Is the following a common data type (i.e. does it have a name)?
Its unique characteristic is, unlike a regular Set, that it contains the "universe" on initialisation with O(C) memory overhead, and a max memory overhead of O(N/2) (which only occurs when you remove every-other element):
> s = new Structure(701)
s = Structure(0-700)
> s.remove(100)
s = Structure(0-99, 101-700)
> s.add(100)
s = Structure(0-700)
> s.remove(200)
s = Structure(0-199, 201-700)
> s.remove(202)
s = Structure(0-199, 201, 203-700)
> s.removeAll()
s = Structure()
Does something like this have a standard name?
I've used this many times in the past and seen it used in things like plane-sweep algorithms for polygon clipping.
Sometimes the abstract data type it represents is just a set, and the data structure is an optimization. I use this for representing the set of matching characters given by a regex expression like [^a-zA-z0-9.-], for example, and to perform intersection, union, and other operations on those sets.
This sort of data structure is implemented on top of some other ordered set or map structure, by simply storing the keys where membership in the set changes instead of the keys in the set itself. In all the other cases where I've seen this sort of thing done, the authors refer to that underlying structure instead of giving a name to the concept itself.
I like the idea of having a name for it, though, since as I said I've used it myself many times. Maybe I would call it an "in & out set" in honor of the hamburger chain I liked the best back when I ate hamburgers.
It's a Compressed Bit Set or Compressed Bitmap.
A Bit Set or Bitmap is a set specifically designed for storing Integers. Most languages offer standard implementations of these. They typically work by assigning a 1 to the Nth bit in an internal array of Integers where N is the number you're adding to the set. 0 indicates the value is not present. The memory usage for these types of Bit Sets is dictated by the largest number you store.
A Compressed Bit Set is one that compacts ranges of 0s and 1s.
In this case, the question demonstrates a type of compression called "run-length-encoding" (thank you #Ralf Kleberhoff), so it is specifically a Run-length Encoded Bitmap.
Common implementations of Compressed Bitmaps (from newest-to-oldest) are:
Roaring Bitmaps (only one to provide "good random access")
EWAH
WAH
Oracle BBC
I have a low-resource embedded system with a graphical user interface. The interface requires font data. To conserve read-only memory (flash), the font data needs to be compressed. I am looking for an algorithm for this purpose.
Properties of the data to be compressed
transparency data for a rectangular pixel map with 8 bits per pixel
there are typically around 200..300 glyphs in a font (typeface sampled in certain size)
each glyph is typically from 6x9 to 15x20 pixels in size
there are a lot of zeros ("no ink") and somewhat less 255's ("completely inked"), otherwise the distribution of octets is quite even due to the nature of anti-aliasing
Requirements for the compression algorithm
The important metrics for the decompression algorithm is the size of the data plus the size of the algorithm (as they will reside in the same limited memory).
There is very little RAM available for the decompression; it is possible to decompress the data for a single glyph into RAM but not much more.
To make things more difficult, the algorithm has to be very fast on a 32-bit microcontroller (ARM Cortex-M core), as the glyphs need to be decompressed while they are being drawn onto the display. Ten or twenty machine cycles per octet is ok, a hundred is certainly too much.
To make things easier, the complete corpus of data is known a priori, and there is a lot of processing power and memory available during the compression phase.
Conclusions and thoughts
The naïve approach of just packing each octet by some variable-length encoding does not give good results due to the relatively high entropy.
Any algorithm taking advantage of data decompressed earlier seems to be out of question as it is not possible to store the decompressed data of other glyphs. This makes LZ algorithms less efficient as they can only reference to a small amount of data.
Constraints on the processing power seem to rule out most bitwise operations, i.e. decompression should handle the data octet-by-octet. This makes Huffman coding difficult and arithmetic coding impossible.
The problem seems to be a good candidate for static dictionary coding, as all data is known beforehand, and the data is somewhat repetitive in nature (different glyphs share same shapes).
