[SciPy-User] Avoiding inner for loops??
eat
e.antero.tammi@gmail....
Mon Aug 20 07:01:14 CDT 2012
Hi,
On Mon, Aug 20, 2012 at 2:47 AM, Martin De Kauwe <mdekauwe@gmail.com> wrote:
> 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
>
> Your script with a slightly modified data and parameters, will produce
[10 13 16 19 22]
[15 19 23 27 31]
[20 25 30 35 40]
snip
[100 121 142 163 184]
[125 151 177 203 229]
[150 181 212 243 274]
which is equivalent to:
In []: p1, p2= ix_(a, b)
In []: n_= newaxis
In []: ans= fake_model(data1[:, n_, n_], data2[:, n_, n_], p1, p2)
In []: ans.reshape(-1, grid_size** 2).T
Out[]:
array([[ 10, 13, 16, 19, 22],
[ 15, 19, 23, 27, 31],
[ 20, 25, 30, 35, 40],
snip
[100, 121, 142, 163, 184],
[125, 151, 177, 203, 229],
[150, 181, 212, 243, 274]])
Regards,
-eat
>
> 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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