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ShortCC1;//2-byte alignment 011 CharDD1;//1-byte alignment 1} testlength1;intLength1 =sizeof(TESTLENGTH1);//2 byte aligned, takes up bytes one by one 10,length = Tentypedefstruct{ CharBB2;//1-byte alignment 1 intAA2;//2-byte alignment 01111 ShortCC2;//2 byte alignment one CharDD2;//1-byte alignment 1} testlength2;intLength2 =sizeof(TESTLENGTH2);//2 byte aligned, takes up bytes by one by one 10,length = Tentypedefstruct{ CharBb3;//1-byte alignment 1 CharDD3;//1-byte alignment 1 intAa3
Finally determined to understand the Markov chain. Of course, the classic of the tutorial will be the reference document, in addition to find a professor of Zhejiang University to write ppt, very good, in Chinese speaking tutorial things out. The following article is also well written.Http://googlechinablog.com/2006/04/blog-post_17.htmlAn article transferred from the above addressThe beauty of Mathematics series three--application of hidden Markov model in l
Pattern recognition is a core part of natural language processing. 6.1 supervised classification Category: select the correct class tag task for the specified input. Supervised Classification: if the classification is based on the training corpus containing each input correct tag. The framework of supervised classification is as follows: {: Word [-1]} The di
there may be a large number of 1001 of the occurrence of aggregation. Therefore, the data can not be randomly divided into different machines, but to be based on the value of the hash to map them to a different machine processing, so that the different computer processing a numerical range. The way you sort it consumes a lot of Io, and it's not efficient. The above distributed method can also be used for s
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