[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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