Guillaume and I just found a bug in some experiment code that was
basically caused by confusing semantics of max().  The same sort of
thing applies to min.  This is an FYI email to help others on the list
avoid this mistake, which is (I think) easy to make.

Python's max() function takes multiple arguments and returns the
largest one of them. (I won't go into the details of how it deals with
corner cases.)

IN CONTRAST

numpy's max() function takes multiple arguments and returns the
largest element[s] from the *first* argument.  The second argument is
used to identify the axis along which to evaluate the [python-style]
max.  The third argument is an array into which the result can be
written.

So for example:
.. code-block:: python

    >>> max(3, 4)
    4
    >>> numpy.max(3, 4)
    3
    >>> a,b,c = [numpy.asarray(i) for i in [0,1,2]]
    >>> numpy.max(a,b,c)
    0
    >>> c
    array(0)

Be careful!

Theano defines a max function (called theano.tensor.max) that is
similar numpy's max.

Theano also defines a function called theano.tensor.largest that is
closer to python's, but not identical since it works elemwise for
tensors.  There is a corresponding 'smallest' function that is like
min()
