If you have a string like this volume-7,notcontact-xxxx,not_lightlevel-1280.0, and this string could vary with other options, like vibrate, light level, screen brightness etc...but the formatting would be the same...(setting-value,setting-value...)
What would be the best way to make a decision based on the current information you have on these settings? (i.e return true or false by checking the predefined string against the current contextual information I have)
So let's say you have the current contextual information...i.e the current volume level, vibrator setting, the light level, screen brightness, etc etc and you want to compare it against a pre-defined string such as the I wrote above,
how would you do it?
Would you parse the string and check for every single possibility? while doable, it doesn't sound very appealing..
Could I use decision trees? If yes, could someone tell me how I would go about it?
Thanks
Parsing it into a HashMap would be my first thought.
You can also use a POJO if you know all the keys in advance. (You'll still have to populate the fields using reflection but you are guaranteed compile time type safety for all the other operations.)
(I might be completely misunderstanding your question, but I can't find any connection between the problem you described and decision trees.)
Related
My goal is to generate a predictive model using tensor flow in Java but I first want to ensure that my goal is achievable. Firstly, if I have a bunch of parameters and each set of parameters is assigned an output is it possible to train a model to predict an output given similar parameters? I am able to get hundreds of thousands samples (if needed) in order to train it so is this possible?
Secondly, after the model is trained how fast can it actually generate results?
Lastly, assuming everything up until this point checks out what is the best method in Java’s tensor flow to train a model with data that has multiple parameters associated with an outcome? Also in the result a given piece of data satisfies two results both can be returned as options ordered from most likely to least.
Also just to clarify I am not asking someone to make this for me I am just trying to make sure that a solution exists and is quick (if it’s slow I could just go back to brute forcing which I am trying to move away from since is kinda slow and resource intensive). Also, if you have any pointers on getting started tackling this I would greatly appreciate it!
Your question is very, very general, but I'll try to offer some insight:
Firstly, if I have a bunch of parameters and each set of parameters is assigned an output is it possible to train a model to predict an output given similar parameters?
Taking a set of parameters (known as the feature set X) and making predictions of another set of parameters (known as the output set Y) is the primary purpose of machine learning. Exactly how to do this requires many steps, how to do it well takes a lot of experience... However if you are asking if it is possible in principle, that depends on the specific feature set X, and output set Y.
I am able to get hundreds of thousands samples (if needed) in order to train it so is this possible?
The trick to machine learning is the data must be of a sufficient quantity and quality. This takes domain specific knowledge to know.
Are you able to provide any specifics about your data to help us understand?
I'd like to know if it's possible (and with which tooling) to do typesafe i18n in Java. Maybe it's not clear so here are some details, assuming we use something based on MessageFormat
1) Translate using typesafe parameters
I'd like to avoid having an interface like String translate(Object key,Object... values) where the values are untyped. It should be impossible to call with a bad parameter type.
Note I'm fine specifying the typing of all the keys. The solution I'm looking for should be scalable and should not increase the backend startup time significantly.
2) It should be known at compile time which keys are still used
I don't want my translation keys base to be like many websites' CSS, growing and growing forever and everybody being frightened to remove keys because we don't know easily if they are still useful or not.
In JS/React land there is babel-plugin-react-intl which permit to extract at compile time the translation keys that are still found in the code. Then we can diff these keys with our translation backend/SaaS and delete the unused keys automatically. Is there anything close to that experience in Java land?
I'm looking for:
any trick you have that could make i18n more manageable in Java regarding these 2 problems I have
current tooling that might help me solve the problem
hints on how to implement something custom if tooling does not exist
Also, is Enum suitable to store a huge fixed list of translation keys?
Translation keys are an open ended domain. For a closed domain an enum would do.
Having something like enums or constant lists likely causes a growth of different enums, constants classes.
And then there is the very important perspective of the translating business:
you would want at least one glossary (not needing translation occurrences), structurally equal phrases grouped,
comments maybe on ambivalent terms and usages (button/menu). This can reduce
the time costs and improve the quality. There also are things like online-help.
