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GPRegression working, gradients still todo
This commit is contained in:
Max Zwiessele
parent
9a70f27010
commit
27724fd456
3 changed files with 33 additions and 21 deletions
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@ -311,7 +311,7 @@ class Model(Parameterized):
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the objective cannot be computed.
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:param x: the parameters of the model.
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:parameter type: np.array
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:type x: np.array
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"""
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try:
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self._set_params_transformed(x)
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@ -329,7 +329,7 @@ class Model(Parameterized):
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Compute the objective function of the model and the gradient of the model at the point given by x.
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:param x: the point at which gradients are to be computed.
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:type np.array:
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:type x: np.array
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"""
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try:
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@ -45,7 +45,7 @@ class Param(numpy.ndarray):
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obj._name_ = name
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obj._parent_ = None
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obj._parent_index_ = None
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obj._updates_parent_ = None
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obj._gradient_parent_ = None
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obj._gradient_ = gradient
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obj._current_slice_ = (slice(obj.shape[0]),)
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obj._realshape_ = obj.shape
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@ -72,7 +72,7 @@ class Param(numpy.ndarray):
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self._realndim_ = getattr(obj, '_realndim_', None)
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self._updated_ = getattr(obj, '_updated_', None)
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self._original_ = getattr(obj, '_original_', None)
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self._updates_parent_ = getattr(obj, '_updates_parent_', None)
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self._gradient_parent_ = getattr(obj, '_gradient_parent_', None)
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def __array_wrap__(self, out_arr, context=None):
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return out_arr.view(numpy.ndarray)
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@ -85,7 +85,7 @@ class Param(numpy.ndarray):
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(self._name_,
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self._parent_,
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self._parent_index_,
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self._updates_parent_,
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self._gradient_parent_,
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self._gradient_,
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self._current_slice_,
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self._realshape_,
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@ -107,7 +107,7 @@ class Param(numpy.ndarray):
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self._realshape_ = state.pop()
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self._current_slice_ = state.pop()
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self._gradient_ = state.pop()
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self._updates_parent_ = state.pop()
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self._gradient_parent_ = state.pop()
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self._parent_index_ = state.pop()
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self._parent_ = state.pop()
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self._name_ = state.pop()
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@ -174,7 +174,7 @@ class Param(numpy.ndarray):
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self._parent_._get_original(self).__setitem__(self._current_slice_, transform.initialize(self), update=False)
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self._parent_._add_constrain(self, transform, warning)
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if update:
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self._updates_parent_.parameters_changed()
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self._parent_.parameters_changed()
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def constrain_positive(self, warning=True):
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"""
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:param warning: print a warning if re-constraining parameters.
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@ -321,7 +321,7 @@ class Param(numpy.ndarray):
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numpy.ndarray.__setitem__(self, s, val)
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self._fire_changed()
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if update:
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self._updates_parent_.parameters_changed()
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self._parent_.parameters_changed()
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#===========================================================================
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# Index Operations:
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#===========================================================================
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@ -467,7 +467,7 @@ class ParamConcatenation(object):
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[numpy.place(p, ind[ps], vals[ps]) and p._fire_changed()
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for p, ps in zip(self.params, self._param_slices_)]
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if update:
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self.params[0]._updates_parent_.parameters_changed()
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self.params[0]._parent_.parameters_changed()
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def _vals(self):
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return numpy.hstack([p._get_params() for p in self.params])
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#===========================================================================
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@ -489,8 +489,8 @@ class ParamConcatenation(object):
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def constrain_bounded(self, lower, upper, warning=True):
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[param.constrain_bounded(lower, upper, warning) for param in self.params]
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constrain_bounded.__doc__ = Param.constrain_bounded.__doc__
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def unconstrain(self, constraints=None):
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[param.unconstrain(constraints) for param in self.params]
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def unconstrain(self, *constraints):
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[param.unconstrain(*constraints) for param in self.params]
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unconstrain.__doc__ = Param.unconstrain.__doc__
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def unconstrain_negative(self):
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[param.unconstrain_negative() for param in self.params]
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@ -12,6 +12,7 @@ import transformations
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import itertools
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from re import compile, _pattern_type
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import re
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from GPy.util.misc import fast_array_equal
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#===============================================================================
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# Printing:
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@ -81,7 +82,7 @@ class Parameterized(object):
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#===========================================================================
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# Parameter connection for model creation:
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#===========================================================================
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def set_as_parameter(self, name, array, gradient, index=None, highest_parent=None):
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def set_as_parameter(self, name, array, gradient, index=None, gradient_parent=None):
