We need to be able to compute the total change in lines of code between two versions (V1 and V2) of a large Java codebase. A tool that uses either Eclipse or Mercurial would be ideal.
Counting the number of lines of code in V1 and V2 is not sufficient, since some sections of code will have been removed and rewritten between versions.
What we really need is to compute something like:
I = Intersection of V1 and V2
D = Difference from I to V2
Then we can compute things such as the percentage change = D/V2
Any recommendations for tools that can do this?
hg log --stat will show you various stats for each commit, including inserted / deleted lines.
I don't know if there's a better solution, but you can parse theses results to achieve what you want.
You can also have a look at this previous answer on SO : Counting changed lines of code over time
After trying some approaches based on Hg, I found that the best solution is to use CLOC (Count Lines of Code): http://cloc.sourceforge.net/
You can give it two folders containing two versions of a project, and it will count all of the lines that are the same, modified, added, removed. It's exactly what I needed.
Yes, ProjectCodeMeter can give you differential sloc between 2 versions of source code, but better than that, it can also give you the difference in development time (which is what i guess you want to really aim for).
Related
I'm working on a Java project where I need to match user queries against several engines.
Each engine has a method similarity(Object a, Object b) which returns: +1 if the objects surely match; -1 if the objects surely DON'T match; any float in-between when there's uncertainty.
Example: user searches "Dragon Ball".
Engine 1 returns "Dragon Ball", "Dragon Ball GT", "Dragon Ball Z", and it claims they are DIFFERENT result (similarity=-1), no matter how similar their names look. This engine is accurate, so it has a high "weight" value.
Engine 2 returns 100 different results. Some of them relate to DBZ, others to DBGT, etc. The engine claims they're all "quite similar" (similarity between 0.5 and 1).
The system queries several other engines (10+)
I'm looking for a way to build clusters out of this system. I need to ensure that values with similarity near -1 will likely end up in different clusters, even if many other values are very similar to all of them.
Is there a well-known clustering algorithm to solve this problem? Is there a Java implementation available? Can I build it on my own, perhaps with the help of a support library? I'm good at Java (15+ years experience) but I'm completely new at clustering.
Thank you!
The obvious approach would be to use "1 - similarity" as a distance function, which will thus go from 0 to 2. Then add them up.
Or you could use 1 + similarity and take the product of these values, ... or, or, or, ...
But since you apparently trust the first score more, you may also want to increase its influence. There is no mathematical solution for this, you habe to choose the weights depending on your data and preferences. If you have training data, you can optimize weights for your approach, and you may want to even discard some rankers if they don't work well or are correlated.
I need to write a logic in java that
Takes 2 versions of xmls like v1.xml & V2.xml
Outputs the differences between the 2 xmls
Displays them on a webpage just like any diff tool, like winmerge would display.
Removed lines - Highlighted in a unique color
Added lines - Highlighted in a unique color
Changed lines - Highlighted in a unique color
What is the best way to achieve this.
Thanks !
You can use XMLUnit to achieve most of your requirements.
Writing an XML parser from scratch is a bad idea if that is what you mean. It sounds really easy at first, but then quickly becomes a nightmare, trust me. I highly recommend taking advantage of existing tools.
http://www.roseindia.net/opensource/xmldiff.php lists several tools, inlcuding 3DM, diffmk, diffxml, VMTools, X-Diff, and XMLUnit. If you do have to write your own parser, you might want to at least look at the code from these projects for ideas. However, it takes much less time and effort to just give them credit and use their tools than to rewrite them yourself. I haven't used any of these tools, so buyer beware.
See also Tool or library for comparing xml files
Write a program with the following objective -
be able to identify whether a word/phrase represents a thing/product. For example -
1) "A glove comprising at least an index finger receptacle, a middle finger receptacle.." <-Be able to identify glove as a thing/product.
2) "In a window regulator, especially for automobiles, in which the window is connected to a drive..." <- be able to identify regulator as a thing.
Doing this tells me that the text is talking about a thing/product. as a contrast, the following text talks about a process instead of a thing/product -> "An extrusion coating process for the production of flexible packaging films of nylon coated substrates consisting of the steps of..."
I have millions of such texts; hence, manually doing it is not feasible. So far, with the help of using NLTK + Python, I have been able to identify some specific cases which use very similar keywords. But I have not been able to do the same with the kinds mentioned in the examples above. Any help will be appreciated!
What you want to do is actually pretty difficult. It is a sort of (very specific) semantic labelling task. The possible solutions are:
create your own labelling algorithm, create training data, test, eval and finally tag your data
use an existing knowledge base (lexicon) to extract semantic labels for each target word
The first option is a complex research project in itself. Do it if you have the time and resources.
