[SciPy-Dev] Seeking help/ advice for applying functions

eat e.antero.tammi@gmail....
Tue Mar 9 08:59:32 CST 2010


Robert Kern <robert.kern <at> gmail.com> writes:

> 
> Your example is not very clear. Can you write a less cryptic one with
> informative variable names and perhaps some comments about what each
> part is doing?
> 

"""
Hi,

I have tried to clarify my code. First part is the relevant one, the rest is
there just to provide some context to run some tests.

Also originally I should have posted this to Scipy-User list. Would it be
more appropriate to continue the discussion there?


Regards,
eat
"""

import numpy as np

## relevant part
# expect indicies to be sparse ~1% but hundred of thousands of elements
# also functions not necessary builtins and data may be large
def how1(functions, data):
    """ firsth approach to apply data to functions"""
    def how(indicies):
        return np.asarray([f(data) for f in functions[indicies]]).T
    return how


def how2(functions, data):
    """ second approach to apply data to functions"""
    def rr(data):
        """ reverse the 'roles' of data and function"""
        def f(g):
            return g(data)
        return f
    daf= rr(data)
    def how(indicies):
        return np.asarray(map(daf, functions[indicies])).T
    return how

# as I understand it, how1 and how2 boils down under the hood pretty
# much to the same code, so only syntatical differencies, right?

# and this is the key question: does there exist a more suitable
# scipy/ numpy solution for this situation?

# perhaps some kind of special vectorization as below (as pseudo code)?
def how3(functions, data):
    """ third approach to apply data to functions"""
    def vecme(functions, data, indicies):
        return functions[indicies](data)
    v= np.vectorize(vecme)
    def how(indicies):
        return np.asarray(v(functions, data, indicies)).T
    return how

## end relevant part
# rest is just some context where hows could be applied
def stream(m, n):
    """ mimic some external stream of 0\ 1 indicators"""
    np.random.seed(123)
    ind= np.asarray(np.random.randint(0, 2, (m, n)), dtype= bool)
    for k in xrange(m):
        yield ind[k, :]

def process(stream, how):
    """ consume stream"""
    for ind in stream:
        yield how(ind)

def run(hows, n):
    """run the hows"""
    for app in hows.keys():
        print 'approach:', app
        for r in process(stream(3, n), hows[app]):
            print np.round(r.squeeze(), 2)

if __name__ == '__main__':
    # some data and functions only as demonstration
    data= np.random.random((3, 1))
    fncs= np.asarray([np.sin, np.cos, np.tan, np.sinh, np.cosh, np.tanh])

    # produce equivalent results
    hows= {'firsth': how1(fncs, data),
           'second': how2(fncs, data)}
#           'third': how3(fncs, data)}
    run(hows, len(fncs))




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