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"""
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:param name: name of the parameter (in print and plots), can be callable without parameters
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:type name: str, callable
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@ -91,7 +92,7 @@ class Parameterized(object):
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:type gradient: callable
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:param index: (optional) index of the parameter when printing
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(:param highest_parent: connect these parameters to this class, but tell
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(:param gradient_parent: connect these parameters to this class, but tell
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updates to highest_parent, this is needed when parameterized classes
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contain parameterized classes, but want to access the parameters
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of their children)
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@ -107,7 +108,7 @@ class Parameterized(object):
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self._parameters_.append(Param(name, array, gradient))
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else:
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self._parameters_.insert(index, Param(name, array, gradient))
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self._connect_parameters(highest_parent=highest_parent)
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self._connect_parameters(gradient_parent=gradient_parent)
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def set_as_parameters(self, *parameters, **kwargs):
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"""
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:param parameters: the parameters to add
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@ -115,13 +116,13 @@ class Parameterized(object):
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Add all parameters to this parameter class, you can insert parameters
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at any given point using the :py:func:`list.insert` syntax
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at any given index using the :py:func:`list.insert` syntax
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"""
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if kwargs.get('index',None) is None:
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self._parameters_.extend(parameters)
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else:
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self._parameters_.insert(kwargs['index'], parameters)
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self._connect_parameters(highest_parent=kwargs.get('highest_parent', self))
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self._connect_parameters(gradient_parent=kwargs.get('gradient_parent', self))
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# def remove_parameter(self, *names_params_indices):
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# """
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# :param names_params_indices: mix of parameter_names, parameter objects, or indices
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@ -137,7 +138,7 @@ class Parameterized(object):
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def parameters_changed(self):
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# will be called as soon as paramters have changed
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pass
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def _connect_parameters(self, highest_parent=None):
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def _connect_parameters(self, gradient_parent=None):
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# connect parameterlist to this parameterized object
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# This just sets up the right connection for the params objects
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# to be used as parameters
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@ -147,11 +148,20 @@ class Parameterized(object):
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sizes = numpy.cumsum([0] + self._parameter_sizes_)
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self._parameter_size_ = sizes[-1]
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self._param_slices_ = [slice(start, stop) for start,stop in zip(sizes, sizes[1:])]
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for i, p in enumerate(self._parameters_):
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i = 0
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for p in self._parameters_:
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#if p._parent_ is None:
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p._parent_ = self
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p._parent_index_ = i
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p._updates_parent_ = highest_parent or self
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i += 1
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p._gradient_parent_ = gradient_parent or p._gradient_parent_ or self
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not_unique = []
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# for k,v in self.__dict__.iteritems():
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# try:
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# if fast_array_equal(v,p):
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# self.__dict__[k] = p
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# except: # parameter comparison, just for convenience
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# pass
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if p.name in self.__dict__:
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if p.base is self.__dict__[p.name] or p is self.__dict__[p.name]:
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self.__dict__[p.name] = p
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@ -218,7 +228,6 @@ class Parameterized(object):
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#===========================================================================
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def _get_param_names_transformed(self):
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return numpy.array([p.name+str(i) for p in self._parameters_ for i in p._indices()])[self._fixes_][0]
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def _get_params(self):
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# don't overwrite this anymore!
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return numpy.hstack([x._get_params() for x in self._parameters_])#numpy.fromiter(itertools.chain(*itertools.imap(lambda x: x._get_params(), self._parameters_)), dtype=numpy.float64, count=sum(self._parameter_sizes_))
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@ -233,6 +242,7 @@ class Parameterized(object):
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return p[self._fixes_]
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return p
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def _set_params_transformed(self, p):
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p = p.copy()
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if self._fixes_ is not None: tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp
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[numpy.put(p, ind, c.f(p[ind])) for c,ind in self._constraints_.iteritems() if c != __fixed__]
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self._set_params(p)
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@ -338,13 +348,15 @@ class Parameterized(object):
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print "Warning: re-constraining parameters:\n{}".format(param._short())
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return rav_i
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def _remove_constrain(self, param, *transforms, **kwargs):
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if transforms is ():
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if not transforms:
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transforms = self._constraints_.properties()
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removed_indices = numpy.array([]).astype(int)
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if "index" in kwargs: index = kwargs['index']
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else: index = self._raveled_index_for(param)
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for constr in transforms:
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removed = self._constraints_.remove(constr, index)
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if constr is __fixed__:
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self._set_unfixed(removed)
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removed_indices = numpy.union1d(removed_indices, removed)
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return removed_indices
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# convienience for iterating over items
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