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James Hensman 2014-02-11 13:44:18 +00:00
commit 103aaebb29
60 changed files with 916 additions and 826 deletions
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68
GPy/core/model.py
View file

@ -260,17 +260,18 @@ class Model(Parameterized):
"""
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa', 'sensitivity']
# param_names = self._get_param_names()
for s in positive_strings:
paramlist = self.grep_param_names(".*"+s)
for param in paramlist:
for p in param.flattened_parameters:
rav_i = set(self._raveled_index_for(p))
for constraint in self.constraints.iter_properties():
rav_i -= set(self._constraint_indices(p, constraint))
rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
if ind.size != 0:
p[np.unravel_index(ind, p.shape)].constrain_positive()
# for s in positive_strings:
# paramlist = self.grep_param_names(".*"+s)
# for param in paramlist:
# for p in param.flattened_parameters:
# rav_i = set(self._raveled_index_for(p))
# for constraint in self.constraints.iter_properties():
# rav_i -= set(self._constraint_indices(p, constraint))
# rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
# ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
# if ind.size != 0:
# p[np.unravel_index(ind, p.shape)].constrain_positive(warning=warning)
# if paramlist:
# self.__getitem__(None, paramlist).constrain_positive(warning=warning)
# currently_constrained = self.all_constrained_indices()
@ -380,7 +381,7 @@ class Model(Parameterized):
sgd.run()
self.optimization_runs.append(sgd)
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):
"""
Check the gradient of the ,odel by comparing to a numerical
estimate. If the verbose flag is passed, invividual
@ -405,8 +406,8 @@ class Model(Parameterized):
dx = step * np.sign(np.random.uniform(-1, 1, x.size))
# evaulate around the point x
f1, g1 = self.objective_and_gradients(x + dx)
f2, g2 = self.objective_and_gradients(x - dx)
f1 = self.objective_function(x + dx)
f2 = self.objective_function(x - dx)
gradient = self.objective_function_gradients(x)
numerical_gradient = (f1 - f2) / (2 * dx)
@ -416,7 +417,7 @@ class Model(Parameterized):
else:
# check the gradient of each parameter individually, and do some pretty printing
try:
names = self._get_param_names_transformed()
names = self._get_param_names()
except NotImplementedError:
names = ['Variable %i' % i for i in range(len(x))]
# Prepare for pretty-printing
@ -430,38 +431,45 @@ class Model(Parameterized):
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
if target_param is None:
param_list = range(len(x))
else:
param_list = self.grep_param_names(target_param, transformed=True, search=True)
if not np.any(param_list):
param_list = self._raveled_index_for(target_param)
if self._has_fixes():
param_list = np.intersect1d(param_list, np.r_[:self.size][self._fixes_], True)
if param_list.size == 0:
print "No free parameters to check"
return
for i in param_list:
gradient = self.objective_function_gradients(x)
np.where(gradient==0, 1e-312, gradient)
ret = True
for i, ind in enumerate(param_list):
xx = x.copy()
xx[i] += step
f1, g1 = self.objective_and_gradients(xx)
xx[i] -= 2.*step
f2, g2 = self.objective_and_gradients(xx)
gradient = self.objective_function_gradients(x)[i]
xx[ind] += step
f1 = self.objective_function(xx)
xx[ind] -= 2.*step
f2 = self.objective_function(xx)
numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * np.where(gradient==0, 1e-312, gradient))
difference = np.abs((f1 - f2) / 2 / step - gradient)
ratio = (f1 - f2) / (2 * step * gradient[ind])
difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[i])
formatted_name = "\033[92m {0} \033[0m".format(names[ind])
ret &= True
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])
formatted_name = "\033[91m {0} \033[0m".format(names[ind])
ret &= False
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient
g = '%.6f' % gradient[ind]
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string
return ret
def input_sensitivity(self):
"""

57
GPy/core/parameterization/array_core.py
View file

@ -15,8 +15,36 @@ class ListArray(np.ndarray):
def __new__(cls, input_array):
obj = np.asanyarray(input_array).view(cls)
return obj
def __eq__(self, other):
return other is self
#def __eq__(self, other):
# return other is self
class ParamList(list):
def __contains__(self, other):
for el in self:
if el is other:
return True
return False
pass
class C(np.ndarray):
__array_priority__ = 1.
def __new__(cls, array):
obj = array.view(cls)
return obj
#def __array_finalize__(self, obj):
# #print 'finalize'
# return obj
def __array_prepare__(self, out_arr, context):
#print 'prepare'
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
def __array_wrap__(self, out_arr, context):
#print 'wrap', type(self), type(out_arr), context
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
class ObservableArray(ListArray, Observable):
"""
@ -25,7 +53,7 @@ class ObservableArray(ListArray, Observable):
will be called every time this array changes. The callable
takes exactly one argument, which is this array itself.
"""
__array_priority__ = 0 # Never give back Param
__array_priority__ = -1 # Never give back ObservableArray
def __new__(cls, input_array):
cls.__name__ = "ObservableArray\n "
obj = super(ObservableArray, cls).__new__(cls, input_array).view(cls)
@ -36,16 +64,19 @@ class ObservableArray(ListArray, Observable):
if obj is None: return
self._observers_ = getattr(obj, '_observers_', None)
def __setitem__(self, s, val, update=True):
if self.ndim:
if not np.all(np.equal(self[s], val)):
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
else:
if not np.all(np.equal(self, val)):
super(ObservableArray, self).__setitem__(Ellipsis, val)
if update:
self._notify_observers()
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
# if self.ndim:
# if not np.all(np.equal(self[s], val)):
# super(ObservableArray, self).__setitem__(s, val)
# if update:
# self._notify_observers()
# else:
# if not np.all(np.equal(self, val)):
# super(ObservableArray, self).__setitem__(Ellipsis, val)
# if update:
# self._notify_observers()
def __getslice__(self, start, stop):
return self.__getitem__(slice(start, stop))
def __setslice__(self, start, stop, val):

64
GPy/core/parameterization/index_operations.py
View file

@ -90,11 +90,6 @@ class ParameterIndexOperations(object):
return self._properties.values()
def properties_for(self, index):
# already_seen = dict()
# for ni in index:
# if ni not in already_seen:
# already_seen[ni] = [prop for prop in self.iter_properties() if ni in self._properties[prop]]
# yield already_seen[ni]
return vectorize(lambda i: [prop for prop in self.iter_properties() if i in self._properties[prop]], otypes=[list])(index)
def add(self, prop, indices):
@ -111,70 +106,11 @@ class ParameterIndexOperations(object):
self._properties[prop] = diff
else:
del self._properties[prop]
#[self._reverse[i].remove(prop) for i in removed if prop in self._reverse[i]]
return removed.astype(int)
# else:
# for a in self.properties():
# if numpy.all(a==prop) and a._parent_index_ == prop._parent_index_:
# ind = create_raveled_indices(indices, shape, offset)
# diff = remove_indices(self[a], ind)
# removed = numpy.intersect1d(self[a], ind, True)
# if not index_empty(diff):
# self._properties[a] = diff
# else:
# del self._properties[a]
# [self._reverse[i].remove(a) for i in removed if a in self._reverse[i]]
# return removed.astype(int)
return numpy.array([]).astype(int)
def __getitem__(self, prop):
return self._properties[prop]
# class TieIndexOperations(object):
# def __init__(self, params):
# self.params = params
# self.tied_from = ParameterIndexOperations()
# self.tied_to = ParameterIndexOperations()
# def add(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# self.tied_from.add(tied_to, rav_from)
# self.tied_to.add(tied_to, rav_to)
# return rav_from, rav_to
# def remove(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# rem_from = self.tied_from.remove(tied_to, rav_from)
# rem_to = self.tied_to.remove(tied_to, rav_to)
# left_from = self.tied_from._properties.pop(tied_to)
# left_to = self.tied_to._properties.pop(tied_to)
# self.tied_from[numpy.delete(tied_to, rem_from)] = left_from
# self.tied_to[numpy.delete(tied_to, rem_to)] = left_to
# return rav_from, rav_to
# def from_to_for(self, index):
# return self.tied_from.properties_for(index), self.tied_to.properties_for(index)
# def iter_from_to_indices(self):
# for k, f in self.tied_from.iteritems():
# yield f, self.tied_to[k]
# def iter_to_indices(self):
# return self.tied_to.iterindices()
# def iter_from_indices(self):
# return self.tied_from.iterindices()
# def iter_from_items(self):
# for f, i in self.tied_from.iteritems():
# yield f, i
# def iter_properties(self):
# return self.tied_from.iter_properties()
# def properties(self):
# return self.tied_from.properties()
# def from_to_indices(self, param):
# return self.tied_from[param], self.tied_to[param]
#
# # def create_raveled_indices(index, shape, offset=0):
# # if isinstance(index, (tuple, list)): i = [slice(None)] + list(index)
# # else: i = [slice(None), index]
# # ind = numpy.array(numpy.ravel_multi_index(numpy.indices(shape)[i], shape)).flat + numpy.int_(offset)
# # return ind
def combine_indices(arr1, arr2):
return numpy.union1d(arr1, arr2)