Up till now XML, like simple docbook / translation memory (tmx/xliff/...), was sufficient for that. And the tooling
including different forms of evaluation was done ourselves.
I hope a more professional answer will be given, but my answer might shed some light
on the desired functionality:
translation centric: as that needs the most work.
version control: some text lists involved.
checking tools: what you mentioned, integrity, missing, almost equal.
I am planning to develop an adventure-like game.
For that I am going to have a lot of instances of classes with different texts (basicly strings).
I dont want to hardcode this many texts, so i am looking for a way to do it better.
The guy in this video ( https://www.youtube.com/watch?v=8CDePunJlck ) is using json to write text files for each class instance manually and parse them automatically into instances. That goes into the right direction.
I´m looking for more information on that, so how is this procedure called?
Its said in the video that this also works with databases?
Is there a way to design a little bit more complex stuff with things like this?
E.g. I have the case that I would like to output different texts if e.g. a local or global variable is over a treshold etc. Can I do this without hardcoding and write an own class for each of my proposed instances?
Thank you!
Your question is quite broad, and it is hard to give a definitive answer. Here are some thoughts - hope you find it helpful.
You are right that you don't want to hardcode strings. The alternative to this is storing strings as external resources, and loading them into your game at start. There are numerous ways the resource can be organized; the choice depends on your programming platform, game architecture etc. For example, you can use simple name-value approach:
AREA_1_DESCRIPTION: You stand next o a small white house.
ITEM_22_DESTRUCTION: The nasty snake disappears with a loud "Bang!"
Using JSON or XML will give you more structured storage, which can be of great help, since you can organize your texts so that it is easier to use them in the code:
<item id="375" name="Great Sword">
<short_description>A Great Sword of Darkness</short_description>
<long_description>The sword has almost black blade with some unknown runes engraved</long_description>
</item>
If your programming system can access a database, then you can do something similar and store texts in the tables; this, however, might make it more difficult to edit texts later. If you want to go this way, I would still recommend using XML or JSON to store the texts, and making the game import texts in DB on the first run.
You probably will also need some sort of simple template-handling engine to be able to re-use some strings. You can start with creating your version of Java String.format() method. Your method might take as a first argument an ID of a string in your string catalog, and use some simple placeholders for the parameters. Suppose you have the following entry in your catalog:
FIRE_GEM_ACTION: "The Fire Gem touches %% and in %% seconds it turns into ashes."
Then you can write a method that will do something like this:
int delaySeconds = 5;
String message = MyTemplateProcessor.process(FIRE_GEM_ACTION, "old map", delaySeconds);
The function will take the string from the catalog, search for the occurrences of the placeholders (%%) and replace them sequentially with the parameters, so in the message you will get: The Fire Gem touches old map and in 5 seconds it turns into ashes.
In general, I would recommend you to have a look at some systems specially designed for creation of adventure games. Inform 7 will be a good starting place: http://inform7.com/learn/
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.
I'm making a console-based Java/Groovy application that does a lot of find/replaces in text files. If, say, the program knows you replaced foo with bar last time, it should by default know you probably want to replace the next foo with bar as well. My idea was to pre-populate the input field of the What would you like to rename this to? prompt so that you don't have to retype it unnecessarily, but I can't find an easy way to do this in Java.
Is this possible, and if so, would it be a recommended practice?
Why don't you just assume that an empty imput is equal to the last input inserted? It would be quite easier to manage that pre-filling a stdin..
in any case if you plan to have a recent list I would suggest you to have a special character combination to tell last one or the previous and so on.
There are a lot of useful approaches to this problem. You can remember only the last value the user replaced, or you can mantain a cache with the last n replacements, in case the user requests a similar replacement. Or you can ignore the previous input and force the user to provide a replacement every time you need. The right approach depends on the frequency of the replacement operations, if the user replaces the same value very often, etc. You have to choose the right solution depending on your problem domain and on the kind of interaction you want to provide to the user.
Implementing these solutions is simple. The most intuitive is saving the replacement cache in a simple text file, loading it during startup and saving the cache at the shutdown. The replacement cache can also be serialized to the file, insted of being written as plain text.