Is it possible to create arrays based of their index as in
int x = 4;
int y = 5;
int someNr = 123;
int foo[x][y] = someNr;
dynamically/on the run, without creating foo[0...3][0...4]?
If not, is there a data structure that allow me to do something similar to this in C?
No.
As written your code make no sense at all. You need foo to be declared somewhere and then you can index into it with foo[x][y] = someNr;. But you cant just make foo spring into existence which is what it looks like you are trying to do.
Either create foo with correct sizes (only you can say what they are) int foo[16][16]; for example or use a different data structure.
In C++ you could do a map<pair<int, int>, int>
Variable Length Arrays
Even if x and y were replaced by constants, you could not initialize the array using the notation shown. You'd need to use:
int fixed[3][4] = { someNr };
or similar (extra braces, perhaps; more values perhaps). You can, however, declare/define variable length arrays (VLA), but you cannot initialize them at all. So, you could write:
int x = 4;
int y = 5;
int someNr = 123;
int foo[x][y];
for (int i = 0; i < x; i++)
{
for (int j = 0; j < y; j++)
foo[i][j] = someNr + i * (x + 1) + j;
}
Obviously, you can't use x and y as indexes without writing (or reading) outside the bounds of the array. The onus is on you to ensure that there is enough space on the stack for the values chosen as the limits on the arrays (it won't be a problem at 3x4; it might be at 300x400 though, and will be at 3000x4000). You can also use dynamic allocation of VLAs to handle bigger matrices.
VLA support is mandatory in C99, optional in C11 and C18, and non-existent in strict C90.
Sparse arrays
If what you want is 'sparse array support', there is no built-in facility in C that will assist you. You have to devise (or find) code that will handle that for you. It can certainly be done; Fortran programmers used to have to do it quite often in the bad old days when megabytes of memory were a luxury and MIPS meant millions of instruction per second and people were happy when their computer could do double-digit MIPS (and the Fortran 90 standard was still years in the future).
You'll need to devise a structure and a set of functions to handle the sparse array. You will probably need to decide whether you have values in every row, or whether you only record the data in some rows. You'll need a function to assign a value to a cell, and another to retrieve the value from a cell. You'll need to think what the value is when there is no explicit entry. (The thinking probably isn't hard. The default value is usually zero, but an infinity or a NaN (not a number) might be appropriate, depending on context.) You'd also need a function to allocate the base structure (would you specify the maximum sizes?) and another to release it.
Most efficient way to create a dynamic index of an array is to create an empty array of the same data type that the array to index is holding.
Let's imagine we are using integers in sake of simplicity. You can then stretch the concept to any other data type.
The ideal index depth will depend on the length of the data to index and will be somewhere close to the length of the data.
Let's say you have 1 million 64 bit integers in the array to index.
First of all you should order the data and eliminate duplicates. That's something easy to achieve by using qsort() (the quick sort C built in function) and some remove duplicate function such as
uint64_t remove_dupes(char *unord_arr, char *ord_arr, uint64_t arr_size)
{
uint64_t i, j=0;
for (i=1;i<arr_size;i++)
{
if ( strcmp(unord_arr[i], unord_arr[i-1]) != 0 ){
strcpy(ord_arr[j],unord_arr[i-1]);
j++;
}
if ( i == arr_size-1 ){
strcpy(ord_arr[j],unord_arr[i]);
j++;
}
}
return j;
}
Adapt the code above to your needs, you should free() the unordered array when the function finishes ordering it to the ordered array. The function above is very fast, it will return zero entries when the array to order contains one element, but that's probably something you can live with.
Once the data is ordered and unique, create an index with a length close to that of the data. It does not need to be of an exact length, although pledging to powers of 10 will make everything easier, in case of integers.
uint64_t* idx = calloc(pow(10, indexdepth), sizeof(uint64_t));
This will create an empty index array.
Then populate the index. Traverse your array to index just once and every time you detect a change in the number of significant figures (same as index depth) to the left add the position where that new number was detected.
If you choose an indexdepth of 2 you will have 10² = 100 possible values in your index, typically going from 0 to 99.
When you detect that some number starts by 10 (103456), you add an entry to the index, let's say that 103456 was detected at position 733, your index entry would be:
index[10] = 733;
Next entry begining by 11 should be added in the next index slot, let's say that first number beginning by 11 is found at position 2023
index[11] = 2023;
And so on.
When you later need to find some number in your original array storing 1 million entries, you don't have to iterate the whole array, you just need to check where in your index the first number starting by the first two significant digits is stored. Entry index[10] tells you where the first number starting by 10 is stored. You can then iterate forward until you find your match.
In my example I employed a small index, thus the average number of iterations that you will need to perform will be 1000000/100 = 10000
If you enlarge your index to somewhere close the length of the data the number of iterations will tend to 1, making any search blazing fast.
What I like to do is to create some simple algorithm that tells me what's the ideal depth of the index after knowing the type and length of the data to index.
Please, note that in the example that I have posed, 64 bit numbers are indexed by their first index depth significant figures, thus 10 and 100001 will be stored in the same index segment. That's not a problem on its own, nonetheless each master has his small book of secrets. Treating numbers as a fixed length hexadecimal string can help keeping a strict numerical order.
You don't have to change the base though, you could consider 10 to be 0000010 to keep it in the 00 index segment and keep base 10 numbers ordered, using different numerical bases is nonetheless trivial in C, which is of great help for this task.
As you make your index depth become larger, the amount of entries per index segment will be reduced
Please, do note that programming, especially lower level like C consists in comprehending the tradeof between CPU cycles and memory use in great part.
Creating the proposed index is a way to reduce the number of CPU cycles required to locate a value at the cost of using more memory as the index becomes larger. This is nonetheless the way to go nowadays, as masive amounts of memory are cheap.
As SSDs' speed become closer to that of RAM, using files to store indexes is to be taken on account. Nevertheless modern OSs tend to load in RAM as much as they can, thus using files would end up in something similar from a performance point of view.
Related
I have a 2tensor in C that looks like:
int n =4;
int l =5;
int p =6;
int q=2;
I then initialize each element of T
//loop over each of the above indices
T[n][l][p][q]=...
However, many of them are zero and there are symmetries such as.
T[4][3][2][1]=-T[3][4][2][1]
How can I save memory on the elements of T which are zero? Ideally I would like to place something like NULL in those positions so they use 0 instead of 8 bytes. Also, later on in the calculation I can check if they are zero or not by checking if they are equal to NULL
How do I implicitly include those symmetries in T with using excess memory?
Edit: the symmetry can perhaps be fixed with a different implementation. But what about the zeros? Is there any implementation to not have them waste memory?
You cannot influence the size of any variable by a value you write to it.
If you want to save memory you have not only to not use it, you have to not define a variable using it.
If you do not define a variable, then you have to not use it ever.
Then you have saved memory.
This is of course obvious.
Now, how to apply that to your problem.
Allow me to simplify, for one because you did not give enough information and explanation, at least not for me to understand every detail. For another, to keep the explanation simple.
So I hope that it suffices if I solve the following problem for you, which I think is kind of the little brother of your problem.
I have a large array in C (not really large, lets say N entries, with N==20).
But for special reasons, I will never need to actually read and write any even indices, they should act as if they contain 0, but I want to save the memory used by them.
So actually I want to only use M of the entries, with M*2==N.
So instead of
int Array[N]; /* all the theoretical elements */
I define
int Array[M]; /* only the actually used elements */
Of course I cannot access any of the elements which are not needed and it will not really be necessary.
But for the logic of my program, I want to be able to program as if I could access them, but be sure that they will always every only read 0 and ignore any written value.
So what I do is wrapping all accesses to the array.
int GetArray(int index)
{
if (index & 1)
{
/* odd, I need to really access the array,
but at a calculated index */
return Array[index/2];
} else
{
/* even, always 0 */
return 0;
}
}
void SetArray(int index, int value)
{
if (index & 1)
{
/* odd, I need to really access the array,
but at a calculated index */ */
Array[index/2] = value;
} else
{
/* even, no need to store anything, stays always "0" */
}
}
So I can read and write as if the array were twice as large, but guarantee not to ever use the faked elements.
And by mapping the indices as
actualindex = wantindex / 2
I ensure that I do not access beyond the size of the actually existing array.
Porting this concept now to the more complicated setup you have described is your job. You know all the details, you can test wether everything works.
I recommend to extend GetArray() and SetArray() by checks on the resulting index, to make sure that it is never outside of the actual array.
You can also add all kinds of self checks to verify that all your rules and expectations are met.
I wrote a simple function to sort an array int a[]; using hash.
For that I stored frequency for every element in new array hash1[] and then I put back in original array in linear time.
#include<bits/stdc++.h>
using namespace std;
int hash1[10000];
void sah(int a[],int n)
{
int maxo=-1;
for(int i=0;i<n;i++)
{
hash1[a[i]]++;
if(maxo<a[i]){maxo=a[i];}
}
int i=0,freq=0,idx=0;
while(i<maxo+1)
{
freq=hash1[i];
if(freq>0)
{
while(freq>0)
{
a[idx++]=i;freq--;
}
}
i++;
}
}
int main()
{
int a[]={6,8,9,22,33,59,12,5,99,12,57,7};
int n=sizeof(a)/sizeof(a[0]);
sah(a,n);
for(int i=0;i<n;i++)
{
printf("%d ",a[i]);
}
}
This algorithm runs in O(max_element). What kind of disadvantages I'm facing here considering only performance( time and space)?
The algorithm you've implemented is called counting sort. Its runtime is O(n + U), where n is the total number of elements and U is the maximum value in the array (assuming the numbers go from 0 to U), and its space usage is Θ(U). Your particular implementation assumes that U = 10,000. Although you've described your approach as "hashing," this really isn't a hash (computing some function of the elements and using that to put them into buckets) as a distribution (spreading elements around according to their values).
If U is a fixed constant - as it is in your case - then the runtime is O(n) and the space usage is O(1), though remember that big-O talks about long-term growth rates and that if U is large the runtime can be pretty high. This makes it attractive if you're sorting very large arrays with a restricted range of values. However, if the range of values can be large, this is not a particularly good approach. Interestingly, you can think of radix sort as an algorithm that repeatedly runs counting sort with U = 10 (if using the base-10 digits of the numbers) or U = 2 (if going in binary) and has a runtime of O(n log U), which is strongly preferable for large values of U.
You can clean up this code in a number of ways. For example, you have an if statement and a while loop with the same condition, which can be combined together into a single while loop. You also might want to put in some assert checks to make sure all the values are in the range from 0 to 9,999, inclusive, since otherwise you'll have a bounds error. Additionally, you could consider making the global array either a local variable (though watch your stack usage) or a static local variable (to avoid polluting the global namespace). You could alternatively have the user pass in a parameter specifying the maximum size or could calculate it yourself.
Issues you may consider:
Input validation. What if the user enters -10 or a very large value.
If the maximum element is large, you will at some point get a performance hit when the L1 cache is exhausted. The hash1-array will compete for memory bandwidth with the a-array. When I have implemented radix-sorting in the past I found that 8-bits per iteration was fastest.
The time complexity is actually O(max_element + number_of_elements). E.g. what if you sorted 2 million ones or zeros. It is not as fast as sorting 2 ones or zeros.
I am struggling to decide between two optimisations for building a numerical solver for the poisson equation.
Essentially, I have a two dimensional array, of which I require n doubles in the first row, n/2 in the second n/4 in the third and so on...
Now my difficulty is deciding whether or not to use a contiguous 2d array grid[m][n], which for a large n would have many unused zeroes but would probably reduce the chance of a cache miss. The other, and more memory efficient method, would be to dynamically allocate an array of pointers to arrays of decreasing size. This is considerably more efficient in terms of memory storage but would it potentially hinder performance?
I don't think I clearly understand the trade-offs in this situation. Could anybody help?
For reference, I made a nice plot of the memory requirements in each case:
There is no hard and fast answer to this one. If your algorithm needs more memory than you expect to be given then you need to find one which is possibly slower but fits within your constraints.
Beyond that, the only option is to implement both and then compare their performance. If saving memory results in a 10% slowdown is that acceptable for your use? If the version using more memory is 50% faster but only runs on the biggest computers will it be used? These are the questions that we have to grapple with in Computer Science. But you can only look at them once you have numbers. Otherwise you are just guessing and a fair amount of the time our intuition when it comes to optimizations are not correct.
Build a custom array that will follow the rules you have set.
The implementation will use a simple 1d contiguous array. You will need a function that will return the start of array given the row. Something like this:
int* Get( int* array , int n , int row ) //might contain logical errors
{
int pos = 0 ;
while( row-- )
{
pos += n ;
n /= 2 ;
}
return array + pos ;
}
Where n is the same n you described and is rounded down on every iteration.
You will have to call this function only once per entire row.
This function will never take more that O(log n) time, but if you want you can replace it with a single expression: http://en.wikipedia.org/wiki/Geometric_series#Formula
You could use a single array and just calculate your offset yourself
size_t get_offset(int n, int row, int column) {
size_t offset = column;
while (row--) {
offset += n;
n << 1;
}
return offset;
}
double * array = calloc(sizeof(double), get_offset(n, 64, 0));
access via
array[get_offset(column, row)]
I'm writing code for a decision tree in C. Right now it gives me the correct result (0% training error, low test error), but it takes a long time to run.
The problem lies in how often I run qsort. My basic algorithm is this:
for every feature
sort that feature column using qsort
remove duplicate feature values in that column
for every unique feature value
split
determine entropy given that split
save the best feature to split + split value
for every training_example
if training_example's value for best feature < best split value, store in Left[]
else store in Right[]
recursively call this function, using only the Left[] training examples
recursively call this function, using only the Right[] training examples
Because the last two lines are iterative calls, and because the tree can extend for dozens and dozens of branches, the number of calls to qsort is huge (especially for my dataset that has > 1000 features).
My idea to reduce the runtime is to create a 2d array (in a separate function) where each column is a sorted feature column. Then, as long as I maintain a vector of row numbers of the training examples in Left[] and Right[] for each recursive call, I can just call this separate function, grab the rows I want in the pre-sorted feature vector, and save the cost of having to qsort each time.
I'm fairly new to C and so I'm not sure how to code this. In MatLab I can just have a global array that any function can change or access, looking for something like that in C.
Global arrays in C are totally possible. There are actually two ways of doing that. In the first case the dimensions of the array are fixed for the application:
#define NROWS 100
#define NCOLS 100
int array[NROWS][NCOLS];
int main(void)
{
int i, j;
for (i = 0; i < NROWS; i++)
for (j = 0; j < NCOLS; j++)
{
array[i][j] = i+j;
}
return 0;
}
In the second example the dimensions may depend on values from the input.
#include <stdlib.h>
int **array;
int main(void)
{
int nrows = 100;
int ncols = 100;
int i, j;
array = malloc(nrows*sizeof(*array));
for (i = 0; i < nrows; i++)
{
array[i] = malloc(ncols*sizeof(*(array[i])));
for (j = 0; j < ncols; j++)
{
array[i][j] = i+j;
}
}
}
Although the access to the arrays in both examples looks deceivingly similar, the implementation of the arrays is quite different. In the first example the array is located in one piece of memory and the strides to access rows is a whole row. In the second example each row access is a pointer to a row, which is one piece of memory. The various rows can however be located in different areas of the memory. In the second example rows might also have a different length. In that case you would need to store the length of each row somewhere too.
I don't fully understand what you are trying to achieve, because I'm not familiar with the terminology of decision tree, feature and the standard approaches to training sets. But you may also want to have a look at other data structures to maintain sorted data:
http://en.wikipedia.org/wiki/Red–black_tree maintains a more or less balanced and sorted tree.
AVL tree a bit slower but more balanced and sorted tree.
Trie a sorted tree on lists of elements.
Hash function to easily map a complex element to an integral value that can be used to sort the elements. Good for finding exact elements, but there is no real order in the elements itself.
P.S1: Coming from Matlab you may want to consider a different language from C to move to. C++ has standard libraries to support above data structures. Java, Python come to mind or even Haskell if you are daring. Pointer handling in C can be quite tedious and error prone.
P.S2: I'm unable to include a - in a URL on StackOverflow. So the Red-black tree links is a bit off and can't be clicked. If someone can edit my post to fix it, then I would appreciate that.
I won't go into details, but I'm attempting to implement an algorithm similar to the Boyer-Moore-Horspool algorithm, only using hex color values instead of characters (i.e., there is a much greater range).
Following the example on Wikipedia, I originally had this:
size_t jump_table[0xFFFFFF + 1];
memset(jump_table, default_value, sizeof(jump_table);
However, 0xFFFFFF is obviously a huge number and this quickly causes C to seg-fault (but not stack-overflow, disappointingly).
Basically, what I need is an efficient associative array mapping integers to integers. I was considering using a hash table, but having a malloc'd struct for each entry just seems overkill to me (I also do not need hashes generated, as each key is a unique integer and there can be no duplicate entries).
Does anyone have any alternatives to suggest? Am I being overly pragmatic about this?
Update
For those interested, I ended up using a hash table via the uthash library.
0xffffff is rather too large to put on the stack on most systems, but you absolutely can malloc a buffer of that size (at least on current computers; not so much on a smartphone). Whether or not you should do it for this task is a separate issue.
Edit: Based on the comment, if you expect the common case to have a relatively small number of entries other than the "this color doesn't appear in the input" skip value, you should probably just go ahead and use a hash map (obviously only storing values that actually appear in the input).
(ignore earlier discussion of other data structures, which was based on an incorrect recollection of the algorithm under discussion -- you want to use a hash table)
If the array you were going to make (of size 0xFFFFFF) was going to be sparse you could try making a smaller array to act as a simple hash table, with the size being 0xFFFFFF / N and the hash function being hexValue / N (or hexValue % (0xFFFFFF / N)). You'll have to be creative to handle collisions though.
This is the only way I can foresee getting out of mallocing structs.
You can malloc(3) 0xFFFFFF blocks of size_t on the heap (for simplicity), and address them as you do with an array.
As for the stack overflow. Basically the program receives a SIGSEGV, which can be a result of a stack overflow or accessing illegal memory or writing on a read-only segment etc... They are all abstracted under the same error message "Segmentation fault".
But why don't you use a higher level language like python that supports associate arrays?
At possibly the cost of some speed, you could try modifying the algorithm to find only matches that are aligned to some boundary (every three or four symbols), then perform the search at byte level.
You could create a sparse array of sorts which has "pages" like this (this example uses 256 "pages", so the upper most byte is the page number):
int *pages[256];
/* call this first to make sure all of the pages start out NULL! */
void init_pages(void) {
for(i = 0; i < 256; ++i) {
pages[i] = NULL;
}
}
int get_value(int index) {
if(pages[index / 0x10000] == NULL) {
pages[index / 0x10000] = calloc(0x10000, 1); /* calloc so it will zero it out */
}
return pages[index / 0x10000][index % 0x10000];
}
void set_value(int index, int value) {
if(pages[index / 0x10000] == NULL) {
pages[index / 0x10000] = calloc(0x10000, 1); /* calloc so it will zero it out */
}
pages[index / 0x10000][index % 0x10000] = value;
}
this will allocate a page the first time it is touched, read or write.
To avoid the overhead of malloc you can use a hashtable where the entries in the table are your structs, assuming they are small. In your case a pair of integers should suffice, with a special value to indicate emptyness of the slot in the table.
How many values are there in your output space, i.e. how many different values do you map to in the range 0-0xFFFFF?
Using randomized universal hashing you can come up with a collision-free hash function with a table no bigger than 2 times the number of values in your output space (for a static table)