The second option will only give you the labels that are available in the knowledge base, and these might not match your wishes. I would give it a try with python, NLTK and Wordnet (interface already available), you might be able to use synset hypernyms for your problem.
This task is called named entity reconition problem.
EDIT: There is no clean definition of NER in NLP community, so one can say this is not NER task, but instance of more general sequence labeling problem. Anyway, there is still no tool that can do this out of the box.
Out of the box, Standford NLP can only recognize following types:
Recognizes named (PERSON, LOCATION, ORGANIZATION, MISC), numerical
(MONEY, NUMBER, ORDINAL, PERCENT), and temporal (DATE, TIME, DURATION,
SET) entities
so it is not suitable for solving this task. There are some commercial solutions that possible can do the job, they can be readily found by googling "product name named entity recognition", some of them offer free trial plans. I don't know any free ready to deploy solution.
Of course, you can create you own model by hand-annotating about 1000 or so product name containing sentences and training some classifier like Conditional Random Field classifier with some basic features (here is documentation page that explains how to that with stanford NLP). This solution should work reasonable well, while it won't be perfect of course (no system will be perfect but some solutions are better then others).
EDIT: This is complex task per se, but not that complex unless you want state-of-the art results. You can create reasonable good model in just 2-3 days. Here is (example) step-by-step instruction how to do this using open source tool:
Download CRF++ and look at provided examples, they are in a simple text format
Annotate you data in a similar manner
a OTHER
glove PRODUCT
comprising OTHER
...
and so on.
Spilt you annotated data into two files train (80%) and dev(20%)
use following baseline template features (paste in template file)
U02:%x[0,0]
U01:%x[-1,0]
U01:%x[-2,0]
U02:%x[0,0]
U03:%x[1,0]
U04:%x[2,0]
U05:%x[-1,0]/%x[0,0]
U06:%x[0,0]/%x[1,0]
4.Run
crf_learn template train.txt model
crf_test -m model dev.txt > result.txt
Look at result.txt. one column will contain your hand-labeled data and other - machine predicted labels. You can then compare these, compute accuracy etc. After that you can feed new unlabeled data into crf_test and get your labels.
As I said, this won't be perfect, but I will be very surprised if that won't be reasonable good (I actually solved very similar task not long ago) and certanly better just using few keywords/templates
ENDNOTE: this ignores many things and some best-practices in solving such tasks, won't be good for academic research, not 100% guaranteed to work, but still useful for this and many similar problems as relatively quick solution.
I am developing a financial manager in my freetime with Java and Swing GUI. When the user adds a new entry, he is prompted to fill in: Moneyamount, Date, Comment and Section (e.g. Car, Salary, Computer, Food,...)
The sections are created "on the fly". When the user enters a new section, it will be added to the section-jcombobox for further selection. The other point is, that the comments could be in different languages. So the list of hard coded words and synonyms would be enormous.
So, my question is, is it possible to analyse the comment (e.g. "Fuel", "Car service", "Lunch at **") and preselect a fitting Section.
My first thought was, do it with a neural network and learn from the input, if the user selects another section.
But my problem is, I don´t know how to start at all. I tried "encog" with Eclipse and did some tutorials (XOR,...). But all of them are only using doubles as in/output.
Anyone could give me a hint how to start or any other possible solution for this?
Here is a runable JAR (current development state, requires Java7) and the Sourceforge Page
Forget about neural networks. This is a highly technical and specialized field of artificial intelligence, which is probably not suitable for your problem, and requires a solid expertise. Besides, there is a lot of simpler and better solutions for your problem.
First obvious solution, build a list of words and synonyms for all your sections and parse for these synonyms. You can then collect comments online for synonyms analysis, or use parse comments/sections provided by your users to statistically detect relations between words, etc...
There is an infinite number of possible solutions, ranging from the simplest to the most overkill. Now you need to define if this feature of your system is critical (prefilling? probably not, then)... and what any development effort will bring you. One hour of work could bring you a 80% satisfying feature, while aiming for 90% would cost one week of work. Is it really worth it?
Go for the simplest solution and tackle the real challenge of any dev project: delivering. Once your app is delivered, then you can always go back and improve as needed.
String myString = new String(paramInput);
if(myString.contains("FUEL")){
//do the fuel functionality
}
In a simple app, if you will be having only some specific sections in your application then you can get string from comments and check it if it contains some keywords and then according to it change the value of Section.
If you have a lot of categories, I would use something like Apache Lucene where you could index all the categories with their name's and potential keywords/phrases that might appear in a users description. Then you could simply run the description through Lucene and use the top matched category as a "best guess".
P.S. Neural Network inputs and outputs will always be doubles or floats with a value between 0 and 1. As for how to implement String matching I wouldn't even know where to start.
