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deleted listarray
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2 changed files with 2 additions and 17 deletions
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@ -6,19 +6,6 @@ __updated__ = '2013-12-16'
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import numpy as np
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import numpy as np
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from parameter_core import Observable
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from parameter_core import Observable
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class ListArray(np.ndarray):
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"""
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ndarray which can be stored in lists and checked if it is in.
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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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def __new__(cls, input_array):
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obj = np.asanyarray(input_array).view(cls)
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return obj
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#def __eq__(self, other):
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# return other is self
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class ParamList(list):
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class ParamList(list):
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def __contains__(self, other):
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def __contains__(self, other):
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@ -29,7 +16,7 @@ class ParamList(list):
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pass
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pass
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class ObservableArray(ListArray, Observable):
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class ObservableArray(np.ndarray, Observable):
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"""
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"""
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An ndarray which reports changes to its observers.
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An ndarray which reports changes to its observers.
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The observers can add themselves with a callable, which
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The observers can add themselves with a callable, which
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@ -46,6 +46,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
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"""
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"""
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__array_priority__ = -1 # Never give back Param
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__array_priority__ = -1 # Never give back Param
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_fixes_ = None
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_fixes_ = None
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_parameters_ = []
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def __new__(cls, name, input_array, default_constraint=None):
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def __new__(cls, name, input_array, default_constraint=None):
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obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
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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._current_slice_ = (slice(obj.shape[0]),)
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@ -144,9 +145,6 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
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# from_name = self.name
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# from_name = self.name
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# self.name = new_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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# 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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def _collect_gradient(self, target):
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target[:] = self.gradient.flat
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target[:] = self.gradient.flat
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#===========================================================================
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#===========================================================================
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