Although there has been a lot of AVL tree libraries available now, nearly all of them are meant to work in the random access memory(RAM). Some of them do provide some mechanism for dumping the whole tree into a file and loading it back to the memory in order to make data in that tree persistent. It serves well when there s just small amount of data. When the tree is somewhat bigger, the dumping/loading process could take a lengthy time and makes your mission-critical program less efficient. How about an AVL tree that can directly use the disk for data storage ? If there s something like that, we won t need to read through the whole tree in order to pick up just a little bit imformation(a node), but read only the sectors that are neccssary for locating a certain node and the sectors in which that node lies. This is my initial motivation for writing a storage-media independent AVL Tree. However, as you step forth, you would find that it not only works fine with disks but also fine with memorys, too.
標(biāo)簽： available libraries Although nearly
上傳時(shí)間： 2014-01-22
上傳用戶：zhoujunzhen
The two C source files in this archive are specific to the TRS-80 Model 4 with high-resolution graphics board. However, the code which performs Huffman enco- ding and decoding is of general interest.
標(biāo)簽： high-resolution specific archive source
上傳時(shí)間： 2014-11-04
上傳用戶：二驅(qū)蚊器
The two C source files in this archive are specific to the TRS-80 Model 4 with high-resolution graphics board. However, the code which performs Huffman enco- ding and decoding is of general interest.
標(biāo)簽： high-resolution specific archive source
上傳時(shí)間： 2013-12-18
上傳用戶：417313137
數(shù)字運(yùn)算，判斷一個(gè)數(shù)是否接近素?cái)?shù) A Niven number is a number such that the sum of its digits divides itself. For example, 111 is a Niven number because the sum of its digits is 3, which divides 111. We can also specify a number in another base b, and a number in base b is a Niven number if the sum of its digits divides its value. Given b (2 <= b <= 10) and a number in base b, determine whether it is a Niven number or not. Input Each line of input contains the base b, followed by a string of digits representing a positive integer in that base. There are no leading zeroes. The input is terminated by a line consisting of 0 alone. Output For each case, print "yes" on a line if the given number is a Niven number, and "no" otherwise. Sample Input 10 111 2 110 10 123 6 1000 8 2314 0 Sample Output yes yes no yes no
上傳時(shí)間： 2015-05-21
上傳用戶：daguda
很好的linux內(nèi)核調(diào)試軟件 兼轅馬，沒有密碼。 The ia64 and ix86 directories contain versions of kdb prior to v2.0 (kdb version v2.0, not the kernel version). Older versions of kdb had complete patches for each architecture it supported, each patch included all the common kdb code. This format was awkward to maintain and use for multiple platforms. Starting with kdb v2.0 there is a common patch against each kernel which contains all the architecture independent code plus separate architecture dependent patches. Either use an old style (v1.8 or v1.9) kdb patch or use a new style (v2.0) common patch plus the corresponding architecture dependent patch.
標(biāo)簽： linux 內(nèi)核 調(diào)試軟件
上傳時(shí)間： 2014-01-21
上傳用戶：wyc199288
A C++ N-grams Package 2.0 This is a simple C++ n-grams package that includes a header, the corresponding cpp file, and a sample driver program. It is a natural language processing tool for creating n-gram profiles for text documents. The details on usage is documented in the header right above each public function defined. This package is based on Dr. Vlado Keselj s Perl package Text::Ngrams which is available in CPAN.
標(biāo)簽： includes correspo N-grams Package
上傳時(shí)間： 2015-06-12
上傳用戶：wfl_yy
This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to define an input signal on which prediction will be performed. The user can choose the number of input units, hidden units and output units, as well as the delay between the input series and the predicted output series. Then it is possible to observe interesting prediction properties.
標(biāo)簽： capabilities illustrates multi-layer perceptrons
上傳時(shí)間： 2015-06-17
上傳用戶：lnnn30
This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
標(biāo)簽： approximation demonstrates capabilities Function
上傳時(shí)間： 2014-01-01
上傳用戶：zjf3110
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
標(biāo)簽： model distributed associative Hopfield
上傳時(shí)間： 2015-06-17
上傳用戶：l254587896
YSS915 (KP2V2) is an LSI for processing Karaoke voice signals. This LSI has an A/D converter (1 channel) for the microphone echo, and a memory for the microphone echo and key control. These features allow achieving the functions needed for the Karaoke system by using only one LSI chip. As for the microphone echoes, many other types of echoes are available in addition to ordinary ones so that YSS915 is applicable to various uses. In addition to these Karaoke programs, YSS915 is able to provide the Movie & Music programs, with which the surround effect is applied to the movie and music sources for giving the users more enjoyment. YSS915 is pin compatible with and register compatible with YSS903 (KP2V).
標(biāo)簽： processing LSI converter Karaoke
上傳時(shí)間： 2015-06-23
上傳用戶：lijianyu172
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