Mailman 3 March 2008 - NumPy-Discussion

confusion about eigenvector
by devnew＠gmail.com March 29, 2008

March 29, 2008

i all I am learning PCA method by reading up Turk&Petland papers etc while trying out PCA on a set of greyscale images using python, and numpy I tried to create eigenvectors and facespace. i have facesarray--- an NXP numpy.ndarray that contains data of images N=numof images,P=pixels in an image avgarray --1XP array containing avg value for each pixel adjustedfaces=facesarray-avgarray adjustedmatrix=matrix(adjustedfaces) adjustedmatrix_trans=adjustedmatrix.transpose() covariancematrix =adjustedmatrix*adjustedmatrix_trans evalues,evect=eigh(covariancematrix) after sorting such that most significant eigenvectors are selected. evectmatrix is now my eigenvectors matrix here is a sample using 4X3 greyscale images evalues [ -1.85852801e-13 6.31143639e+02 3.31182765e+03 5.29077871e+03] evect [[ 0.5 -0.06727772 0.6496399 -0.56871936] [ 0.5 -0.77317718 -0.37697426 0.10043632] [ 0.5 0.27108233 0.31014514 0.76179023] [ 0.5 0.56937257 -0.58281078 -0.29350719]] evectmatrix (sorted according to largest evalue first) [[-0.56871936 0.6496399 -0.06727772 0.5 ] [ 0.10043632 -0.37697426 -0.77317718 0.5 ] [ 0.76179023 0.31014514 0.27108233 0.5 ] [-0.29350719 -0.58281078 0.56937257 0.5 ]] then i can create facespace by facespace=evectmat*adjustedfaces till now i 've been following the steps as mentioned in the PCA tutorial(by Lindsay smith & others) what i want to know is that in the above evectmatrix is each row ([-0.56871936 0.6496399 -0.06727772 0.5 ] etc) an eigenvector? or does a column in the above matrix represent an eigenvector? to put it differently, should i represent an eigenvector by evectmatrix[i] or by (get_column_i_of(evectmatrix)).transpose() if someone can make this clear please do D

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Quikest way to create a diagonal matrix ?
by Pierre GM March 28, 2008

March 28, 2008

All, What's the quickest way to create a diagonal matrix ? I already have the elements above the main diagonal. Of course, I could use loops: >>>m=5 >>>z = numpy.arange(m*m).reshape(m,m) >>>for k in range(m): >>> for j in range(k+1,m): >>> z[j,k] = z[k,j] But I was looking for something more efficient. Thanks a lot in advance !

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Transforming an array of numbers to an array of formatted strings
by Alexander Michael March 28, 2008

March 28, 2008

Is there a better way than looping to perform the following transformation? >>> import numpy >>> int_data = numpy.arange(1,11, dtype=int) # just an example >>> str_data = int_data.astype('S4') >>> for i in xrange(len(int_data)): ... str_data[i] = 'S%03d' % int_data[i] >>> print str_data ['S001' 'S002' 'S003' 'S004' 'S005' 'S006' 'S007' 'S008' 'S009' 'S010'] That is, I want to format an array of numbers as strings. Thanks, Alex

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Arcos returns nan
by João Quinta da Fonseca March 28, 2008

March 28, 2008

I have a function that returns the dot product of two unit vectors. When I try to use arcos on the values returned I sometimes get the warning: "Warning: invalid value encountered in arccos", and the angle returned is nan. I found out that this happens for essentially co- linear vectors, for which the dot product function returns 1.0. This looks like 1.0 no matter how I print it but if I do: >>N.dot(a,b)>1, I get: >>True. Now I guess this arises because of the way computers store floats but shouldn't numpy take care of this somehow? Is it a bug? I don't seem to have this problem with Matlab or Fortran. João

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Re: [Numpy-discussion] f2py functions, docstrings, and epydoc
by Tom Loredo March 27, 2008

