mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-20 15:38:08 +02:00
all parameters in memory
This commit is contained in:
Max Zwiessele
parent
f7223ea377
commit
546d5dfff3
9 changed files with 135 additions and 103 deletions
|
|
@ -6,6 +6,12 @@ __updated__ = '2013-12-16'
|
|||
import numpy as np
|
||||
from parameter_core import Observable
|
||||
|
||||
class _Array(np.ndarray):
|
||||
def __init__(self, dtype=float, buffer=None, offset=0,
|
||||
strides=None, order=None, *args, **kwargs):
|
||||
super(_Array, self).__init__(dtype=dtype, buffer=buffer, offset=offset,
|
||||
strides=strides, order=order, *args, **kwargs)
|
||||
|
||||
class ObservableArray(np.ndarray, Observable):
|
||||
"""
|
||||
An ndarray which reports changes to its observers.
|
||||
|
|
@ -14,16 +20,14 @@ class ObservableArray(np.ndarray, Observable):
|
|||
takes exactly one argument, which is this array itself.
|
||||
"""
|
||||
__array_priority__ = -1 # Never give back ObservableArray
|
||||
def __new__(cls, input_array):
|
||||
def __new__(cls, input_array, *a, **kw):
|
||||
if not isinstance(input_array, ObservableArray):
|
||||
obj = np.atleast_1d(input_array).view(cls)
|
||||
obj = np.atleast_1d(np.require(input_array, dtype=np.float64, requirements=['C', 'W'])).view(cls)
|
||||
else: obj = input_array
|
||||
cls.__name__ = "ObservableArray\n "
|
||||
super(ObservableArray, obj).__init__(*a, **kw)
|
||||
return obj
|
||||
|
||||
def __init__(self, *a, **kw):
|
||||
super(ObservableArray, self).__init__(*a, **kw)
|
||||
|
||||
def __array_finalize__(self, obj):
|
||||
# see InfoArray.__array_finalize__ for comments
|
||||
if obj is None: return
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
|
||||
import itertools
|
||||
import numpy
|
||||
from parameter_core import OptimizationHandlable, Gradcheckable, adjust_name_for_printing
|
||||
from parameter_core import OptimizationHandlable, adjust_name_for_printing
|
||||
from array_core import ObservableArray
|
||||
|
||||
###### printing
|
||||
|
|
@ -43,13 +43,12 @@ class Param(OptimizationHandlable, ObservableArray):
|
|||
_fixes_ = None
|
||||
_parameters_ = []
|
||||
def __new__(cls, name, input_array, default_constraint=None):
|
||||
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
|
||||
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array, name=name, default_constraint=default_constraint))
|
||||
cls.__name__ = "Param"
|
||||
obj._current_slice_ = (slice(obj.shape[0]),)
|
||||
obj._realshape_ = obj.shape
|
||||
obj._realsize_ = obj.size
|
||||
obj._realndim_ = obj.ndim
|
||||
obj._updated_ = False
|
||||
from lists_and_dicts import SetDict
|
||||
obj._tied_to_me_ = SetDict()
|
||||
obj._tied_to_ = []
|
||||
|
|
@ -86,7 +85,6 @@ class Param(OptimizationHandlable, ObservableArray):
|
|||
self._realshape_ = getattr(obj, '_realshape_', None)
|
||||
self._realsize_ = getattr(obj, '_realsize_', None)
|
||||
self._realndim_ = getattr(obj, '_realndim_', None)
|
||||
self._updated_ = getattr(obj, '_updated_', None)
|
||||
self._original_ = getattr(obj, '_original_', None)
|
||||
self._name = getattr(obj, 'name', None)
|
||||
self._gradient_array_ = getattr(obj, '_gradient_array_', None)
|
||||
|
|
@ -121,14 +119,12 @@ class Param(OptimizationHandlable, ObservableArray):
|
|||
self._realndim_,
|
||||
self._tied_to_me_,
|
||||
self._tied_to_,
|
||||
self._updated_,
|
||||
)
|
||||
)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(Param, self).__setstate__(state[0])
|
||||
state = list(state[1])
|
||||
self._updated_ = state.pop()
|
||||
self._tied_to_ = state.pop()
|
||||
self._tied_to_me_ = state.pop()
|
||||
self._realndim_ = state.pop()
|
||||
|
|
|
|||
|
|
@ -40,11 +40,9 @@ class Observable(object):
|
|||
as an observer. Every time the observable changes, it sends a notification with
|
||||
self as only argument to all its observers.
