kgb:城市恐怖的克劳斯游戏机器人

克格勃 Klaus Game Bot（它的首字母缩写词是 KGB）是第一个用 python 2.7 编写的机器人（ ）的返工，旨在成为一个灵活、稳定和高性能的游戏机器人城市恐怖 4.x 服务器。 技能、比率和其他“统计”特征被自愿排除在开发之外，以保持对性能和灵活性的关注。 KGB 是托管多个游戏实例的服务器的理想解决方案，因为更新是集中管理的，因此游戏服务器的管理员无需担心它们。 他们的机器人始终保持最新状态，并始终获得最新的错误修复... 如何运行克格勃 所有的配置参数都保存在一个unix shell-script文件中，编译这部分由部落管理员或游戏服务器管理员负责。 一个基本示例类似于以下脚本： #!/bin/bash # ============ # yourfolder/run_bot.sh SERVER_ADDRESS="Your server IP" # i.e.

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上传者： weixin_42165508 时间： 2021-06-02

KGB-Archiver1.2.1.24授权汉化版

KGB 是一款具有难以置信的高压缩比的免费压缩工具，它超越了以高压缩比著称的 7Zip 和 UHARC，尤其擅长多媒体文件和可执行文件的压缩。需要注意的是虽然它的压缩比超高，但是对硬件的要求也相应的比其它压缩工具高，译者推荐将此工具用于超大型文件的压缩，即使连 KGB 本身一起提供给好友或者下载方，也才多一点点而已（KGB 的压缩与解压模块是分开的，各自独立），相比待压缩文件本身的大小仍是十分合算的。KGB 支持 256 位的 AES 加密，众所周知这是目前最强的加密算法之一，因此兼作加密工具也是可以的 ^_^ KGB 有自己独创的压缩格式 KGB 及 KGE，第三方工具无法打开，也就无从破解密码（市场上针对 RAR/ZIP 的密码破解软件已经很多了），做影音类下载站的兄弟用 KGB 压缩并加密多媒体文件不失为一个有意思的解决方案 :)

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上传者： xixi0909 时间： 2012-08-23

KGB Archiver v2.0 Beta 2

一款具有难以置信的高压缩比的免费压缩工具，超越了以高压缩比著称的 7Zip 和 UHARC，尤其擅长多媒体文件和可执行文件的压缩，支持 256 位的 AES 加密，众所周知这是目前最强的加密算法之一，因此兼作加密工具也是可以的需要安装.net v2.0

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上传者： weixin_38743602 时间： 2019-11-01