167
GPy/core/parameterization/param.py
View file

@ -3,8 +3,8 @@
import itertools
import numpy
from parameter_core import Constrainable, adjust_name_for_printing
from array_core import ObservableArray
from parameter_core import Constrainable, Gradcheckable, adjust_name_for_printing
from array_core import ObservableArray, ParamList
###### printing
__constraints_name__ = "Constraint"
@ -20,12 +20,15 @@ class Float(numpy.float64, Constrainable):
self._base = base
class Param(ObservableArray, Constrainable):
class Param(ObservableArray, Constrainable, Gradcheckable):
"""
Parameter object for GPy models.
:param name: name of the parameter to be printed
:param input_array: array which this parameter handles
:param str name: name of the parameter to be printed
:param input_array: array which this parameter handles
:type input_array: numpy.ndarray
:param default_constraint: The default constraint for this parameter
:type default_constraint:
You can add/remove constraints by calling constrain on the parameter itself, e.g:
@ -40,19 +43,10 @@ class Param(ObservableArray, Constrainable):
See :py:class:`GPy.core.parameterized.Parameterized` for more details on constraining etc.
This ndarray can be stored in lists and checked if it is in.
>>> import numpy as np
>>> x = np.random.normal(size=(10,3))
>>> x in [[1], x, [3]]
True
WARNING: This overrides the functionality of x==y!!!
Use numpy.equal(x,y) for element-wise equality testing.
"""
__array_priority__ = 0 # Never give back Param
__array_priority__ = -1 # Never give back Param
_fixes_ = None
def __new__(cls, name, input_array, *args, **kwargs):
def __new__(cls, name, input_array, default_constraint=None):
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
obj._current_slice_ = (slice(obj.shape[0]),)
obj._realshape_ = obj.shape
@ -66,8 +60,8 @@ class Param(ObservableArray, Constrainable):
obj.gradient = None
return obj
def __init__(self, name, input_array):
super(Param, self).__init__(name=name)
def __init__(self, name, input_array, default_constraint=None):
super(Param, self).__init__(name=name, default_constraint=default_constraint)
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
@ -75,7 +69,7 @@ class Param(ObservableArray, Constrainable):
super(Param, self).__array_finalize__(obj)
self._direct_parent_ = getattr(obj, '_direct_parent_', None)
self._parent_index_ = getattr(obj, '_parent_index_', None)
self._highest_parent_ = getattr(obj, '_highest_parent_', None)
self._default_constraint_ = getattr(obj, '_default_constraint_', None)
self._current_slice_ = getattr(obj, '_current_slice_', None)
self._tied_to_me_ = getattr(obj, '_tied_to_me_', None)
self._tied_to_ = getattr(obj, '_tied_to_', None)
@ -98,7 +92,7 @@ class Param(ObservableArray, Constrainable):
(self.name,
self._direct_parent_,
self._parent_index_,
self._highest_parent_,
self._default_constraint_,
self._current_slice_,
self._realshape_,
self._realsize_,
@ -119,7 +113,7 @@ class Param(ObservableArray, Constrainable):
self._realsize_ = state.pop()
self._realshape_ = state.pop()
self._current_slice_ = state.pop()
self._highest_parent_ = state.pop()
self._default_constraint_ = state.pop()
self._parent_index_ = state.pop()
self._direct_parent_ = state.pop()
self.name = state.pop()
@ -153,28 +147,9 @@ class Param(ObservableArray, Constrainable):
@property
def _parameters_(self):
return []
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
def _collect_gradient(self, target):
target[:] = self.gradient.flat
#===========================================================================
# Fixing Parameters:
#===========================================================================
def constrain_fixed(self, warning=True):
"""
Constrain this paramter to be fixed to the current value it carries.
:param warning: print a warning for overwriting constraints.
"""
self._highest_parent_._fix(self,warning)
fix = constrain_fixed
def unconstrain_fixed(self):
"""
This parameter will no longer be fixed.
"""
self._highest_parent_._unfix(self)
unfix = unconstrain_fixed
#===========================================================================
# Tying operations -> bugged, TODO
#===========================================================================
def tie_to(self, param):
@ -195,7 +170,7 @@ class Param(ObservableArray, Constrainable):
# this tie will be created to the parameter the param is tied
# to.
assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param,param.__class__)
assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param, param.__class__)
param = numpy.atleast_1d(param)
if param.size != 1:
raise NotImplementedError, "Broadcast tying is not implemented yet"
@ -248,7 +223,7 @@ class Param(ObservableArray, Constrainable):
t_rav_i = t._raveled_index()
tr_rav_i = tied_to_me._raveled_index()
new_index = list(set(t_rav_i) | set(tr_rav_i))
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index,t._realshape_)]
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
self._tied_to_me_[tmp] = self._tied_to_me_[t] | set(self._raveled_index())
del self._tied_to_me_[t]
return
@ -261,7 +236,7 @@ class Param(ObservableArray, Constrainable):
import ipdb;ipdb.set_trace()
new_index = list(set(t_rav_i) - set(tr_rav_i))
if new_index:
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index,t._realshape_)]
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
self._tied_to_me_[tmp] = self._tied_to_me_[t]
del self._tied_to_me_[t]
if len(self._tied_to_me_[tmp]) == 0:
@ -269,12 +244,12 @@ class Param(ObservableArray, Constrainable):
else:
del self._tied_to_me_[t]
def _on_tied_parameter_changed(self, val, ind):
if not self._updated_: #not fast_array_equal(self, val[ind]):
if not self._updated_: # not fast_array_equal(self, val[ind]):
val = numpy.atleast_1d(val)
self._updated_ = True
if self._original_:
self.__setitem__(slice(None), val[ind], update=False)
else: # this happens when indexing created a copy of the array
else: # this happens when indexing created a copy of the array
self._direct_parent_._get_original(self).__setitem__(self._current_slice_, val[ind], update=False)
self._notify_tied_parameters()
self._updated_ = False
@ -303,11 +278,11 @@ class Param(ObservableArray, Constrainable):
def __getitem__(self, s, *args, **kwargs):
if not isinstance(s, tuple):
s = (s,)
if not reduce(lambda a,b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
s += (Ellipsis,)
new_arr = super(Param, self).__getitem__(s, *args, **kwargs)
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
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)
@ -328,8 +303,8 @@ class Param(ObservableArray, Constrainable):
elif isinstance(si, (list,numpy.ndarray,tuple)):
a = si[0]
else: a = si
if a<0:
a = self._realshape_[i]+a
if a < 0:
a = self._realshape_[i] + a
internal_offset += a * extended_realshape[i]
return internal_offset
def _raveled_index(self, slice_index=None):
@ -337,8 +312,8 @@ class Param(ObservableArray, Constrainable):
# of this object
extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
ind = self._indices(slice_index)
if ind.ndim < 2: ind=ind[:,None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape*x), 1, ind), dtype=int)
if ind.ndim < 2: ind = ind[:, None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape * x), 1, ind), dtype=int)
def _expand_index(self, slice_index=None):
# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
# it basically translates slices to their respective index arrays and turns negative indices around
@ -351,11 +326,11 @@ class Param(ObservableArray, Constrainable):
if isinstance(a, slice):
start, stop, step = a.indices(b)
return numpy.r_[start:stop:step]
elif isinstance(a, (list,numpy.ndarray,tuple)):
elif isinstance(a, (list, numpy.ndarray, tuple)):
a = numpy.asarray(a, dtype=int)
a[a<0] = b + a[a<0]
elif a<0:
a = b+a
a[a < 0] = b + a[a < 0]
elif a < 0:
a = b + a
return numpy.r_[a]
return numpy.r_[:b]
return itertools.imap(f, itertools.izip_longest(slice_index[:self._realndim_], self._realshape_, fillvalue=slice(self.size)))
@ -379,7 +354,7 @@ class Param(ObservableArray, Constrainable):
#===========================================================================
@property
def _description_str(self):
if self.size <= 1: return ["%f"%self]
if self.size <= 1: return ["%f" % self]
else: return [str(self.shape)]
def _parameter_names(self, add_name):
return [self.name]
@ -391,31 +366,31 @@ class Param(ObservableArray, Constrainable):
return [self.shape]
@property
def _constraints_str(self):
return [' '.join(map(lambda c: str(c[0]) if c[1].size==self._realsize_ else "{"+str(c[0])+"}", self._highest_parent_._constraints_iter_items(self)))]
return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self._highest_parent_._constraints_iter_items(self)))]
@property
def _ties_str(self):
return [t._short() for t in self._tied_to_] or ['']
@property
def name_hirarchical(self):
if self.has_parent():
return self._direct_parent_.hirarchy_name()+adjust_name_for_printing(self.name)
return self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name)
return adjust_name_for_printing(self.name)
def __repr__(self, *args, **kwargs):
name = "\033[1m{x:s}\033[0;0m:\n".format(
x=self.name_hirarchical)
return name + super(Param, self).__repr__(*args,**kwargs)
return name + super(Param, self).__repr__(*args, **kwargs)
def _ties_for(self, rav_index):
#size = sum(p.size for p in self._tied_to_)
# size = sum(p.size for p in self._tied_to_)
ties = numpy.empty(shape=(len(self._tied_to_), numpy.size(rav_index)), dtype=Param)
for i, tied_to in enumerate(self._tied_to_):
for t, ind in tied_to._tied_to_me_.iteritems():
if t._parent_index_ == self._parent_index_:
matches = numpy.where(rav_index[:,None] == t._raveled_index()[None, :])
matches = numpy.where(rav_index[:, None] == t._raveled_index()[None, :])
tt_rav_index = tied_to._raveled_index()
ind_rav_matches = numpy.where(tt_rav_index == numpy.array(list(ind)))[0]
if len(ind) != 1: ties[i, matches[0][ind_rav_matches]] = numpy.take(tt_rav_index, matches[1], mode='wrap')[ind_rav_matches]
else: ties[i, matches[0]] = numpy.take(tt_rav_index, matches[1], mode='wrap')
return map(lambda a: sum(a,[]), zip(*[[[tie.flatten()] if tx!=None else [] for tx in t] for t,tie in zip(ties,self._tied_to_)]))
return map(lambda a: sum(a, []), zip(*[[[tie.flatten()] if tx != None else [] for tx in t] for t, tie in zip(ties, self._tied_to_)]))
def _constraints_for(self, rav_index):
return self._highest_parent_._constraints_for(self, rav_index)
def _indices(self, slice_index=None):
@ -425,12 +400,12 @@ class Param(ObservableArray, Constrainable):
if isinstance(slice_index, (tuple, list)):
clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)]
if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice)
and len(set(map(len,clean_curr_slice))) <= 1):
and len(set(map(len, clean_curr_slice))) <= 1):
return numpy.fromiter(itertools.izip(*clean_curr_slice),
dtype=[('',int)]*self._realndim_,count=len(clean_curr_slice[0])).view((int, self._realndim_))
dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
expanded_index = list(self._expand_index(slice_index))
return numpy.fromiter(itertools.product(*expanded_index),
dtype=[('',int)]*self._realndim_,count=reduce(lambda a,b: a*b.size,expanded_index,1)).view((int, self._realndim_))
dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
def _max_len_names(self, gen, header):
return reduce(lambda a, b:max(a, len(b)), gen, len(header))
def _max_len_values(self):
@ -443,9 +418,9 @@ class Param(ObservableArray, Constrainable):
if self._realsize_ < 2:
return name
ind = self._indices()
if ind.size > 4: indstr = ','.join(map(str,ind[:2])) + "..." + ','.join(map(str,ind[-2:]))
else: indstr = ','.join(map(str,ind))
return name+'['+indstr+']'
if ind.size > 4: indstr = ','.join(map(str, ind[:2])) + "..." + ','.join(map(str, ind[-2:]))
else: indstr = ','.join(map(str, ind))
return name + '[' + indstr + ']'
def __str__(self, constr_matrix=None, indices=None, ties=None, lc=None, lx=None, li=None, lt=None):
filter_ = self._current_slice_
vals = self.flat
@ -458,10 +433,10 @@ class Param(ObservableArray, Constrainable):
if lx is None: lx = self._max_len_values()
if li is None: li = self._max_len_index(indices)
if lt is None: lt = self._max_len_names(ties, __tie_name__)
header = " {i:^{2}s} | \033[1m{x:^{1}s}\033[0;0m | {c:^{0}s} | {t:^{3}s}".format(lc,lx,li,lt, x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__) # nice header for printing
header = " {i:^{2}s} | \033[1m{x:^{1}s}\033[0;0m | {c:^{0}s} | {t:^{3}s}".format(lc, lx, li, lt, x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__) # nice header for printing
if not ties: ties = itertools.cycle([''])
return "\n".join([header]+[" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {t:^{4}s} ".format(lc,lx,__precision__,li,lt, x=x, c=" ".join(map(str,c)), t=(t or ''), i=i) for i,x,c,t in itertools.izip(indices,vals,constr_matrix,ties)]) # return all the constraints with right indices
#except: return super(Param, self).__str__()
return "\n".join([header] + [" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {t:^{4}s} ".format(lc, lx, __precision__, li, lt, x=x, c=" ".join(map(str, c)), t=(t or ''), i=i) for i, x, c, t in itertools.izip(indices, vals, constr_matrix, ties)]) # return all the constraints with right indices
# except: return super(Param, self).__str__()
class ParamConcatenation(object):
def __init__(self, params):
@ -472,8 +447,8 @@ class ParamConcatenation(object):
See :py:class:`GPy.core.parameter.Param` for more details on constraining.
"""
#self.params = params
self.params = []
# self.params = params
self.params = ParamList([])
for p in params:
for p in p.flattened_parameters:
if p not in self.params:
@ -493,7 +468,7 @@ class ParamConcatenation(object):
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 p._notify_tied_parameters()
for p, ps in zip(self.params, self._param_slices_)]
for p, ps in zip(self.params, self._param_slices_)]
if update:
self.params[0]._highest_parent_.parameters_changed()
def _vals(self):
@ -501,46 +476,68 @@ class ParamConcatenation(object):
#===========================================================================
# parameter operations:
#===========================================================================
def update_all_params(self):
self.params[0]._highest_parent_.parameters_changed()
def constrain(self, constraint, warning=True):
[param.constrain(constraint) for param in self.params]
[param.constrain(constraint, update=False) for param in self.params]
self.update_all_params()
constrain.__doc__ = Param.constrain.__doc__
def constrain_positive(self, warning=True):
[param.constrain_positive(warning) for param in self.params]
[param.constrain_positive(warning, update=False) for param in self.params]
self.update_all_params()
constrain_positive.__doc__ = Param.constrain_positive.__doc__
def constrain_fixed(self, warning=True):
[param.constrain_fixed(warning) for param in self.params]
constrain_fixed.__doc__ = Param.constrain_fixed.__doc__
fix = constrain_fixed
def constrain_negative(self, warning=True):
[param.constrain_negative(warning) for param in self.params]
[param.constrain_negative(warning, update=False) for param in self.params]
self.update_all_params()
constrain_negative.__doc__ = Param.constrain_negative.__doc__
def constrain_bounded(self, lower, upper, warning=True):
[param.constrain_bounded(lower, upper, warning) for param in self.params]
[param.constrain_bounded(lower, upper, warning, update=False) for param in self.params]
self.update_all_params()
constrain_bounded.__doc__ = Param.constrain_bounded.__doc__
def unconstrain(self, *constraints):
[param.unconstrain(*constraints) for param in self.params]
unconstrain.__doc__ = Param.unconstrain.__doc__
def unconstrain_negative(self):
[param.unconstrain_negative() for param in self.params]
unconstrain_negative.__doc__ = Param.unconstrain_negative.__doc__
def unconstrain_positive(self):
[param.unconstrain_positive() for param in self.params]
unconstrain_positive.__doc__ = Param.unconstrain_positive.__doc__
def unconstrain_fixed(self):
[param.unconstrain_fixed() for param in self.params]
unconstrain_fixed.__doc__ = Param.unconstrain_fixed.__doc__
unfix = unconstrain_fixed
def unconstrain_bounded(self, lower, upper):
[param.unconstrain_bounded(lower, upper) for param in self.params]
unconstrain_bounded.__doc__ = Param.unconstrain_bounded.__doc__
def untie(self, *ties):
[param.untie(*ties) for param in self.params]
__lt__ = lambda self, val: self._vals()<val
__le__ = lambda self, val: self._vals()<=val
__eq__ = lambda self, val: self._vals()==val
__ne__ = lambda self, val: self._vals()!=val
__gt__ = lambda self, val: self._vals()>val
__ge__ = lambda self, val: self._vals()>=val
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance)
#checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__
__lt__ = lambda self, val: self._vals() < val
__le__ = lambda self, val: self._vals() <= val
__eq__ = lambda self, val: self._vals() == val
__ne__ = lambda self, val: self._vals() != val
__gt__ = lambda self, val: self._vals() > val
__ge__ = lambda self, val: self._vals() >= val
def __str__(self, *args, **kwargs):
def f(p):
ind = p._raveled_index()
@ -569,7 +566,7 @@ if __name__ == '__main__':
print "random done"
p = Param("q_mean", X)
p1 = Param("q_variance", numpy.random.rand(*p.shape))
p2 = Param("Y", numpy.random.randn(p.shape[0],1))
p2 = Param("Y", numpy.random.randn(p.shape[0], 1))
p3 = Param("variance", numpy.random.rand())
p4 = Param("lengthscale", numpy.random.rand(2))