|
||||
"""
|
||||
_updated = True
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(Observable, self).__init__()
|
||||
self._observer_callables_ = []
|
||||
def __del__(self, *args, **kwargs):
|
||||
del self._observer_callables_
|
||||
|
||||
def add_observer(self, observer, callble, priority=0):
|
||||
self._insert_sorted(priority, observer, callble)
|
||||
|
|
@ -161,7 +159,9 @@ class Parentable(object):
|
|||
"""
|
||||
_parent_ = None
|
||||
_parent_index_ = None
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(Parentable, self).__init__()
|
||||
|
||||
def has_parent(self):
|
||||
"""
|
||||
Return whether this parentable object currently has a parent.
|
||||
|
|
@ -205,6 +205,7 @@ class Gradcheckable(Parentable):
|
|||
"""
|
||||
def __init__(self, *a, **kw):
|
||||
super(Gradcheckable, self).__init__(*a, **kw)
|
||||
|
||||
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
|
||||
"""
|
||||
Check the gradient of this parameter with respect to the highest parent's
|
||||
|
|
@ -272,6 +273,9 @@ class Indexable(object):
|
|||
Enable enraveled indexes and offsets for this object.
|
||||
The raveled index of an object is the index for its parameters in a flattened int array.
|
||||
"""
|
||||
def __init__(self, *a, **kw):
|
||||
super(Indexable, self).__init__()
|
||||
|
||||
def _raveled_index(self):
|
||||
"""
|
||||
Flattened array of ints, specifying the index of this object.
|
||||
|
|
@ -314,7 +318,7 @@ class Constrainable(Nameable, Indexable):
|
|||
:func:`constrain()` and :func:`unconstrain()` are main methods here
|
||||
"""
|
||||
def __init__(self, name, default_constraint=None, *a, **kw):
|
||||
super(Constrainable, self).__init__(name=name, *a, **kw)
|
||||
super(Constrainable, self).__init__(name=name, default_constraint=default_constraint, *a, **kw)
|
||||
self._default_constraint_ = default_constraint
|
||||
from index_operations import ParameterIndexOperations
|
||||
self.constraints = ParameterIndexOperations()
|
||||
|
|
@ -534,8 +538,11 @@ class OptimizationHandlable(Constrainable, Observable):
|
|||
"""
|
||||
This enables optimization handles on an Object as done in GPy 0.4.
|
||||
|
||||
transformed: make sure the transformations and constraints etc are handled
|
||||
`..._transformed`: make sure the transformations and constraints etc are handled
|
||||
"""
|
||||
def __init__(self, name, default_constraint=None, *a, **kw):
|
||||
super(OptimizationHandlable, self).__init__(name, default_constraint=default_constraint, *a, **kw)
|
||||
|
||||
def transform(self):
|
||||
[np.put(self._param_array_, ind, c.finv(self._param_array_[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
|
||||
|
||||
|
|
@ -625,6 +632,24 @@ class OptimizationHandlable(Constrainable, Observable):
|
|||
[np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None]
|
||||
self._set_params_transformed(x) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
|
||||
|
||||
#===========================================================================
|
||||
# For shared memory arrays. This does nothing in Param, but sets the memory
|
||||
# for all parameterized objects
|
||||
#===========================================================================
|
||||
def _propagate_param_grad(self, parray, garray):
|
||||
pi_old_size = 0
|
||||
for pi in self._parameters_:
|
||||
pislice = slice(pi_old_size, pi_old_size+pi.size)
|
||||
|
||||
self._param_array_[pislice] = pi._param_array_.ravel()#, requirements=['C', 'W']).flat
|
||||
self._gradient_array_[pislice] = pi._gradient_array_.ravel()#, requirements=['C', 'W']).flat
|
||||
|
||||
pi._param_array_.data = parray[pislice].data
|
||||
pi._gradient_array_.data = garray[pislice].data
|
||||
|
||||
pi._propagate_param_grad(parray[pislice], garray[pislice])
|
||||