kgb档案压缩console版+源码

KGB Archiver console version ?005-2006 Tomasz Pawlak, tomekp17@gmail.com, mod by Slawek (poczta-sn@gazeta.pl) based on PAQ6 by Matt Mahoney PAQ6v2 - File archiver and compressor. (C) 2004, Matt Mahoney, mmahoney@cs.fit.edu This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 2 as published by the Free Software Foundation at http://www.gnu.org/licenses/gpl.txt or (at your option) any later version. This program is distributed without any warranty. USAGE To compress: PAQ6 -3 archive file file... (1 or more file names), or or (MSDOS): dir/b | PAQ6 -3 archive (read file names from input) or (UNIX): ls | PAQ6 -3 archive To decompress: PAQ6 archive (no option) To list contents: more < archive Compression: The files listed are compressed and stored in the archive, which is created. The archive must not already exist. File names may specify a path, which is stored. If there are no file names on the command line, then PAQ6 prompts for them, reading until the first blank line or end of file. The -3 is optional, and is used to trade off compression vs. speed and memory. Valid options are -0 to -9. Higher numbers compress better but run slower and use more memory. -3 is the default, and gives a reasonable tradeoff. Recommended options are: -0 to -2 for fast (2X over -3) but poor compression, uses 2-6 MB memory -3 for reasonably fast and good compression, uses 18 MB (default) -4 better compression but 3.5X slower, uses 64 MB -5 slightly better compression, 6X slower than -3, uses 154 MB -6 about like -5, uses 202 MB memory -7 to -9 use 404 MB, 808 MB, 1616 MB, about the same speed as -5 Decompression: No file names are specified. The archive must exist. If a path is stored, the file is extracted to the appropriate directory, which must exist. PAQ6 does not create directories. If the file to be extracted already exists, it is not replaced; rather it is compared with the archived file, and the offset of the first difference is reported. The decompressor requires as much memory as was used to compress. There is no option. It is not possible to add, remove, or update files in an existing archive. If you want to do this, extract the files, delete the archive, and create a new archive with just the files you want. TO COMPILE gxx -O PAQ6.cpp DJGPP 2.95.2 bcc32 -O2 PAQ6.cpp Borland 5.5.1 sc -o PAQ6.cpp Digital Mars 8.35n g++ -O produces the fastest executable among free compilers, followed by Borland and Mars. However Intel 8 will produce the fastest and smallest Windows executable overall, followed by Microsoft VC++ .net 7.1 /O2 /G7 PAQ6 DESCRIPTION 1. OVERVIEW A PAQ6 archive has a header, listing the names and lengths of the files it contains in human-readable format, followed by the compressed data. The first line of the header is "PAQ6 -m" where -m is the memory option. The data is compressed as if all the files were concatenated into one long string. PAQ6 uses predictive arithmetic coding. The string, y, is compressed by representing it as a base 256 number, x, such that: P(s < y) <= x < P(s <= y) (1) where s is chosen randomly from the probability distribution P, and x has the minimum number of digits (bytes) needed to satisfy (1). Such coding is within 1 byte of the Shannon limit, log 1/P(y), so compression depends almost entirely on the goodness of the model, P, i.e. how well it estimates the probability distribution of strings that might be input to the compressor. Coding and decoding are illustrated in Fig. 1. An encoder, given P and y, outputs x. A decoder, given P and x, outputs y. Note that given P in equation (1), that you can find either x from y or y from x. Note also that both computations can be done incrementally. As the leading characters of y are known, the range of possible x narrows, so the leading digits can be output as they become known. For decompression, as the digits of x are read, the set of possible y satisfying (1) is restricted to an increasingly narrow lexicographical range containing y. All of the strings in this range will share a growing prefix. Each time the prefix grows, we can output a character. y +--------------------------+ Uncomp- | V ressed | +---------+ p +----------+ x Compressed Data --+--->| Model |----->| Encoder |----+ Data +---------+ +----------+ | | +----------+ V y +---------+ p +----------+ y Uncompressed +--->| Model |----->| Decoder |----+---> Data | +---------+ +----------+ | | | +-------------------------------------+ Fig. 1. Predictive arithmetic compression