Mailman 3 March 2017 - scikit-learn

Bookmarklet to view documentation on CircleCI
by Joel Nothman April 7, 2017

April 7, 2017

At https://gist.github.com/jnothman/bf76d02f60af6476221ec65c63c77e60 I've created a bookmarklet which, when viewing a pull request page for which the CircleCI build has finished, will identify the circle build number and open a new tab with the changed documentation files corresponding to that PR.

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urgent help in scikit-learn
by Shuchi Mala April 5, 2017

April 5, 2017

Hi everyone, I have the data with following attributes: (Latitude, Longitude). Now I am performing clustering using DBSCAN for my data. I have following doubts: 1. How can I add data to the data set of the package? 2. How I can calculate Rand index for my data? 3. How to use make_blobs command for my data? Sample of my data is : Latitude Longitude 37.76901 -122.429299 37.76904 -122.42913 37.76878 -122.429092 37.7763 -122.424249 37.77627 -122.424657 With Best Regards, Shuchi Mala Research Scholar Department of Civil Engineering MNIT Jaipur

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GSoC 2017
by Jacob Schreiber April 3, 2017

April 3, 2017

Starting yesterday, students were able to submit their proposals on the GSoC website. Please review this site <https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-(GS…> thoroughly before making a submission. We're eager to hear what prospective students have in mind for a contribution to sklearn. As we've said before, mentor time is at a premium this year. If you've posted a proposal and we haven't responded, please keep poking us. I know that personally I tend to wake up to between 30-70 emails and have to triage based on my availability, and that Gael likely scoffs at this small number. Things fall through the cracks. If you haven't heard back that doesn't mean we don't want your submission, please submit or ask for feedback! A strong factor in determining if you're going to be chosen will be your availability with the code and methods you'd like to work on. It is less likely that we will take someone unfamiliar with the code base this year, as there is a large starting cost to getting familiar with an intricate code-base. In your application please emphasize your prior experience with either sklearn code, cython code (if applicable for your project) or machine learning code in general. Let us know if you have any other questions. Jacob

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GSoC proposal - linear model
by Konstantinos Katrioplas March 31, 2017

March 31, 2017

Dear all, here is a draft of my proposal <https://gist.github.com/kkatrio/9710b0a37f042a2df784cda7082d5523>on improving online learning for linear models with softmax and AdaGrad. I look forward to your feedback, Konstantinos

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Data type returned by PLSR different from other estimators
by GOMEZ TAMAYO, JOSE CARLOS March 31, 2017

March 31, 2017

Hi there, I have recently run into a problem when dealing with PLSR (and other cross-decomposition methods) prediction output. Unlike other estimators which return a numpy array containing the predictions, PLSR returns a list of single-value lists containing the predictions. I do not know why this done this way (perhaps there is a reason unknown to me) but the fact the estimator is returning a different data type should be advised in the documentation. It is easily solved through modifying the source code or just by overriding the method. Cheers, Jose Carlos Gómez

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GSoC 2017 Proposal: Improve online learning for linear models
by Yizheng Zhao March 30, 2017

March 30, 2017

Hi developers, It is excited that I have opportunity work with you! I am Yizheng Zhao, a graduate student at Carnegie Mellon University majoring in Software Engineering and I’ve got my Bachelor’s degree in Math in 2016 at Jilin University. I love python and machine learning and that why I wanna make my own contribution to community. I have 2 years experience developing with python and I am quite familiar with scikit-learn as a user. In college, I learned several machine learning algorithms and their mathematical derivation. I believe I can do better than others with my strong Math background and coding skills. Here is my proposal. https://github.com/YizhengZHAO/scikit-learn/wiki/GSoC-2017-:-Improve-online… <https://github.com/YizhengZHAO/scikit-learn/wiki/GSoC-2017-:-Improve-online…> BTW, could you please give me more explanation about “A tool to set the learning rate on a few epochs”? I am happy to get suggestions from the community. Sincerely, Yizheng

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Announcement: scikit-image 0.13.0
by Juan Nunez-Iglesias March 29, 2017