pi_old_size += pi.size
|
||||
|
||||
class Parameterizable(OptimizationHandlable):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(Parameterizable, self).__init__(*args, **kwargs)
|
||||
|
|
@ -811,25 +836,24 @@ class Parameterizable(OptimizationHandlable):
|
|||
p._parent_index_ = i
|
||||
|
||||
pslice = slice(old_size, old_size+p.size)
|
||||
pi_old_size = old_size
|
||||
for pi in p.flattened_parameters:
|
||||
pislice = slice(pi_old_size, pi_old_size+pi.size)
|
||||
|
||||
self._param_array_[pislice] = pi._param_array_.flat
|
||||
self._gradient_array_[pislice] = pi._gradient_array_.flat
|
||||
|
||||
pi._param_array_.data = self._param_array_[pislice].data
|
||||
pi._gradient_array_.data = self._gradient_array_[pislice].data
|
||||
|
||||
pi_old_size += pi.size
|
||||
|
||||
# first connect all children
|
||||
p._propagate_param_grad(self._param_array_[pslice], self._gradient_array_[pslice])
|
||||
|
||||
# then connect children to self
|
||||
self._param_array_[pslice] = p._param_array_.ravel()#, requirements=['C', 'W']).ravel(order='C')
|
||||
self._gradient_array_[pslice] = p._gradient_array_.ravel()#, requirements=['C', 'W']).ravel(order='C')
|
||||
|
||||
if not p._param_array_.flags['C_CONTIGUOUS']:
|
||||
import ipdb;ipdb.set_trace()
|
||||
p._param_array_.data = self._param_array_[pslice].data
|
||||
p._gradient_array_.data = self._gradient_array_[pslice].data
|
||||
|
||||
self._param_slices_.append(pslice)
|
||||
|
||||
self._add_parameter_name(p, ignore_added_names=ignore_added_names)
|
||||
old_size += p.size
|
||||
|
||||
|
||||
#===========================================================================
|
||||
# notification system
|
||||
#===========================================================================
|
||||
|
|
|
|||
|
|
@ -65,8 +65,8 @@ class Parameterized(Parameterizable, Pickleable):
|
|||
# **Never** call parameters_changed() yourself
|
||||
__metaclass__ = ParametersChangedMeta
|
||||
#===========================================================================
|
||||
def __init__(self, name=None, *a, **kw):
|
||||
super(Parameterized, self).__init__(name=name, parent=None, parent_index=None, *a, **kw)
|
||||
def __init__(self, name=None, parameters=[], *a, **kw):
|
||||
super(Parameterized, self).__init__(name=name, *a, **kw)
|
||||
self._in_init_ = True
|
||||
self._parameters_ = ArrayList()
|
||||
self.size = sum(p.size for p in self._parameters_)
|
||||
|
|
@ -76,6 +76,7 @@ class Parameterized(Parameterizable, Pickleable):
|
|||
self._param_slices_ = []
|
||||
self._connect_parameters()
|
||||
del self._in_init_
|
||||
self.add_parameters(*parameters)
|
||||
|
||||
def build_pydot(self, G=None):
|
||||
import pydot # @UnresolvedImport
|
||||
|
|
@ -205,25 +206,29 @@ class Parameterized(Parameterizable, Pickleable):
|
|||
return found_params
|
||||
|
||||
def __getitem__(self, name, paramlist=None):
|
||||
if paramlist is None:
|
||||
paramlist = self.grep_param_names(name)
|
||||
if len(paramlist) < 1: raise AttributeError, name
|
||||
if len(paramlist) == 1:
|
||||
if isinstance(paramlist[-1], Parameterized):
|
||||
paramlist = paramlist[-1].flattened_parameters
|
||||
if len(paramlist) != 1:
|
||||
return ParamConcatenation(paramlist)
|
||||
return paramlist[-1]
|
||||
return ParamConcatenation(paramlist)
|
||||
if isinstance(name, (int, slice, tuple, np.ndarray)):
|
||||
return self._param_array_[name]
|
||||
else:
|
||||
if paramlist is None:
|
||||
paramlist = self.grep_param_names(name)
|
||||
if len(paramlist) < 1: raise AttributeError, name
|
||||
if len(paramlist) == 1:
|
||||
if isinstance(paramlist[-1], Parameterized):
|
||||
paramlist = paramlist[-1].flattened_parameters
|
||||
if len(paramlist) != 1:
|
||||
return ParamConcatenation(paramlist)
|
||||
return paramlist[-1]
