Evelyna waugha która brokeback mieszanek najlepszym była porażkę teł przechodzenia paq9a Dec. 2007 and is a component ZPAQ. The idea is to use SSE as a direct prediction method rather than to refine existing prediction. However, SSE does not work well with high order contexts because the large table size uses too much memory. generally, a large model with lots of free parameters overfit the training data and have no predictive power for future input. As a general rule, a model should not be larger than the input it is trained on. ISSE does not use a 2-D table. Instead it first maps a context to a bit history as with indirect context map. Then the 8-bit bit history is used as a context to select the pair of weights for a 2 input mixer taking the input prediction and a fixed constant as its two inputs. The weights are initialized to meaning that the initial output prediction is equal to the input. PAQ9A and the default compression mode of ZPAQ both start with order 0 model prediction and refine it using a chain of ISSE components increasing order. ZPAQ, the weights are 20 bit signed, fixed point numbers with range -8 to 8 and precision 2 like a MIX. The fixed input is 4 and the learning rate is fixed at λ 2. Match Model. A match model finds the last occurrence of a high order context and predicts whatever symbol came next. The accuracy of the prediction depends on the length of the context match. Longer matches generally give more confidence to the prediction. Typically a match model of order 6 is mixed with lower order context models. A match model is faster and uses less memory than a corresponding context model but does not model well for low orders. Match models are used PAQ and ZPAQ. They consist of a rotating history buffer and a hash table mapping contexts to pointers into the buffer. ZPAQ, a pointer to the match is maintained until a mismatching bit is found. The model then look for a new match at the start of the next byte. On each byte boundary, the buffer is updated with the modeled byte and the hash table is updated that the current context hash points to the end of the buffer. ZPAQ allows both the hash table size and buffer size to be user specified For best compression, the history buffer should be as large as the input and the hash table size is typically 1 of this. Because each pointer is 4 bytes, both data structures use the same amount of memory. Match models PAQ maintain multiple context hashes of different orders and multiple pointers into the buffer. The prediction is indirect by mapping the match length to a prediction through a direct context model. ZPAQ uses a simpler match model with just one pointer and one hash, although it is possible to have multiple, independent match models. The prediction for a match of L bytes is that the next bit be the same with probability 1 8L. The user specify the context length by using a rolling hash that depends on the desired number of characters. If h is the context hash, c is the input byte, then the update: h h with their usual meanings C C++. Division or mod by 0 is 0. means The post-processor if it is present, is called once per decoded byte with that byte the A register. At the end of each segment, it is called once more with -1 A. The decompresser output is whatever is output by the OUT instruction. The context model is always present. It is called once per decoded byte. It puts its result H. OUT has no effect. HCOMP sees as input the PCOMP code followed by a contiguous stream