61
GPy/core/parameterization/parameter_core.py
View file

@ -48,19 +48,24 @@ class Pickleable(object):
#===============================================================================
class Parentable(object):
def __init__(self, direct_parent=None, highest_parent=None, parent_index=None):
def __init__(self, direct_parent=None, parent_index=None):
super(Parentable,self).__init__()
self._direct_parent_ = direct_parent
self._parent_index_ = parent_index
self._highest_parent_ = highest_parent
def has_parent(self):
return self._direct_parent_ is not None and self._highest_parent_ is not None
return self._direct_parent_ is not None
@property
def _highest_parent_(self):
if self._direct_parent_ is None:
return self
return self._direct_parent_._highest_parent_
class Nameable(Parentable):
_name = None
def __init__(self, name, direct_parent=None, highest_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, highest_parent, parent_index)
def __init__(self, name, direct_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, parent_index)
self._name = name or self.__class__.__name__
@property
@ -73,9 +78,41 @@ class Nameable(Parentable):
if self.has_parent():
self._direct_parent_._name_changed(self, from_name)
class Gradcheckable(Parentable):
#===========================================================================
# Gradchecking
#===========================================================================
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
if self.has_parent():
return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance)
def _checkgrad(self, param):
raise NotImplementedError, "Need log likelihood to check gradient against"
class Constrainable(Nameable):
def __init__(self, name):
def __init__(self, name, default_constraint=None):
super(Constrainable,self).__init__(name)
self._default_constraint_ = default_constraint
#===========================================================================
# Fixing Parameters:
#===========================================================================
def constrain_fixed(self, value=None, warning=True):
"""
Constrain this paramter to be fixed to the current value it carries.
:param warning: print a warning for overwriting constraints.
"""
if value is not None:
self[:] = value
self._highest_parent_._fix(self,warning)
fix = constrain_fixed
def unconstrain_fixed(self):
"""
This parameter will no longer be fixed.
"""
self._highest_parent_._unfix(self)
unfix = unconstrain_fixed
#===========================================================================
# Constrain operations -> done
#===========================================================================
@ -98,30 +135,30 @@ class Constrainable(Nameable):
if update:
self.parameters_changed()
def constrain_positive(self, warning=True):
def constrain_positive(self, warning=True, update=True):
"""
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to the default positive constraint.
"""
self.constrain(Logexp(), warning)
self.constrain(Logexp(), warning=warning, update=update)
def constrain_negative(self, warning=True):
def constrain_negative(self, warning=True, update=True):
"""
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to the default negative constraint.
"""
self.constrain(NegativeLogexp(), warning)
self.constrain(NegativeLogexp(), warning=warning, update=update)
def constrain_bounded(self, lower, upper, warning=True):
def constrain_bounded(self, lower, upper, warning=True, update=True):
"""
:param lower, upper: the limits to bound this parameter to
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to lie within the given range.
"""
self.constrain(Logistic(lower, upper), warning)
self.constrain(Logistic(lower, upper), warning=warning, update=update)
def unconstrain(self, *transforms):
"""

49
GPy/core/parameterization/parameterized.py
View file

@ -8,9 +8,10 @@ import cPickle
import itertools
from re import compile, _pattern_type
from param import ParamConcatenation, Param
from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing
from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing, Gradcheckable
from index_operations import ParameterIndexOperations,\
index_empty
from array_core import ParamList
#===============================================================================
# Printing:
@ -23,7 +24,7 @@ FIXED = False
UNFIXED = True
#===============================================================================
class Parameterized(Constrainable, Pickleable, Observable):
class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
"""
Parameterized class
@ -69,7 +70,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
super(Parameterized, self).__init__(name=name)
self._in_init_ = True
self._constraints_ = None#ParameterIndexOperations()
self._parameters_ = []
self._parameters_ = ParamList()
self.size = sum(p.size for p in self._parameters_)
if not self._has_fixes():
self._fixes_ = None
@ -170,6 +171,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
for t, ind in param._constraints_.iteritems():
self.constraints.add(t, ind+self._offset_for(param))
param._constraints_.clear()
if param._default_constraint_ is not None:
self._add_constrain(param, param._default_constraint_, False)
if self._has_fixes() and np.all(self._fixes_): # ==UNFIXED
self._fixes_= None
@ -188,10 +191,10 @@ class Parameterized(Constrainable, Pickleable, Observable):
note: if it is a string object it will not (!) be regexp-matched
automatically.
"""
self._parameters_ = [p for p in self._parameters_
self._parameters_ = ParamList([p for p in self._parameters_
if not (p._parent_index_ in names_params_indices
or p.name in names_params_indices
or p in names_params_indices)]
or p in names_params_indices)])
self._connect_parameters()
def parameters_changed(self):
@ -216,7 +219,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
for i,p in enumerate(self._parameters_):
p._direct_parent_ = self
p._parent_index_ = i
p._connect_highest_parent(self)
not_unique = []
sizes.append(p.size+sizes[-1])
self._param_slices_.append(slice(sizes[-2], sizes[-1]))
@ -231,14 +233,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
self.__dict__[pname] = p
self._added_names_.add(pname)
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
# no parameters for this class
return
for p in self._parameters_:
p._connect_highest_parent(highest_parent)
#===========================================================================
# Pickling operations
#===========================================================================
@ -256,6 +250,16 @@ class Parameterized(Constrainable, Pickleable, Observable):
cPickle.dump(self, f, protocol)
def copy(self):
"""Returns a (deep) copy of the current model """
#dc = dict()
#for k, v in self.__dict__.iteritems():
#if k not in ['_highest_parent_', '_direct_parent_']:
#dc[k] = copy.deepcopy(v)
#dc = copy.deepcopy(self.__dict__)
#dc['_highest_parent_'] = None
#dc['_direct_parent_'] = None
#s = self.__class__.new()
#s.__dict__ = dc
return copy.deepcopy(self)
def __getstate__(self):
if self._has_get_set_state():
@ -310,8 +314,11 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Optimization handles:
#===========================================================================
def _get_param_names_transformed(self):
def _get_param_names(self):
n = numpy.array([p.name_hirarchical+'['+str(i)+']' for p in self.flattened_parameters for i in p._indices()])
return n
def _get_param_names_transformed(self):
n = self._get_param_names()
if self._has_fixes():
return n[self._fixes_]
return n
@ -372,6 +379,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
that is an int array, containing the indexes for the flattened
param inside this parameterized logic.
"""
if isinstance(param, ParamConcatenation):
return numpy.hstack((self._raveled_index_for(p) for p in param.params))
return param._raveled_index() + self._offset_for(param)
def _raveled_index(self):
@ -381,7 +390,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
"""
return numpy.r_[:self.size]
#===========================================================================
# Handle ties:
# Fixing parameters:
#===========================================================================
def _set_fixed(self, param_or_index):
if not self._has_fixes(): self._fixes_ = numpy.ones(self.size, dtype=bool)
@ -406,9 +415,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
if self._has_fixes():
return self._fixes_[self._raveled_index_for(param)]
return numpy.ones(self.size, dtype=bool)[self._raveled_index_for(param)]
#===========================================================================
# Fixing parameters:
#===========================================================================
def _fix(self, param, warning=True):
f = self._add_constrain(param, __fixed__, warning)
self._set_fixed(f)
@ -419,6 +425,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Convenience for fixed, tied checking of param:
#===========================================================================
def fixed_indices(self):
return np.array([x.is_fixed for x in self._parameters_])
def _is_fixed(self, param):
# returns if the whole param is fixed
if not self._has_fixes():
@ -448,7 +456,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
# if removing constraints before adding new is not wanted, just delete the above line!
self.constraints.add(transform, rav_i)
param = self._get_original(param)
param._set_params(transform.initialize(param._get_params()))
if not (transform == __fixed__):
param._set_params(transform.initialize(param._get_params()), update=False)
if warning and any(reconstrained):
# if you want to print the whole params object, which was reconstrained use:
# m = str(param[self._backtranslate_index(param, reconstrained)])

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

@ -162,7 +162,9 @@ class Logistic(Transformation):
def initialize(self, f):
if np.any(np.logical_or(f < self.lower, f > self.upper)):
print "Warning: changing parameters to satisfy constraints"
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
#return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
#FIXME: Max, zeros_like right?
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(np.zeros_like(f)), f)
def __str__(self):
return '{},{}'.format(self.lower, self.upper)

5
GPy/core/parameterization/variational.py
View file

@ -3,10 +3,8 @@ Created on 6 Nov 2013
@author: maxz
'''
import numpy as np
from parameterized import Parameterized
from param import Param
from ...util.misc import param_to_array
class Normal(Parameterized):
'''
@ -26,6 +24,7 @@ class Normal(Parameterized):
See GPy.plotting.matplot_dep.variational_plots
"""
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import variational_plots
from ...plotting.matplot_dep import variational_plots
return variational_plots.plot(self,*args)

4
GPy/core/svigp.py
View file

@ -154,13 +154,13 @@ class SVIGP(GP):
self.psi2 = None
def dL_dtheta(self):
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
dL_dtheta = self.kern._param_grad_helper(self.dL_dKmm, self.Z)
if self.has_uncertain_inputs:
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X_batch, self.X_variance_batch)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1, self.Z, self.X_batch, self.X_variance_batch)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X_batch, self.X_variance_batch)
else:
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.X_batch, self.Z)
dL_dtheta += self.kern._param_grad_helper(self.dL_dpsi1, self.X_batch, self.Z)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X_batch)
return dL_dtheta

34
GPy/examples/dimensionality_reduction.py
View file

@ -15,54 +15,54 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
num_inducing = 5
if plot:
output_dim = 1
input_dim = 2
input_dim = 3
else:
input_dim = 2
input_dim = 1
output_dim = 25
# generate GPLVM-like data
X = _np.random.rand(num_inputs, input_dim)
lengthscales = _np.random.rand(input_dim)
k = (GPy.kern.rbf(input_dim, .5, lengthscales, ARD=True)
+ GPy.kern.white(input_dim, 0.01))
#+ GPy.kern.white(input_dim, 0.01)
)
K = k.K(X)
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.rbf(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m = GPy.models.BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
#===========================================================================
# randomly obstruct data with percentage p
p = .8
Y_obstruct = Y.copy()
Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
#===========================================================================
m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
if plot:
import matplotlib.pyplot as pb
m.plot()
pb.title('PCA initialisation')
m2.plot()
pb.title('PCA initialisation')
#m2.plot()
#pb.title('PCA initialisation')
if optimize:
m.optimize('scg', messages=verbose)
m2.optimize('scg', messages=verbose)
#m2.optimize('scg', messages=verbose)
if plot:
m.plot()
pb.title('After optimisation')
m2.plot()
pb.title('After optimisation')
#m2.plot()
#pb.title('After optimisation')
return m, m2
return m
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
import GPy
@ -264,16 +264,16 @@ def bgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m['noise'] = Y.var() / 100.
m.Gaussian_noise = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.q.plot("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m

34
GPy/examples/non_gaussian.py
View file

@ -37,39 +37,43 @@ def student_t_approx(optimize=True, plot=True):
# Kernel object
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
kernel3 = kernel1.copy()
kernel4 = kernel1.copy()
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
#Gaussian GP model on clean data
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1.ensure_default_constraints()
m1.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m1.randomize()
#Gaussian GP model on corrupt data
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m2.ensure_default_constraints()
m2.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m2.randomize()
#Student t GP model on clean data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution)
m3 = GPy.models.GPRegression(X, Y.copy(), kernel3, likelihood=stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
m3.ensure_default_constraints()
m3.constrain_bounded('t_noise', 1e-6, 10.)
m3.constrain_fixed('white', 1e-5)
m3['t_noise'].constrain_bounded(1e-6, 10.)
m3['white'] = 1e-5
m3['white'].constrain_fixed()
m3.randomize()
#Student t GP model on corrupt data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
m4 = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
m4.ensure_default_constraints()
m4.constrain_bounded('t_noise', 1e-6, 10.)
m4.constrain_fixed('white', 1e-5)
m4['t_noise'].constrain_bounded(1e-6, 10.)
m4['white'] = 1e-5
m4['white'].constrain_fixed()
m4.randomize()
if optimize:

7
GPy/examples/regression.py
View file

@ -281,11 +281,12 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.Laplace(Y,noise_model)
kern = GPy.kern.rbf(1)
poisson_lik = GPy.likelihoods.Poisson()
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
# create simple GP Model
m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)
if optimize:
m.optimize(optimizer)

16
GPy/inference/latent_function_inference/fitc.py
View file

@ -97,10 +97,10 @@ class VarDTC(object):
K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
_dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * Kmmipsi1.T[i:(i+1),:]
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
self._dKmm_dX += self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
self._dpsi1_dtheta += self.kern._param_grad_helper(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern._param_grad_helper(_dKmm,self.Z)
self._dKmm_dX += self.kern.gradients_X(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.gradients_X(_dpsi1.T,self.Z,self.X[i:i+1,:])
# the partial derivative vector for the likelihood
if self.likelihood.num_params == 0:
@ -144,15 +144,15 @@ class VarDTC(object):
def dL_dtheta(self):
dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X)
dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z)
dL_dtheta += self.kern._param_grad_helper(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern._param_grad_helper(self._dL_dKmm,X=self.Z)
dL_dtheta += self._dKmm_dtheta
dL_dtheta += self._dpsi1_dtheta
return dL_dtheta
def dL_dZ(self):
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.dK_dX(self._dL_dKmm,X=self.Z)
dL_dZ = self.kern.gradients_X(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.gradients_X(self._dL_dKmm,X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
return dL_dZ

97
GPy/inference/latent_function_inference/laplace.py
View file

@ -11,9 +11,8 @@
#http://gaussianprocess.org/gpml/code.
import numpy as np
from ...util.linalg import mdot, jitchol, pddet, dpotrs, dtrtrs, dpotri, symmetrify
from ...util.linalg import mdot, jitchol, dpotrs, dtrtrs, dpotri, symmetrify
from ...util.misc import param_to_array
from functools import partial as partial_func
from posterior import Posterior
import warnings
from scipy import optimize
@ -32,7 +31,7 @@ class LaplaceInference(object):
self._mode_finding_tolerance = 1e-7
self._mode_finding_max_iter = 40
self.bad_fhat = True
self._previous_Ki_fhat = None
def inference(self, kern, X, likelihood, Y, Y_metadata=None):
"""
@ -50,16 +49,17 @@ class LaplaceInference(object):
Ki_f_init = np.zeros_like(Y)
else:
Ki_f_init = self._previous_Ki_fhat
f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
#Compute hessian and other variables at mode
log_marginal, Ki_W_i, K_Wi_i, dL_dK, woodbury_vector = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, Y_metadata)
log_marginal, woodbury_vector, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
#likelihood.gradient = self.likelihood_gradients()
kern.update_gradients_full(dL_dK, X)
likelihood.update_gradients(dL_dthetaL)
self._previous_Ki_fhat = Ki_fhat.copy()
return Posterior(woodbury_vector=woodbury_vector, woodbury_inv = K_Wi_i, K=K), log_marginal, {'dL_dK':dL_dK}
return Posterior(woodbury_vector=woodbury_vector, woodbury_inv=woodbury_inv, K=K), log_marginal, {'dL_dK':dL_dK}
def rasm_mode(self, K, Y, likelihood, Ki_f_init, Y_metadata=None):
"""
@ -84,7 +84,6 @@ class LaplaceInference(object):
Ki_f = Ki_f_init.copy()
f = np.dot(K, Ki_f)
#define the objective function (to be maximised)
def obj(Ki_f, f):
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, extra_data=Y_metadata)
@ -133,13 +132,15 @@ class LaplaceInference(object):
return f, Ki_f
def mode_computations(self, f_hat, Ki_f, K, Y, likelihood, Y_metadata):
def mode_computations(self, f_hat, Ki_f, K, Y, likelihood, kern, Y_metadata):
"""
At the mode, compute the hessian and effective covariance matrix.
returns: logZ : approximation to the marginal likelihood
Cov : the approximation to the covariance matrix
woodbury_vector : variable required for calculating the approximation to the covariance matrix
woodbury_inv : variable required for calculating the approximation to the covariance matrix
dL_dthetaL : array of derivatives (1 x num_kernel_params)
dL_dthetaL : array of derivatives (1 x num_likelihood_params)
"""
#At this point get the hessian matrix (or vector as W is diagonal)
W = -likelihood.d2logpdf_df2(f_hat, Y, extra_data=Y_metadata)
@ -153,45 +154,54 @@ class LaplaceInference(object):
#compute the log marginal
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, extra_data=Y_metadata) - np.sum(np.log(np.diag(L)))
#compute dL_dK
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
d3lik_d3fhat = likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata)
dL_dfhat = 0.5*(np.diag(Ki_W_i)[:, None]*d3lik_d3fhat) #why isn't this -0.5? s2 in R&W p126 line 9.
#Compute vival matrices for derivatives
dW_df = -likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata) # -d3lik_d3fhat
woodbury_vector = likelihood.dlogpdf_df(f_hat, Y, extra_data=Y_metadata)
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(np.eye(Y.shape[0]) - np.dot(K, K_Wi_i))
dL_dfhat = -0.5*(np.diag(Ki_W_i)[:, None]*dW_df) #why isn't this -0.5? s2 in R&W p126 line 9.
#BiK, _ = dpotrs(L, K, lower=1)
#dL_dfhat = 0.5*np.diag(BiK)[:, None]*dW_df
I_KW_i = np.eye(Y.shape[0]) - np.dot(K, K_Wi_i)
dL_dK = explicit_part + implicit_part
return log_marginal, Ki_W_i, K_Wi_i, dL_dK, woodbury_vector
def likelihood_gradients(self):
"""
Gradients with respect to likelihood parameters (dL_dthetaL)
:rtype: array of derivatives (1 x num_likelihood_params)
"""
dL_dfhat, I_KW_i = self._shared_gradients_components()
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(self.f_hat, self.data, extra_data=self.extra_data)
num_params = len(self._get_param_names())
# make space for one derivative for each likelihood parameter
dL_dthetaL = np.zeros(num_params)
for thetaL_i in range(num_params):
####################
#compute dL_dK#
####################
if kern.size > 0 and not kern.is_fixed:
#Explicit
dL_dthetaL_exp = ( np.sum(dlik_dthetaL[:, thetaL_i])
#- 0.5*np.trace(mdot(self.Ki_W_i, (self.K, np.diagflat(dlik_hess_dthetaL[thetaL_i]))))
+ np.dot(0.5*np.diag(self.Ki_W_i)[:,None].T, dlik_hess_dthetaL[:, thetaL_i])
)
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
dfhat_dthetaL = mdot(I_KW_i, self.K, dlik_grad_dthetaL[:, thetaL_i])
dL_dthetaL_imp = np.dot(dL_dfhat, dfhat_dthetaL)
dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(I_KW_i)
return dL_dthetaL
dL_dK = explicit_part + implicit_part
else:
dL_dK = np.zeros(likelihood.size)
####################
#compute dL_dthetaL#
####################
if likelihood.size > 0 and not likelihood.is_fixed:
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(f_hat, Y, extra_data=Y_metadata)
num_params = likelihood.size
# make space for one derivative for each likelihood parameter
dL_dthetaL = np.zeros(num_params)
for thetaL_i in range(num_params):
#Explicit
dL_dthetaL_exp = ( np.sum(dlik_dthetaL[thetaL_i])
# The + comes from the fact that dlik_hess_dthetaL == -dW_dthetaL
+ 0.5*np.sum(np.diag(Ki_W_i).flatten()*dlik_hess_dthetaL[:, thetaL_i].flatten())
)
#Implicit
dfhat_dthetaL = mdot(I_KW_i, K, dlik_grad_dthetaL[:, thetaL_i])
#dfhat_dthetaL = mdot(Ki_W_i, dlik_grad_dthetaL[:, thetaL_i])
dL_dthetaL_imp = np.dot(dL_dfhat.T, dfhat_dthetaL)
dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
else:
dL_dthetaL = np.zeros(likelihood.size)
return log_marginal, woodbury_vector, K_Wi_i, dL_dK, dL_dthetaL
def _compute_B_statistics(self, K, W, log_concave):
"""
@ -225,6 +235,5 @@ class LaplaceInference(object):
#K_Wi_i_2 , _= dpotri(L2)
#symmetrify(K_Wi_i_2)
return K_Wi_i, L, LiW12

2
GPy/inference/latent_function_inference/varDTC.py
View file

@ -80,7 +80,7 @@ class VarDTC(object):
# no backsubstitution because of bound explosion on tr(A) if not...
LmInv, _ = dtrtri(Lm, lower=1)
A = LmInv.T.dot(psi2_beta.dot(LmInv))
print A.sum()
#print A.sum()
else:
if het_noise:
tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))

64
GPy/kern/kern.py
View file

@ -8,6 +8,7 @@ from parts.prod import Prod as prod
from parts.linear import Linear
from parts.kernpart import Kernpart
from ..core.parameterization import Parameterized
from GPy.core.parameterization.param import Param
class kern(Parameterized):
def __init__(self, input_dim, parts=[], input_slices=None):
@ -84,7 +85,7 @@ class kern(Parameterized):
# represents the gradient in the transformed space (i.e. that given by
# get_params_transformed())
#
# :param g: the gradient vector for the current model, usually created by dK_dtheta
# :param g: the gradient vector for the current model, usually created by _param_grad_helper
# """
# x = self._get_params()
# [np.place(g, index, g[index] * constraint.gradfactor(x[index]))
@ -160,6 +161,9 @@ class kern(Parameterized):
return newkern
def __call__(self, X, X2=None):
return self.K(X, X2)
def __mul__(self, other):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)
@ -291,7 +295,7 @@ class kern(Parameterized):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
[p.update_gradients_variational(dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z) for p in self._parameters_]
def dK_dtheta(self, dL_dK, X, X2=None):
def _param_grad_helper(self, dL_dK, X, X2=None):
"""
Compute the gradient of the covariance function with respect to the parameters.
@ -307,9 +311,9 @@ class kern(Parameterized):
assert X.shape[1] == self.input_dim
target = np.zeros(self.size)
if X2 is None:
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
[p._param_grad_helper(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
else:
[p.dK_dtheta(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
[p._param_grad_helper(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
return self._transform_gradients(target)
@ -481,6 +485,7 @@ from GPy.core.model import Model
class Kern_check_model(Model):
"""This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Model.__init__(self, 'kernel_test_model')
num_samples = 20
num_samples2 = 10
if kernel==None:
@ -494,12 +499,10 @@ class Kern_check_model(Model):
dL_dK = np.ones((X.shape[0], X2.shape[0]))
self.kernel=kernel
self.add_parameter(kernel)
self.X = X
self.X2 = X2
self.dL_dK = dL_dK
#self.constrained_indices=[]
#self.constraints=[]
Model.__init__(self, 'kernel_test_model')
def is_positive_definite(self):
v = np.linalg.eig(self.kernel.K(self.X))[0]
@ -508,15 +511,6 @@ class Kern_check_model(Model):
else:
return True
def _get_params(self):
return self.kernel._get_params()
def _get_param_names(self):
return self.kernel._get_param_names()
def _set_params(self, x):
self.kernel._set_params(x)
def log_likelihood(self):
return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()
@ -529,7 +523,7 @@ class Kern_check_dK_dtheta(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
def _log_likelihood_gradients(self):
return self.kernel.dK_dtheta(self.dL_dK, self.X, self.X2)
return self.kernel._param_grad_helper(self.dL_dK, self.X, self.X2)
class Kern_check_dKdiag_dtheta(Kern_check_model):
"""This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters."""
@ -537,6 +531,8 @@ class Kern_check_dKdiag_dtheta(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0]))
def parameters_changed(self):
self.kernel.update_gradients_full(self.dL_dK, self.X)
def log_likelihood(self):
return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
@ -548,23 +544,16 @@ class Kern_check_dK_dX(Kern_check_model):
"""This class allows gradient checks for the gradient of a kernel with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.remove_parameter(kernel)
self.X = Param('X', self.X)
self.add_parameter(self.X)
def _log_likelihood_gradients(self):
return self.kernel.dK_dX(self.dL_dK, self.X, self.X2).flatten()
return self.kernel.gradients_X(self.dL_dK, self.X, self.X2).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)
class Kern_check_dKdiag_dX(Kern_check_model):
class Kern_check_dKdiag_dX(Kern_check_dK_dX):
"""This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
Kern_check_dK_dX.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0]))
@ -574,15 +563,6 @@ class Kern_check_dKdiag_dX(Kern_check_model):
def _log_likelihood_gradients(self):
return self.kernel.dKdiag_dX(self.dL_dK, self.X).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)
def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
"""
This function runs on kernels to check the correctness of their
@ -657,7 +637,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -673,7 +653,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -689,7 +669,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:

