[mlpack-git] (blog) master: keon week eight (ff26822)

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Repository : https://github.com/mlpack/blog
On branch  : master
Link       : https://github.com/mlpack/blog/compare/2d909de93566bc7617cc7b9fb9b048630da0043d...ff26822729b0a434910a185db735efe74d68e0a3

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commit ff26822729b0a434910a185db735efe74d68e0a3
Author: Keon Kim <kwk236 at gmail.com>
Date:   Thu Jul 21 02:17:20 2016 +0900

    keon week eight


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+Title: Dataset and Experimentation Tools : Week-8 Highlights
+Date: 2016-07-20 16:00:00
+Tags: gsoc, dataset, data
+Author: Keon Kim
+
+This week, I:
+
+DatasetMapper & Imputer
+
+1) Optimized Imputer a little bit. The details are discussed in the pull request [#694](https://github.com/mlpack/mlpack/pull/694).
+
+3) Debugged and polished some comments.
+
+Descriptive Statistics
+
+1) Made statistics.hpp and statistics_impl.hpp, which is basically a more convinient version of armadillo statistics functions.
+It also has more features like calculating skewness and kurtosis.
+They are made to provide convinience, so the computational efficiency is little hurt.
+I made the results to sync with the results given by the excel.
+The commits I've done are in [describe branch](https://github.com/keonkim/mlpack/commits/describe)
+
+2) The first version of the statistics class calculated every statistics at its constructor.
+The benchmark scores are recorded [here](https://github.com/keonkim/mlpack/commit/2a89412fe6375178f2657bc48c3d698430419da0#commitcomment-18315506).
+
+3) Changed iomanip to boost::format for formatting the output.
+
+I've been studying little more about how ANN and RNNs are implemented in mlpack (just personal interest).
+Deep learning is more fun than I thought, hopefully I can contribute to neural net parts of the mlpack in the future. 
+
+Later, I will work a little more on statistics module, mainly to optimize a little more and polish the comments and outputs.
+
+And, I will work on mlpack_preprocess_verify executable, which is just a small extension of Imputer module.
+In this program, it does not change or replace any values, but only detects the invalid values.