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https://github.com/SheffieldML/GPy.git
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596 lines
28 KiB
Python
596 lines
28 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import itertools
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import numpy
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from parameter_core import Constrainable, Gradcheckable, adjust_name_for_printing
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from array_core import ObservableArray, ParamList
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###### printing
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__constraints_name__ = "Constraint"
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__index_name__ = "Index"
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__tie_name__ = "Tied to"
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__precision__ = numpy.get_printoptions()['precision'] # numpy printing precision used, sublassing numpy ndarray after all
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__print_threshold__ = 5
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######
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class Float(numpy.float64, Constrainable):
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def __init__(self, f, base):
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super(Float,self).__init__(f)
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self._base = base
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class Param(ObservableArray, Constrainable, Gradcheckable):
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"""
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Parameter object for GPy models.
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:param name: name of the parameter to be printed
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:param input_array: array which this parameter handles
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You can add/remove constraints by calling constrain on the parameter itself, e.g:
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- self[:,1].constrain_positive()
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- self[0].tie_to(other)
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- self.untie()
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- self[:3,:].unconstrain()
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- self[1].fix()
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Fixing parameters will fix them to the value they are right now. If you change
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the fixed value, it will be fixed to the new value!
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See :py:class:`GPy.core.parameterized.Parameterized` for more details on constraining etc.
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This ndarray can be stored in lists and checked if it is in.
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>>> import numpy as np
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>>> x = np.random.normal(size=(10,3))
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>>> x in [[1], x, [3]]
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True
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WARNING: This overrides the functionality of x==y!!!
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Use numpy.equal(x,y) for element-wise equality testing.
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"""
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__array_priority__ = 0 # Never give back Param
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_fixes_ = None
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def __new__(cls, name, input_array, *args, **kwargs):
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obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
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obj._current_slice_ = (slice(obj.shape[0]),)
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obj._realshape_ = obj.shape
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obj._realsize_ = obj.size
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obj._realndim_ = obj.ndim
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obj._updated_ = False
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from index_operations import SetDict
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obj._tied_to_me_ = SetDict()
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obj._tied_to_ = []
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obj._original_ = True
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obj.gradient = None
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return obj
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def __init__(self, name, input_array):
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super(Param, self).__init__(name=name)
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def __array_finalize__(self, obj):
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# see InfoArray.__array_finalize__ for comments
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if obj is None: return
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super(Param, self).__array_finalize__(obj)
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self._direct_parent_ = getattr(obj, '_direct_parent_', None)
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self._parent_index_ = getattr(obj, '_parent_index_', None)
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self._current_slice_ = getattr(obj, '_current_slice_', None)
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self._tied_to_me_ = getattr(obj, '_tied_to_me_', None)
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self._tied_to_ = getattr(obj, '_tied_to_', None)
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self._realshape_ = getattr(obj, '_realshape_', None)
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self._realsize_ = getattr(obj, '_realsize_', None)
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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._name = getattr(obj, 'name', None)
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self.gradient = getattr(obj, 'gradient', 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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#===========================================================================
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# Pickling operations
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#===========================================================================
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def __reduce_ex__(self):
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func, args, state = super(Param, self).__reduce__()
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return func, args, (state,
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(self.name,
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self._direct_parent_,
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self._parent_index_,
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self._current_slice_,
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self._realshape_,
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self._realsize_,
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self._realndim_,
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self._tied_to_me_,
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self._tied_to_,
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self._updated_,
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)
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)
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def __setstate__(self, state):
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super(Param, self).__setstate__(state[0])
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state = list(state[1])
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self._updated_ = state.pop()
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self._tied_to_ = state.pop()
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self._tied_to_me_ = state.pop()
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self._realndim_ = state.pop()
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self._realsize_ = state.pop()
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self._realshape_ = state.pop()
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self._current_slice_ = state.pop()
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self._parent_index_ = state.pop()
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self._direct_parent_ = state.pop()
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self.name = state.pop()
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#===========================================================================
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# get/set parameters
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#===========================================================================
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def _set_params(self, param, update=True):
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self.flat = param
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self._notify_tied_parameters()
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self._notify_observers()
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def _get_params(self):
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return self.flat
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# @property
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# def name(self):
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# """
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# Name of this parameter.