4
GPy/kern/parts/Brownian.py
View file

@ -43,7 +43,7 @@ class Brownian(Kernpart):
def Kdiag(self,X,target):
target += self.variance*X.flatten()
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
if X2 is None:
X2 = X
target += np.sum(np.fmin(X,X2.T)*dL_dK)
@ -51,7 +51,7 @@ class Brownian(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target += np.dot(X.flatten(), dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError, "TODO"
#target += self.variance
#target -= self.variance*theta(X-X2.T)

8
GPy/kern/parts/Matern32.py
View file

@ -74,7 +74,7 @@ class Matern32(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
@ -96,7 +96,7 @@ class Matern32(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X[None, :, :]) / self.lengthscale), -1))[:, :, None]
@ -105,8 +105,8 @@ class Matern32(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

8
GPy/kern/parts/Matern52.py
View file

@ -74,7 +74,7 @@ class Matern52(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target,self.variance,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
@ -96,7 +96,7 @@ class Matern52(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X[None,:,:])/self.lengthscale),-1))[:,:,None]
@ -104,8 +104,8 @@ class Matern52(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
gradients_X = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(gradients_X*dL_dK.T[:,:,None],0)
def dKdiag_dX(self,dL_dKdiag,X,target):
pass

2
GPy/kern/parts/ODE_1.py
View file

@ -90,7 +90,7 @@ class ODE_1(Kernpart):
np.add(self.varianceU*self.varianceY*(k1+k2+k3), target, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.abs(X - X2.T)

4
GPy/kern/parts/eq_ode1.py
View file

@ -124,7 +124,7 @@ class Eq_ode1(Kernpart):
#target += np.diag(self.B)[np.asarray(index,dtype=np.int).flatten()]
pass
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
# First extract times and indices.
self._extract_t_indices(X, X2, dL_dK=dL_dK)
@ -193,7 +193,7 @@ class Eq_ode1(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,index,target):
pass
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
pass
def _extract_t_indices(self, X, X2=None, dL_dK=None):

8
GPy/kern/parts/exponential.py
View file

@ -75,7 +75,7 @@ class Exponential(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
@ -95,13 +95,13 @@ class Exponential(Kernpart):
# NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

2
GPy/kern/parts/finite_dimensional.py
View file

@ -50,7 +50,7 @@ class FiniteDimensional(Kernpart):
def Kdiag(self,X,target):
product = np.diag(self.K(X, X))
np.add(target,product,target)
def dK_dtheta(self,X,X2,target):
def _param_grad_helper(self,X,X2,target):
"""Return shape is NxMx(Ntheta)"""
if X2 is None: X2 = X
FX = np.column_stack([f(X) for f in self.F])

4
GPy/kern/parts/fixed.py
View file

@ -31,10 +31,10 @@ class Fixed(Kernpart):
def K(self, X, X2, target):
target += self.variance * self.fixed_K
def dK_dtheta(self, partial, X, X2, target):
def _param_grad_helper(self, partial, X, X2, target):
target += (partial * self.fixed_K).sum()
def dK_dX(self, partial, X, X2, target):
def gradients_X(self, partial, X, X2, target):
pass
def dKdiag_dX(self, partial, X, target):

8
GPy/kern/parts/gibbs.py
View file

@ -85,7 +85,7 @@ class Gibbs(Kernpart):
"""Compute the diagonal of the covariance matrix for X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
self._dK_computations(dL_dK)
@ -97,7 +97,7 @@ class Gibbs(Kernpart):
target+= np.hstack([(dL_dK*self._K_dvar).sum(), gmapping])
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
# First account for gradients arising from presence of X in exponent.
self._K_computations(X, X2)
@ -105,8 +105,8 @@ class Gibbs(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_co
dK_dX = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(dK_dX*dL_dK.T[:, :, None], 0)
gradients_X = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(gradients_X*dL_dK.T[:, :, None], 0)
# Now account for gradients arising from presence of X in lengthscale.
self._dK_computations(dL_dK)
if X2 is None:

4
GPy/kern/parts/hetero.py
View file

@ -80,7 +80,7 @@ class Hetero(Kernpart):
"""Helper function for computing the diagonal elements of the covariance."""
return self.mapping.f(X).flatten()**2
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
if (X2 is None) or (X2 is X):
dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]
@ -90,7 +90,7 @@ class Hetero(Kernpart):
"""Gradient of diagonal of covariance with respect to parameters."""
target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
if X2==None or X2 is X:
dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]

8
GPy/kern/parts/hierarchical.py
View file

@ -50,19 +50,19 @@ class Hierarchical(Kernpart):
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
X, X2, slices, slices2 = self._sort_slices(X,X2)
[[[[k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
[[[[k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#if X2 is None:
#X2,slices2 = X,slices
#else:
#X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
#[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#
def dKdiag_dX(self,dL_dKdiag,X,target):
raise NotImplementedError

8
GPy/kern/parts/independent_outputs.py
View file

@ -70,22 +70,22 @@ class IndependentOutputs(Kernpart):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[self.k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
[[[self.k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dKdiag_dX(self,dL_dKdiag,X,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])

20
GPy/kern/parts/kernpart.py
View file

@ -20,7 +20,6 @@ class Kernpart(Parameterized):
# the number of optimisable parameters
# the name of the covariance function.
# link to parameterized objects
self._parameters_ = []
#self._X = None
def connect_input(self, X):
@ -76,14 +75,14 @@ class Kernpart(Parameterized):
raise NotImplementedError
def Kdiag(self,X,target):
raise NotImplementedError
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
raise NotImplementedError
def dKdiag_dtheta(self,dL_dKdiag,X,target):
# In the base case compute this by calling dK_dtheta. Need to
# In the base case compute this by calling _param_grad_helper. Need to
# override for stationary covariances (for example) to save
# time.
for i in range(X.shape[0]):
self.dK_dtheta(dL_dKdiag[i], X[i, :][None, :], X2=None, target=target)
self._param_grad_helper(dL_dKdiag[i], X[i, :][None, :], X2=None, target=target)
def psi0(self,Z,mu,S,target):
raise NotImplementedError
def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):
@ -106,12 +105,19 @@ class Kernpart(Parameterized):
raise NotImplementedError
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
raise NotImplementedError
def dKdiag_dX(self, dL_dK, X, target):
raise NotImplementedError
def update_gradients_full(self, dL_dK, X):
"""Set the gradients of all parameters when doing full (N) inference."""
raise NotImplementedError
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
"""Set the gradients of all parameters when doing sparse (M) inference."""
raise NotImplementedError
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
"""Set the gradients of all parameters when doing variational (M) inference with uncertain inputs."""
raise NotImplementedError
class Kernpart_stationary(Kernpart):
def __init__(self, input_dim, lengthscale=None, ARD=False):

89
GPy/kern/parts/linear.py
View file

@ -8,6 +8,7 @@ from kernpart import Kernpart
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
class Linear(Kernpart):
"""
@ -43,8 +44,9 @@ class Linear(Kernpart):
else:
variances = np.ones(self.input_dim)
self.variances = Param('variances', variances)
self.add_parameters(self.variances)
self.variances = Param('variances', variances, Logexp())
self.variances.gradient = np.zeros(self.variances.shape)
self.add_parameter(self.variances)
self.variances.add_observer(self, self.update_variance)
# initialize cache
@ -57,20 +59,34 @@ class Linear(Kernpart):
def on_input_change(self, X):
self._K_computations(X, None)
# def _get_params(self):
# return self.variances
#
# def _set_params(self, x):
# assert x.size == (self.num_params)
# self.variances = x
#def parameters_changed(self):
# self.variances2 = np.square(self.variances)
#
# def _get_param_names(self):
# if self.num_params == 1:
# return ['variance']
# else:
# return ['variance_%i' % i for i in range(self.variances.size)]
def update_gradients_full(self, dL_dK, X):
#self.variances.gradient[:] = 0
self._param_grad_helper(dL_dK, X, self.variances.gradient)
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
tmp = dL_dKdiag[:, None] * X ** 2
if self.ARD:
self.variances.gradient = tmp.sum(0)
else:
self.variances.gradient = tmp.sum()
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
self._param_grad_helper(dL_dKnm, X, Z, self.variances.gradient)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
self._psi_computations(Z, mu, S)
# psi0:
tmp = dL_dpsi0[:, None] * self.mu2_S
if self.ARD: self.variances.gradient[:] = tmp.sum(0)
else: self.variances.gradient[:] = tmp.sum()
#psi1
self._param_grad_helper(dL_dpsi1, mu, Z, self.variances.gradient)
#psi2
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
else: self.variances.gradient += tmp.sum()
#from Kmm
self._K_computations(Z, None)
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
def K(self, X, X2, target):
if self.ARD:
@ -88,7 +104,7 @@ class Linear(Kernpart):
def Kdiag(self, X, target):
np.add(target, np.sum(self.variances * np.square(X), -1), target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
if self.ARD:
if X2 is None:
[np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.input_dim)]
@ -100,14 +116,7 @@ class Linear(Kernpart):
self._K_computations(X, X2)
target += np.sum(self._dot_product * dL_dK)
def dKdiag_dtheta(self, dL_dKdiag, X, target):
tmp = dL_dKdiag[:, None] * X ** 2
if self.ARD:
target += tmp.sum(0)
else:
target += tmp.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
if X2 is None:
target += 2*(((X[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
else:
@ -124,14 +133,6 @@ class Linear(Kernpart):
self._psi_computations(Z, mu, S)
target += np.sum(self.variances * self.mu2_S, 1)
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
self._psi_computations(Z, mu, S)
tmp = dL_dpsi0[:, None] * self.mu2_S
if self.ARD:
target += tmp.sum(0)
else:
target += tmp.sum()
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
target_mu += dL_dpsi0[:, None] * (2.0 * mu * self.variances)
target_S += dL_dpsi0[:, None] * self.variances
@ -140,17 +141,13 @@ class Linear(Kernpart):
"""the variance, it does nothing"""
self._psi1 = self.K(mu, Z, target)
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
"""the variance, it does nothing"""
self.dK_dtheta(dL_dpsi1, mu, Z, target)
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
"""Do nothing for S, it does not affect psi1"""
self._psi_computations(Z, mu, S)
target_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
self.dK_dX(dL_dpsi1.T, Z, mu, target)
self.gradients_X(dL_dpsi1.T, Z, mu, target)
def psi2(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
@ -164,25 +161,17 @@ class Linear(Kernpart):
def dpsi2_dtheta_new(self, dL_dpsi2, Z, mu, S, target):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
self.K(mu,Z,tmp)
self.dK_dtheta(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
self._param_grad_helper(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
result= 2.*(dL_dpsi2[:,:,:,None]*S[:,None,None,:]*self.variances*Z[None,:,None,:]*Z[None,None,:,:]).sum(0).sum(0).sum(0)
if self.ARD:
target += result.sum(0).sum(0).sum(0)
else:
target += result.sum()
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
self._psi_computations(Z, mu, S)
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
if self.ARD:
target += tmp.sum(0).sum(0).sum(0)
else:
target += tmp.sum()
def dpsi2_dmuS_new(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
self.K(mu,Z,tmp)
self.dK_dX(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
self.gradients_X(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
Zs = Z*self.variances
Zs_sq = Zs[:,None,:]*Zs[None,:,:]
@ -288,11 +277,11 @@ class Linear(Kernpart):
if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2)):
self._X = X.copy()
if X2 is None:
self._dot_product = tdot(X)
self._dot_product = tdot(param_to_array(X))
self._X2 = None
else:
self._X2 = X2.copy()
self._dot_product = np.dot(X, X2.T)
self._dot_product = np.dot(param_to_array(X), param_to_array(X2.T))
def _psi_computations(self, Z, mu, S):
# here are the "statistics" for psi1 and psi2

4
GPy/kern/parts/mlp.py
View file

@ -77,7 +77,7 @@ class MLP(Kernpart):
self._K_diag_computations(X)
target+= self.variance*self._K_diag_dvar
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
denom3 = self._K_denom*self._K_denom*self._K_denom
@ -107,7 +107,7 @@ class MLP(Kernpart):
target[0] += np.sum(self._K_dvar*dL_dK)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_asin_arg

2
GPy/kern/parts/periodic_Matern32.py
View file

@ -112,7 +112,7 @@ class PeriodicMatern32(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

2
GPy/kern/parts/periodic_Matern52.py
View file

@ -114,7 +114,7 @@ class PeriodicMatern52(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

2
GPy/kern/parts/periodic_exponential.py
View file

@ -110,7 +110,7 @@ class PeriodicExponential(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

4
GPy/kern/parts/poly.py
View file

@ -86,7 +86,7 @@ class POLY(Kernpart):
self._K_diag_computations(X)
target+= self.variance*self._K_diag_dvar
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
base = self.variance*self.degree*self._K_poly_arg**(self.degree-1)
@ -99,7 +99,7 @@ class POLY(Kernpart):
target[2] += base_cov_grad.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_poly_arg