|
||||
return ParamConcatenation(paramlist)
|
||||
|
||||
def __setitem__(self, name, value, paramlist=None):
|
||||
if isinstance(name, (slice, tuple, np.ndarray)):
|
||||
self._param_array_[name] = value
|
||||
self.notify_observers()
|
||||
else:
|
||||
try: param = self.__getitem__(name, paramlist)
|
||||
except AttributeError as a: raise a
|
||||
param[:] = value
|
||||
|
||||
|
||||
def __setattr__(self, name, val):
|
||||
# override the default behaviour, if setting a param, so broadcasting can by used
|
||||
if hasattr(self, '_parameters_'):
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ class VariationalPrior(Parameterized):
|
|||
updates the gradients for mean and variance **in place**
|
||||
"""
|
||||
raise NotImplementedError, "override this for variational inference of latent space"
|
||||
|
||||
|
||||
class NormalPrior(VariationalPrior):
|
||||
def KL_divergence(self, variational_posterior):
|
||||
var_mean = np.square(variational_posterior.mean).sum()
|
||||
|
|
@ -63,20 +63,38 @@ class SpikeAndSlabPrior(VariationalPrior):
|
|||
|
||||
|
||||
class VariationalPosterior(Parameterized):
|
||||
def __init__(self, means=None, variances=None, name=None, **kw):
|
||||
super(VariationalPosterior, self).__init__(name=name, **kw)
|
||||
def __init__(self, means=None, variances=None, name=None, *a, **kw):
|
||||
super(VariationalPosterior, self).__init__(name=name, *a, **kw)
|
||||
self.mean = Param("mean", means)
|
||||
self.variance = Param("variance", variances, Logexp())
|
||||
self.ndim = self.mean.ndim
|
||||
self.shape = self.mean.shape
|
||||
self.variance = Param("variance", variances, Logexp())
|
||||
self.add_parameters(self.mean, self.variance)
|
||||
self.num_data, self.input_dim = self.mean.shape
|
||||
self.add_parameters(self.mean, self.variance)
|
||||
if self.has_uncertain_inputs():
|
||||
assert self.variance.shape == self.mean.shape, "need one variance per sample and dimenion"
|
||||
|
||||
def has_uncertain_inputs(self):
|
||||
return not self.variance is None
|
||||
|
||||
def __getitem__(self, s):
|
||||
if isinstance(s, (int, slice, tuple, list, np.ndarray)):
|
||||
import copy
|
||||
n = self.__new__(self.__class__, self.name)
|
||||
dc = self.__dict__.copy()
|
||||
dc['mean'] = self.mean[s]
|
||||
dc['variance'] = self.variance[s]
|
||||
dc['_parameters_'] = copy.copy(self._parameters_)
|
||||
n.__dict__.update(dc)
|
||||
n._parameters_[dc['mean']._parent_index_] = dc['mean']
|
||||
n._parameters_[dc['variance']._parent_index_] = dc['variance']
|
||||
n.ndim = n.mean.ndim
|
||||
n.shape = n.mean.shape
|
||||
n.num_data = n.mean.shape[0]
|
||||
n.input_dim = n.mean.shape[1] if n.ndim != 1 else 1
|
||||
return n
|
||||
else:
|
||||
return super(VariationalPrior, self).__getitem__(s)
|
||||
|
||||
class NormalPosterior(VariationalPosterior):
|
||||
'''
|
||||
|
|
@ -107,7 +125,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
|
|||
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
|
||||
self.gamma = Param("binary_prob",binary_prob, Logistic(1e-10,1.-1e-10))
|
||||
self.add_parameter(self.gamma)
|
||||
|
||||
|
||||
def plot(self, *args):
|
||||
"""
|
||||
Plot latent space X in 1D:
|
||||
|
|
|
|||
|
|
@ -324,14 +324,14 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
|
|||
|
||||
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
|
||||
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
|
||||
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
|
||||
|
||||
k = kern.Linear(Q, ARD=True) + kern.Bias(Q, _np.exp(-2)) + kern.White(Q, _np.exp(-2))
|
||||
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
|
||||
m.ensure_default_constraints()
|
||||
|
||||
for i, bgplvm in enumerate(m.bgplvms):
|
||||
m['{}_noise'.format(i)] = bgplvm.likelihood.Y.var() / 500.
