I think I'm missing something fundamental about CouchDB views.
Let's say I'm storing cars in a database. I want to get all cars that are blue or red, but NOT green, AND they are Hondas. But this query is dynamic using keys. How do?
Map Function:
function(car) {
emit([car.color, car.make],car);
}
I can't find a way to format keys to make anything like this possible. I'm not married to this map function either, I just want to know how someone would handle a request like this on the fly. Do you have to just narrow it down as far as possible in Couch and then do more process with the returned data? Seems like there should be a way to do this...
One option would be to include the keys parameter with your query:
http://localhost:5984/cars/_design/all-cars/_view/by-color-and-make?keys=[["red","honda"],["blue","honda"]]
More likely though, you have a search page with a bunch of different characteristics, and the user is picking and choosing values to search by. They might choose 4 door, all wheel drive, and priced below 10,000.
You won't be able to make views to satisfy all the different combinations. couchdb-lucene is a good option to solve this problem.
And if you use cloudant.com, it's automatically included: https://cloudant.com/for-developers/search/
Related
I'm new to OSM querying, but would like to query vector data for a large area. Thus I need to limit the results I would like to get by tagging the request.
http://www.informationfreeway.org/api/0.6/way[tag=value][bbox=x,y,z,j]
I'd like to filter for specific tag/values when querying for a way. Though I don't know which tags/values exist. Is there a list listing the most common of them?
You are approaching your problem from the wrong direction. The number of different tags is almost unlimited. According to taginfo there are currently 75 380 856 different tags. I'm pretty sure you are not interested in most of them. Likewise you are probably not even interested in many of the most common tags.
What data do you want to query?
The OSM wiki should be your starting point for generating a list of tags you are interested in. For a generic overview take a look at the map features. Are you interested in streets? Then visit at the highway key. Routing? Then take a look at the routing wiki page.
Always remember that these lists aren't complete. People can use any tag they like (but should use well-established tags whenever possible of course).
Also consider using Overpass API instead of XAPI. Overpass API is much more powerful.
I was wondering if it is possible to find a specific place using the google places api.
I know the name of the place, the address or website url, and the coordinates.
I need this to get the ratings this place has.
Is this possible? If not, is it going to be?
I think your best bet would be to do a nearbysearch with the location (lat,long), a small radius, and the name and types parameters to narrow it down. If you are targeting a specific place, then you can just manually find it in the results and use its reference for a Details request in your solution.
If the target place can be dynamic, for example based on user input, then you might want to show the user the list of results and let them choose the correct one. I don't think there's a way to guarantee that you will always get exactly the result you're looking for as, say, the first result in the list. Experiment with different types of requests and parameters and try to get a sense for the behaviour of the responses to find what will work best for your solution.
Is there a data structure within LiveCode that can be used as a "holder" for associated data, letting me handle it collectively? I come from a Java / Javascript / C background so I am looking for a Class or Struct sort of data structure.
I've found examples of Groups, which seem to have some of this functionality, but it feels a bit like I'm bending the language to meet my needs.
As a specific example, suppose I had an image field on my screen that would randomly display an image and, when pressed, play an associated sound clip. I'd expect to create a list of "structures" that contained the path to the image and the path to the associated sound clip, and use that data to populate the image field and to decide what sound clip to play.
Would a Group be the correct structure to use in this case? Or am I approaching this in a way that isn't really fitting with the way LiveCode works?
It takes a little getting used to, but the xTalk world is much simpler and more open than any ordinary procedural language. So much of what you once had to manage is no longer required.
So when splash21 said that you could store all your image and sound references in a custom property, he was really saying that the LiveCode environment contains intrinsic, high level functionality that makes these sorts of things instantly accessible, and the only thing required of you is to call for them, and they simply work.
The only way to appreciate this is to make a few simple programs, to really see what is possible. Make your application. Everything you mentioned can be accomplished with perhaps a dozen lines of code in a single handler. I recommend that you join the LiveCode use list and forums. The community is vibrant and eager to help, frequently with full blown solutions to specific problems, but more importantly, as guides and mentors to new users
Craig Newman
Arrays in LiveCode are actually associative arrays (like hash maps). A key is associated with a value. The value might be as well an array.
Chapter 5.5.7 of the User's Guide says
Array elements may contain nested or sub-elements, making them multi-dimensional.
This type of array is ideal for processing hierarchical data structures such as trees or
XML. To access a sub-element, simply declare it using an additional set of square
brackets.
put "ABC" into myVariable["myKeyName"][“aChildElement”]
see also
How to store pictures in a stack?
Dave- I'm hoping to get a struct-like container implemented in the near future. Meanwhile you can, as splash21 mentioned, use custom properties (or better yet, custom property sets) to do what you want. This will give you a pseudo-struct for each object and you can implement the file and sound specifications into the properties. And if you use that in conjunction with a behavior object you'll end up very close to a real inheritable class formation.
I am new at the idea of programming algorithms. I can work with simplistic ideas, but my current project requires that I create something a bit more complicated.
I'm trying to create a categorization system based on keywords and subsets of 'general' categories that filter down into more detailed categories that requires as little work as possible from the user.
I.E.
Sports >> Baseball >> Pitching >> Nolan Ryan
So, if a user decides they want to talk about "Baseball" and they filter the search, I would like to also include 'Sports"
User enters: "baseball"
User is then taken to Sports >> Baseball
Now I understand that this would be impossible without a living - breathing dynamic program that connects those two categories in some way. It would also require 'some' user input initially, and many more inputs throughout the lifetime of the software in order to maintain it and keep it up to date.
But Alas, asking for such an algorithm would be frivolous without detailing very concrete specifics about what I'm trying to do. And i'm not trying to ask for a hand out.
Instead, I am curious if people are aware of similar systems that have already been implemented and if there is documentation out there describing how it has been done. Or even some real life examples of your own projects.
In short, I have a 'plan' but it requires more user input than I really want. I feel getting more info on the subject would be the best course of action before jumping head first into developing this program.
Thanks
IMHO It isn't as hard as you think. What you want is called Tagging and you can do it Automatically just by setting the correlation between tags (i.e. a Tag can have its meaningful information plus its reation with other ones. Then, if user select a Tag well, you related that with others via looking your ADT collection (can be as simple as an array).
Tag:
Sport
Related Tags
Football
Soccer
...
I'm hoping this helps!
It sounds like what you want to do is create a tree/menu structure, and then be able to rapidly retrieve the "breadcrumb" for any given key in the tree.
Here's what I would think:
Create the tree with all the branches. It's okay if you want branches to share keys - as long as you can give the user a "choice" of "Multiple found, please choose which one... ?"
For every key in the tree, generate the breadcrumb. This is time-consuming, and if the tree is very large and updating regularly then it may be something better done offline, in the cloud, or via hadoop, etc.
Store the key and the breadcrumb in a key/value store such as redis, or in memory/cached as desired. You'll want every value to have an array if you want to share keys across categories/branches.
When the user selects a key - the key is looked up in the store, and if the resulting value contains only one match, then you simply construct the breadcrumb to take the user where you want them to go. If it has multiple, you give them a choice.
I would even say, if you need something more organic, say a user can create "new topic" dynamically from anywhere else, then you might want to not use a tree at all after the initial import - instead just update your key/value store in real-time.
I have a huge amount of documents (mainly pdfs and doc's) I want to classify, so I can search over them according to certain tags. These tags could either be of my own (I put the tags to the document) or extracted from the text.
I've just seen a post related to this (Classify data using Apache Mahout), but perhaps there is something even more simple.
Mahout might be overkill for your problem - but you can get a fairly quick, easy solution by using OpenNLP.
http://opennlp.sourceforge.net/api/index.html
Specifically, look at the opennlp.tools.doccat package. Essentially, you have to go through and manually tag a small(ish) set of the items for each category you desire. If they are really distinct, you can get away with a small sample size.
You can use the DocumentCategorizerME.train() static function to train a collection of documents, where each requires a category tag and the text block to train on. Then, you can initialize the DocumentCategorizerME with the trained model and begin classifying all the rest of your documents.
Once you do this, you can (I think) write the model to a file so you don't have to ever do that again.
This post on extracting keywords and classifying webpages is related and may be helpful. In your example it sounds like you can use tags in lieu of the keyword extraction piece (although you may want to use both in combination). Weka is easy to use, I would definitely recommend giving it a look.