and decompression Note that the model, which estimates P, is identical for compression and decompression. Modeling can be expressed incrementally by the chain rule: P(y) = P(y_1) P(y_2|y_1) P(y_3|y_1 y_2) ... P(y_n|y_1 y_2 ... y_n-1) (2) where y_i means the i'th character of the string y. The output of the model is a distribution over the next character, y_i, given the context of characters seen so far, y_1 ... y_i-1. To simplify coding, PAQ6 uses a binary string alphabet. Thus, the output of a model is an estimate of P(y_i = 1 | context) (henceforth p), where y_i is the i'th bit, and the context is the previous i - 1 bits of uncompressed data. 2. PAQ6 MODEL The PAQ6 model consists of a weighted mix of independent submodels which make predictions based on different contexts. The submodels are weighted adaptively to favor those making the best predictions. The output of two independent mixers (which use sets of weights selected by different contexts) are averaged. This estimate is then adjusted by secondary symbol estimation (SSE), which maps the probability to a new probability based on previous experience and the current context. This final estimate is then fed to the encoder as illustrated in Fig. 2. Uncompressed input -----+--------------------+-------------+-------------+ | | | | V V | | +---------+ n0, n1 +----------+ | | | Model 1 |--------->| Mixer 1 |\ p | | +---------+ \ / | | \ V V \ / +----------+ \ +-----+ +------------+ +---------+ \ / \| | p | | Comp- | Model 2 | \/ + | SSE |--->| Arithmetic |--> ressed +---------+ /\ | | | Encoder | output ... / \ /| | | | / \ +----------+ / +-----+ +------------+ +---------+ / \ | Mixer 2 | / | Model N |--------->| |/ p +---------+ +----------+ Fig. 2. PAQ6 Model details for compression. The model is identical for decompression, but the encoder is replaced with a decoder. In Sections 2-6, the description applies to the default memory option (-5, or MEM = 5). For smaller values of MEM, some components are omitted and the number of contexts is less. 3. MIXER The mixers compute a probability by a weighted summation of the N models. Each model outputs two numbers, n0 and n1 represeting the relative probability of a 0 or 1, respectively. These are combined using weighted summations to estimate the probability p that the next bit will be a 1: SUM_i=1..N w_i n1_i (3) p = -------------------, n_i = n0_i + n1_i SUM_i=1..N w_i n_i The weights w_i are adjusted after each bit of uncompressed data becomes known in order to reduce the cost (code length) of that bit. The cost of a 1 bit is -log(p), and the cost of a 0 is -log(1-p). We find the gradient of the weight space by taking the partial derivatives of the cost with respect to w_i, then adjusting w_i in the direction of the gradient to reduce the cost. This adjustment is: w_i := w_i + e[ny_i/(SUM_j (w_j+wo) ny_j) - n_i/(SUM_j (w_j+wo) n_j)] where e and wo are small constants, and ny_i means n0_i if the actual bit is a 0, or n1_i if the bit is a 1. The weight offset wo prevents the gradient from going to infinity as the weights go to 0. e is set to around .004, trading off between faster adaptation (larger e) and less noise for better compression of stationary data (smaller e). There are two mixers, whose outputs are averaged together before being input to the SSE stage. Each mixer maintains a set of weights which is selected by a context. Mixer 1 maintains 16 weight vectors, selected by the 3 high order bits of the previous byte and on whether the data is text or binary. Mixer 2 maintains 16 weight vectors, selected by the 2 high order bits of each of the previous 2 bytes. To distinguish text from binary data, we use the heuristic that space characters are more common in text than NUL bytes, while NULs are more common in binary data. We compare the position of the 4th from last space with the position of the 4th from last 0 byte. 4. CONTEXT MODELS Individual submodels output a prediction in the form of two numbers, n0 and n1, representing relative probabilities of 0 and 1. Generally this is done by storing a pair of counters (c0,c1) in a hash table indexed by context. When a 0 or 1 is encountered in a context, the appropriate count is increased by 1. Also, in order to favor newer data over old, the opposite count is decreased by the following heuristic: If the count > 25 then replace with sqrt(count) + 6 (rounding down) Else if the count > 1 then replace with count / 2 (rounding down) The outputs are derived from the counts