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# This can be a callable without parameters. The callable will be called
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# every time the name property is accessed.
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# """
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# if callable(self.name):
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# return self.name()
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# return self.name
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# @name.setter
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# def name(self, new_name):
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# from_name = self.name
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# self.name = new_name
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# self._direct_parent_._name_changed(self, from_name)
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@property
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def _parameters_(self):
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return []
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def _collect_gradient(self, target):
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target[:] = self.gradient.flat
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#===========================================================================
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# Tying operations -> bugged, TODO
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#===========================================================================
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def tie_to(self, param):
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"""
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:param param: the parameter object to tie this parameter to.
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Can be ParamConcatenation (retrieved by regexp search)
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Tie this parameter to the given parameter.
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Broadcasting is not allowed, but you can tie a whole dimension to
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one parameter: self[:,0].tie_to(other), where other is a one-value
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parameter.
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Note: For now only one parameter can have ties, so all of a parameter
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will be removed, when re-tieing!
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"""
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#Note: this method will tie to the parameter which is the last in
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# the chain of ties. Thus, if you tie to a tied parameter,
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# this tie will be created to the parameter the param is tied
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# to.
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assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param, param.__class__)
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param = numpy.atleast_1d(param)
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if param.size != 1:
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raise NotImplementedError, "Broadcast tying is not implemented yet"
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try:
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if self._original_:
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self[:] = param
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else: # this happens when indexing created a copy of the array
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self._direct_parent_._get_original(self)[self._current_slice_] = param
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except ValueError:
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raise ValueError("Trying to tie {} with shape {} to {} with shape {}".format(self.name, self.shape, param.name, param.shape))
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if param is self:
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raise RuntimeError, 'Cyclic tieing is not allowed'
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# if len(param._tied_to_) > 0:
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# if (self._direct_parent_._get_original(self) is param._direct_parent_._get_original(param)
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# and len(set(self._raveled_index())&set(param._tied_to_[0]._raveled_index()))!=0):
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# raise RuntimeError, 'Cyclic tieing is not allowed'
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# self.tie_to(param._tied_to_[0])
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# return
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if not param in self._direct_parent_._get_original(self)._tied_to_:
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self._direct_parent_._get_original(self)._tied_to_ += [param]
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param._add_tie_listener(self)
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self._highest_parent_._set_fixed(self)
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cs = self._highest_parent_._constraints_for(param, param._raveled_index())
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for cs in self._highest_parent_._constraints_for(param, param._raveled_index()):
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[self.constrain(c, warning=False) for c in cs]
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# for t in self._tied_to_me_.keys():
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# if t is not self:
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# t.untie(self)
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# t.tie_to(param)
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def untie(self, *ties):
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"""
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remove all ties.
