Unexpected behavior in 3D matrices

[delacylab]

Describe the bug Tslearn supports unequal time series by filling nans at the end. But this does not seem to work with 3D matrices where the dimension is greater than 1 as shown below.

To Reproduce X, y = random_walk_blobs(n_ts_per_blob=10, sz=3, d=2, n_blobs=2) #(20, 3, 2) shape X[19,2,1] = np.nan #Change last row, last timestep, last dimension to nan for row 19 clf = tslearn.svm.TimeSeriesSVC(C=1.0, gamma="auto", kernel="gak") clf.fit(X,y)

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

Expected behavior Nans are allowed at the end of matrices throughout tslearn examples and are used to fill unequal timeseries, so I am puzzled why this does not work.

Environment (please complete the following information):

• OS: Ubuntu 20.04
• tslearn version 0.5.3.2

Additional context Experimenting has shown that I can only avoid the nan error if the row's entire timestep for all dimensions is nan like with: X[19,2,:] = np.nan That works. So it appears that for each row, a timestep's features have to all be nan or all be a real number where it seems like that shouldn't be the case.