[SciPy-User] Avoiding inner for loops??

[Martin De Kauwe]

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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