Python Pre-Define Function

Question

In my code I use the following structure to avoid conditions in a for loop:

if patch_type == "zeros":
    patch_fct = np.zeros
elif patch_type == "ones":
    patch_fct = np.ones
elif patch_type == "rand":
    patch_fct = np.random.random_sample
else:
    raise "Error"

for k in range(10**9):
    m, n = comp_size()
    bla = patch_fct((m,n))

where patch_fct can be easily used with tupels.

Now I want to use the same approach to create a patch_fct that takes a tupel and returns uniformly distributed random numbers between -1 and 1. How can I do that?

I would like to do something like:

patch_fct = 2. * (np.random.random_sample - 0.5)

The approach from above does not seem to be the right one.

Could you show us your approach for the actual problem and how it does not work? Currently we only have your approach for a problem that you already seem to have solved. — timgeb
– timgeb, Commented Nov 7, 2018 at 11:11
How about def patch_fct(tup): return 2*(np.random.random_sample(tup) - 0.5)? — timgeb
– timgeb, Commented Nov 7, 2018 at 11:15
Where does the input tuple get used in the output? Per your description, you don't seem to use it for anything at all. — Jongware
– Jongware, Commented Nov 7, 2018 at 11:18

timgeb · Accepted Answer · 2018-11-07 11:27:27Z

If you need a function that does not already exist, you can just define it and use its name from now on.

For example:

if foo:
    def patch_fct(tup):
        return 2*(np.random.random_sample(tup) - 0.5)
elif bar:
    def patch_fct(tup):
        # do something else
else:
    patch_fct = another_existing_function

The chain of ifs and elses can be written a bit more smoothly with the help of a dictionary.

For your original code, you could write

patch_functions = {'zeros': np.zeros, 
                   'ones': np.ones, 
                   'rand': np.random.random_sample}

and then use it like this:

>>> patch_functions['zeros'](5)
>>> array([0., 0., 0., 0., 0.])

This will automatically throw a KeyError if you are trying to access a key that does not exist in the dictionary.

You can also put self-defined functions inside a dictionary, either by defining them prior to insertion or using anonymous lambda functions. Demo:

>>> def fun1(tup):
...:    return sum(tup) + 1
>>> 
>>> my_functions = {'my_sum': fun1, 'my_random': lambda tup: 2*(np.random.random_sample(tup) - 0.5)}
>>> my_functions['my_sum']((2, 5))
>>> 8
>>> my_functions['my_random']((2, 5))
>>> 
array([[-0.20203832, -0.23868021,  0.72052191,  0.72931098, -0.57160796],
       [-0.45117601, -0.95461634, -0.52232593, -0.24011216, -0.83875935]])

kabanus · Accepted Answer · 2018-11-07 11:32:14Z

1

numpy provides such a function explicitly:

numpy.random.uniform(low=-1, high=1, size=None)

size is the amount of times to draw - this can be a tuple stating the dimensions of the resulting array. size=(10,10) will yield a 10x10 matrix.

If I understand you correctly then:

def patch_fct(size):
    return numpy.random.uniform(low=-1, high=1, size=size)

and size can be a tuple (or not).

In general searching numpy with some math/probability thing will yield the correct answer on the first hit.

edited Nov 7, 2018 at 11:32

answered Nov 7, 2018 at 11:22

kabanus

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6 Comments

Gilfoyle Over a year ago

I still wonder how to use this function as I used the other functions in my question? patch_fct=np.random.uniform(-1,1,size=None); print(patch_fct(10,10)) does not work.

kabanus Over a year ago

@Samuel what is patch_fct supposed to do? your question is unclear.

Gilfoyle Over a year ago

I want to avoid conditions in my code. Therefore I want to be able to use patch_fct((m,n)) instead of np.zero or np.ones etc.

kabanus Over a year ago

@Samuel You still did not say what is patch_fct(m,n) supposed to do. Does it return an mxn array with numbers drawn from the uniform distribution? if so see my edit. Does it return an mxn array with the same number, which is randomly selected? please be specific.

kabanus Over a year ago

@Samuel so that is exactly the function from numpy - just use a tuple with the size parameter.

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