It seems to me that following will do:
hard word statistics
maybe a stemming class (English/Spanish) which reduce a word like "lunches" to "lunch".
a list of most frequent non-words (the, at, a, for, ...)
The best fit is a linear problem, so theoretical fit for a neural net, but why not take immediately the numerical best fit.
A machine learning algorithm such as an Artificial Neural Network doesn't seem like the best solution here. ANNs can be used for multi-class classification (i.e. 'to which of the provided pre-trained classes does the input represent?' not just 'does the input represent an X?') which fits your use case. The problem is that they are supervised learning methods and as such you need to provide a list of pairs of keywords and classes (Sections) that spans every possible input that your users will provide. This is impossible and in practice ANNs are re-trained when more data is available to produce better results and create a more accurate decision boundary / representation of the function that maps the inputs to outputs. This also assumes that you know all possible classes before you start and each of those classes has training input values that you provide.
The issue is that the input to your ANN (a list of characters or a numerical hash of the string) provides no context by which to classify. There's no higher level information provided that describes the word's meaning. This means that a different word that hashes to a numerically close value can be misclassified if there was insufficient training data.
(As maclema said, the output from an ANN will always be floats with each value representing proximity to a class - or a class with a level of uncertainty.)
A better solution would be to employ some kind of word-relation or synonym graph. A Bag of words model might be useful here.
Edit: In light of your comment that you don't know the Sections before hand,
an easy solution to program would be to provide a list of keywords in a file that gets updated as people use the program. Simply storing a mapping of provided comments -> Sections, which you will already have in your database, would allow you to filter out non-keywords (and, or, the, ...). One option is to then find a list of each Section that the typed keywords belong to and suggest multiple Sections and let the user pick one. The feedback that you get from user selections would enable improvements of suggestions in the future. Another would be to calculate a Bayesian probability - the probability that this word belongs to Section X given the previous stored mappings - for all keywords and Sections and either take the modal Section or normalise over each unique keyword and take the mean. Calculations of probabilities will need to be updated as you gather more information ofcourse, perhaps this could be done with every new addition in a background thread.
Project Background:
I am writing a map tile overlay class for java that can use gdal2tile.py tiles. Basically I will end up with thousands of jpg files that are in a file structure like
"Zoom Level/X coordinate/Y coordinate"
The coordinates are ints but will not necessarily start at 0 or 1.
I will have to search for tiles that are within a certain range to find out which ones I need to render.
My Problem:
I tried iterating using the file structure itself but it is wicked slow (not surprising).
I tried iterating using an ArrayList of strings of the file structure and .contains() but it seems to be even slower (not too surprising).
Optimally I would like to use a data structure that would let me choose a range on multiple dimensions so that I can call something like.
Tiles.getWhere(Zoom Level,min X,max X,min Y,maxY);
I assume that some sort of Collection or TreeMap would be the right choice but I'm not experienced enough with Java to know for sure and I'd prefer not to have to benchmark a lot of different approaches.
I could use SQLite to do it but that seems like overkill.
My Question:
What is the most efficient way to check for the existence of datasets given multiple dimensional constraints?
May be you are looking for a map with multiple keys.
Commons-collections provides a map with multiple lookup keys:
http://commons.apache.org/collections/apidocs/org/apache/commons/collections/map/MultiKeyMap.html
a map guarantees a O(1) insertion and O(1) selection timings.
Thinking of your problem I could find out three directions to which you could aim your search next (this is not a hand-by-hand guide but rather a out-of-the-box brain opener for a stucked situation you have faced):
1) Usage of Java built in structures. Yes, indeed, a list is the worst case of a searching method. A Map, as the name suggests, is far more convenient for maps. It is not only the name, but the indexing to a Map is signifigantly less time consuming compared to a List. You can imagine your map as a cube, where you have to handle about half of the dots inside it, if you use List and probably only a narrow layer of it when you search by indexing a Map. There is a magnitude of difference. So, my answer here: Map is a key word towards the correct direction (assuming you want to do it in this way after reading on my answer).
2) Usage of a Map Server solution. This is probably too far from your approach, but entire frameworks are made for solving your type of question. An example is GeoServer. It has a ready made solution for the entire problem. It is a stable solution for the great big problem possibly in your hand: showing a map to a user from a source.
3) Sticking to the GDAL framework you were using, you could select slightly different py-file, like gdal_proximity.py and - wow! - you have a searching possibility in your hand! This particular one searches by a center point and a distance, but will do the stuff you need =)
There is a starting point, how I would make it. Could this serve for something?
Sounds to me like you are looking for something like an Interval Tree.
http://en.wikipedia.org/wiki/Interval_tree
I have implemented one of these in the past but only in one dimension. The Wikipedia reference mentions extensions to more dimensions.
Paul