._s_not_empty(s):
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
def __getslice__(self, start, stop):

15
GPy/core/parameterization/param.py
View file

@ -137,8 +137,6 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
#===========================================================================
def _set_params(self, param, update=True):
self.flat = param
#self._notify_tied_parameters()
self._notify_observers()
def _get_params(self):
return self.flat
@ -161,11 +159,9 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
except AttributeError: pass # returning 0d array or float, double etc
return new_arr
def __setitem__(self, s, val, update=True):
super(Param, self).__setitem__(s, val, update=update)
#self._notify_tied_parameters()
if update and self._s_not_empty(s):
self._notify_parameters_changed()
def __setitem__(self, s, val):
super(Param, self).__setitem__(s, val)
#self._notify_observers()
#===========================================================================
# Index Operations:
@ -185,6 +181,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
a = self._realshape_[i] + a
internal_offset += a * extended_realshape[i]
return internal_offset
def _raveled_index(self, slice_index=None):
# return an index array on the raveled array, which is formed by the current_slice
# of this object
@ -354,7 +351,7 @@ class ParamConcatenation(object):
val = val._vals()
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
vals = self._vals(); vals[s] = val; del val
[numpy.place(p, ind[ps], vals[ps]) and update and p._notify_parameters_changed()
[numpy.place(p, ind[ps], vals[ps]) and update and p._notify_observers()
for p, ps in zip(self.params, self._param_slices_)]
def _vals(self):
return numpy.hstack([p._get_params() for p in self.params])
@ -363,7 +360,7 @@ class ParamConcatenation(object):
#===========================================================================
def update_all_params(self):
for p in self.params:
p._notify_parameters_changed()
p._notify_observers()
def constrain(self, constraint, warning=True):
[param.constrain(constraint, update=False) for param in self.params]

23
GPy/core/parameterization/parameter_core.py
View file

@ -18,8 +18,13 @@ class Observable(object):
def add_observer(self, observer, callble):
self._observer_callables_[observer].append(callble)
def remove_observer(self, observer, callble):
del self._observer_callables_[observer][callble]
def remove_observer(self, observer, callble=None):
if callble is None:
del self._observer_callables_[observer]
else:
self._observer_callables_[observer].remove(callble)
if len(self._observer_callables_[observer]) == 0:
self.remove_observer(observer)
def _notify_observers(self):
[[callble(self) for callble in callables]
@ -72,8 +77,7 @@ class Parentable(object):
return self._direct_parent_._highest_parent_
def _notify_parameters_changed(self):
if self.has_parent():
self._direct_parent_._notify_parameters_changed()
raise NotImplementedError, "shouldnt happen, abstract superclass"
class Nameable(Parentable):
def __init__(self, name, *a, **kw):
@ -309,7 +313,7 @@ class Constrainable(Nameable, Indexable):
print "WARNING: reconstraining parameters {}".format(self.parameter_names() or self.name)
which.add(transform, self._raveled_index())
if update:
self._notify_parameters_changed()
self._notify_observers()
def _remove_from_index_operations(self, which, transforms):
if len(transforms) == 0:
@ -325,7 +329,7 @@ class Constrainable(Nameable, Indexable):
return removed
class Parameterizable(Constrainable):
class Parameterizable(Constrainable, Observable):
def __init__(self, *args, **kwargs):
super(Parameterizable, self).__init__(*args, **kwargs)
from GPy.core.parameterization.array_core import ParamList
@ -386,7 +390,7 @@ class Parameterizable(Constrainable):
def _set_params(self, params, update=True):
# don't overwrite this anymore!
import itertools
[p._set_params(params[s], update=update) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
[p._set_params(params[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
self.parameters_changed()
def copy(self):
@ -420,10 +424,9 @@ class Parameterizable(Constrainable):
return s
def _notify_parameters_changed(self):
def _notify_parameters_changed(self, which):
self.parameters_changed()
if self.has_parent():
self._direct_parent_._notify_parameters_changed()
self._notify_observers()
def parameters_changed(self):
"""

4
GPy/core/parameterization/parameterized.py
View file

@ -11,7 +11,7 @@ from parameter_core import Constrainable, Pickleable, Parentable, Observable, Pa
from transformations import __fixed__
from array_core import ParamList
class Parameterized(Parameterizable, Pickleable, Observable, Gradcheckable):
class Parameterized(Parameterizable, Pickleable, Gradcheckable):
"""
Parameterized class
@ -92,6 +92,7 @@ class Parameterized(Parameterizable, Pickleable, Observable, Gradcheckable):
self.constraints.update(param.constraints, start)
self.priors.update(param.priors, start)
self._parameters_.insert(index, param)
param.add_observer(self, self._notify_parameters_changed)
self.size += param.size
else:
raise RuntimeError, """Parameter exists already added and no copy made"""
@ -120,6 +121,7 @@ class Parameterized(Parameterizable, Pickleable, Observable, Gradcheckable):
del self._parameters_[param._parent_index_]
param._disconnect_parent()
param.remove_observer(self, self._notify_parameters_changed)
self.constraints.shift_left(start, param.size)
self._connect_fixes()
self._connect_parameters()

1
GPy/kern/_src/linear.py
View file

@ -9,7 +9,6 @@ from ...util.linalg import tdot
from ...util.misc import fast_array_equal, param_to_array
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.caching import cache_this
class Linear(Kern):
"""

