This is the first year of Python being supported for the assignments in this course.
Since all the materials are written for Matlab, using Python is mainly for experienced Python users with knowledge of Numpy module
Be careful with indexing standards differences for Matlab and for Python.
Using Python is voluntary and a student who will decide to use it has to count with some restrictions
Upload system and assignment templates are in alpha version and we cannot guarantee a bug-free experience
We support only Python 2.7 for now (version supporting Python 3.5+ is in progress and may so the whole semester)
We will try to build an automatic evaluation script for every lab, but it is not guaranteed as it is quite demanding job. In case we fail to support the automatic evaluation we will change into personal evaluation instead.
In case of troubles with the upload system, it will be possible to submit the solutions personally.
Not working with correct shapes of input/output data: Shapes of all input/output data are defined in the docstring of individual methods. Please strictly stick to the prescribed shapes.
If you want to use i.e. np array <1xn> as (n,), you can use np.squeeze() in the beginning of the method.
For returning data in certain shape, you can use np.expand_dims() or np.at_leastXd() (where X is the number of dimmensions).