March 29, 2017

We're happy to (finally) announce the release of scikit-image v0.13.0! Special thanks to our many contributors for making it possible! This release is the result of over a year of work, with over 200 pull requests by 82 contributors. Linux and macOS wheels are available now on PyPI <https://pypi.python.org/pypi/scikit-image>, together with a source distribution. Use "pip install -U scikit-image" to get the latest version! Packages on conda-forge, Windows wheels, and Debian packages should be available within the next few days. scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our gallery of examples http://scikit-image.org/docs/dev/auto_examples/ Highlights ---------- - Improved n-dimensional image support. This release adds nD support to: * ``regionprops`` computation for centroids (#2083) * ``segmentation.clear_border`` (#2087) * Hessian matrix (#2194) - In addition, the following new functions support nD images: * new wavelet denoising function, ``restoration.denoise_wavelet`` (#1833, #2190, #2238, #2240, #2241, #2242, #2462) * new thresholding functions, ``filters.threshold_sauvola`` and ``filters.threshold_niblack`` (#2266, #2441) * new local maximum, local minimum, hmaxima, hminima functions (#2449) - Grey level co-occurrence matrix (GLCM) now works with uint16 images - ``filters.try_all_threshold`` to rapidly see output of various thresholding methods - Frangi and Hessian filters (2D only) (#2153) - New *compact watershed* algorithm in ``segmentation.watershed`` (#2211) - New *shape index* algorithm in ``feature.shape_index`` (#2312) New functions and features -------------------------- - Add threshold minimum algorithm (#2104) - Implement mean and triangle thresholding (#2126) - Add Frangi and Hessian filters (#2153) - add bbox_area to region properties (#2187) - colorconv: Add rgba2rgb() (#2181) - Lewiner marching cubes algorithm (#2052) - image inversion (#2199) - wavelet denoising (from #1833) (#2190) - routine to estimate the noise standard deviation from an image (#1837) - Add compact watershed and clean up existing watershed (#2211) - Added the missing 'grey2rgb' function. (#2316) - Shape index (#2312) - Fundamental and essential matrix 8-point algorithm (#1357) - Add YUV, YIQ, YPbPr, YCbCr colorspaces - Detection of local extrema from morphology (#2449) - shannon entropy (#2416) Documentation improvements -------------------------- - add details about github SSH keys in contributing page (#2073) - Add example for felzenszwalb image segmentation (#2096) - Sphinx gallery for example gallery (#2078) - Improved region boundary RAG docs (#2106) - Add gallery Lucy-Richardson deconvolution algorithm (#2376) - Gallery: Use Horse to illustrate Convex Hull (#2431) - Add working with OpenCV in user guide (#2519) Code improvements ----------------- - Remove lena image from test suite (#1985) - Remove duplicate mean calculation in skimage.feature.match_template (#1980) - Add nD support to clear_border (#2087) - Add uint16 images support for co-occurrence matrix (#2095) - Add default parameters for Gaussian and median filters (#2151) - try_all to choose the best threshold algorithm (#2110) - Add support for multichannel in Felzenszwalb segmentation (#2134) - Improved SimilarityTransform, new EuclideanTransform class (#2044) - ENH: Speed up Hessian matrix computation (#2194) - add n-dimensional support to denoise_wavelet (#2242) - Speedup ``inpaint_biharmonic`` (#2234) - Update hessian matrix code to include order kwarg (#2327) - Handle cases for label2rgb where input labels are negative and/or nonconsecutive (#2370) - Added watershed_line parameter (#2393) API Changes ----------- - Remove deprecated ``filter`` module. Use ``filters`` instead. (#2023) - Remove ``skimage.filters.canny`` links. Use ``feature.canny`` instead. (#2024) - Removed Python 2.6 support and related checks (#2033) - Remove deprecated {h/v}sobel, {h/v}prewitt, {h/v}scharr, roberts_{positive/negative} filters (#2159) - Remove deprecated ``_mode_deprecations`` (#2156) - Remove deprecated None defaults in ``rescale_intensity`` (#2161) - Parameters ``ntiles_x`` and ``ntiles_y`` have been removed from ``exposure.equalize_adapthist`` - The minimum NumPy version is now 1.11, and the minimum SciPy version is now 0.17 Deprecations ------------ - clip_negative will be set to false by default in version 0.15 (func: dtype_limits) (#2228) - Deprecate "dynamic_range" in favor of "data_range" (#2384) - The default value of the ``circle`` argument to ``radon`` and ``iradon`` transforms will be ``True`` in 0.15 (#2235) - The default value of ``multichannel`` for ``denoise_bilateral`` and ``denoise_nl_means`` will be ``False`` in 0.15 - The default value of ``block_norm`` in ``feature.hog`` will be L2-Hysteresis in 0.15. - The ``threshold_adaptive`` function is deprecated. Use ``threshold_local`` instead. - The default value of ``mode`` in ``transform.swirl``, ``resize``, and ``rescale`` will be "reflect" in 0.15. For a complete list of contributors and pull requests merged in this release, please see our release notes online: https://github.com/scikit-image/scikit-image/blob/master/doc/release/releas… Please spread the word, including on Twitter <https://twitter.com/jnuneziglesias/status/847145183250665472>! Enjoy! Juan.

