Mercurial > pylearn
diff pylearn/sandbox/scan_inputs_groups.py @ 771:72730f38d1fb
opt of the FillMissing op. Now 80-90% faster python implementation.
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Wed, 10 Jun 2009 13:42:56 -0400 |
| parents | 742972b6906a |
| children | b6670cb57101 |
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--- a/pylearn/sandbox/scan_inputs_groups.py Tue Jun 09 21:39:50 2009 -0400 +++ b/pylearn/sandbox/scan_inputs_groups.py Wed Jun 10 13:42:56 2009 -0400 @@ -613,19 +613,46 @@ mask = mask[0] if self.fill_with_is_array: - ignore_k = len(out.shape) - len(self.fill_with.shape) - assert ignore_k >= 0 - - if self.fill_with_is_array: - for (idx, v) in numpy.ndenumerate(out): - if numpy.isnan(v): - out[idx] = self.fill_with[idx[ignore_k:]] - mask[idx] = 0 + #numpy.ndenumerate is slower then a loop + #so we optimise for some number of dimension frequently used + if out.ndim==1: + assert self.fill_with.ndim==1 + for i in range(out.shape[0]): + if numpy.isnan(out[i]): + out[i] = self.fill_with[i] + mask[i] = 0 + elif out.ndim==2 and self.fill_with.ndim==1: + for i in range(out.shape[0]): + for j in range(out.shape[1]): + if numpy.isnan(out[i,j]): + out[i,j] = self.fill_with[j] + mask[i,j] = 0 + else: + ignore_k = out.ndim - self.fill_with.ndim + assert ignore_k >= 0 + for (idx, v) in numpy.ndenumerate(out): + if numpy.isnan(v): + out[idx] = self.fill_with[idx[ignore_k:]] + mask[idx] = 0 else: - for (idx, v) in numpy.ndenumerate(out): - if numpy.isnan(v): - out[idx] = self.fill_with - mask[idx] = 0 + #numpy.ndenumerate is slower then a loop + #so we optimise for some number of dimension frequently used + if out.ndim==1: + for i in range(out.shape[0]): + if numpy.isnan(out[i]): + out[i] = self.fill_with + mask[i] = 0 + elif out.ndim==2: + for i in range(out.shape[0]): + for j in range(out.shape[1]): + if numpy.isnan(out[i,j]): + out[i,j] = self.fill_with + mask[i,j] = 0 + else: + for (idx, v) in numpy.ndenumerate(out): + if numpy.isnan(out[idx]): + out[idx] = self.fill_with + mask[idx] = 0 def grad(self, inputs, (out_grad, mask_grad, )): return [out_grad]