fjxmlzn / DoppelGANger

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
http://arxiv.org/abs/1909.13403
BSD 3-Clause Clear License
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sample_len and sample_time #9

Closed patricxu closed 3 years ago

patricxu commented 3 years ago

Get confused by the meaning of the variables.

sample_len: The time series batch size. self.sample_time = int(self.data_feature.shape[1] / self.sample_len)

In my view: data_feature.shape[1]: the length(steps) of the time series of a training sample. and it's fixed value for all the sample. so the sample_time should be 0 <= sample_time < data_features.shape[1]

Did I misunderstand?

fjxmlzn commented 3 years ago

Sorry, the variable names are kind of confusing.

data_feature.shape[1]: You are correct. sample_len: The time series batch size, i.e. the number of time steps generated at each RNN rollout (parameter S in Section 4.1 in the paper). sample_time: The maximum number of RNN rollouts needed for generating a sample.

patricxu commented 3 years ago

Thank you!