March 27, 2008

Pearu- > smll_offset = smll_offset > exec `smll_offset.__doc__` Thanks for the quick and helpful response! I'll give it a try. I don't grasp why it works, though. I suppose I don't need to, but... I'm guessing the exec adds stuff to the current namespace that isn't there until a fortran object's attributes are explicitly accessed. While I have your attention... could you clear this up, also just for my curiousity? It's probably related. > f2py generated functions (that, by the way, are > actually instances of `fortran` type and define __call__ method). I had wondered about this when I first encountered this issue, and thought maybe I could figure out how to put some hook into epydoc so it would document anything with a __call__ method. But it looks like 'fortran' objects *don't* have a __call__ (here _cbmlike is my f2py-generated module): In [1]: from inference.count._cbmlike import smllike In [2]: smllike Out[2]: <fortran object at 0x947a668> In [3]: dir smllike ------> dir(smllike) Out[3]: ['__doc__', '_cpointer'] In [4]: smllike.__call__ --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /home/inference/loredo/tex/meetings/head08/<ipython console> in <module>() AttributeError: __call__ Yet despite this apparent absence of __call__, I can magically call smllike just fine. Would you provide a quick explanation of what f2py and the fortran object are doing here? Thanks, Tom ------------------------------------------------- This mail sent through IMP: http://horde.org/imp/

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greedy loadtxt
by lorenzo bolla March 27, 2008

March 27, 2008

Hi all! I realized that numpy.loadtxt do not read the last character of an input file. This is annoying if the input file do not end with a newline. For example: data.txt ------- 1 2 3<EOF> In [33]: numpy.loadtxt('data.txt') Out[33]: array([ 1., 2.]) While: data.txt ------- 1 2 3 <EOF> In [33]: numpy.loadtxt('data.txt') Out[33]: array([ 1., 2., 3.]) Should I use numpy.fromfile, instead? L. -- Lorenzo Bolla lbolla(a)gmail.com http://lorenzobolla.emurse.com/

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f2py functions, docstrings, and epydoc
by Tom Loredo March 27, 2008

March 27, 2008

Hi folks- Can anyone offer any tips on how I can get epydoc to produce API documentation for functions in an f2py-produced module? Currently they get listed in the generated docs as "Variables": Variables psigc = <fortran object at 0xa3e46b0> sigctp = <fortran object at 0xa3e4698> smll_offset = <fortran object at 0xa3e46c8> Yet each of these objects is callable, and has a docstring. The module itself has docs that give a 1-line signature for each function, but that's only part of the docstring. One reason I'd like to see the full docstrings documented by epydoc is that, for key functions, I'm loading the functions into a module and *changing* the docstrings, to have info beyond the limited f2py-generated docstrings. On a related question, is there a way to provide input to f2py for function docstrings? The manual hints that triple-quoted multiline blocks in the .pyf can be used to provide documentation, but when I add them, they don't appear to be used. Thanks, Tom ------------------------------------------------- This mail sent through IMP: http://horde.org/imp/

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reconstruct image from eigenfaces
by royG March 27, 2008

March 27, 2008

hi i am trying to reconstruct face images from eigenfaces derrived from original set of face images. i represented orig images by an ndarray with each row for each image and each column for pixel intensity.I sorted the eigenvectors such that each row of sortedeigenvectors is an eigenvector(first row being the most significant). Thus my facespace has each row correspond to an eigenface image. facespace=dot(sortedeigenvectors_rowwise,adjfaces) where adjfaces=origfaces-averageface also i calculated the weights matrix by wk=dot(facespace[:selectednumberofEVectors,:],adjfaces.transpose() ).transpose() weights=abs(wk) Now I am trying to reconstruct the face images from this data.Since i am still learning this technique i couldn't figure out how to do the reconstruction can someone help/advise? RG

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accumarray
by Gabriel J.L. Beckers March 27, 2008

March 27, 2008

Does numpy have something like Matlab's accumarray? http://www.mathworks.com/access/helpdesk/help/techdoc/ref/accumarray.html Best, Gabriel

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Improving Docs on Wiki
by vel.accel＠gmail.com March 26, 2008

March 26, 2008

Hi, I want to know if creating individual documentation for each numpy routine on the scipy.org wiki would, for some administrative reason (or other) be frowned upon. Here is an example of what I'd like to do for all of numpy's routines. http://www.scipy.org/sort. After each routine is properly documented there, We can have various index, category, and cross references. Oh boy :-) -Dieter

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