i see the function CGPathEqualToPath which i successfully used to compare data from 2 UIbezierPaths (technically, i compared a path to itself).
Is there any way to modify this function to find out how similar 2 paths are? and perhaps make a threshold to say, ok, these paths are close enough to be considered the same?
(i'm using iOS)
also, unrelated. i have a mutable array of bezierpaths. what is the notation for accessing a particular element of the array? i'm new to this. thanks
You might be able to accomplish the comparison by drawing each path into a separate bitmap and then seeing how many bits they have in common. You could make a ratio of the total bits in both bitmaps to the bits in both bitmaps to get a degree of similarity. 2:1 would be identical (two bitmaps completely overlapping), 2:0 would mean nothing in common.
I don't think you can create a likeness function as you don't have access to the underlying structure or functions that provide access to those values. If you can elaborate the use case, maybe there is an alternate solution.
As for accessing an object at a particular index in an array, you can do it using –
id myObject = [array objectAtIndex:particularIndex];
Related
I have a bit of code that looks like this:
DO I=0,500
arg1((I*54+1):(I*54+54)) = premultz*sinphi(I+1)
ENDDO
In short, I have an array premultz of dimension 54. I have an array sinphi of dimension 501. I want to take the first value of of sinphi times all the entries of premultz and store it in the first 54 entries of arg1, then the second value of of sinphi times all the entries of premultz and store it in the second54 entries of arg1, and so on.
These are flattened matrices. I have flattened them in the interest of speed, as one of the primary goals of this project is very fast code.
My question is this: is there a more efficient way of coding this sort of calculation in Fortran90? I know that Fortran has a lot of nifty array operations that can be done that I'm not fully aware of.
Thanks in advance.
This expression, if I've got things right, ought to create arg1 in one statement
arg1 = reshape(spread(premultz,dim=2,ncopies=501)*&
&spread(sinphi,dim=1,ncopies=54),[1,54*501])
I've hardwired the dimensions here, that may or may not suit your purposes. The inner expression generates the outer product of premultz and sinphi, which is then reshaped into a vector. You may find you need to reshape the transpose of the outer product, I haven't checked things very carefully.
However, based on my experience with this sort of clever use of Fortran's array intrinsics I doubt that this, or most other clever uses of Fortran's array intrinsics, will outperform the straightforward loop implementation you already have. For many of these operations the compiler is going to generate copies of arrays, and copying data is relatively expensive. Of course, this is an assertion you may want to test.
I'll leave it to you to decide if the one-liner is more comprehensible than the loops. Sometimes the expressivity of the array syntax comes at an acceptable cost in performance, sometimes it doesn't.
Is it preferable to store redundant information, (which can be otherwise generated from existing data,) or to instead convert the existing data each time you need access?
I've simplified my specific problem as best as I can below, hoping that the provided answers are useful as future-reference material.
Example:
Let's say we've developed a program that places data into Squares on a grid (like a super-descriptive game of Tic-Tac-Toe or something) and assigns various details, and a unique identification number to each:
Throughout our program, we often perform logic based on a square's X and/or Y coordinates (checking for 3 in a row) and other times we only need the ID (perhaps to access a string at "SquareName[ID]") - We aren't exactly certain which of these two is accessed more often, but it's a rather close competition.
Up until now we've simply stored the ID inside the square class, and converted it with some simple formulas whenever just the X or Y are needed. Say we want to get coordinates for one square in particular:
int CurrentX = (this.Square.ID - 1) % 3) + 1; // X coordinate, 1 through 3
int CurrentY = (this.Square.ID + 1) / 3; // Y, 1 through 3
Since the squares don't move around or change ID after setup, part of me believes it would be simpler just to store all 3 values inside the Square class, but my other part cringes at the redundancy since access to X and Y is already easy enough to calculate from the existing ID.
(Note, This program itself is not very memory or resource intensive, nor does the size of the grid get much larger, so it mostly comes down to which option is a better practice or rule of thumb.)
What would you do?
As a rule of thumb, for a system where the data is read/write, store your basic data without redundancy.
When performance or other considerations become a practical issue, then you should denormalize as necessary. (i.e. wait for it to be a problem, don't pre-optimize overly much).
Your goal should be the most maintainable code possible. That usually means writing the least code possible. Having extra code to maintain redundant copies of data points will make your code more brittle.
If those are values which can be determined at the moment of creation and then do not change anymore, I would go for variables populated in the constructor. It's not redundant info in so far as that it isn't stored anywhere else, but that's not my main point. When reading my code, I'd usually expect that whenever something is computed at the time of request, it might change per request. It is easy to find the point in the source where the field is populated and where it is changed, especially if it does never change, but you might end up slightly confused when looking at some calculation which will return always the same result, as it's variables can't change, and wonder whether you're just missing a case or this is really static.
Also, using a descriptive variable name, you can get rid of the comments. Not that I generally aim at not commenting, but source code which doesn't even need comments is a pretty save signal for easy to understand code, which might (/should) be your aim.
I'm pretty sure this is a silly newbie question but I didn't know it so I had to ask...
Why do we use data structures, like Linked List, Binary Search Tree, etc? (when no dynamic allocation is needed)
I mean: wouldn't it be faster if we kept a single variable for a single object? Wouldn't that speed up access time? Eg: BST possibly has to run through some pointers first before it gets to the actual data.
Except for when dynamic allocation is needed, is there a reason to use them?
Eg: using linked list/ BST / std::vector in a situation where a simple (non-dynamic) array could be used.
Each thing you are storing is being kept in it's own variable (or storage location). Data structures apply organization to your data. Imagine if you had 10,000 things you were trying to track. You could store them in 10,000 separate variables. If you did that, then you'd always be limited to 10,000 different things. If you wanted more, you'd have to modify your program and recompile it each time you wanted to increase the number. You might also have to modify the code to change the way in which the calculations are done if the order of the items changes because the new one is introduced in the middle.
Using data structures, from simple arrays to more complex trees, hash tables, or custom data structures, allows your code to both be more organized and extensible. Using an array, which can either be created to hold the required number of elements or extended to hold more after it's first created keeps you from having to rewrite your code each time the number of data items changes. Using an appropriate data structure allows you to design algorithms based on the relationships between the data elements rather than some fixed ordering, giving you more flexibility.
A simple analogy might help to understand. You could, for example, organize all of your important papers by putting each of them into separate filing cabinet. If you did that you'd have to memorize (i.e., hard-code) the cabinet in which each item can be found in order to use them effectively. Alternatively, you could store each in the same filing cabinet (like a generic array). This is better in that they're all in one place, but still not optimum, since you have to search through them all each time you want to find one. Better yet would be to organize them by subject, putting like subjects in the same file folder (separate arrays, different structures). That way you can look for the file folder for the correct subject, then find the item you're looking for in it. Depending on your needs you can use different filing methods (data structures/algorithms) to better organize your information for it's intended use.
I'll also note that there are times when it does make sense to use individual variables for each data item you are using. Frequently there is a mixture of individual variables and more complex structures, using the appropriate method depending on the use of the particular item. For example, you might store the sum of a collection of integers in a variable while the integers themselves are stored in an array. A program would need to be pretty simple though before the introduction of data structures wouldn't be appropriate.
Sorry, but you didn't just find a great new way of doing things ;) There are several huge problems with this approach.
How could this be done without requring programmers to massively (and nontrivially) rewrite tons of code as soon as the number of allowed items changes? Even when you have to fix your data structure sizes at compile time (e.g. arrays in C), you can use a constant. Then, changing a single constant and recompiling is sufficent for changes to that size (if the code was written with this in mind). With your approach, we'd have to type hundreds or even thousands of lines every time some size changes. Not to mention that all this code would be incredibly hard to read, write, maintain and verify. The old truism "more lines of code = more space for bugs" is taken up to eleven in such a setting.
Then there's the fact that the number is almost never set in stone. Even when it is a compile time constant, changes are still likely. Writing hundreds of lines of code for a minor (if it exists at all) performance gain is hardly ever worth it. This goes thrice if you'd have to do the same amount of work again every time you want to change something. Not to mention that it isn't possible at all once there is any remotely dynamic component in the size of the data structures. That is to say, it's very rarely possible.
Also consider the concept of implicit and succinct data structures. If you use a set of hard-coded variables instead of abstracting over the size, you still got a data structure. You merely made it implicit, unrolled the algorithms operating on it, and set its size in stone. Philosophically, you changed nothing.
But surely it has a performance benefit? Well, possible, although it will be tiny. But it isn't guaranteed to be there. You'd save some space on data, but code size would explode. And as everyone informed about inlining should know, small code sizes are very useful for performance to allow the code to be in the cache. Also, argument passing would result in excessive copying unless you'd figure out a trick to derive the location of most variables from a few pointers. Needless to say, this would be nonportable, very tricky to get right even on a single platform, and liable to being broken by any change to the code or the compiler invocation.
Finally, note that a weaker form is sometimes done. The Wikipedia page on implicit and succinct data structures has some examples. On a smaller scale, some data structures store much data in one place, such that it can be accessed with less pointer chasing and is more likely to be in the cache (e.g. cache-aware and cache-oblivious data structures). It's just not viable for 99% of all code and taking it to the extreme adds only a tiny, if any, benefit.
The main benefit to datastructures, in my opinion, is that you are relationally grouping them. For instance, instead of having 10 separate variables of class MyClass, you can have a datastructure that groups them all. This grouping allows for certain operations to be performed because they are structured together.
Not to mention, having datastructures can potentially enforce type security, which is powerful and necessary in many cases.
And last but not least, what would you rather do?
string string1 = "string1";
string string2 = "string2";
string string3 = "string3";
string string4 = "string4";
string string5 = "string5";
Console.WriteLine(string1);
Console.WriteLine(string2);
Console.WriteLine(string3);
Console.WriteLine(string4);
Console.WriteLine(string5);
Or...
List<string> myStringList = new List<string>() { "string1", "string2", "string3", "string4", "string5" };
foreach (string s in myStringList)
Console.WriteLine(s);
I'm using OpenCV to compare two blobs in two images. Suppose I've known
a pair of blobs that are likely to be similar, and I know their indices
in the contour arrays (generated by cvFindContours()), how can I get
access to one contour in a constant time?
The most cumbersome way is to use the link operation (contours=contours->h_next) multiple times, but I wonder if there is a faster way to retrieve one contour in an array.
I use CV_RETR_EXTERNAL and CV_CHAIN_APPROX_NONE in calling cvFindContours().
Thanks!
-J.C.
I think the function cvGetSeqElem does what you want. Quoting the OpenCV docs: "The function has O(1) time complexity assuming that the number of blocks is much smaller than the number of elements." I suppose "blocks" means "contours" in this context.
Also, take a look at cvCvtSeqToArray (link), which copies a sequence to one continuous block of memory.
I am developing a game for the web. The map of this game will be a minimum of 2000km by 2000km. I want to be able to encode elevation and terrain type at some level of granularity - 100m X 100m for example.
For a 2000km by 2000km map storing this information in 100m2 buckets would mean 20000 by 20000 elements or a total of 400,000,000 records in a database.
Is there some other way of storing this type of information?
MORE INFORMATION
The map itself will not ever be displayed in its entirety. Units will be moved on the map in a turn based fashion and the players will get feedback on where they are located and what the local area looks like. Terrain will dictate speed and prohibition of movement.
I guess I am trying to say that the map will be used for the game and not necessarily for a graphical or display purposes.
It depends on how you want to generate your terrain.
For example, you could procedurally generate it all (using interpolation of a low resolution terrain/height map - stored as two "bitmaps" - with random interpolation seeded from the xy coords to ensure that terrain didn't morph), and use minimal storage.
If you wanted areas of terrain that were completely defined, you could store these separately and use them where appropriate, randomly generating the rest.)
If you want completely defined terrain, then you're going to need to look into some kind of compression/streaming technique to only pull terrain you are currently interested in.
I would treat it differently, by separating terrain type and elevation.
Terrain type, I assume, does not change as rapidly as elevation - there are probably sectors of the same type of terrain that stretch over much longer than the lowest level of granularity. I would map those sectors into database records or some kind of hash table, depending on performance, memory and other requirements.
Elevation I would assume is semi-contiuous, as it changes gradually for the most part. I would try to map the values into set of continuous functions (different sets between parts that are not continues, as in sudden change in elevation). For any set of coordinates for which the terrain is the same elevation or can be described by a simple function, you just need to define the range this function covers. This should reduce much the amount of information you need to record to describe the elevation at each point in the terrain.
So basically I would break down the map into different sectors which compose of (x,y) ranges, once for terrain type and once for terrain elevation, and build a hash table for each which can return the appropriate value as needed.
If you want the kind of granularity that you are looking for, then there is no obvious way of doing it.
You could try a 2-dimensional wavelet transform, but that's pretty complex. Something like a Fourier transform would do quite nicely. Plus, you probably wouldn't go about storing the terrain with a one-record-per-piece-of-land way; it makes more sense to have some sort of database field which can store an encoded matrix.
I think the usual solution is to break your domain up into "tiles" of manageable sizes. You'll have to add a little bit of logic to load the appropriate tiles at any given time, but not too bad.
You shouldn't need to access all that info at once--even if each 100m2 bucket occupied a single pixel on the screen, no screen I know of could show 20k x 20k pixels at once.
Also, I wouldn't use a database--look into height mapping--effectively using a black & white image whose pixel values represent heights.
Good luck!
That will be awfully lot of information no matter which way you look at it. 400,000,000 grid cells will take their toll.
I see two ways of going around this. Firstly, since it is a web-based game, you might be able to get a server with a decently sized HDD and store the 400M records in it just as you would normally. Or more likely create some sort of your own storage mechanism for efficiency. Then you would only have to devise a way to access the data efficiently, which could be done by taking into account the fact that you doubtfully will need to use it all at once. ;)
The other way would be some kind of compression. You have to be careful with this though. Most out-of-the-box compression algorithms won't allow you to decompress an arbitrary location in the stream. Perhaps your terrain data has some patterns in it you can use? I doubt it will be completely random. More likely I predict large areas with the same data. Perhaps those can be encoded as such?