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25 lines
847 B
25 lines
847 B
import torch
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def preprocess_data(data, skip=True):
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# Assuming 'data' is a tensor of shape [n_samples, 3]
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if not skip:
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# Compute argmin and argmax for each row
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argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
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argmax_values = torch.argmax(data, dim=1, keepdim=True).float()
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# Normalize or scale argmin and argmax if necessary
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# For example, here I am just dividing by the number of features
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argmin_values /= data.shape[1] - 1
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argmax_values /= data.shape[1] - 1
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# Concatenate the argmin and argmax values to the original data
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new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
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else:
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new_data = data
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return new_data
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PURE_RGB = preprocess_data(
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torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=torch.float32)
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)
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