Questions
How can a good dictionary be constructed? I know finding the optimal dictionary for certain data is a np complete problem, but are there any reasonably good approximations? I have tried the zstandard's dictionary builder, but the results were not very good.
Is there something in my conclusions that I've gotten wrong? (Am I on the wrong track and omitting something obvious?)
Best algorithm this far
Just to give some background information, the best useful algorithm I have been able to figure out is as follows:
All samples in the font data for a single glyph are concatenated (flattened) into a one-dimensional array (vector, table).
Each sample has three possible states: 0, 255, and "something else".
This information is packed five consecutive samples at a time into a 5-digit base-three number (0..3^5).
As there are some extra values available in an octet (2^8 = 256, 3^5 = 243), they are used to signify longer strings of 0's and 255's.
For each "something else" value the actual value (1..254) is stored in a separate vector.
This data is fast to decompress, as the base-3 values can be decoded into base-4 values by a smallish (243 x 3 = 729 octets) lookup table. The compression ratios are highly dependent on the font size, but with my typical data I can get around 1:2. As this is significantly worse than LZ variants (which get around 1:3), I would like to try the static dictionary approach.
Of course, the usual LZ variants use Huffman or arithmetic coding, which naturally makes the compressed data smaller. On the other hand, I have all the data available, and the compression speed is not an issue. This should make it possible to find much better dictionaries.
Due to the nature of the data I could be able to use a lossy algorithm, but in that case the most likely lossy algorithm would be reducing the number of quantization levels in the pixel data. That won't change the underlying compression problem much, and I would like to avoid the resulting bit-alignment hassle.
I do admit that this is a borderline case of being a good answer to my question, but as I have researched the problem somewhat, this answer both describes the approach I chose and gives some more information on the nature of the problem should someone bump into it.
"The right answer" a.k.a. final algorithm
What I ended up with is a variant of what I describe in the question. First, each glyph is split into trits 0, 1, and intermediate. This ternary information is then compressed with a 256-slot static dictionary. Each item in the dictionary (or look-up table) is a binary encoded string (0=0, 10=1, 11=intermediate) with a single 1 added to the most significant end.
The grayscale data (for the intermediate trits) is interspersed between the references to the look-up table. So, the data essentially looks like this:
<LUT reference><gray value><gray value><LUT reference>...
The number of gray scale values naturally depends on the number of intermediate trits in the ternary data looked up from the static dictionary.
Decompression code is very short and can easily be written as a state machine with only one pointer and one 32-bit variable giving the state. Something like this:
static uint32_t trits_to_decode;
static uint8_t *next_octet;
/* This should be called when starting to decode a glyph
data : pointer to the compressed glyph data */
void start_glyph(uint8_t *data)
{
next_octet = data; // set the pointer to the beginning of the glyph
trits_to_decode = 1; // this triggers reloading a new dictionary item
}
/* This function returns the next 8-bit pixel value */
uint8_t next_pixel(void)
{
uint8_t return_value;
// end sentinel only? if so, we are out of ternary data
if (trits_to_decode == 1)
// get the next ternary dictionary item
trits_to_decode = dictionary[*next_octet++];
// get the next pixel from the ternary word
// check the LSB bit(s)
if (trits_to_decode & 1)
{
trits_to_decode >>= 1;
// either full value or gray value, check the next bit
if (trits_to_decode & 1)
{
trits_to_decode >>= 1;
// grayscale value; get next from the buffer
return *next_octet++;
}
// if we are here, it is a full value
trits_to_decode >>= 1;
return 255;
}
// we have a zero, return it
trits_to_decode >>= 1;
return 0;
}
(The code has not been tested in exactly this form, so there may be typos or other stupid little errors.)
There is a lot of repetition with the shift operations. I am not too worried, as the compiler should be able to clean it up. (Actually, left shift could be even better, because then the carry bit could be used after shifting. But as there is no direct way to do that in C, I don't bother.)
One more optimization relates to the size of the dictionary (look-up table). There may be short and long items, and hence it can be built to support 32-bit, 16-bit, or 8-bit items. In that case the dictionary has to be ordered so that small numerical values refer to 32-bit items, middle values to 16-bit items and large values to 8-bit items to avoid alignment problems. Then the look-up code looks like this:
static uint8_t dictionary_lookup(uint8_t octet)
{
if (octet < NUMBER_OF_32_BIT_ITEMS)
return dictionary32[octet];
if (octet < NUMBER_OF_32_BIT_ITEMS + NUMBER_OF_16_BIT_ITEMS)
return dictionary16[octet - NUMBER_OF_32_BIT_ITEMS];
return dictionary8[octet - NUMBER_OF_16_BIT_ITEMS - NUMBER_OF_32_BIT_ITEMS];
}
Of course, if every font has its own dictionary, the constants will become variables looked up form the font information. Any half-decent compiler will inline that function, as it is called only once.
If the number of quantization levels is reduced, it can be handled, as well. The easiest case is with 4-bit gray levels (1..14). This requires one 8-bit state variable to hold the gray levels. Then the gray level branch will become:
// new state value
static uint8_t gray_value;
...
// new variable within the next_pixel() function
uint8_t return_value;
...
// there is no old gray value available?
if (gray_value == 0)
gray_value = *next_octet++;
// extract the low nibble
return_value = gray_value & 0x0f;
// shift the high nibble into low nibble
gray_value >>= 4;
return return_value;
This actually allows using 15 intermediate gray levels (a total of 17 levels), which maps very nicely into linear 255-value system.
Three- or five-bit data is easier to pack into a 16-bit halfword and set MSB always one. Then the same trick as with the ternary data can be used (shift until you get 1).
It should be noted that the compression ratio starts to deteriorate at some point. The amount of compression with the ternary data does not depend on the number of gray levels. The gray level data is uncompressed, and the number of octets scales (almost) linearly with the number of bits. For a typical font the gray level data at 8 bits is 1/2 .. 2/3 of the total, but this is highly dependent on the typeface and size.
So, reduction from 8 to 4 bits (which is visually quite imperceptible in most cases) reduces the compressed size typically by 1/4..1/3, whereas the further reduction offered by going down to three bits is significantly less. Two-bit data does not make sense with this compression algorithm.
How to build the dictionary?
If the decompression algorithm is very straightforward and fast, the real challenges are in the dictionary building. It is easy to prove that there is such thing as an optimal dictionary (dictionary giving the least number of compressed octets for a given font), but wiser people than me seem to have proven that the problem of finding such dictionary is NP-complete.
With my arguably rather lacking theoretical knowledge on the field I thought there would be great tools offering reasonably good approximations. There might be such tools, but I could not find any, so I rolled my own mickeymouse version. EDIT: the earlier algorithm was rather goofy; a simpler and more effective was found
start with a static dictionary of '0', g', '1' (where 'g' signifies an intermediate value)
split the ternary data for each glyph into a list of trits
find the most common consecutive combination of items (it will most probably be '0', '0' at the first iteration)
replace all occurrences of the combination with the combination and add the combination into the dictionary (e.g., data '0', '1', '0', '0', 'g' will become '0', '1', '00', 'g' if '0', '0' is replaced by '00')
remove any unused items in the dictionary (they may occur at least in theory)
repeat steps 3-5 until the dictionary is full (i.e. at least 253 rounds)
This is still a very simplistic approach and it probably gives a very sub-optimal result. Its only merit is that it works.
How well does it work?
One answer is well enough, but to elaborate that a bit, here are some numbers. This is a font with 864 glyphs, typical glyph size of 14x11 pixels, and 8 bits per pixel.
raw uncompressed size: 127101
number of intermediate values: 46697
Shannon entropies (octet-by-octet):
total: 528914 bits = 66115 octets
ternary data: 176405 bits = 22051 octets
intermediate values: 352509 bits = 44064 octets
simply compressed ternary data (0=0, 10=1, 11=intermediate) (127101 trits): 207505 bits = 25939 octets
dictionary compressed ternary data: 18492 octets
entropy: 136778 bits = 17097 octets
dictionary size: 647 octets
full compressed data: 647 + 18492 + 46697 = 65836 octets
compression: 48.2 %
The comparison with octet-by-octet entropy is quite revealing. The intermediate value data has high entropy, whereas the ternary data can be compressed. This can also be interpreted by the high number of values 0 and 255 in the raw data (as compared to any intermediate values).
We do not do anything to compress the intermediate values, as there do not seem to be any meaningful patterns. However, we beat entropy by a clear margin with ternary data, and even the total amount of data is below entropy limit. So, we could do worse.
Reducing the number of quantization levels to 17 would reduce the data size to approximately 42920 octets (compression over 66 %). The entropy is then 41717 octets, so the algorithm gets slightly worse as is expected.
In practice, smaller font sizes are difficult to compress. This should be no surprise, as larger fraction of the information is in the gray scale information. Very big font sizes compress efficiently with this algorithm, but there run-length compression is a much better candidate.
What would be better?
If I knew, I would use it! But I can still speculate.
Jubatian suggests there would be a lot of repetition in a font. This must be true with the diacritics, as aàäáâå have a lot in common in almost all fonts. However, it does not seem to be true with letters such as p and b in most fonts. While the basic shape is close, it is not enough. (Careful pixel-by-pixel typeface design is then another story.)
Unfortunately, this inevitable repetition is not very easy to exploit in smaller size fonts. I tried creating a dictionary of all possible scan lines and then only referencing to those. Unfortunately, the number of different scan lines is high, so that the overhead added by the references outweighs the benefits. The situation changes somewhat if the scan lines themselves can be compressed, but there the small number of octets per scan line makes efficient compression difficult. This problem is, of course, dependent on the font size.
My intuition tells me that this would still be the right way to go, if both longer and shorter runs than full scan lines are used. This combined with using 4-bit pixels would probably give very good results—only if there were a way to create that optimal dictionary.
One hint to this direction is that LZMA2 compressed file (with xz at the highest compression) of the complete font data (127101 octets) is only 36720 octets. Of course, this format fulfils none of the other requirements (fast to decompress, can be decompressed glyph-by-glyph, low RAM requirements), but it still shows there is more redundance in the data than what my cheap algorithm has been able to exploit.
Dictionary coding is typically combined with Huffman or arithmetic coding after the dictionary step. We cannot do it here, but if we could, it would save another 4000 octets.
You can consider using something already developed for a scenario similar to Yours
https://github.com/atomicobject/heatshrink
https://spin.atomicobject.com/2013/03/14/heatshrink-embedded-data-compression/
You could try lossy compression using a sparse representation with custom dictionary.
The output of each glyph is a superposition of 1-N blocks from the dictionary;
most cpu time spent in preprocessing
predetermined decoding time (max, average or constant N) additions per pixel
controllable compressed size (dictionary size + xyn codes per glyph)
It seems that the simplest lossy method would be to reduce the number of bits-per-pixel. With glyphs of that size, 16 levels are likely to be sufficient. That would halve the data immediately, then you might apply your existing algorithm in the values 0, 16 or "something else" to perhaps halve it again.
I would go for Clifford's answer, that is, converting the font to 4 bits per pixel first which is sufficient for this task.
Then, since this is a font, you have lots of row repetitions, that is when rows defining one character match those of another character. Take for example the letter 'p' and 'b', the middle part of these letters should be the same (you will have even more matches if the target language uses loads of diacritics). Your encoder then could first collect all distinct rows of the font, store these, and then each character image is formed by a list of pointers to the rows.
The efficiency depends on the font of course, depending on the source, you might need some preprocessing to get it compress better with this method.
If you want more, you might rather choose to go for 3 bits per pixel or even 2 bits per pixel, depending on your goals (and some will for hand-tuning the font images), these might still be satisfactory.
This method in overall of course works very well for real-time display (you only need to traverse a pointer to get the row data).
I wish to have a 2-D data structure to store SAT formulas. What I want is similar to 2-D arrays. But I wish to do dynamic memory allocation.
I was thinking in terms of array of vectors of the following form :-
typedef vector<int> intVector;
intVector *clause;
clause = new intVector [numClause + 1] ;
Thus clause[0] will be one vector, clause[1] will be another and so on. And each vector may have a different size. But I am unsure if this is the right thing to do in terms of memory allocation.
Can an array of vectors be made, so that the vectors have different sizes ? How bad is it on memory management ?
Thanks.
To memory management will be fine using STL (dynamic memory alloc), performance will go down because is dynamic and doesn't do direct access.
If you're in complete dispair you may use a vector of pairs, with the first as an static array and the second, the count of used elements. If some array grows bigger, you may reallocate with more memory and increase the count. This will be a big mess, but may work. You'll lose less memory and make direct acess to the second level of arrays, but is ugly and dirt.
I'm a J newbie, and am trying to import one of my large datasets for further experimentation. It is a 2D matrix of doubles, approximately 80000x50000. So far, I have found two different methods to load data into J.
The first is to convert the data into J format (replacing negatives with underscores, putting exponential notation numbers into J format, etc) and then load with (adapted from J: Handy method to enter a matrix?):
(".;._2) fread 'path/to/file'
The second method is to use tables/dsv.
I am experiencing the same problem with both methods: namely, that these methods work with small matrices, but fail at approximately 10M values. It seems the input just gets truncated to some arbitrary limit. How can I load matrices of arbitrary size? If I have to convert to some binary format, that's OK, as long as there is a description of the format somewhere.
I should add that this is a 64-bit system and build of J, and I can successfully create a matrix of random numbers of the appropriate size, so it doesn't seem to be a limitation on matrix size per se but only during I/O.
Thanks!
EDIT: I did not find what exactly was causing this, but thanks to Dane, I did find a workaround by using JMF ( 'data/jmf' package). It turns out that JMF is just straight binary data with no header and native (?) or little-endian data can be mapped directly with JFL map_jmf_ 'x';'whatever.bin'
You're running out of memory. A quick test to see how much space integers take up yields the following:
7!:2 'i. 80000 5000'
8589936256
That is, an 80,000 by 5,000 matrix of integers requires 8 GB of memory. Your 80,000 by 50,000 matrix, if it were of integers, would require approximately 80 GB of memory.
Your next question should be about performing array or matrix operations on a matrix too big to load into memory.
I have many large 1GB+ matrices of doubles (floats), many of them 0.0, that need to be stored efficiently. I indend on keeping the double type since some of the elements do require to be a double (but I can consider changing this if it could lead to a significant space saving). A string header is optional. The matrices have no missing elements, NaNs, NAs, nulls, etc: they are all doubles.
Some columns will be sparse, others will not be. The proportion of columns that are sparse will vary from file to file.
What is a space efficient alternative to CSV? For my use, I need to parse this matrix quickly into R, python and Java, so a file format specific to a single language is not appropriate. Access may need to be by row or column.
I am also not looking for a commercial solution.
My main objective is to save HDD space without blowing out io times. RAM usage once imported is not the primary consideration.
The most important question is if you always expand the whole matrix into memory or if you need a random access to the compacted form (and how). Expanding is way simpler, so I'm concentrating on this.
You could use a bitmap stating if a number is present or zero. This costs 1 bit per entry and thus can increase the file size by 1/64 in case of no zeros or shrink it to 1/64 in case of all zeros. If there are runs of zeros, you may store the number of following zeros and the number non-zeros instead, e.g., by packing two 4-bit numbers into one byte.
As the double representation is standard, you can use binary representation in both languages. If many of your numbers are actually ints, you may consider something like I did.
If consecutive numbers are related, you could consider storing their differences.
I indend on keeping the double type since some of the elements do require to be a double (but I can consider changing this if it could lead to a significant space saving).
Obviously, switching to float would trade half precision for haltf the memory. This is probably too imprecise, so you could instead omit a few bits from the mantisa and get e.g. 6 bytes per entry. Alternatively, you could reduce the exponent to a single byte instead as the range 1e-38 to 3e38 should suffice.