30
GPy/kern/parts/prod.py
View file

@ -54,15 +54,15 @@ class Prod(Kernpart):
self.k1.update_gradients_full(dL_dK*self._K2, X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self._K1, X[:,self.slice2])
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""Derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -81,21 +81,21 @@ class Prod(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
if X2 is None:
if not isinstance(self.k1,Coregionalize) and not isinstance(self.k2,Coregionalize):
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
else:#if isinstance(self.k1,Coregionalize) or isinstance(self.k2,Coregionalize):
#NOTE The indices column in the inputs makes the ki.dK_dX fail when passing None instead of X[:,self.slicei]
#NOTE The indices column in the inputs makes the ki.gradients_X fail when passing None instead of X[:,self.slicei]
X2 = X
self.k1.dK_dX(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
else:
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -103,8 +103,8 @@ class Prod(Kernpart):
self.k1.Kdiag(X[:,self.slice1],K1)
self.k2.Kdiag(X[:,self.slice2],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

20
GPy/kern/parts/prod_orthogonal.py
View file

@ -41,15 +41,15 @@ class prod_orthogonal(Kernpart):
self._K_computations(X,X2)
target += self._K1 * self._K2
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -67,11 +67,11 @@ class prod_orthogonal(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.input_dim],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.input_dim:],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -79,8 +79,8 @@ class prod_orthogonal(Kernpart):
self.k1.Kdiag(X[:,0:self.k1.input_dim],K1)
self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

4
GPy/kern/parts/rational_quadratic.py
View file

@ -52,7 +52,7 @@ class RationalQuadratic(Kernpart):
def Kdiag(self,X,target):
target += self.variance
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
if X2 is None: X2 = X
dist2 = np.square((X-X2.T)/self.lengthscale)
@ -68,7 +68,7 @@ class RationalQuadratic(Kernpart):
target[0] += np.sum(dL_dKdiag)
# here self.lengthscale and self.power have no influence on Kdiag so target[1:] are unchanged
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist2 = np.square((X-X.T)/self.lengthscale)

18
GPy/kern/parts/rbf.py
View file

@ -8,6 +8,7 @@ from kernpart import Kernpart
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
class RBF(Kernpart):
"""
@ -50,9 +51,12 @@ class RBF(Kernpart):
else:
lengthscale = np.ones(self.input_dim)
self.variance = Param('variance', variance)
self.variance = Param('variance', variance, Logexp())
self.lengthscale = Param('lengthscale', lengthscale)
self.lengthscale.add_observer(self, self.update_lengthscale)
self.update_lengthscale(self.lengthscale)
self.add_parameters(self.variance, self.lengthscale)
self.parameters_changed() # initializes cache
@ -114,7 +118,7 @@ class RBF(Kernpart):
self._K_computations(X, Z)
self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
@ -123,7 +127,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -138,7 +142,7 @@ class RBF(Kernpart):
d_length = self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) +1./self.lengthscale)
dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
if not self.ARD:
self.lengthscale.gradeint = dpsi1_dlength.sum()
self.lengthscale.gradient = dpsi1_dlength.sum()
else:
self.lengthscale.gradient = dpsi1_dlength.sum(0).sum(0)
@ -157,7 +161,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -168,8 +172,8 @@ class RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

8
GPy/kern/parts/rbf_inv.py
View file

@ -97,7 +97,7 @@ class RBFInv(RBF):
# return ['variance'] + ['inv_lengthscale%i' % i for i in range(self.inv_lengthscale.size)]
# TODO: Rewrite computations so that lengthscale is not needed (but only inv. lengthscale)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
target[0] += np.sum(self._K_dvar * dL_dK)
if self.ARD:
@ -142,14 +142,14 @@ class RBFInv(RBF):
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK) * (-self.lengthscale2)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if X2 is None:
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

4
GPy/kern/parts/rbfcos.py
View file

@ -73,7 +73,7 @@ class RBFCos(Kernpart):
def Kdiag(self,X,target):
np.add(target,self.variance,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
target[0] += np.sum(dL_dK*self._dvar)
if self.ARD:
@ -88,7 +88,7 @@ class RBFCos(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
#TODO!!!
raise NotImplementedError

2
GPy/kern/parts/spline.py
View file

@ -50,7 +50,7 @@ class Spline(Kernpart):
def Kdiag(self,X,target):
target += self.variance*X.flatten()**3/3.
def dK_dtheta(self,X,X2,target):
def _param_grad_helper(self,X,X2,target):
target += 0.5*(t*s**2) - s**3/6. + (s_t)**3*theta(s_t)/6.
def dKdiag_dtheta(self,X,target):

4
GPy/kern/parts/ss_rbf.py
View file

@ -144,8 +144,8 @@ class SS_RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

20
GPy/kern/parts/symmetric.py
View file

@ -40,7 +40,7 @@ class Symmetric(Kernpart):
self.k.K(X,AX2,target)
self.k.K(AX,AX2,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
AX = np.dot(X,self.transform)
if X2 is None:
@ -48,13 +48,13 @@ class Symmetric(Kernpart):
ZX2 = AX
else:
AX2 = np.dot(X2, self.transform)
self.k.dK_dtheta(dL_dK,X,X2,target)
self.k.dK_dtheta(dL_dK,AX,X2,target)
self.k.dK_dtheta(dL_dK,X,AX2,target)
self.k.dK_dtheta(dL_dK,AX,AX2,target)
self.k._param_grad_helper(dL_dK,X,X2,target)
self.k._param_grad_helper(dL_dK,AX,X2,target)
self.k._param_grad_helper(dL_dK,X,AX2,target)
self.k._param_grad_helper(dL_dK,AX,AX2,target)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
AX = np.dot(X,self.transform)
if X2 is None:
@ -62,10 +62,10 @@ class Symmetric(Kernpart):
ZX2 = AX
else:
AX2 = np.dot(X2, self.transform)
self.k.dK_dX(dL_dK, X, X2, target)
self.k.dK_dX(dL_dK, AX, X2, target)
self.k.dK_dX(dL_dK, X, AX2, target)
self.k.dK_dX(dL_dK, AX ,AX2, target)
self.k.gradients_X(dL_dK, X, X2, target)
self.k.gradients_X(dL_dK, AX, X2, target)
self.k.gradients_X(dL_dK, X, AX2, target)
self.k.gradients_X(dL_dK, AX ,AX2, target)
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""

4
GPy/kern/parts/sympykern.py
View file

@ -348,7 +348,7 @@ class spkern(Kernpart):
def Kdiag(self,X,target):
self._weave_inline(self._Kdiag_code, X, target)
def dK_dtheta(self,partial,X,Z,target):
def _param_grad_helper(self,partial,X,Z,target):
if Z is None:
self._weave_inline(self._dK_dtheta_code_X, X, target, Z, partial)
else:
@ -357,7 +357,7 @@ class spkern(Kernpart):
def dKdiag_dtheta(self,partial,X,target):
self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
def dK_dX(self,partial,X,Z,target):
def gradients_X(self,partial,X,Z,target):
if Z is None:
self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
else:

17
GPy/likelihoods/gaussian.py
View file

@ -130,7 +130,10 @@ class Gaussian(Likelihood):
:rtype: float
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
return -0.5*(np.sum((y-link_f)**2/self.variance) + self.ln_det_K + self.N*np.log(2.*np.pi))
N = y.shape[0]
ln_det_cov = N*np.log(self.variance)
return -0.5*(np.sum((y-link_f)**2/self.variance) + ln_det_cov + N*np.log(2.*np.pi))
def dlogpdf_dlink(self, link_f, y, extra_data=None):
"""
@ -175,7 +178,8 @@ class Gaussian(Likelihood):
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
hess = -(1.0/self.variance)*np.ones((self.N, 1))
N = y.shape[0]
hess = -(1.0/self.variance)*np.ones((N, 1))
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
@ -194,7 +198,8 @@ class Gaussian(Likelihood):
:rtype: Nx1 array
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
d3logpdf_dlink3 = np.diagonal(0*self.I)[:, None]
N = y.shape[0]
d3logpdf_dlink3 = np.zeros((N,1))
return d3logpdf_dlink3
def dlogpdf_link_dvar(self, link_f, y, extra_data=None):
@ -215,7 +220,8 @@ class Gaussian(Likelihood):
assert np.asarray(link_f).shape == np.asarray(y).shape
e = y - link_f
s_4 = 1.0/(self.variance**2)
dlik_dsigma = -0.5*self.N/self.variance + 0.5*s_4*np.sum(np.square(e))
N = y.shape[0]
dlik_dsigma = -0.5*N/self.variance + 0.5*s_4*np.sum(np.square(e))
return np.sum(dlik_dsigma) # Sure about this sum?
def dlogpdf_dlink_dvar(self, link_f, y, extra_data=None):
@ -255,7 +261,8 @@ class Gaussian(Likelihood):
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
s_4 = 1.0/(self.variance**2)
d2logpdf_dlink2_dvar = np.diag(s_4*self.I)[:, None]
N = y.shape[0]
d2logpdf_dlink2_dvar = np.ones((N,1))*s_4
return d2logpdf_dlink2_dvar
def dlogpdf_link_dtheta(self, f, y, extra_data=None):

25
GPy/likelihoods/likelihood.py
View file

@ -44,6 +44,10 @@ class Likelihood(Parameterized):
def _gradients(self,partial):
return np.zeros(0)
def update_gradients(self, partial):
if self.size > 0:
raise NotImplementedError('Must be implemented for likelihoods with parameters to be optimized')
def _preprocess_values(self,Y):
"""
In case it is needed, this function assess the output values or makes any pertinent transformation on them.
@ -303,31 +307,31 @@ class Likelihood(Parameterized):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
return self.dlogpdf_link_dtheta(link_f, y, extra_data=extra_data)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([1, 0])
return np.zeros([1, 0])
def dlogpdf_df_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
return np.zeros([f.shape[0], 0])
def d2logpdf_df2_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
d2link_df2 = self.gp_link.d2transf_df2(f)
@ -336,7 +340,7 @@ class Likelihood(Parameterized):
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
return np.zeros([f.shape[0], 0])
def _laplace_gradients(self, f, y, extra_data=None):
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, extra_data=extra_data)
@ -345,9 +349,12 @@ class Likelihood(Parameterized):
#Parameters are stacked vertically. Must be listed in same order as 'get_param_names'
# ensure we have gradients for every parameter we want to optimize
assert dlogpdf_dtheta.shape[1] == len(self._get_param_names())
assert dlogpdf_df_dtheta.shape[1] == len(self._get_param_names())
assert d2logpdf_df2_dtheta.shape[1] == len(self._get_param_names())
try:
assert len(dlogpdf_dtheta) == self.size #1 x num_param array
assert dlogpdf_df_dtheta.shape[1] == self.size #f x num_param matrix
assert d2logpdf_df2_dtheta.shape[1] == self.size #f x num_param matrix
except Exception as e:
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta

7
GPy/likelihoods/poisson.py
View file

@ -19,8 +19,11 @@ class Poisson(Likelihood):
.. Note::
Y is expected to take values in {0,1,2,...}
"""
def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
super(Poisson, self).__init__(gp_link,analytical_mean,analytical_variance)
def __init__(self, gp_link=None):
if gp_link is None:
gp_link = link_functions.Log_ex_1()
super(Poisson, self).__init__(gp_link, name='Poisson')
def _preprocess_values(self,Y):
return Y

