How can I execute MapReduce on MATLAB R2014a?
Because I think MATLAB R2014a does not support DataStore, Map, and Reduce functions.
You're correct. The functionality for datastore and mapreduce was introduced in MATLAB R2014b. So you cannot use these functions with R2014a.
For more info, see the Release Notes:
http://www.mathworks.com/help/matlab/release-notes.html
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I have a visual analytics program that handles the visualization of 3D datasets (e.g., MRI datasets). For multiple reasons, I want to be able to watch the result (3D-grid image visualizable using ray tracking techniques, e.g., the ray-marching algorithm) in Matlab. I see that Matlab relies on .fig files, but I do not find any documentation of its structure.
Does people know where .fig files are documented? Is there any C++ library that proposes such a functionality? My C++ code runs on a linux workstation and I do not own any Matlab license: the generated files are not meant to me but to others owning such a license.
Thanks!
I am new to Map reduce program.I want to know if I can run map reduce program as a normal java program without using Hadoop. What all libraries should I include?Is it possible?
It is possible, but in that you need to write each end every code block starting from map-->SS-->Reduce. Tobe very simple hadoop is a framework built on provides lot API to run the mapreduce job. It will take care of passing the input from file, Suffle and sort and then reduce function. you just need to understand the various API of haddop and the flow of data thats it.
I'm wondering if it is possibile to write a java program that do a BulkLoad on HBase. I'm on a hadoop cluster but I don't need to write a MapReduce Job for some reason.
Thanks
BulkLoad works with HFile. So If you have HFiles, you can directly use LoadIncrementalHFiles to handle the bulk load.
Generally we use Map reduce, which can convert the data into above format, and perform Bulk Load.
If you have csv file, you can use ImportTsv utility to process your data into HFiles. use this link, for more information
It depends at which format you data is in currently.
Point to note is, Bulk Load, do not use Write ahead Logs(WAL). They skip this step and add data at a faster rate. if you have any other framework depending on the above WAL, consider other options of adding data in Hbase. Happy Coding.
I'm using libsvm library in my project and have recently discovered that it provides out-of-the-box cross validation.
I'm checking the documentation and it says clearly that I have to call svm-train with -n switch to use CV feature
.
When I call it with -v switch I cannot get a model file which is needed by svm-predict.
Implementing Support Vector Machine from scratch is beyond the scope of my project, so I'd rather fix this one if it is broken or ask the community for support.
Can anybody help with that?
Here's the link to the library, implemented in C and C++, and here is the paper that describes how to use it.
Cause libsvm use cv only for parameter selection.
From libsvm FAQ:
Q: After doing cross validation, why there is no model file outputted ?
Cross validation is used for selecting good parameters. After finding them, you want to re-train the whole data without the -v option.
If you are going to use cv for estimating quality of classifier on your data you should implement external cross validation by splitting data, train on some part and test on other.
It's been a while since I used libsvm so I don't think I have the answer you're looking, but if you run the cross-validation and are satisfied with the results, running lib-svm with the same parameters without the -v will yield the same model.
I used the matlab neural network to train on some data but I want to run this neural network in c++ program,how to do that?
You can use ML to generate your feature set (input layer) and then use an open source C++ NN implementation to do training/classification. (E.g., http://takinginitiative.net/2008/04/23/basic-neural-network-tutorial-c-implementation-and-source-code/) If you want to use ML to train and C++ to classify it shouldn't be too difficult to write some additional code to write out the trained network in a way that can be read in by the C++ classifier.
You can use the Matlab Compiler that generates code you can embed in your C++ application
I'm using Matlab R2013a. If you are still facing with this issue, try to look at this location
Matlab\R2013a\toolbox\nnet\nnet\nnderivative\+nnMex2
inside Matlab directory. I found there file "yy.cpp" which contains mexFunction which does the thing. Very likely that Matlab calls this function to simulate network.
Seems that it's possible to integrate this function into your project after slight refactoring. At least I plan to do so. :-)