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Announcement: scikit-image 0.13.0
by Juan Nunez-Iglesias March 29, 2017

March 29, 2017

We're happy to (finally) announce the release of scikit-image v0.13.0! Special thanks to all our contributors who made this possible. Linux and macOS wheels are available now on PyPI, as well as a source distribution. A conda-forge package, Windows wheels, and Debian packages should arrive in the coming days. scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our gallery of examples http://scikit-image.org/docs/dev/auto_examples/ Highlights ---------- This release is the result of a year of work, with over 200 pull requests by 82 contributors. Highlights include: - Improved n-dimensional image support. This release adds nD support to: * ``regionprops`` computation for centroids (#2083) * ``segmentation.clear_border`` (#2087) * Hessian matrix (#2194, #2327) - In addition, the following new functions support nD images: * new wavelet denoising function, ``restoration.denoise_wavelet`` (#1833, #2190, #2238, #2240, #2241, #2242, #2462) * new thresholding functions, ``filters.threshold_sauvola`` and ``filters.threshold_niblack`` (#2266, #2441) * new local maximum, local minimum, hmaxima, hminima functions (#2449) - Grey level co-occurrence matrix (GLCM) now works with uint16 images - ``filters.try_all_threshold`` to rapidly see output of various thresholding methods - Frangi and Hessian filters (2D only) (#2153) - New *compact watershed* algorithm in ``segmentation.watershed`` (#2211) - New *shape index* algorithm in ``feature.shape_index`` (#2312) New functions and features -------------------------- - Add threshold minimum algorithm (#2104) - Implement mean and triangle thresholding (#2126) - Add Frangi and Hessian filters (#2153) - add bbox_area to region properties (#2187) - colorconv: Add rgba2rgb() (#2181) - Lewiner marching cubes algorithm (#2052) - image inversion (#2199) - wavelet denoising (from #1833) (#2190) - routine to estimate the noise standard deviation from an image (#1837) - Add compact watershed and clean up existing watershed (#2211) - Added the missing 'grey2rgb' function. (#2316) - Shape index (#2312) - Fundamental and essential matrix 8-point algorithm (#1357) - Add YUV, YIQ, YPbPr, YCbCr colorspaces - Detection of local extrema from morphology (#2449) - shannon entropy (#2416) Documentation improvements -------------------------- - add details about github SSH keys in contributing page (#2073) - Add example for felzenszwalb image segmentation (#2096) - Sphinx gallery for example gallery (#2078) - Improved region boundary RAG docs (#2106) - Add gallery Lucy-Richardson deconvolution algorithm (#2376) - Gallery: Use Horse to illustrate Convex Hull (#2431) - Add working with OpenCV in user guide (#2519) Code improvements ----------------- - Remove lena image from test suite (#1985) - Remove duplicate mean calculation in skimage.feature.match_template (#1980) - Add nD support to clear_border (#2087) - Add uint16 images support for co-occurrence matrix (#2095) - Add default parameters for Gaussian and median filters (#2151) - try_all to choose the best threshold algorithm (#2110) - Add support for multichannel in Felzenszwalb segmentation (#2134) - Improved SimilarityTransform, new EuclideanTransform class (#2044) - ENH: Speed up Hessian matrix computation (#2194) - add n-dimensional support to denoise_wavelet (#2242) - Speedup ``inpaint_biharmonic`` (#2234) - Update hessian matrix code to include order kwarg (#2327) - Handle cases for label2rgb where input labels are negative and/or nonconsecutive (#2370) - Added watershed_line parameter (#2393) API Changes ----------- - Remove deprecated ``filter`` module. Use ``filters`` instead. (#2023) - Remove ``skimage.filters.canny`` links. Use ``feature.canny`` instead. (#2024) - Removed Python 2.6 support and related checks (#2033) - Remove deprecated {h/v}sobel, {h/v}prewitt, {h/v}scharr, roberts_{positive/negative} filters (#2159) - Remove deprecated ``_mode_deprecations`` (#2156) - Remove deprecated None defaults in ``rescale_intensity`` (#2161) - Parameters ``ntiles_x`` and ``ntiles_y`` have been removed from ``exposure.equalize_adapthist`` - The minimum NumPy version is now 1.11, and the minimum SciPy version is now 0.17 Deprecations ------------ - clip_negative will be set to false by default in version 0.15 (func: dtype_limits) (#2228) - Deprecate "dynamic_range" in favor of "data_range" (#2384) - The default value of the ``circle`` argument to ``radon`` and ``iradon`` transforms will be ``True`` in 0.15 (#2235) - The default value of ``multichannel`` for ``denoise_bilateral`` and ``denoise_nl_means`` will be ``False`` in 0.15 - The default value of ``block_norm`` in ``feature.hog`` will be L2-Hysteresis in 0.15. - The ``threshold_adaptive`` function is deprecated. Use ``threshold_local`` instead. - The default value of ``mode`` in ``transform.swirl``, ``resize``, and ``rescale`` will be "reflect" in 0.15. For a complete list of contributors to this release, and PRs merged, please see the online release notes: https://github.com/scikit-image/scikit-image/blob/master/doc/release/releas… Enjoy!