5
GPy/kern/_src/rbf.py
View file

@ -6,6 +6,7 @@ import numpy as np
from scipy import weave
from ...util.misc import param_to_array
from stationary import Stationary
from GPy.util.caching import Cache_this
class RBF(Stationary):
"""
@ -166,7 +167,7 @@ class RBF(Stationary):
return target
#@cache_this TODO
@Cache_this(limit=1)
def _psi1computations(self, Z, vp):
mu, S = vp.mean, vp.variance
l2 = self.lengthscale **2
@ -179,7 +180,7 @@ class RBF(Stationary):
#@cache_this TODO
@Cache_this(limit=1)
def _psi2computations(self, Z, vp):
mu, S = vp.mean, vp.variance

5
GPy/kern/_src/ssrbf.py
View file

@ -6,7 +6,6 @@ from kern import Kern
import numpy as np
from ...util.linalg import tdot
from ...util.config import *
from ...util.caching import cache_this
from stationary import Stationary
class SSRBF(Stationary):
@ -155,7 +154,7 @@ class SSRBF(Stationary):
# Precomputations #
#---------------------------------------#
@cache_this(1)
#@cache_this(1)
def _K_computations(self, X, X2):
"""
K(X,X2) - X is NxQ
@ -175,7 +174,7 @@ class SSRBF(Stationary):
self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), axis=1)[:, None] + np.sum(np.square(X2), axis=1)[None, :])
self._K_dvar = np.exp(-0.5 * self._K_dist2)
@cache_this(1)
#@cache_this(1)
def _psi_computations(self, Z, mu, S, gamma):
"""
Z - MxQ

5
GPy/kern/_src/stationary.py
View file

@ -9,6 +9,7 @@ from ...util.linalg import tdot
from ... import util
import numpy as np
from scipy import integrate
from ...util.caching import Cache_this
class Stationary(Kern):
def __init__(self, input_dim, variance, lengthscale, ARD, name):
@ -39,15 +40,18 @@ class Stationary(Kern):
def dK_dr(self, r):
raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class"
@Cache_this(limit=5, ignore_args=())
def K(self, X, X2=None):
r = self._scaled_dist(X, X2)
return self.K_of_r(r)
@Cache_this(limit=5, ignore_args=(0,))
def _dist(self, X, X2):
if X2 is None:
X2 = X
return X[:, None, :] - X2[None, :, :]
@Cache_this(limit=5, ignore_args=(0,))
def _unscaled_dist(self, X, X2=None):
"""
Compute the square distance between each row of X and X2, or between
@ -61,6 +65,7 @@ class Stationary(Kern):
X2sq = np.sum(np.square(X2),1)
return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
@Cache_this(limit=5, ignore_args=())
def _scaled_dist(self, X, X2=None):
"""
Efficiently compute the scaled distance, r.

2
GPy/models/gplvm.py
View file

@ -41,7 +41,7 @@ class GPLVM(GP):
def parameters_changed(self):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self._dL_dK, self.X, None)
self.X.gradient = self.kern.gradients_X(self.dL_dK, self.X, None)
def _getstate(self):
return GP._getstate(self)

4
GPy/models/sparse_gp_regression.py
View file

@ -8,6 +8,7 @@ from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference import VarDTC
from ..util.misc import param_to_array
from ..core.parameterization.variational import VariationalPosterior
class SparseGPRegression(SparseGP):
"""
@ -45,6 +46,9 @@ class SparseGPRegression(SparseGP):
likelihood = likelihoods.Gaussian()
if not (X_variance is None):
X = VariationalPosterior(X,X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
def _getstate(self):

65
GPy/util/caching.py
View file

@ -1,21 +1,23 @@
from ..core.parameterization.parameter_core import Observable
class Cacher(object):
def __init__(self, operation, limit=5, reset_on_first=False):
def __init__(self, operation, limit=5, ignore_args=()):
self.limit = int(limit)
self._reset_on_first = reset_on_first
self.ignore_args = ignore_args
self.operation=operation
self.cached_inputs = []
self.cached_outputs = []
self.inputs_changed = []
def __call__(self, *args):
if self._reset_on_first:
assert isinstance(args[0], Observable)
args[0].add_observer(self, self.reset)
cached_args = args
if len(self.ignore_args) != 0:
ca = [a for i,a in enumerate(args) if i not in self.ignore_args]
cached_args = []
for a in ca:
if not any(a is ai for ai in cached_args):
cached_args.append(a)
else:
cached_args = args[1:]
cached_args = args
if not all([isinstance(arg, Observable) for arg in cached_args]):
@ -36,7 +38,7 @@ class Cacher(object):
self.cached_inputs.append(cached_args)
self.cached_outputs.append(self.operation(*args))
self.inputs_changed.append(False)
[a.add_observer(self, self.on_cache_changed) for a in args]
[a.add_observer(self, self.on_cache_changed) for a in cached_args]
return self.cached_outputs[-1]
def on_cache_changed(self, arg):
@ -48,42 +50,15 @@ class Cacher(object):
self.cached_outputs = []
self.inputs_changed = []
def cache_this(limit=5, reset_on_self=False):
def limited_cache(f):
c = Cacher(f, limit, reset_on_first=reset_on_self)
class Cache_this(object):
def __init__(self, limit=5, ignore_args=()):
self.limit = limit
self.ignore_args = ignore_args
self.c = None
def __call__(self, f):
def f_wrap(*args):
return c(*args)
f_wrap._cacher = c
if self.c is None:
self.c = Cacher(f, self.limit, ignore_args=self.ignore_args)
return self.c(*args)
f_wrap._cacher = self
return f_wrap
return limited_cache
#Xbase = X
#while Xbase is not None:
#try:
#i = self.cached_inputs.index(X)
#break
#except ValueError:
#Xbase = X.base
#continue
#self.inputs_changed[i] = True