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"""
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[t._direct_parent_._get_original(t)._remove_tie_listener(self) for t in self._tied_to_]
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new_ties = []
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for t in self._direct_parent_._get_original(self)._tied_to_:
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for tied in t._tied_to_me_.keys():
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if t._parent_index_ is tied._parent_index_:
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new_ties.append(tied)
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self._direct_parent_._get_original(self)._tied_to_ = new_ties
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self._direct_parent_._get_original(self)._highest_parent_._set_unfixed(self)
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# self._direct_parent_._remove_tie(self, *params)
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def _notify_tied_parameters(self):
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for tied, ind in self._tied_to_me_.iteritems():
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tied._on_tied_parameter_changed(self.base, list(ind))
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def _add_tie_listener(self, tied_to_me):
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for t in self._tied_to_me_.keys():
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if tied_to_me._parent_index_ is t._parent_index_:
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t_rav_i = t._raveled_index()
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tr_rav_i = tied_to_me._raveled_index()
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new_index = list(set(t_rav_i) | set(tr_rav_i))
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tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
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self._tied_to_me_[tmp] = self._tied_to_me_[t] | set(self._raveled_index())
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del self._tied_to_me_[t]
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return
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self._tied_to_me_[tied_to_me] = set(self._raveled_index())
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def _remove_tie_listener(self, to_remove):
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for t in self._tied_to_me_.keys():
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if t._parent_index_ == to_remove._parent_index_:
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t_rav_i = t._raveled_index()
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tr_rav_i = to_remove._raveled_index()
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import ipdb;ipdb.set_trace()
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new_index = list(set(t_rav_i) - set(tr_rav_i))
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if new_index:
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tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
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self._tied_to_me_[tmp] = self._tied_to_me_[t]
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del self._tied_to_me_[t]
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if len(self._tied_to_me_[tmp]) == 0:
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del self._tied_to_me_[tmp]
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else:
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del self._tied_to_me_[t]
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def _on_tied_parameter_changed(self, val, ind):
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if not self._updated_: # not fast_array_equal(self, val[ind]):
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val = numpy.atleast_1d(val)
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self._updated_ = True
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if self._original_:
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self.__setitem__(slice(None), val[ind], update=False)
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else: # this happens when indexing created a copy of the array
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self._direct_parent_._get_original(self).__setitem__(self._current_slice_, val[ind], update=False)
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self._notify_tied_parameters()
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self._updated_ = False
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#===========================================================================
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# Prior Operations
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#===========================================================================
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def set_prior(self, prior):
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"""
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:param prior: prior to be set for this parameter
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Set prior for this parameter.
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"""
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if not hasattr(self._highest_parent_, '_set_prior'):
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raise AttributeError("Parent of type {} does not support priors".format(self._highest_parent_.__class__))
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self._highest_parent_._set_prior(self, prior)
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def unset_prior(self, *priors):
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"""
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:param priors: priors to remove from this parameter
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Remove all priors from this parameter
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"""
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self._highest_parent_._remove_prior(self, *priors)
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#===========================================================================
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# Array operations -> done
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#===========================================================================
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def __getitem__(self, s, *args, **kwargs):
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if not isinstance(s, tuple):
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s = (s,)
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if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
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s += (Ellipsis,)
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new_arr = super(Param, self).__getitem__(s, *args, **kwargs)
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try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
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except AttributeError: pass # returning 0d array or float, double etc
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return new_arr
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def __setitem__(self, s, val, update=True):
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super(Param, self).__setitem__(s, val, update=update)
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self._notify_tied_parameters()
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if update:
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self._highest_parent_.parameters_changed()
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#===========================================================================
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# Index Operations:
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#===========================================================================
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def _internal_offset(self):
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internal_offset = 0
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extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
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for i, si in enumerate(self._current_slice_[:self._realndim_]):
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if numpy.all(si == Ellipsis):
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continue
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if isinstance(si, slice):
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a = si.indices(self._realshape_[i])[0]
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elif isinstance(si, (list,numpy.ndarray,tuple)):
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a = si[0]
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else: a = si
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if a < 0:
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a = self._realshape_[i] + a
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internal_offset += a * extended_realshape[i]
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return internal_offset
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def _raveled_index(self, slice_index=None):
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# return an index array on the raveled array, which is formed by the current_slice
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# of this object
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extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
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ind = self._indices(slice_index)
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if ind.ndim < 2: ind = ind[:, None]
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return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape * x), 1, ind), dtype=int)
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def _expand_index(self, slice_index=None):
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# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