in a way that favors highly predictive contexts, i.e. those where one count is large and the other is small. For the case of c1 >= c0 the following heuristic is used. If c0 = 0 then n0 = 0, n1 = 4 c0 Else n0 = 1, n1 = c1 / c0 For the case of c1 < c0 we use the same heuristic swapping 0 and 1. In the following example, we encounter a long string of zeros followed by a string of ones and show the model output. Note how n0 and n1 predict the relative outcome of 0 and 1 respectively, favoring the most recent data, with weight n = n0 + n1 Input c0 c1 n0 n1 ----- -- -- -- -- 0000000000 10 0 40 0 00000000001 5 1 5 1 000000000011 2 2 1 1 0000000000111 1 3 1 3 00000000001111 1 4 1 4 Table 1. Example of counter state (c0,c1) and outputs (n0,n1) In order to represent (c0,c1) as an 8-bit state, counts are restricted to the values 0-40, 44, 48, 56, 64, 96, 128, 160, 192, 224, or 255. Large counts are incremented probabilistically. For example, if c0 = 40 and a 0 is encountered, then c0 is set to 44 with probability 1/4. Decreases in counter values are deterministic, and are rounded down to the nearest representable state. Counters are stored in a hash table indexed by contexts starting on byte boundaries and ending on nibble (4-bit) boundaries. Each hash element contains 15 counter states, representing the 15 possible values for the 0-3 remaining bits of the context after the last nibble boundary. Hash collisions are detected by storing an 8-bit checksum of the context. Each bucket contains 4 elements in a move-to-front queue. When a new element is to be inserted, the priority of the two least recently accessed elements are compared by using n (n0+n1) of the initial counter as the priority, and the lower priority element is discarded. Hash buckets are aligned on 64 byte addresses to minimize cache misses. 5. RUN LENGTH MODELS A second type of model is used to efficiently represent runs of up to 255 identical bytes within a context. For example, given the sequence "abc...abc...abc..." then a run length model would map "ab" -> ("c", 3) using a hash table indexed by "ab". If a new value is seen, e.g. "abd", then the state is updated to the new character and a count of 1, i.e. "ab" -> ("d", 1). A run length context is accessed 8 times, once for each bit. If the bits seen so far are consistent with the modeled character, then the output of a run length model is (n0,n1) = (0,n) if the next bit is a 1, or (n,0) if the next bit is a 0, where n is the count (1 to 255). If the bits seen so far are not consistent with the predicted byte, then the output is (0,0). These counts are added to the counter state counts to produce the model output. Run lengths are stored in a hash table without collision detection, so an element occupies 2 bytes. Generally, most models store one run length for every 8 counter pairs, so 20% of the memory is allocated to them. Run lengths are used only for memory option (-MEM) of 5 or higher. 6. SUBMODEL DETAILS Submodels differ mainly in their contexts. These are as follows: a. DefaultModel. (n0,n1) = (1,1) regardless of context. b. CharModel (N-gram model). A context consists of the last 0 to N whole bytes, plus the 0 to 7 bits of the partially read current byte. The maximum N depends on the -MEM option as shown in the table below. The order 0 and 1 contexts use a counter state lookup table rather than a hash table. Order Counters Run lengths ----- -------- ----------- 0 2^8 1 2^16 2 2^(MEM+15) 2^(MEM+12), MEM >= 5 3 2^(MEM+17) 2^(MEM+14), MEM >= 5 4 2^(MEM+18) 2^(MEM+15), MEM >= 5 5 2^(MEM+18), MEM >= 1 2^(MEM+15), MEM >= 5 6 2^(MEM+18), MEM >= 3 2^(MEM+15), MEM >= 5 7 2^(MEM+18), MEM >= 3 2^(MEM+15), MEM >= 5 8 2^20, MEM = 5 2^17, MEM = 5 2^(MEM+14), MEM >= 6 2^(MEM+14), MEM >= 6 9 2^20, MEM = 5 2^17, MEM = 5 2^(MEM+14), MEM >= 6 2^(MEM+14), MEM >= 6 Table 2. Number of modeled contexts of length 0-9 c. MatchModel (long context). A context is the last n whole bytes (plus extra bits) where n >=8. Up to 4 matching contexts are found by indexing into a rotating input buffer whose size depends on MEM. The index is a hash table of 32-bit pointers with 1/4 as many elements as the buffer (and therefore occupying an equal amount of memory). The table is indexed by a hashes of 8 byte contexts. No collision detection is used. In order to detect very long matches at a long distance (for example, versions of a file compressed together), 1/16 of the pointers (chosen randomly) are indexed by a hash of a 32 byte context. For