|
||||
|
||||
#Ylist = [Ylist[0]]
|
||||
k = [kern.Linear(Q, ARD=True) + kern.White(Q, 1e-4) for _ in range(len(Ylist))]
|
||||
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="", initz='permute', **kw)
|
||||
|
||||
m['.*noise'] = [Y.var()/500. for Y in Ylist]
|
||||
#for i, Y in enumerate(Ylist):
|
||||
# m['.*Y_{}.*Gaussian.*noise'.format(i)] = Y.var(1) / 500.
|
||||
|
||||
if optimize:
|
||||
print "Optimizing Model:"
|
||||
|
|
|
|||
|
|
@ -1,12 +1,9 @@
|
|||
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
|
||||
import sys
|
||||
import numpy as np
|
||||
import itertools
|
||||
from linear import Linear
|
||||
from ...core.parameterization import Parameterized
|
||||
from ...core.parameterization.param import Param
|
||||
from kern import Kern
|
||||
|
||||
class Add(Kern):
|
||||
|
|
@ -42,8 +39,11 @@ class Add(Kern):
|
|||
else:
|
||||
return sum([p.K(X[:, i_s], X2[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
|
||||
|
||||
def update_gradients_full(self, dL_dK, X):
|
||||
[p.update_gradients_full(dL_dK, X[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
|
||||
def update_gradients_full(self, dL_dK, X, X2=None):
|
||||
if X2 is None:
|
||||
[p.update_gradients_full(dL_dK, X[:,i_s], X2) for p, i_s in zip(self._parameters_, self.input_slices)]
|
||||
else:
|
||||
[p.update_gradients_full(dL_dK, X[:,i_s], X2[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
|
||||
|
||||
def gradients_X(self, dL_dK, X, X2=None):
|
||||
"""Compute the gradient of the objective function with respect to X.
|
||||
|
|
@ -68,19 +68,18 @@ class Add(Kern):
|
|||
|
||||
|
||||
def psi0(self, Z, variational_posterior):
|
||||
return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
|
||||
return np.sum([p.psi0(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
|
||||
|
||||
def psi1(self, Z, variational_posterior):
|
||||
return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
|
||||
return np.sum([p.psi1(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
|
||||
|
||||
def psi2(self, Z, variational_posterior):
|
||||
psi2 = np.sum([p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
|
||||
psi2 = np.sum([p.psi2(Z[:, i_s], variational_posterior[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
|
||||
|
||||
# compute the "cross" terms
|
||||
from white import White
|
||||
from static import White, Bias
|
||||
from rbf import RBF
|
||||
#from rbf_inv import RBFInv
|
||||
from bias import Bias
|
||||
from linear import Linear
|
||||
#ffrom fixed import Fixed
|
||||
|
||||
|
|
@ -91,24 +90,20 @@ class Add(Kern):
|
|||
# rbf X bias
|
||||
#elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
|
||||
elif isinstance(p1, Bias) and isinstance(p2, (RBF, Linear)):
|
||||
tmp = p2.psi1(Z[:,i2], mu[:,i2], S[:,i2])
|
||||
tmp = p2.psi1(Z[:,i2], variational_posterior[:, i_s])
|
||||
psi2 += p1.variance * (tmp[:, :, None] + tmp[:, None, :])
|
||||
#elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
|
||||
elif isinstance(p2, Bias) and isinstance(p1, (RBF, Linear)):
|
||||
tmp = p1.psi1(Z[:,i1], mu[:,i1], S[:,i1])
|
||||
tmp = p1.psi1(Z[:,i1], variational_posterior[:, i_s])
|
||||
psi2 += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
|
||||
else:
|
||||
raise NotImplementedError, "psi2 cannot be computed for this kernel"
|
||||
return psi2
|
||||
|
||||
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
from white import White
|
||||
from rbf import RBF
|
||||
#from rbf_inv import RBFInv
|
||||
#from bias import Bias
|
||||
from linear import Linear
|
||||
#ffrom fixed import Fixed
|
||||
|
||||
from static import White, Bias
|
||||
mu, S = variational_posterior.mean, variational_posterior.variance
|
||||
|
||||
for p1, is1 in zip(self._parameters_, self.input_slices):
|
||||
|
||||
#compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2!