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# it basically translates slices to their respective index arrays and turns negative indices around
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# it tells you in the second return argument if it has only seen arrays as indices
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if slice_index is None:
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slice_index = self._current_slice_
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def f(a):
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a, b = a
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if a not in (slice(None), Ellipsis):
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if isinstance(a, slice):
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start, stop, step = a.indices(b)
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return numpy.r_[start:stop:step]
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elif isinstance(a, (list, numpy.ndarray, tuple)):
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a = numpy.asarray(a, dtype=int)
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a[a < 0] = b + a[a < 0]
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elif a < 0:
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a = b + a
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return numpy.r_[a]
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return numpy.r_[:b]
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return itertools.imap(f, itertools.izip_longest(slice_index[:self._realndim_], self._realshape_, fillvalue=slice(self.size)))
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#===========================================================================
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# Convienience
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#===========================================================================
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@property
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def is_fixed(self):
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return self._highest_parent_._is_fixed(self)
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def round(self, decimals=0, out=None):
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view = super(Param, self).round(decimals, out).view(Param)
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view.__array_finalize__(self)
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return view
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def _has_fixes(self):
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return False
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round.__doc__ = numpy.round.__doc__
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def _get_original(self, param):
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return self
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#===========================================================================
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# Printing -> done
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#===========================================================================
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@property
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def _description_str(self):
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if self.size <= 1: return ["%f" % self]
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else: return [str(self.shape)]
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def _parameter_names(self, add_name):
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return [self.name]
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@property
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def flattened_parameters(self):
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return [self]
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@property
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def parameter_shapes(self):
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return [self.shape]
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@property
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def _constraints_str(self):
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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)))]
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@property
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def _ties_str(self):
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return [t._short() for t in self._tied_to_] or ['']
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@property
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def name_hirarchical(self):
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if self.has_parent():
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return self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name)
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return adjust_name_for_printing(self.name)
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def __repr__(self, *args, **kwargs):
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name = "\033[1m{x:s}\033[0;0m:\n".format(
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x=self.name_hirarchical)
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return name + super(Param, self).__repr__(*args, **kwargs)
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def _ties_for(self, rav_index):
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# size = sum(p.size for p in self._tied_to_)
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ties = numpy.empty(shape=(len(self._tied_to_), numpy.size(rav_index)), dtype=Param)
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for i, tied_to in enumerate(self._tied_to_):
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for t, ind in tied_to._tied_to_me_.iteritems():
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if t._parent_index_ == self._parent_index_:
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matches = numpy.where(rav_index[:, None] == t._raveled_index()[None, :])
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tt_rav_index = tied_to._raveled_index()
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ind_rav_matches = numpy.where(tt_rav_index == numpy.array(list(ind)))[0]
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if len(ind) != 1: ties[i, matches[0][ind_rav_matches]] = numpy.take(tt_rav_index, matches[1], mode='wrap')[ind_rav_matches]
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else: ties[i, matches[0]] = numpy.take(tt_rav_index, matches[1], mode='wrap')
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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_)]))
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def _constraints_for(self, rav_index):
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return self._highest_parent_._constraints_for(self, rav_index)
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def _indices(self, slice_index=None):
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# get a int-array containing all indices in the first axis.
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if slice_index is None:
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slice_index = self._current_slice_
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if isinstance(slice_index, (tuple, list)):
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clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)]
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if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice)
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and len(set(map(len, clean_curr_slice))) <= 1):
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return numpy.fromiter(itertools.izip(*clean_curr_slice),
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dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
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expanded_index = list(self._expand_index(slice_index))
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return numpy.fromiter(itertools.product(*expanded_index),
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dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
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def _max_len_names(self, gen, header):
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return reduce(lambda a, b:max(a, len(b)), gen, len(header))
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def _max_len_values(self):
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return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.name_hirarchical))
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def _max_len_index(self, ind):
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return reduce(lambda a, b:max(a, len(str(b))), ind, len(__index_name__))
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|
def _short(self):
|
|
# short string to print
|
|
name = self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name)
|
|
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 + ']'
|
|
def __str__(self, constr_matrix=None, indices=None, ties=None, lc=None, lx=None, li=None, lt=None):
|
|
filter_ = self._current_slice_
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|
vals = self.flat
|
|
if indices is None: indices = self._indices(filter_)
|
|
ravi = self._raveled_index(filter_)
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|
if constr_matrix is None: constr_matrix = self._constraints_for(ravi)
|
|
if ties is None: ties = self._ties_for(ravi)
|
|
ties = [' '.join(map(lambda x: x._short(), t)) for t in ties]
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|
if lc is None: lc = self._max_len_names(constr_matrix, __constraints_name__)
|
|
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
|
|
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__()
|
|
|
|
class ParamConcatenation(object):
|
|
def __init__(self, params):
|
|
"""
|
|
Parameter concatenation for convienience of printing regular expression matched arrays
|
|
you can index this concatenation as if it was the flattened concatenation
|
|
of all the parameters it contains, same for setting parameters (Broadcasting enabled).