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decision trees
by Andrew Howe March 29, 2017

March 29, 2017

Is one-hot encoding still the most accurate way to pass categorical variables to decision trees in scikit-learn (i.e. without causing spurious ordering/interpolation)? Thanks. Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD www.andrewhowe.com http://www.linkedin.com/in/ahowe42 https://www.researchgate.net/profile/John_Howe12/ I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~>

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Re: [scikit-learn] Using Scikit-Learn to predict magnetism in chemical systems
by Tommaso Costanzo March 28, 2017

March 28, 2017

Dear Henrique, Yes, my previous representation looks like a Z-matrix format (BTW in scikit-learn you will need to have the same number of columns at every line, so you will need to fill somehow the first line). However, I will take this email as the opportunity to stress the fact that you do not have to stick to a specific file format, but the the features/columns/(2nd index of a 2D matrix) they have to represent the properties/parameters that they will directly affect changes in what you are trying to predict. In fact in your columns you can even add more features compared to the priviosly cited and probably you will describe the system in a better way. What I can think right now, just for the seak of a better explanation, you can use: bond lenght, atoms type, number of unpaired electrons, total number of electrons, diedral angle of the two atoms, number of atoms between the pair (e.g. if you have Mn--O--Mn there is an oxygen between the two Mn that you might want to look at the coupling) and so on and so forth. The number of parameters you will have to use will solely depend on your system and what you need to describe, but it will not affect in any case scikit-learn routines. Basically every 2D matrix of number will work on scikil-learn, but in order to make these number to have physical meaning that will depend on what the number represent. Let me know if it make more sense Sincerely Tommaso On Mar 28, 2017 12:51 PM, "Henrique C. S. Junior" <henriquecsj(a)gmail.com> wrote: @Tommaso, this is something like Internal Coordinates[1], right? @Bill, thanks for the hint, I'll definitely take a look at this. [1] - https://en.wikipedia.org/wiki/Z-matrix_(chemistry) On Tue, Mar 28, 2017 at 2:12 AM, Bill Ross <ross(a)cgl.ucsf.edu> wrote: > Image processing deals with xy coordinates by (as I understand) training > with multiple permutations of the raw data, in the form of translations and > rotations in the 2d space. If training with 3d data, there would be that > much more translating and rotating to do, in order to divorce the learning > from the incidentals. > > Bill > > On 3/27/17 4:35 PM, Tommaso Costanzo wrote: > > Dear Henrique, > I am sorry for the poor email I wrote before. What I was saying is simply > the fact that if you are trying to use the coordinates as "features" from > an .xyz file then by machine learning you will learn at wich coordinate > certain atoms will occur so you can only make prediction on the coordinate. > However, if I correctly understood, the "features" representing the > coupling J are distance, angle, and electron number. Definitely this > properties can be derived from the XYZ file format from simple geometric > calculations and the number of electrons will depend from the type of atom. > So, what I was trying to say is that instead of using the XYZ file as input > for scikit-learn, I was suggesting to do the calculation of angle, > distances, electrons' number in advance (with other software(s) or directly > in python) and use the new calculated matrix as input for scikit-learn. In > this case the machine will learn how J(AB) varies as a function of angle, > distance, number of electrons. > For example > > distance angle n el. > 1 90 1 > 1 90 1 > 2 90 1 > .... ... ... > > If you are using a supervised learning you will have to add a 4th column ( > in reality a separate column vector) with your J(AB) on which you can train > your model and then predict the unknown samples > > For example > distance angle n el. J(AB) > 1 90 1 1 > 1 90 1 1 > 2 90 1 0.5 > .... ... ... ... > > Now if you train the model on the second matrix, and then you try to > predict the first one you should expect a results like: > > 1 > 1 > 0.5 > > Of course in this case the "features" are perfectly equal, hence the > example is completely unrealistic. However, I hope that it will help to > understand what I was explaining in the previous email. > If you want you can directly contact me at this email, and I hope that you > got additional hints from Robert, that he seems to be even more > knowledgeable than me. > > Sincerely > Tommaso > > > > 2017-03-27 18:44 GMT-04:00 Henrique C. S. Junior <henriquecsj(a)gmail.com>: > >> Dear Tommaso, thank you for your kind reply. >> I know I have a lot to study before actually starting any code and that's >> why any suggestion is so valuable. >> So, you're suggesting that a simplification of the system using only the >> paramagnetic centers can be a good approach? (I'm not sure if I understood >> it correctly). >> My main idea was, at first, try to represent the systems as realistically >> as possible (using coordinates). I know that the software will not know >> what a bond is or what an intermolecular interaction is but, let's say, >> after including 1000s of examples in the training, I was expecting that (as >> an example) finding a C 0.000 and an H at 1.000 should start to "make >> sense" because it leads to an experimental trend. And I totally agree that >> my way to represent the system is not the better. >> >> Thank you so much for all the help. >> >> On Mon, Mar 27, 2017 at 4:15 PM, Tommaso Costanzo < >> tommaso.costanzo01(a)gmail.com> wrote: >> >>> Dear Henrique, >>> >>> >>> I agree with Robert on the use of a supervised algorithm and I would >>> also suggest you to try a semisupervised one if you have trouble in >>> labeling your data. >>> >>> >>> Moreover, as a chemist I think that the input you are thinking to use is >>> not the in the best form for machine learning because you are trying to >>> predict coupling J(AB) but in the future space you have only coordinates >>> (XYZ). What I suggest is to generate the pair of atoms externally and then >>> use a matrix of the form (Mx3), where M are the pairs of atoms you want to >>> predict your J and 3 are the features of the two atoms (distance, angle, >>> unpaired electrons). For a supervised approach you will need a training set >>> where the J is know so your training data will be of the form Mx4 and the >>> fourth feature will be the J you know. >>> >>> Hope that this is clear, if not I will be happy to help more >>> >>> >>> Sincerely >>> >>> Tommaso >>> >>> 2017-03-27 13:46 GMT-04:00 Henrique C. S. Junior <henriquecsj(a)gmail.com> >>> : >>> >>>> Dear Robert, thank you. Yes, I'd like to talk about some specifics on >>>> the project. >>>> Thank you again. >>>> >>>> On Mon, Mar 27, 2017 at 2:25 PM, Robert Slater <rdslater(a)gmail.com> >>>> wrote: >>>> >>>>> You definitely can use some of the tools in sci-kit learn for >>>>> supervised machine learning. The real trick will be how well your training >>>>> system is representative of your future predictions. All of the various >>>>> regression algorithms would be of some value and you make even consider an >>>>> ensemble to help generalize. There will be some important questions to >>>>> answer--what kind of loss function do you want to look at? I assumed >>>>> regression (continuous response) but it could also classify--paramagnetic, >>>>> diamagnetic, ferromagnetic, etc... >>>>> >>>>> Another task to