37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
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import torch
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def get_random_messages(count, order):
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"""
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Generate a list of messages.
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:param count: Number of messages to generate.
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:param order: Number of possible messages.
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:return: One-dimensional vector with each entry being the index of the
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generated message which is between 0, inclusive, and `order`, exclusive.
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>>> get_random_messages(5)
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torch.tensor([0, 2, 0, 3, 4])
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"""
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return torch.randint(0, order, (count,))
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def messages_to_onehot(messages, order):
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"""
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Convert messages represented as indexes to one-hot encoding.
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:param messages: List of messages to convert.
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:param order: Number of possible messages.
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:return: One-hot encoded messages.
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>>> messages_to_onehot(torch.tensor([0, 2, 0, 3, 4]))
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torch.tensor([
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[1., 0., 0., 0., 0.],
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[0., 0., 1., 0., 0.],
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[1., 0., 0., 0., 0.],
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[0., 0., 0., 1., 0.],
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[0., 0., 0., 0., 1.],
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])
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"""
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return torch.nn.functional.one_hot(messages, num_classes=order).float()
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