|
|
|
|
See :py:class:`GPy.core.parameter.Param` for more details on constraining.
|
|
"""
|
|
# self.params = params
|
|
self.params = ParamList([])
|
|
for p in params:
|
|
for p in p.flattened_parameters:
|
|
if p not in self.params:
|
|
self.params.append(p)
|
|
self._param_sizes = [p.size for p in self.params]
|
|
startstops = numpy.cumsum([0] + self._param_sizes)
|
|
self._param_slices_ = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
|
|
#===========================================================================
|
|
# Get/set items, enable broadcasting
|
|
#===========================================================================
|
|
def __getitem__(self, s):
|
|
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
|
|
params = [p._get_params()[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p._get_params()[ind[ps]])]
|
|
if len(params)==1: return params[0]
|
|
return ParamConcatenation(params)
|
|
def __setitem__(self, s, val, update=True):
|
|
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_)]
|
|
if update:
|
|
self.params[0]._highest_parent_.parameters_changed()
|
|
def _vals(self):
|
|
return numpy.hstack([p._get_params() for p in self.params])
|
|
#===========================================================================
|
|
# parameter operations:
|
|
#===========================================================================
|
|
def update_all_params(self):
|
|
self.params[0]._highest_parent_.parameters_changed()
|
|
|
|
def constrain(self, constraint, warning=True):
|
|
[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, 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, 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, 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]
|
|
|
|
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()
|
|
return p._constraints_for(ind), p._ties_for(ind)
|
|
params = self.params
|
|
constr_matrices, ties_matrices = zip(*map(f, params))
|
|
indices = [p._indices() for p in params]
|
|
lc = max([p._max_len_names(cm, __constraints_name__) for p, cm in itertools.izip(params, constr_matrices)])
|
|
lx = max([p._max_len_values() for p in params])
|
|
li = max([p._max_len_index(i) for p, i in itertools.izip(params, indices)])
|
|
lt = max([p._max_len_names(tm, __tie_name__) for p, tm in itertools.izip(params, ties_matrices)])
|
|
strings = [p.__str__(cm, i, tm, lc, lx, li, lt) for p, cm, i, tm in itertools.izip(params,constr_matrices,indices,ties_matrices)]
|
|
return "\n".join(strings)
|
|
return "\n{}\n".format(" -"+"- | -".join(['-'*l for l in [li,lx,lc,lt]])).join(strings)
|
|
def __repr__(self):
|
|
return "\n".join(map(repr,self.params))
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
|
from GPy.core.parameterized import Parameterized
|
|
from GPy.core.parameter import Param
|
|
|
|
#X = numpy.random.randn(2,3,1,5,2,4,3)
|
|
X = numpy.random.randn(3,2)
|
|
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))
|
|
|
|
p3 = Param("variance", numpy.random.rand())
|
|
p4 = Param("lengthscale", numpy.random.rand(2))
|
|
|
|
m = Parameterized()
|
|
rbf = Parameterized(name='rbf')
|
|
|
|
rbf.add_parameter(p3,p4)
|
|
m.add_parameter(p,p1,rbf)
|
|
|
|
print "setting params"
|
|
#print m.q_v[3:5,[1,4,5]]
|
|
print "constraining variance"
|
|
#m[".*variance"].constrain_positive()
|
|
#print "constraining rbf"
|
|
#m.rbf_l.constrain_positive()
|
|
#m.q_variance[1,[0,5,11,19,2]].tie_to(m.rbf_v)
|
|
#m.rbf_v.tie_to(m.rbf_l[0])
|
|
#m.rbf_l[0].tie_to(m.rbf_l[1])
|
|
#m.q_v.tie_to(m.rbf_v)
|
|
# m.rbf_l.tie_to(m.rbf_va)
|
|
# pt = numpy.array(params._get_params_transformed())
|
|
# ptr = numpy.random.randn(*pt.shape)
|
|
# params.X.tie_to(params.rbf_v)
|