[SciPy-User] Avoiding inner for loops??

[eat]

Hi,

On Sun, Aug 19, 2012 at 12:07 PM, Martin De Kauwe <mdekauwe at gmail.com>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
>
> You may like to utilize functionality of ix_ and newaxis, like:
In []: p1, p2, p3= ix_(param1, param2, param3)
In []: n_= newaxis
In []: ans= fake_model(data1[:, n_, n_, n_], data2[:, n_, n_, n_], p1, p2,
p3)
In []: ss2= ((obs[:, n_, n_, n_]- ans).sum(0)** 2).reshape(grid_size** 3)
In []: allclose(ss, ss2)
Out[]: True


My 2 cents,
-eat

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