each match found, the output is (n0,n1) = (w,0) or (0,w) (depending on the next bit) with a weight of w = length^2 / 4 (maximum 511), depending on the length of the context in bytes. The four outputs are added together. d. RecordModel. This models data with fixed length records, such as tables. The model attempts to find the record length by searching for characters that repeat in the pattern x..x..x..x where the interval between 4 successive occurrences is identical and at least 2. Because of uncertainty in this method, the two most recent values (which must be different) are used. The following 5 contexts are modeled; 1. The two bytes above the current bit for each repeat length. 2. The byte above and the previous byte (to the left) for each repeat length. 3. The byte above and the current position modulo the repeat length, for the longer of the two lengths only. e. SparseModel. This models contexts with gaps. It considers the following contexts, where x denotes the bytes considered and ? denotes the bit being predicted (plus preceding bits, which are included in the context). x.x? (first and third byte back) x..x? x...x? x....x? xx.? x.x.? xx..? c ... c?, gap length c ... xc?, gap length Table 3. Sparse model contexts The last two examples model variable gap lengths between the last byte and its previous occurrence. The length of the gap (up to 255) is part of the context. e. AnalogModel. This is intended to model 16-bit audio (mono or stereo), 24-bit color images, 8-bit data (such as grayscale images). Contexts drop the low order bits, and include the position within the file modulo 2, 3, or 4. There are 8 models, combined into 4 by addition before mixing. An x represents those bits which are part of the context. 16 bit audio: xxxxxx.. ........ xxxxxx.. ........ ? (position mod 2) xxxx.... ........ xxxxxx.. ........ ? (position mod 2) xxxxxx.. ........ ........ ........ xxxxxx.. ........ xxxxxx.. ........ ? (position mod 4 for stereo audio) 24 bit color: xxxx.... ........ ........ xxxxxxxx ........ ........ ? (position mod 3) xxxxxx.. xxxx.... xxxx.... ? (position mod 3) 8 bit data: xxx..... xxxxx... xxxxxxx. ? CCITT images (1 bit per pixel, 216 bytes wide, e.g. calgary/pic) xxxxxxxx (skip 215 bytes...) xxxxxxxx (skip 215 bytes...) ? Table 4. Analog models. f. WordModel. This is intended to model text files. There are 3 contexts: 1. The current word 2. The previous and current words 3. The second to last and current words (skipping a word) A word is defined in two different ways, resulting in a total of 6 different contexts: 1. Any sequence of characters with ASCII code > 32 (not white space). Upper case characters are converted to lower case. 2. Any sequence of A-Z and a-z (case sensitive). g. ExeModel. This models 32-bit Intel .exe and .dll files by changing relative 32-bit CALL addresses to absolute. These instructions have the form (in hex) "E8 xx yy zz 00" or "E8 xx yy zz FF" where the 32-bit operand is stored least significant byte first. These are converted to absolute addresses by adding the position of the E8 byte, and then stored in a 256 element table indexed by the low order byte (xx) along with an 8-bit count. If another E8 xx ... 00/FF with the same value of xx is encountered, then the old value is replaced and the count set back to 1. During modeling, when "E8 xx" is encountered, the bytes yy, zz, and 00/FF are predicted by adjusting xx to absolute address, then looking up the address in the table indexed by xx. If the context matches the table entry up to the current bit, then the next bit from the table is predicted with weight n for yy, 4n for zz, and 16n for 00/FF, where n is the count. 7. SSE The purpose of the SSE stage is to further adjust the probability output from the mixers to agree with actual experience. Ideally this should not be necessary, but in reality this can improve compression. For example, when "compressing" random data, the output probability should be 0.5 regardless of what the models say. SSE will learn this by mapping all input probabilities to 0.5. | Output __ | p / | / | __/ | / | / | | | / |/ Input p +------------- Fig. 3. Example of an SSE mapping. SSE maps the probability p back to p using a piecewise linear function with 32 segments. Each vertex is represented by a pair of 8-bit counters (n0, n1) except that now the counters use a stationary model. When the input is p and a 0 or 1 is observed, then the corresponding count (n0 or n1) of the two vertices on either side of