|
||||
|
|
@ -121,20 +116,15 @@ class Add(Kern):
|
|||
elif isinstance(p2, Bias):
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
|
||||
else:
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is1]) * 2.
|
||||
|
||||
|
||||
p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
|
||||
p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
|
||||
|
||||
|
||||
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
from white import White
|
||||
from rbf import RBF
|
||||
#from rbf_inv import rbfinv
|
||||
from bias import Bias
|
||||
from linear import Linear
|
||||
#ffrom fixed import fixed
|
||||
|
||||
from static import White, Bias
|
||||
|
||||
target = np.zeros(Z.shape)
|
||||
for p1, is1 in zip(self._parameters_, self.input_slices):
|
||||
|
||||
|
|
@ -148,22 +138,17 @@ class Add(Kern):
|
|||
elif isinstance(p2, Bias):
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
|
||||
else:
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is2]) * 2.
|
||||
|
||||
|
||||
target += p1.gradients_z_variational(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
|
||||
target += p1.gradients_Z_expectations(eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
|
||||
return target
|
||||
|
||||
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
from white import white
|
||||
from rbf import rbf
|
||||
#from rbf_inv import rbfinv
|
||||
#from bias import bias
|
||||
from linear import linear
|
||||
#ffrom fixed import fixed
|
||||
|
||||
target_mu = np.zeros(mu.shape)
|
||||
target_S = np.zeros(S.shape)
|
||||
from static import White, Bias
|
||||
|
||||
target_mu = np.zeros(variational_posterior.shape)
|
||||
target_S = np.zeros(variational_posterior.shape)
|
||||
for p1, is1 in zip(self._parameters_, self.input_slices):
|
||||
|
||||
#compute the effective dL_dpsi1. extra terms appear becaue of the cross terms in psi2!
|
||||
|
|
@ -171,15 +156,15 @@ class Add(Kern):
|
|||
for p2, is2 in zip(self._parameters_, self.input_slices):
|
||||
if p2 is p1:
|
||||
continue
|
||||
if isinstance(p2, white):
|
||||
if isinstance(p2, White):
|
||||
continue
|
||||
elif isinstance(p2, bias):
|
||||
elif isinstance(p2, Bias):
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.variance * 2.
|
||||
else:
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(z[:,is2], mu[:,is2], s[:,is2]) * 2.
|
||||
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], variational_posterior[:, is2]) * 2.
|
||||
|
||||
|
||||
a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], s[:,is1], z[:,is1])
|
||||
a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z[:,is1], variational_posterior[:, is1])
|
||||
target_mu += a
|
||||
target_S += b
|
||||
return target_mu, target_S
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ class Kern(Parameterized):
|
|||
"""
|
||||
Returns the sensitivity for each dimension of this kernel.
|
||||
"""
|
||||
return self.kern.input_sensitivity()
|
||||
return np.zeros(self.input_dim)
|
||||
|
||||
def __add__(self, other):
|
||||
""" Overloading of the '+' operator. for more control, see self.add """
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ class White(Static):
|
|||
def psi2(self, Z, variational_posterior):
|
||||
return np.zeros((variational_posterior.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
|
||||
|
||||
def update_gradients_full(self, dL_dK, X):
|
||||
def update_gradients_full(self, dL_dK, X, X2=None):
|
||||
self.variance.gradient = np.trace(dL_dK)
|
||||
|
||||
def update_gradients_diag(self, dL_dKdiag, X):
|
||||
|
|
@ -79,10 +79,10 @@ class Bias(Static):
|
|||
self.variance.gradient = dL_dK.sum()
|
||||
|
||||
def update_gradients_diag(self, dL_dKdiag, X):
|
||||
self.variance.gradient = dL_dK.sum()
|
||||
self.variance.gradient = dL_dKdiag.sum()
|
||||
|
||||
def psi2(self, Z, variational_posterior):
|
||||
ret = np.empty((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
|
||||
ret = np.empty((variational_posterior.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
|
||||
ret[:] = self.variance**2
|
||||
return ret
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue