OptDMD.predict does not work with default argument factorization='evd'

[cgahr]

If I run OptDMD with default argument factorization='evd', the error 'OptDMD' object has no attribute '_eigs' is raised. This is an easy fix, replace self._eigs with self.eigs. However, doing so introduces a new bug.

Running OptDMD with factorization='svd' works fine.

Describe the bug

dmd = OptDMD(svd_rank=7)
dmd.fit(X)
dmd.predict(X)

The last statement causes an error.

To Reproduce
see previous code snippet.

Expected behavior
There shouldn't be an exception.

Output

Traceback (most recent call last):
  ...
  File "/home/user/anaconda3/envs/test/lib/python3.9/site-packages/pydmd/optdmd.py", line 235, in predict
    Y = np.linalg.multi_dot(
  File "<__array_function__ internals>", line 5, in multi_dot
  File "/home/user/anaconda3/envs/test/lib/python3.9/site-packages/numpy/linalg/linalg.py", line 2724, in multi_dot
    _assert_2d(*arrays)
  File "/home/user/anaconda3/envs/test/lib/python3.9/site-packages/numpy/linalg/linalg.py", line 190, in _assert_2d
    raise LinAlgError('%d-dimensional array given. Array must be '
numpy.linalg.LinAlgError: 1-dimensional array given. Array must be two-dimensional

Additional context
I looked into what causes this bug and there seems to be a mismatch in dimensions:

Y = np.linalg.multi_dot(
    [
        self._output_space,
        np.diag(self.eigs),
        self._input_space.T.conj(),
        X,
    ]
)

We have the following shapes:

self._output_space.shape = (128, 7)
np.diag(self.eigs).shape = (7, 7)
self._input_space.T.conj().shape = (7, )
X.shape = (128, )

This does not match. My best guess is, that the shape of the third matrix should be self._input_space.shape == (128, 7) so that the result after the multiplication has a shape of (128, ).