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johannfaouzi avatar johannfaouzi commented on May 13, 2024 1

Hi,

The error comes from this private method:

def _check_constant(self, X):
if np.any(np.max(X, axis=1) - np.min(X, axis=1) == 0):
raise ValueError("At least one sample is constant.")

If this error is raised, it means that there is at least one sample (i.e. one time series) that is constant. The MarkovTransitionField algorithm discretizes time series into n_bins bins, which would lead to a single bin if a time series is constant. It may be better to raise a warning rather than an error.

If you remove the constant time series from your 2D-array, this error will no longer be raised. You could find the corresponding indices using np.where(np.var(x_train_np, axis=1) == 0)[0].

I hope that this helps a bit.

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wwjd1234 avatar wwjd1234 commented on May 13, 2024

I have this issue as well when padding my time series with zeros so that they are all the same length

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