The thread-hijacking was inadvertent, I assure you! :)
It's still not clear to me that setting pervoxel=False will work.
From http://www.scipy.org/Numpy_Example_List#mean the axis argument
determines whether the mean is calculated over all values in the array,
for each row, or for each column. myDataset.samples is a 2d array
(volumes x voxels). In the /mvpa/datasets/miscfx.py zscore method (which
I think is the code used when calling zscore(myDataset)) the axis
argument is set to {} when pervoxel is false, which calculates a single
mean for the entire array. When pervoxel is true the axis argument is
set to 0, which calculates the mean for each column in the
dataset.samples. I think for row-wise scaling the argument would need to
be 1, to calculate the mean and standard deviation row-wise.
thanks,
Jo
On 11/30/2010 6:53 PM, Yaroslav Halchenko wrote:
Hi J.A., The Great Thief of a Thread! ;)
I am sorry for our scarce docstring of zscore :-/
pervoxel=False is actually what you are looking for I believe --
row-wise standartization.
just be careful with it -- using it for generalization assessment is ok,
but features are not 'voxels' anylonger and you shouldn't look at their
sensitivities from then.
On Tue, 30 Nov 2010, J.A. Etzel wrote:
Setting perchunk=False and pervoxel=True normalizes the entire
column: each voxel has a mean of zero and standard deviation of one
over all volumes in the dataset.
Setting perchunk=False and pervoxel=False normalizes over all
columns: all voxels together have a mean of zero and standard
deviation of one over all volumes in the dataset.
Setting perchunk=True and pervoxel=False normalizes over all columns
within each chunk: all voxels together have a mean of zero and
standard deviation of one over all volumes in each chunk.
This gives various options for normalizing column-wise, but is it
possible to normalize row-wise? In other words, normalize so that
the voxels in each sample (volume) have a mean of zero and standard
deviation one? Chunks are irrelevant for this case, since each
sample is normalized separately. If this is already in pyMVPA, can
you point me in the right direction?
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