Hi,
thank you for your responses. I did take a look at your previous work in this regard as well as the todo-list and it seems you've made quite some progress. That's great and I really look forward to seeing the final result, but to be quite honest with you, I'm afraid "I've added a couple of minor features to a module that was otherwise pretty well developed" doesn't really make for a good Bachelor's thesis. What I would like my students to do is to develop one algorithm (or several algorithms) from beginning to end. So how about we implement some more standard clustering algorithms, for instance c-means, DIANA or fuzzy subspace clustering? I've searched for these and a couple of other related keywords in the pull requests and it appears no is working on that yet. Correct me if I'm wrong. I'll talk to my student, but my guess is he'll like the idea more than the multilayer perceptron. Greetings, Patrick Raghav R V 於 2015/3/16 上午 07:54 寫道: > Also there is a PR by Andy working towards completing the same (MLP) > here - https://github.com/scikit-learn/scikit-learn/pull/3939 > > BTW, that PR does have a nice todo list, which you might want to take > a look at :) > > > > R > > On Mon, Mar 16, 2015 at 2:39 AM, Joel Nothman <joel.noth...@gmail.com> wrote: >> I think #3306 (Extreme Learning Machines) needs review, and after that's >> merged, focus should return to the MLP PR. I've not been following either of >> those PRs extremely closely, but I gather that both are quite mature, but >> not small items for review. >> >> On 16 March 2015 at 07:53, Michael Eickenberg <michael.eickenb...@gmail.com> >> wrote: >>> Maybe others can comment on the status of this PR and to what extent help >>> may be needed to finish it? >>> >>> Michael >>> >>> On Sun, Mar 15, 2015 at 9:47 PM, Michael Eickenberg >>> <michael.eickenb...@gmail.com> wrote: >>>> Dear Patrick, >>>> >>>> there is an almost finished pull request for multilayer perceptrons from >>>> last years GSoC by Issam Laradji: >>>> https://github.com/scikit-learn/scikit-learn/pull/3204 >>>> >>>> Michael >>>> >>>> On Sun, Mar 15, 2015 at 8:57 PM, Patrick Urbanke >>>> <patrick-axel.urba...@wiwi.uni-goettingen.de> wrote: >>>>> Hello, >>>>> >>>>> >>>>> I'm writing, because I would like to contribute a multilayer perceptron >>>>> module to scikit-learn. On your website it says that I should contact >>>>> you to avoid duplicating work, so here I am. >>>>> >>>>> I'm a research associate and PhD candidate at the University of >>>>> Göttingen, Germany. All of my research is related to machine learning >>>>> and I often use scikit-learn to benchmark my own algorithms. I also use >>>>> scikit-learn for teaching, so thank you for all for your great work. >>>>> >>>>> I've noticed that scikit-learn still lacks a multilayer perceptron. >>>>> Since this is a very popular algorithm, I've decided that it would be a >>>>> good idea to have one of my students develop such a module for his >>>>> Bachelor's thesis under my supervision. He is very talented and I have >>>>> no doubt that he can do it. Also, he can build on some code I have have >>>>> already written. >>>>> >>>>> Here are the functionalities we would implement: >>>>> - Classifier and regressor >>>>> - Trained using SGD with minibatch updating >>>>> - One hidden layer >>>>> - Different activation functions (sigmoid, tanh, Gaussian RBM, >>>>> multiquadric RBM, linear) and the ability to mix them (so you could have >>>>> a neural network with 5 sigmoid functions, 10 Gaussian RBM and 5 >>>>> multiquadric RBM) >>>>> - L2 regularization >>>>> >>>>> Nice to have: >>>>> - Support for scipy sparse matrices >>>>> >>>>> We would develop the main functionalities in C++ and then write an >>>>> interface using Cython. Obviously, we would adhere to the coding >>>>> guidelines >>>>> >>>>> (http://scikit-learn.org/stable/developers/index.html#coding-guidelines). >>>>> >>>>> Anything else we should consider? >>>>> >>>>> >>>>> Greetings, >>>>> Patrick Urbanke >>>>> >>>>> >>>>> >>>>> ------------------------------------------------------------------------------ >>>>> Dive into the World of Parallel Programming The Go Parallel Website, >>>>> sponsored >>>>> by Intel and developed in partnership with Slashdot Media, is your hub >>>>> for all >>>>> things parallel software development, from weekly thought leadership >>>>> blogs to >>>>> news, videos, case studies, tutorials and more. Take a look and join the >>>>> conversation now. http://goparallel.sourceforge.net/ >>>>> _______________________________________________ >>>>> Scikit-learn-general mailing list >>>>> Scikit-learn-general@lists.sourceforge.net >>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>> >>> >>> ------------------------------------------------------------------------------ >>> Dive into the World of Parallel Programming The Go Parallel Website, >>> sponsored >>> by Intel and developed in partnership with Slashdot Media, is your hub for >>> all >>> things parallel software development, from weekly thought leadership blogs >>> to >>> news, videos, case studies, tutorials and more. Take a look and join the >>> conversation now. http://goparallel.sourceforge.net/ >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >> >> ------------------------------------------------------------------------------ >> Dive into the World of Parallel Programming The Go Parallel Website, >> sponsored >> by Intel and developed in partnership with Slashdot Media, is your hub for >> all >> things parallel software development, from weekly thought leadership blogs >> to >> news, videos, case studies, tutorials and more. Take a look and join the >> conversation now. http://goparallel.sourceforge.net/ >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming The Go Parallel Website, sponsored > by Intel and developed in partnership with Slashdot Media, is your hub for all > things parallel software development, from weekly thought leadership blogs to > news, videos, case studies, tutorials and more. Take a look and join the > conversation now. http://goparallel.sourceforge.net/ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