p are incremented. When a count exceeds the maximum of 255, both counts are halved. The output probability is a linear interpolation of n1/n between the vertices on either side. The vertices are scaled to be longer in the middle of the graph and short near the ends. The intial counts are set so that p maps to itself. SSE is context sensitive. There are 2048 separately maintained SSE functions, selected by the 0-7 bits of the current (partial) byte and the 2 high order bits of the previous byte, and on whether the data is text or binary, using the same heuristic as for selecting the mixer context. The final output to the encoder is a weighted average of the SSE input and output, with the output receiving 3/4 of the weight: p := (3 SSE(p) + p) / 4. (4) 8. MEMORY USAGE The -m option (MEM = 0 through 9) controls model and memory usage. Smaller numbers compress faster and use less memory, while higher numbers compress better. For MEM < 5, only one mixer is used. For MEM < 4, bit counts are stored in nonstationary counters, but no run length is stored (decreasing memory by 20%). For MEM < 1, SSE is not used. For MEM < 5, the record, sparse, and analog models are not used. For MEM < 4, the word model is not used. The order of the char model ranges from 4 to 9 depending on MEM for MEM as shown in Table 6. Run Memory used by........................ Total MEM Mixers Len Order Char Match Record Sparse Analog Word SSE Memory (MB) --- ------ --- ----- ---- ----- ------ ------ ------ ---- --- ----------- 0 1 no 4 .5 1 1.5 1 1 no 5 1 2 .12 3 2 1 no 5 2 4 .12 6 3 1 no 7 10 8 .12 18 4 1 no 7 20 16 6 6 1 15 .12 64 5 2 yes 9 66 32 13 11 2 30 .12 154 6 2 yes 9 112 32 13 11 4 30 .12 202 7 2 yes 9 224 64 25 22 9 60 .12 404 8 2 yes 9 448 128 50 45 18 120 .12 808 9 2 yes 9 992 256 100 90 36 240 .12 1616 Table 5. Memory usage depending on MEM (-0 to -9 option). 9. EXPERIMENTAL RESULTS Results on the Calgary corpos are shown below for some top data compressors as of Dec. 30, 2003. Options are set for maximum compression. When possible, the files are all compressed into a single archive. Run times are on a 705 MHz Duron with 256 MB memory, and include 3 seconds to run WRT when applicable. PAQ6 was compiled with DJGPP (g++) 2.95.2 -O. Original size Options 3,141,622 Time Author ------------- ------- --------- ---- ------ gzip 1.2.4 -9 1,017,624 2 Jean Loup Gailly epm r9 c 668,115 49 Serge Osnach rkc a -M80m -td+ 661,102 91 Malcolm Taylor slim 20 a 659,213 159 Serge Voskoboynikov compressia 1.0 beta 650,398 66 Yaakov Gringeler durilca v.03a (as in README) 647,028 30 Dmitry Shkarin PAQ5 661,811 361 Matt Mahoney WRT11 + PAQ5 638,635 258 Przemyslaw Skibinski + PAQ6 -0 858,954 52 -1 750,031 66 -2 725,798 76 -3 709,806 97 -4 655,694 354 -5 648,951 625 -6 648,892 636 WRT11 + PAQ6 -6 626,395 446 WRT20 + PAQ6 -6 617,734 439 Table 6. Compressed size of the Calgary corpus. WRT11 is a word reducing transform written by Przemyslaw Skibinski. It uses an external English dictionary to replace words with 1-3 byte symbols to improve compression. rkc, compressia, and durilca use a similar approach. WRT20 is a newer version of WRT11. 10. ACKNOWLEDGMENTS Thanks to Serge Osnach for introducing me to SSE (in PAQ1SSE/PAQ2) and the sparse models (PAQ3N). Also, credit to Eugene Shelwein, Dmitry Shkarin for suggestions on using multiple character SSE contexts. Credit to Eugene, Serge, and Jason Schmidt for developing faster and smaller executables of previous versions. Credit to Werner Bergmans and Berto Destasio for testing and evaluating them, including modifications that improve compression at the cost of more memory. Credit to Alexander Ratushnyak who found a bug in PAQ4 decompression, and also in PAQ6 decompression for very small files (both fixed). Thanks to Berto for writing PAQ5-EMILCONT-DEUTERIUM from which this program is derived (which he derived from PAQ5). His improvements to PAQ5 include a new Counter state table and additional contexts for CharModel and SparseModel. I refined the state table by adding more representable states and modified the return counts to give greater weight when there is a large difference between the two counts. I expect there will be better versions in the future. If you make any changes, please change the name of the program (e.g. PAQ7), including the string in the archive header by redefining PROGNAME below. This will prevent any confusion about versions or archive compatibility. Also, give yourself credit in the help message.

