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checkgrad (╯°□°）╯︵ ┻━┻

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Max Zwiessele 2014-02-13 21:59:08 +00:00
parent 9af4c34f90
commit ffd09c7820
4 changed files with 9 additions and 5 deletions
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4
GPy/core/model.py
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@ -455,8 +455,8 @@ class Model(Parameterized):
if self._has_fixes(): if self._has_fixes():
indices = np.r_[:self.size] indices = np.r_[:self.size]
which = (param_index[:,None]==indices[self._fixes_][None,:]).nonzero() which = (param_index[:,None]==indices[self._fixes_][None,:]).nonzero()
transformed_index = (indices-(~self._fixes_).cumsum())[which[1]] param_index = param_index[which[0]]
param_index = indices[which[0]] transformed_index = (indices-(~self._fixes_).cumsum())[param_index]
print param_index, transformed_index print param_index, transformed_index
else: else:
transformed_index = param_index transformed_index = param_index

2
GPy/core/parameterization/array_core.py
View file

@ -28,8 +28,8 @@ class ObservableArray(np.ndarray, Observable):
""" """
__array_priority__ = -1 # Never give back ObservableArray __array_priority__ = -1 # Never give back ObservableArray
def __new__(cls, input_array): def __new__(cls, input_array):
cls.__name__ = "ObservableArray\n "
obj = np.atleast_1d(input_array).view(cls) obj = np.atleast_1d(input_array).view(cls)
cls.__name__ = "ObservableArray\n "
obj._observers_ = {} obj._observers_ = {}
return obj return obj
def __array_finalize__(self, obj): def __array_finalize__(self, obj):

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

@ -43,6 +43,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
_parameters_ = [] _parameters_ = []
def __new__(cls, name, input_array, default_constraint=None): def __new__(cls, name, input_array, default_constraint=None):
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array)) obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
cls.__name__ = "Param"
obj._current_slice_ = (slice(obj.shape[0]),) obj._current_slice_ = (slice(obj.shape[0]),)
obj._realshape_ = obj.shape obj._realshape_ = obj.shape
obj._realsize_ = obj.size obj._realsize_ = obj.size
@ -75,6 +76,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
self._original_ = getattr(obj, '_original_', None) self._original_ = getattr(obj, '_original_', None)
self._name = getattr(obj, 'name', None) self._name = getattr(obj, 'name', None)
self.gradient = getattr(obj, 'gradient', None) self.gradient = getattr(obj, 'gradient', None)
self.constraints = getattr(obj, 'constraints', None)
def __array_wrap__(self, out_arr, context=None): def __array_wrap__(self, out_arr, context=None):
return out_arr.view(numpy.ndarray) return out_arr.view(numpy.ndarray)
@ -391,6 +393,9 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
slice_index = self._current_slice_ slice_index = self._current_slice_
if isinstance(slice_index, (tuple, list)): if isinstance(slice_index, (tuple, list)):
clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)] clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)]
for i in range(self._realndim_-len(clean_curr_slice)):
i+=len(clean_curr_slice)
clean_curr_slice += range(self._realshape_[i])
if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice) 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), return numpy.fromiter(itertools.izip(*clean_curr_slice),

1
GPy/examples/regression.py
View file

@ -473,7 +473,6 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
Z = np.random.uniform(-3., 3., (7, 1)) Z = np.random.uniform(-3., 3., (7, 1))
k = GPy.kern.rbf(1) k = GPy.kern.rbf(1)
import ipdb;ipdb.set_trace()
# create simple GP Model - no input uncertainty on this one # create simple GP Model - no input uncertainty on this one
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.rbf(1), Z=Z) m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.rbf(1), Z=Z)