60
GPy/likelihoods/student_t.py
View file

@ -8,6 +8,7 @@ import link_functions
from scipy import stats, integrate
from scipy.special import gammaln, gamma
from likelihood import Likelihood
from ..core.parameterization import Param
class StudentT(Likelihood):
"""
@ -19,26 +20,30 @@ class StudentT(Likelihood):
p(y_{i}|\\lambda(f_{i})) = \\frac{\\Gamma\\left(\\frac{v+1}{2}\\right)}{\\Gamma\\left(\\frac{v}{2}\\right)\\sqrt{v\\pi\\sigma^{2}}}\\left(1 + \\frac{1}{v}\\left(\\frac{(y_{i} - f_{i})^{2}}{\\sigma^{2}}\\right)\\right)^{\\frac{-v+1}{2}}
"""
def __init__(self,gp_link=None,analytical_mean=True,analytical_variance=True, deg_free=5, sigma2=2):
self.v = deg_free
self.sigma2 = sigma2
def __init__(self,gp_link=None, deg_free=5, sigma2=2):
if gp_link is None:
gp_link = link_functions.Identity()
super(StudentT, self).__init__(gp_link, name='Student_T')
self.sigma2 = Param('t_noise', float(sigma2))
self.v = Param('deg_free', float(deg_free))
self.add_parameter(self.sigma2)
self.add_parameter(self.v)
self.v.constrain_fixed()
self._set_params(np.asarray(sigma2))
super(StudentT, self).__init__(gp_link,analytical_mean,analytical_variance)
self.log_concave = False
def _get_params(self):
return np.asarray(self.sigma2)
def parameters_changed(self):
self.variance = (self.v / float(self.v - 2)) * self.sigma2
def _get_param_names(self):
return ["t_noise_std2"]
def _set_params(self, x):
self.sigma2 = float(x)
@property
def variance(self, extra_data=None):
return (self.v / float(self.v - 2)) * self.sigma2
def update_gradients(self, partial):
"""
Pull out the gradients, be careful as the order must match the order
in which the parameters are added
"""
self.sigma2.gradient = partial[0]
self.v.gradient = partial[1]
def pdf_link(self, link_f, y, extra_data=None):
"""
@ -82,10 +87,14 @@ class StudentT(Likelihood):
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
#FIXME:
#Why does np.log(1 + (1/self.v)*((y-link_f)**2)/self.sigma2) suppress the divide by zero?!
#But np.log(1 + (1/float(self.v))*((y-link_f)**2)/self.sigma2) throws it correctly
#print - 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
objective = (+ gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
- 0.5*np.log(self.sigma2 * self.v * np.pi)
- 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
- gammaln(self.v * 0.5)
- 0.5*np.log(self.sigma2 * self.v * np.pi)
- 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
)
return np.sum(objective)
@ -222,17 +231,20 @@ class StudentT(Likelihood):
def dlogpdf_link_dtheta(self, f, y, extra_data=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, extra_data=extra_data)
return np.asarray([[dlogpdf_dvar]])
dlogpdf_dv = np.zeros_like(dlogpdf_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dvar, dlogpdf_dv))
def dlogpdf_dlink_dtheta(self, f, y, extra_data=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)
return dlogpdf_dlink_dvar
dlogpdf_dlink_dv = np.zeros_like(dlogpdf_dlink_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dlink_dvar, dlogpdf_dlink_dv))
def d2logpdf_dlink2_dtheta(self, f, y, extra_data=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)
return d2logpdf_dlink2_dvar
d2logpdf_dlink2_dv = np.zeros_like(d2logpdf_dlink2_dvar) #FIXME: Not done yet
return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
def predictive_variance(self, mu, sigma, predictive_mean=None):
"""
Compute predictive variance of student_t*normal p(y*|f*)p(f*)
@ -252,7 +264,7 @@ class StudentT(Likelihood):
return true_var
def _predictive_mean_analytical(self, mu, sigma):
def predictive_mean(self, mu, sigma):
"""
Compute mean of the prediction
"""

2
GPy/mappings/kernel.py
View file

@ -57,4 +57,4 @@ class Kernel(Mapping):
return np.hstack((self._df_dA.flatten(), self._df_dbias))
def df_dX(self, dL_df, X):
return self.kern.dK_dX((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)
return self.kern.gradients_X((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)

55
GPy/models/bayesian_gplvm.py
View file

@ -2,7 +2,6 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import itertools
from gplvm import GPLVM
from .. import kern
from ..core import SparseGP
@ -23,15 +22,10 @@ class BayesianGPLVM(SparseGP, GPLVM):
:type init: 'PCA'|'random'
"""
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, name='bayesian gplvm', **kwargs):
if type(likelihood_or_Y) is np.ndarray:
likelihood = Gaussian(likelihood_or_Y)
else:
likelihood = likelihood_or_Y
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=Gaussian(), name='bayesian gplvm', **kwargs):
if X == None:
X = self.initialise_latent(init, input_dim, likelihood.Y)
X = self.initialise_latent(init, input_dim, Y)
self.init = init
if X_variance is None:
@ -44,9 +38,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
if kernel is None:
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
SparseGP.__init__(self, X=X, likelihood=likelihood, kernel=kernel, Z=Z, X_variance=X_variance, name=name, **kwargs)
self.q = Normal(self.X, self.X_variance)
self.add_parameter(self.q, gradient=self._dbound_dmuS, index=0)
self.q = Normal(X, X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, X_variance, name, **kwargs)
self.add_parameter(self.q, index=0)
self.ensure_default_constraints()
def _getstate(self):
@ -94,9 +88,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
return dKL_dmu, dKL_dS
def dL_dmuS(self):
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.dL_dpsi0, self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.dL_dpsi2, self.Z, self.X, self.X_variance)
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.grad_dict['dL_dpsi0'], self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu = dL_dmu_psi0 + dL_dmu_psi1 + dL_dmu_psi2
dL_dS = dL_dS_psi0 + dL_dS_psi1 + dL_dS_psi2
@ -107,10 +101,25 @@ class BayesianGPLVM(SparseGP, GPLVM):
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = SparseGP.log_likelihood(self)
kl = self.KL_divergence()
return ll - kl
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
self._log_marginal_likelihood -= self.KL_divergence()
#The derivative of the bound wrt the inducing inputs Z
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu, dL_dS = self.dL_dmuS()
dKL_dmu, dKL_dS = self.dKL_dmuS()
self.q.means.gradient = dL_dmu - dKL_dmu
self.q.variances.gradient = dL_dS - dKL_dS
# def log_likelihood(self):
# ll = SparseGP.log_likelihood(self)
# kl = self.KL_divergence()
# return ll - kl
def _dbound_dmuS(self):
dKL_dmu, dKL_dS = self.dKL_dmuS()
@ -181,18 +190,18 @@ class BayesianGPLVM(SparseGP, GPLVM):
"""
dmu_dX = np.zeros_like(Xnew)
for i in range(self.Z.shape[0]):
dmu_dX += self.kern.dK_dX(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
dmu_dX += self.kern.gradients_X(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
return dmu_dX
def dmu_dXnew(self, Xnew):
"""
Individual gradient of prediction at Xnew w.r.t. each sample in Xnew
"""
dK_dX = np.zeros((Xnew.shape[0], self.num_inducing))
gradients_X = np.zeros((Xnew.shape[0], self.num_inducing))
ones = np.ones((1, 1))
for i in range(self.Z.shape[0]):
dK_dX[:, i] = self.kern.dK_dX(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(dK_dX, self.Cpsi1Vf)
gradients_X[:, i] = self.kern.gradients_X(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(gradients_X, self.Cpsi1Vf)
def plot_steepest_gradient_map(self, *args, ** kwargs):
"""

2
GPy/models/bcgplvm.py
View file

@ -44,7 +44,7 @@ class BCGPLVM(GPLVM):
GP._set_params(self, x[self.mapping.num_params:])
def _log_likelihood_gradients(self):
dL_df = self.kern.dK_dX(self.dL_dK, self.X)
dL_df = self.kern.gradients_X(self.dL_dK, self.X)
dL_dtheta = self.mapping.df_dtheta(dL_df, self.likelihood.Y)
return np.hstack((dL_dtheta.flatten(), GP._log_likelihood_gradients(self)))

2
GPy/models/gplvm.py
View file

@ -60,7 +60,7 @@ class GPLVM(GP):
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
target[:,:,i]=self.kern.gradients_X(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):

37
GPy/models/gradient_checker.py
View file

@ -1,9 +1,10 @@
# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GPy.core.model import Model
from ..core.model import Model
import itertools
import numpy
from ..core.parameterization import Param
def get_shape(x):
if isinstance(x, numpy.ndarray):
@ -59,7 +60,7 @@ class GradientChecker(Model):
grad.randomize()
grad.checkgrad(verbose=1)
"""
Model.__init__(self)
Model.__init__(self, 'GradientChecker')
if isinstance(x0, (list, tuple)) and names is None:
self.shapes = [get_shape(xi) for xi in x0]
self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
@ -72,8 +73,10 @@ class GradientChecker(Model):
else:
self.names = names
self.shapes = [get_shape(x0)]
for name, xi in zip(self.names, at_least_one_element(x0)):
self.__setattr__(name, xi)
self.__setattr__(name, Param(name, xi))
self.add_parameter(self.__getattribute__(name))
# self._param_names = []
# for name, shape in zip(self.names, self.shapes):
# self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
@ -93,20 +96,18 @@ class GradientChecker(Model):
def _log_likelihood_gradients(self):
return numpy.atleast_1d(self.df(*self._get_x(), **self.kwargs)).flatten()
#def _get_params(self):
#return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
def _get_params(self):
return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
#def _set_params(self, x):
#current_index = 0
#for name, shape in zip(self.names, self.shapes):
#current_size = numpy.prod(shape)
#self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
#current_index += current_size
def _set_params(self, x):
current_index = 0
for name, shape in zip(self.names, self.shapes):
current_size = numpy.prod(shape)
self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
current_index += current_size
def _get_param_names(self):
_param_names = []
for name, shape in zip(self.names, self.shapes):
_param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
return _param_names
#def _get_param_names(self):
#_param_names = []
#for name, shape in zip(self.names, self.shapes):
#_param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
#return _param_names

2
GPy/models/sparse_gplvm.py
View file

@ -52,7 +52,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
def dL_dX(self):
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0, self.X)
dL_dX += self.kern.dK_dX(self.dL_dpsi1, self.X, self.Z)
dL_dX += self.kern.gradients_X(self.dL_dpsi1, self.X, self.Z)
return dL_dX

3
GPy/plotting/matplot_dep/variational_plots.py
View file

@ -1,4 +1,5 @@
import pylab as pb
import pylab as pb, numpy as np
from ...util.misc import param_to_array
def plot(parameterized, fignum=None, ax=None, colors=None):
"""

2
GPy/testing/kernel_tests.py
View file

@ -5,7 +5,7 @@ import unittest
import numpy as np
import GPy
verbose = False
verbose = True
try:
import sympy