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上传者： haozekun 时间： 2008-12-21

KGB Scalper.rar_MQL__MQL_

KGB SCALPER con presets para diferente tipo de capital

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上传者： pudn01 时间： 2021-08-09

Python库 | kgb-2.0.3-py2.7.egg

资源分类：Python库 所属语言：Python 使用前提：需要解压 资源全名：kgb-2.0.3-py2.7.egg 资源来源：官方 安装方法：https://lanzao.blog.csdn.net/article/details/101784059

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上传者： qq_38161040 时间： 2022-03-25

Python库 | kgb-6.0-py3.6.egg

资源分类：Python库 所属语言：Python 使用前提：需要解压 资源全名：kgb-6.0-py3.6.egg 资源来源：官方 安装方法：https://lanzao.blog.csdn.net/article/details/101784059

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上传者： qq_38161040 时间： 2022-03-25

Python库 | kgb-3.0-py3.6.egg

python库，解压后可用。 资源全名：kgb-3.0-py3.6.egg

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上传者： qq_38161040 时间： 2022-02-21

KGB高压缩比软件

流行的高压缩比软件，性能优良，适用于有专门需求的同学，应该很管用的。

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上传者： snoopy_19870410 时间： 2012-01-08

fake-kgb-bdd

fake-kgb-bdd

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上传者： weixin_42129113 时间： 2021-03-09

KGB Archiver-开源

KGB Archiver是具有令人难以置信的高压缩率的压缩工具。 就功能而言，它甚至超过了诸如7zip和UHARC之类的高效压缩工具。 KGB存档器使用AES-256加密来加密存档。

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上传者： weixin_42134097 时间： 2021-05-02

KGB Archiver v1.2.1.24

一款具有难以置信的高压缩比的免费压缩工具，超越了以高压缩比著称的 7Zip 和 UHARC，尤其擅长多媒体文件和可执行文件的压缩，支持 256 位的 AES 加密，众所周知这是目前最强的加密算法之一，因此兼作加密工具也是可以的

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上传者： weixin_38743506 时间： 2019-11-01

kgb:超轻量级 javascript 决策树构建器

名称 .js 概要 一个超轻量级的 javascript 工具，用于构建向导-y 'if that then this' 工作流和多步骤表单。 用法 kgb.js 消除了构建基于答案显示/隐藏步骤的多步骤表单的乏味。 它累积每一步的答案，并将这些答案提供给客户端代码以执行任意处理。 给定一个多步骤的形式，组织如下： < ! -- -- > < form data-kgb-key = 'question-1' > < input type = 'radio' name = 'question' value = 'a' > < input type = 'radio' name = 'question' value = 'b' > < br> < input type = 'submit' > < / form >

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上传者： weixin_42135073 时间： 2021-06-30

Python库 | kgb-1.0-py2.6.egg

资源分类：Python库 所属语言：Python 使用前提：需要解压 资源全名：kgb-1.0-py2.6.egg 资源来源：官方 安装方法：https://lanzao.blog.csdn.net/article/details/101784059

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上传者： qq_38161040 时间： 2022-03-25

KGB Archiver v1.1.2.19 汉化版

KGB是一款具有难以置信的高压缩比的免费压缩工具，它超越了以高压缩比著称的7Zip和UHARC，尤其擅长多媒体文件和可执行文件的压缩。需要注意的是虽然它的压缩比超高，但是对硬件的要求也相应的比其它压缩工具高，译者推荐将此工具用于超大型文件的压缩，即使连KGB本身一起提供给好友或者下载方，也才多一点点而已（KGB的压缩与解压模块是分开的，各自独立），相比待压缩文件本身的大小仍是十分合算的。KGB支持256 位的AES加密，众所周知这是目前最强的加密算法之一，因此兼作加密工具也是可以的 ^_^

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上传者： chenxh 时间： 2006-03-06

PyPI 官网下载 | kgb-3.0-py3.6.egg

资源来自pypi官网。 资源全名：kgb-3.0-py3.6.egg

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上传者： qq_38161040 时间： 2022-01-08