add dataset and dataloader for sample points (#195)
* add dataset and dataloader for sample points * unittests
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@@ -22,6 +22,8 @@ class AbstractProblem(metaclass=ABCMeta):
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# varible to check if sampling is done. If no location
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# element is presented in Condition this variable is set to true
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self._have_sampled_points = {}
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for condition_name in self.conditions:
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self._have_sampled_points[condition_name] = False
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# put in self.input_pts all the points that we don't need to sample
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self._span_condition_points()
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@@ -102,15 +104,10 @@ class AbstractProblem(metaclass=ABCMeta):
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"""
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for condition_name in self.conditions:
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condition = self.conditions[condition_name]
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if hasattr(condition, 'equation') and hasattr(condition, 'input_points'):
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if hasattr(condition, 'input_points'):
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samples = condition.input_points
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elif hasattr(condition, 'output_points') and hasattr(condition, 'input_points'):
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samples = (condition.input_points, condition.output_points)
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# skip if we need to sample
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elif hasattr(condition, 'location'):
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self._have_sampled_points[condition_name] = False
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continue
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self.input_pts[condition_name] = samples
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self.input_pts[condition_name] = samples
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self._have_sampled_points[condition_name] = True
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def discretise_domain(self, n, mode = 'random', variables = 'all', locations = 'all'):
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"""
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@@ -204,7 +201,7 @@ class AbstractProblem(metaclass=ABCMeta):
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def add_points(self, new_points):
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"""
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Adding points to the already sampled points
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Adding points to the already sampled points.
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:param dict new_points: a dictionary with key the location to add the points
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and values the torch.Tensor points.
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