250
GPy/testing/likelihood_tests.py
View file

@ -4,10 +4,11 @@ import GPy
from GPy.models import GradientChecker
import functools
import inspect
from GPy.likelihoods.noise_models import gp_transformations
from GPy.likelihoods import link_functions
from ..core.parameterization import Param
from functools import partial
#np.random.seed(300)
np.random.seed(7)
#np.random.seed(7)
def dparam_partial(inst_func, *args):
"""
@ -22,12 +23,14 @@ def dparam_partial(inst_func, *args):
the f or Y that are being used in the function whilst we tweak the
param
"""
def param_func(param, inst_func, args):
inst_func.im_self._set_params(param)
def param_func(param_val, param_name, inst_func, args):
#inst_func.im_self._set_params(param)
#inst_func.im_self.add_parameter(Param(param_name, param_val))
inst_func.im_self[param_name] = param_val
return inst_func(*args)
return functools.partial(param_func, inst_func=inst_func, args=args)
def dparam_checkgrad(func, dfunc, params, args, constraints=None, randomize=False, verbose=False):
def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None, randomize=False, verbose=False):
"""
checkgrad expects a f: R^N -> R^1 and df: R^N -> R^N
However if we are holding other parameters fixed and moving something else
@ -38,27 +41,34 @@ def dparam_checkgrad(func, dfunc, params, args, constraints=None, randomize=Fals
The number of parameters and N is the number of data
Need to take a slice out from f and a slice out of df
"""
#print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
#func.__name__, dfunc.__name__)
print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
func.__name__, dfunc.__name__)
partial_f = dparam_partial(func, *args)
partial_df = dparam_partial(dfunc, *args)
gradchecking = True
for param in params:
fnum = np.atleast_1d(partial_f(param)).shape[0]
dfnum = np.atleast_1d(partial_df(param)).shape[0]
zipped_params = zip(params, params_names)
for param_ind, (param_val, param_name) in enumerate(zipped_params):
#Check one parameter at a time, make sure it is 2d (as some gradients only return arrays) then strip out the parameter
fnum = np.atleast_2d(partial_f(param_val, param_name))[:, param_ind].shape[0]
dfnum = np.atleast_2d(partial_df(param_val, param_name))[:, param_ind].shape[0]
for fixed_val in range(dfnum):
#dlik and dlik_dvar gives back 1 value for each
f_ind = min(fnum, fixed_val+1) - 1
print "fnum: {} dfnum: {} f_ind: {} fixed_val: {}".format(fnum, dfnum, f_ind, fixed_val)
#Make grad checker with this param moving, note that set_params is NOT being called
#The parameter is being set directly with __setattr__
grad = GradientChecker(lambda x: np.atleast_1d(partial_f(x))[f_ind],
lambda x : np.atleast_1d(partial_df(x))[fixed_val],
param, 'p')
#This is not general for more than one param...
#Check only the parameter and function value we wish to check at a time
grad = GradientChecker(lambda p_val: np.atleast_2d(partial_f(p_val, param_name))[f_ind, param_ind],
lambda p_val: np.atleast_2d(partial_df(p_val, param_name))[fixed_val, param_ind],
param_val, [param_name])
if constraints is not None:
for constraint in constraints:
constraint('p', grad)
for constrain_param, constraint in constraints:
if grad.grep_param_names(constrain_param):
constraint(constrain_param, grad)
else:
print "parameter didn't exist"
print constrain_param, " ", constraint
if randomize:
grad.randomize()
if verbose:
@ -76,7 +86,7 @@ class TestNoiseModels(object):
Generic model checker
"""
def setUp(self):
self.N = 5
self.N = 15
self.D = 3
self.X = np.random.rand(self.N, self.D)*10
@ -94,7 +104,7 @@ class TestNoiseModels(object):
self.var = np.random.rand(1)
#Make a bigger step as lower bound can be quite curved
self.step = 1e-3
self.step = 1e-4
def tearDown(self):
self.Y = None
@ -107,17 +117,20 @@ class TestNoiseModels(object):
####################################################
# Constraint wrappers so we can just list them off #
####################################################
def constrain_fixed(regex, model):
model[regex].constrain_fixed()
def constrain_negative(regex, model):
model.constrain_negative(regex)
model[regex].constrain_negative()
def constrain_positive(regex, model):
model.constrain_positive(regex)
model[regex].constrain_positive()
def constrain_bounded(regex, model, lower, upper):
"""
Used like: partial(constrain_bounded, lower=0, upper=1)
"""
model.constrain_bounded(regex, lower, upper)
model[regex].constrain_bounded(lower, upper)
"""
Dictionary where we nest models we would like to check
@ -134,71 +147,81 @@ class TestNoiseModels(object):
}
"""
noise_models = {"Student_t_default": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
#"constraints": [("t_noise", constrain_positive), ("deg_free", partial(constrain_fixed, value=5))]
},
"laplace": True
},
"Student_t_1_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [1.0],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_small_deg_free": {
"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_small_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [0.01],
"constraints": [constrain_positive]
"vals": [0.0001],
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_large_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [10.0],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_approx_gauss": {
"model": GPy.likelihoods.student_t(deg_free=1000, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_log": {
"model": GPy.likelihoods.student_t(gp_link=gp_transformations.Log(), deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Gaussian_default": {
"model": GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N),
"model": GPy.likelihoods.Gaussian(variance=self.var),
"grad_params": {
"names": ["noise_model_variance"],
"names": ["variance"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("variance", constrain_positive)]
},
"laplace": True,
"ep": True
},
#"Gaussian_log": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -207,7 +230,7 @@ class TestNoiseModels(object):
#"laplace": True
#},
#"Gaussian_probit": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Probit(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Probit(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -216,7 +239,7 @@ class TestNoiseModels(object):
#"laplace": True
#},
#"Gaussian_log_ex": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -225,31 +248,31 @@ class TestNoiseModels(object):
#"laplace": True
#},
"Bernoulli_default": {
"model": GPy.likelihoods.bernoulli(),
"model": GPy.likelihoods.Bernoulli(),
"link_f_constraints": [partial(constrain_bounded, lower=0, upper=1)],
"laplace": True,
"Y": self.binary_Y,
"ep": True
},
"Exponential_default": {
"model": GPy.likelihoods.exponential(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True,
},
"Poisson_default": {
"model": GPy.likelihoods.poisson(),
"link_f_constraints": [constrain_positive],
"Y": self.integer_Y,
"laplace": True,
"ep": False #Should work though...
},
"Gamma_default": {
"model": GPy.likelihoods.gamma(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True
}
#"Exponential_default": {
#"model": GPy.likelihoods.exponential(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True,
#},
#"Poisson_default": {
#"model": GPy.likelihoods.poisson(),
#"link_f_constraints": [constrain_positive],
#"Y": self.integer_Y,
#"laplace": True,
#"ep": False #Should work though...
#},
#"Gamma_default": {
#"model": GPy.likelihoods.gamma(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True
#}
}
for name, attributes in noise_models.iteritems():
@ -286,8 +309,8 @@ class TestNoiseModels(object):
else:
ep = False
if len(param_vals) > 1:
raise NotImplementedError("Cannot support multiple params in likelihood yet!")
#if len(param_vals) > 1:
#raise NotImplementedError("Cannot support multiple params in likelihood yet!")
#Required by all
#Normal derivatives
@ -302,13 +325,13 @@ class TestNoiseModels(object):
yield self.t_d3logpdf_df3, model, Y, f
yield self.t_d3logpdf_dlink3, model, Y, f, link_f_constraints
#Params
yield self.t_dlogpdf_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_df_dparams, model, Y, f, param_vals, param_constraints
yield self.t_d2logpdf2_df2_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_dlogpdf_df_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_d2logpdf2_df2_dparams, model, Y, f, param_vals, param_names, param_constraints
#Link params
yield self.t_dlogpdf_link_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_dlink_dparams, model, Y, f, param_vals, param_constraints
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_link_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_dlogpdf_dlink_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, param_vals, param_names, param_constraints
#laplace likelihood gradcheck
yield self.t_laplace_fit_rbf_white, model, self.X, Y, f, self.step, param_vals, param_names, param_constraints
@ -370,33 +393,33 @@ class TestNoiseModels(object):
# df_dparams #
##############
@with_setup(setUp, tearDown)
def t_dlogpdf_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_dlogpdf_df_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_df_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_d2logpdf2_df2_dparams(self, model, Y, f, params, param_constraints):
def t_d2logpdf2_df2_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
################
@ -454,32 +477,32 @@ class TestNoiseModels(object):
# dlink_dparams #
#################
@with_setup(setUp, tearDown)
def t_dlogpdf_link_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_link_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.logpdf_link, model.dlogpdf_link_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_dlogpdf_dlink_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_dlink_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.dlogpdf_dlink, model.dlogpdf_dlink_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, params, param_constraints):
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.d2logpdf_dlink2, model.d2logpdf_dlink2_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@ -493,18 +516,23 @@ class TestNoiseModels(object):
Y = Y/Y.max()
white_var = 1e-6
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), model)
m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=laplace_likelihood)
laplace_likelihood = GPy.inference.latent_function_inference.LaplaceInference()
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, inference_method=laplace_likelihood)
m.ensure_default_constraints()
m.constrain_fixed('white', white_var)
m['white'].constrain_fixed(white_var)
for param_num in range(len(param_names)):
name = param_names[param_num]
m[name] = param_vals[param_num]
constraints[param_num](name, m)
#Set constraints
for constrain_param, constraint in constraints:
constraint(constrain_param, m)
print m
m.randomize()
#Set params
for param_num in range(len(param_names)):
name = param_names[param_num]
m[name] = param_vals[param_num]
#m.optimize(max_iters=8)
print m
m.checkgrad(verbose=1, step=step)
@ -525,10 +553,10 @@ class TestNoiseModels(object):
Y = Y/Y.max()
white_var = 1e-6
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
ep_likelihood = GPy.likelihoods.EP(Y.copy(), model)
m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=ep_likelihood)
ep_inf = GPy.inference.latent_function_inference.EP()
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, inference_method=ep_inf)
m.ensure_default_constraints()
m.constrain_fixed('white', white_var)
m['white'].constrain_fixed(white_var)
for param_num in range(len(param_names)):
name = param_names[param_num]
@ -559,8 +587,8 @@ class LaplaceTests(unittest.TestCase):
self.var = 0.2
self.var = np.random.rand(1)
self.stu_t = GPy.likelihoods.student_t(deg_free=5, sigma2=self.var)
self.gauss = GPy.likelihoods.gaussian(gp_transformations.Log(), variance=self.var, D=self.D, N=self.N)
self.stu_t = GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var)
self.gauss = GPy.likelihoods.Gaussian(gp_link=link_functions.Log(), variance=self.var)
#Make a bigger step as lower bound can be quite curved
self.step = 1e-6
@ -584,7 +612,7 @@ class LaplaceTests(unittest.TestCase):
noise = np.random.randn(*self.X.shape)*self.real_std
self.Y = np.sin(self.X*2*np.pi) + noise
self.f = np.random.rand(self.N, 1)
self.gauss = GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N)
self.gauss = GPy.likelihoods.Gaussian(variance=self.var)
dlogpdf_df = functools.partial(self.gauss.dlogpdf_df, y=self.Y)
d2logpdf_df2 = functools.partial(self.gauss.d2logpdf_df2, y=self.Y)
@ -605,23 +633,27 @@ class LaplaceTests(unittest.TestCase):
#Yc = Y.copy()
#Yc[75:80] += 1
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
#FIXME: Make sure you can copy kernels when params is fixed
#kernel2 = kernel1.copy()
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
m1.constrain_fixed('white', 1e-6)
m1['noise'] = initial_var_guess
m1.constrain_bounded('noise', 1e-4, 10)
m1.constrain_bounded('rbf', 1e-4, 10)
gauss_distr1 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
exact_inf = GPy.inference.latent_function_inference.ExactGaussianInference()
m1 = GPy.core.GP(X, Y.copy(), kernel=kernel1, likelihood=gauss_distr1, inference_method=exact_inf)
m1['white'].constrain_fixed(1e-6)
m1['variance'] = initial_var_guess
m1['variance'].constrain_bounded(1e-4, 10)
m1['rbf'].constrain_bounded(1e-4, 10)
m1.ensure_default_constraints()
m1.randomize()
gauss_distr = GPy.likelihoods.gaussian(variance=initial_var_guess, D=1, N=Y.shape[0])
laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), gauss_distr)
m2 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel2, likelihood=laplace_likelihood)
gauss_distr2 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m2 = GPy.core.GP(X, Y.copy(), kernel=kernel2, likelihood=gauss_distr2, inference_method=laplace_inf)
m2.ensure_default_constraints()
m2.constrain_fixed('white', 1e-6)
m2.constrain_bounded('rbf', 1e-4, 10)
m2.constrain_bounded('noise', 1e-4, 10)
m2['white'].constrain_fixed(1e-6)
m2['rbf'].constrain_bounded(1e-4, 10)
m2['variance'].constrain_bounded(1e-4, 10)
m2.randomize()
if debug:
@ -667,7 +699,7 @@ class LaplaceTests(unittest.TestCase):
#Check Y's are the same
np.testing.assert_almost_equal(Y, m2.likelihood.Y, decimal=5)
np.testing.assert_almost_equal(m1.Y, m2.Y, decimal=5)
#Check marginals are the same
np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
#Check marginals are the same with random

85
GPy/testing/psi_stat_gradient_tests.py
View file

@ -9,42 +9,44 @@ import numpy
import GPy
import itertools
from GPy.core import Model
from GPy.core.parameterization.param import Param
from GPy.core.parameterization.transformations import Logexp
class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
super(PsiStatModel, self).__init__(name='psi stat test')
self.which = which
self.X = X
self.X_variance = X_variance
self.Z = Z
self.X = Param("X", X)
self.X_variance = Param('X_variance', X_variance, Logexp())
self.Z = Param("Z", Z)
self.N, self.input_dim = X.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
super(PsiStatModel, self).__init__()
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
def _get_param_names(self):
Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.num_inducing), range(self.input_dim))]
return Xnames + Znames + self.kern._get_param_names()
def _get_params(self):
return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
def _set_params(self, x, save_old=True, save_count=0):
start, end = 0, self.X.size
self.X = x[start:end].reshape(self.N, self.input_dim)
start, end = end, end + self.X_variance.size
self.X_variance = x[start: end].reshape(self.N, self.input_dim)
start, end = end, end + self.Z.size
self.Z = x[start: end].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(x[end:])
self.add_parameters(self.X, self.X_variance, self.Z, self.kern)
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def _log_likelihood_gradients(self):
def parameters_changed(self):
psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
self.X.gradient = psimu
self.X_variance.gradient = psiS
#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
except AttributeError: psiZ = numpy.zeros_like(self.Z)
self.Z.gradient = psiZ
#psiZ = numpy.ones(self.num_inducing * self.input_dim)
thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
N,M = self.X.shape[0], self.Z.shape[0]
dL_dpsi0, dL_dpsi1, dL_dpsi2 = numpy.zeros([N]), numpy.zeros([N,M]), numpy.zeros([N,M,M])
if self.which == 'psi0': dL_dpsi0 += 1
if self.which == 'psi1': dL_dpsi1 += 1
if self.which == 'psi2': dL_dpsi2 += 1
self.kern.update_gradients_variational(numpy.zeros([1,1]),
dL_dpsi0,
dL_dpsi1,
dL_dpsi2, self.X, self.X_variance, self.Z)
class DPsiStatTest(unittest.TestCase):
input_dim = 5
@ -57,61 +59,66 @@ class DPsiStatTest(unittest.TestCase):
Y = X.dot(numpy.random.randn(input_dim, input_dim))
# kernels = [GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.rbf(input_dim, ARD=True), GPy.kern.bias(input_dim)]
kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)]
kernels = [
GPy.kern.linear(input_dim),
GPy.kern.rbf(input_dim),
#GPy.kern.bias(input_dim),
#GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
#GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)
]
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
#m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
import ipdb;ipdb.set_trace()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi1(self):
for k in self.kernels:
m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
if __name__ == "__main__":

5
GPy/util/caching.py
View file

@ -1,10 +1,9 @@
import numpy as np
from ..core.parameterization.array_core import ObservableArray
from ..core.parameterization.array_core import ObservableArray, ParamList
class Cacher(object):
def __init__(self, operation, limit=5):
self.limit = int(limit)
self.operation=operation
self.cached_inputs = []
self.cached_inputs = ParamList([])
self.cached_outputs = []
self.inputs_changed = []