50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
import torch
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class LabelTensor():
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def __init__(self, x, labels):
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if len(labels) != x.shape[1]:
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print(len(labels), x.shape[1])
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raise ValueError
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self.__labels = labels
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self.tensor = x
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def __getitem__(self, key):
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if key in self.labels:
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return self.tensor[:, self.labels.index(key)]
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else:
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return self.tensor.__getitem__(key)
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def __repr__(self):
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return self.tensor
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def __str__(self):
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return self.tensor, self.labels
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@property
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def labels(self):
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return self.__labels
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@staticmethod
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def hstack(labeltensor_list):
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concatenated_tensor = torch.cat([lt.tensor for lt in labeltensor_list], axis=1)
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concatenated_label = sum([lt.labels for lt in labeltensor_list], [])
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return LabelTensor(concatenated_tensor, concatenated_label)
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if __name__ == "__main__":
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import numpy as np
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a = np.random.uniform(size=(20, 3))
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a = np.random.uniform(size=(20, 3))
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p = torch.from_numpy(a)
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t = LabelTensor(p, labels=['u', 'p', 't'])
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print(t)
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print(t['u'])
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t *= 2
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print(t['u'])
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print(t[:, 0])
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