think about might be dimension reduction. >>>>> There is no guarantee you will get fantastic results--every problem is >>>>> unique and much will depend on exactly what you want out of the >>>>> solution--it may be that we get '10%' accuracy at best--for some systems >>>>> that is quite good, others it is horrible. >>>>> >>>>> If you'd like to talk specifics, feel free to contact me at this >>>>> email. I have a background in magnetism (PhD in magnetic multilayers--i >>>>> was physics, but as you are probably aware chemisty and physics blend in >>>>> this area) and have a fairly good knowledge of sci-kit learn and machine >>>>> learning. >>>>> >>>>> >>>>> >>>>> On Mon, Mar 27, 2017 at 10:50 AM, Henrique C. S. Junior < >>>>> henriquecsj(a)gmail.com> wrote: >>>>> >>>>>> I'm a chemist with some rudimentary programming skills (getting >>>>>> started with python) and in the middle of the year I'll be starting a Ph.D. >>>>>> project that uses computers to describe magnetism in molecular systems. >>>>>> >>>>>> Most of the time I get my results after several simulations and >>>>>> experiments, so, I know that one of the hardest tasks in molecular >>>>>> magnetism is to predict the nature of magnetic interactions. That's why >>>>>> I'll try to tackle this problem with Machine Learning (because such >>>>>> interactions are dependent, basically, of distances, angles and number of >>>>>> unpaired electrons). The idea is to feed the computer with a large training >>>>>> set (with number of unpaired electrons, XYZ coordinates of each molecule >>>>>> and experimental magnetic couplings) and see if it can predict the magnetic >>>>>> couplings (J(AB)) of new systems: >>>>>> (see example in the attached image) >>>>>> >>>>>> Can Scikit-Learn handle the task, knowing that the matrix used to >>>>>> represent atomic coordinates will probably have a different number of atoms >>>>>> (because some molecules have more atoms than others)? Or is this a job >>>>>> better suited for another software/approach? >>>>>> >>>>>> >>>>>> -- >>>>>> *Henrique C. S. Junior* >>>>>> Industrial Chemist - UFRRJ >>>>>> M. Sc. Inorganic Chemistry - UFRRJ >>>>>> Data Processing Center - PMP >>>>>> Visite o Mundo Químico <http://mundoquimico.com.br> >>>>>> >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> scikit-learn(a)python.org >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> >>>>>> >>>>> >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn(a)python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>>> >>>> >>>> >>>> -- >>>> *Henrique C. S. Junior* >>>> Industrial Chemist - UFRRJ >>>> M. Sc. Inorganic Chemistry - UFRRJ >>>> Data Processing Center - PMP >>>> Visite o Mundo Químico <http://mundoquimico.com.br> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn(a)python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> >>> >>> -- >>> Please do NOT send Microsoft Office Attachments: >>> http://www.gnu.org/philosophy/no-word-attachments.html >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> >> -- >> *Henrique C. S. Junior* >> Industrial Chemist - UFRRJ >> M. Sc. Inorganic Chemistry - UFRRJ >> Data Processing Center - PMP >> Visite o Mundo Químico <http://mundoquimico.com.br> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > Please do NOT send Microsoft Office Attachments: > http://www.gnu.org/philosophy/no-word-attachments.html > > > _______________________________________________ > scikit-learn mailing listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- *Henrique C. S. Junior* Industrial Chemist - UFRRJ M. Sc. Inorganic Chemistry - UFRRJ Data Processing Center - PMP Visite o Mundo Químico <http://mundoquimico.com.br> _______________________________________________ scikit-learn mailing list scikit-learn(a)python.org https://mail.python.org/mailman/listinfo/scikit-learn

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