[SciPy-User] Avoiding inner for loops??
Martin De Kauwe
mdekauwe@gmail....
Sun Aug 19 18:47:45 CDT 2012
Perhaps simplifying, in 2D this is what I want if using loops
def fake_model(data1, data2, p1, p2):
""" complete nonsense """
return data1 + data2 * p1 * p2
grid_size = 5
nobs = 5
obs = np.zeros(nobs)
data1 = np.arange(nobs)
data2 = np.arange(nobs)
a = np.arange(grid_size)
b = np.arange(grid_size)
c = np.arange(grid_size)
ss = np.zeros(0)
for p1 in a:
for p2 in b:
ans = fake_model(data1, data2, p1, p2)
#ss = np.append(ss, np.sum(obs - ans)**2)
print ans
which would produce
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 2 4 6 8]
.
snip
.
[0 1 2 3 4]
[ 0 5 10 15 20]
[ 0 9 18 27 36]
[ 0 13 26 39 52]
[ 0 17 34 51 68]
And so I figured something like...
a = np.ones((grid_size,grid_size)) * np.arange(grid_size)[None,:]
b = np.ones((grid_size,grid_size)) * np.arange(grid_size)[:,None]
ans = fake_model(data1, data2, a, b)
Although this doesn't seem to work, but I think this might be along the
right lines? This produces
[[ 0. 1. 2. 3. 4.]
[ 0. 2. 6. 12. 20.]
[ 0. 3. 10. 21. 36.]
[ 0. 4. 14. 30. 52.]
[ 0. 5. 18. 39. 68.]]
On Sunday, August 19, 2012 7:07:59 PM UTC+10, Martin De Kauwe wrote:
>
> Hi,
>
> I need to avoid (at least) two inner for loops in what I am trying to do
> otherwise my processing takes forever. What is the best way to transfer
> what I am doing into a more "numpy way"? Essentially I am trying to call a
> model again for various different parameter combinations. The example is
> fictional, by the grid_size would ideally grow > 500 and by doing so the
> processing speed becomes very slow the way I have set things up..
>
> thanks.
>
> example.
>
>
> import numpy as np
>
> def fake_model(data1, data2, p1, p2, p3):
> """ complete nonsense """
> return data1 + data2 * p1 * p2 * p3
>
> data1 = np.random.rand(10) # the size of this arrays varies might be 10
> might be 15 etc
> data2 = np.random.rand(10) # the size of this arrays varies might be 10
> might be 15 etc
> obs = np.random.rand(10) # the size of this arrays varies might be 10
> might be 15 etc
>
> grid_size = 10 # Ideally this would be a large number
> param1 = np.linspace(5.0, 350, grid_size)
> param2 = np.linspace(5.0, 550, grid_size)
> param3 = np.linspace(1E-8, 10.5, grid_size)
> ss = np.zeros(0)
>
> for p1 in param1:
> for p2 in param2:
> for p3 in param3:
> ans = fake_model(data1, data2, p1, p2, p3)
>
> ss = np.append(ss, np.sum(obs - ans)**2)
> print np.sum(obs - ans)**2
>
>
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