a0cbf1c44a
* Reimplement conditions * Refactor datasets and implement LabelBatch --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
from pina.problem import AbstractProblem
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from pina import Condition
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from pina import Graph
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class SupervisedProblem(AbstractProblem):
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"""
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A problem definition for supervised learning in PINA.
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This class allows an easy and straightforward definition of a Supervised problem,
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based on a single condition of type `InputTargetCondition`
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:Example:
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>>> import torch
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>>> input_data = torch.rand((100, 10))
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>>> output_data = torch.rand((100, 10))
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>>> problem = SupervisedProblem(input_data, output_data)
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"""
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conditions = dict()
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output_variables = None
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def __init__(self, input_, output_):
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"""
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Initialize the SupervisedProblem class
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:param input_: Input data of the problem
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:type input_: torch.Tensor | Graph
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:param output_: Output data of the problem
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:type output_: torch.Tensor
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"""
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if isinstance(input_, Graph):
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input_ = input_.data
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self.conditions["data"] = Condition(input=input_, target=output_)
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super().__init__()
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