没有合适的资源?快使用搜索试试~ 我知道了~
首页DNA元基催化与肽计算:章节精华与关键技术
DNA元基催化与肽计算:章节精华与关键技术
需积分: 0 0 下载量 47 浏览量
更新于2024-06-30
收藏 15.23MB PDF 举报
DNA元基催化与肽计算是一个多学科融合的领域,涉及生物学、计算机科学以及人工智能技术。本书第五修订版本V00058121主要探讨了以下几个核心主题: 1. 德塔自然语言图灵系统:这一章节着重介绍了德塔系统的基础,包括知识来源、分词技术的催化优化方法。分词是关键步骤,通过催化切词算法提高效率,并通过排序算法进行组织。神经网络索引用于加速搜索过程,尤其是对分词在文本搜索中的实时应用,如动态POS(Part-of-Speech)分析。 2. Java数据分析算法引擎系统:章节讨论了微分催化排序的概念,这是一种基于计算力和算能优化的方法,应用于线性和非线性问题,比如音频卷积处理和多媒体图片的智能相诊。这里还提到仿生听觉技术,以及ETL视觉流在数据处理中的应用。 3. 德塔ETL人工智能可视化数据流分析引擎系统:章节关注数据流分析的界面设计,ETL引擎的交互逻辑,以及ETLUnicorn计算节点的结构,强调神经元皮肤和拓扑的概念。一键执行功能使得数据处理更加高效。 4. 德塔Socket流可编程数据库语言引擎系统:该部分涉及SocketrestTCP握手协议,文件数据库的管理,以及VPCS服务器的功能和调度架构。PLSQL语言在此环境中起到关键作用,支持数据库操作和编程。 这些章节不仅涵盖了基础的生物信息学(如DNA元基催化)和计算机科学(如算法设计和数据库管理),还融入了深度学习(如神经网络、RNN、DNN)和人工智能技术(如图灵机和ETL)。整本书旨在提供一个跨学科的视角,展示如何将这些技术结合在肽计算中,实现高效的数据分析和处理。读者可以从中了解到自然语言处理、机器学习算法在实际应用中的操作技巧和优化策略。
资源详情
资源推荐
DNA 元基催化与肽计算_第 5 修订版本 V00058 16
avoiding, tunning the constant values, balancing the computing sets and the discrete conditions differentiations(Demorgan,
Frequency flows etc). and now those things widely were used in Deta’s catalytic family technology community (parser, word
segments, mindreading, NLP computing etc).
神经网络索引
1 德塔分词的词汇字典用 map 进行索引, 因为 jdk8+的 map 对象的 key 支持 2 分搜索, 搜索速度到了峰值. refer
page, 129, 131
2 德塔分词的索引不断的将大 map 进行细化分类, 如词长 map, 词类 map, 词性 map, 让搜索再次加速. refer page 55,
3 德塔分词的索引 map 支持 2 次组合计算, 支持分布式服务器进行索引 cache. 关于 2 次组合计算作者不建议单机使用.
refer page 92,
4 德塔分词 map 的 key 用 string 的 char 对应 ASCII int 进行标识来执行 find key, 方便二分搜索存储和 StringBuilder
高速计算, 实现底层核统一. refer page 92
Nero Network Index Forest
1 Deta Parser did a word segment indexed map by using humanoid speech verbal dictionary, for the reason why using JDK8+ tool
to do the map search logic, is that it had already integrated the binary search tree, balanced map tree arrangement and other
technologies.
2 Deta Parser’s balanced binary search tree method makes an observer mode of averaged classification with all types of the
reflection java concurrent maps, those maps include the char word length, verbal types and Part of speech corpus, etc. The author
did it to accelerate the NERO marching speed for searching the words.
3 Deta Parser supports the secondary indexing computing combinations, this way could be suitable for the distributed cache
searching systems. The author does not suggest this technology be used on a single desktop.
4 For the computing logic, Finally Deta Parser functions use string builder to accelerate the searching engine.
神经网络索引的价值主要体现在 2 个地方, 切词的关联索引上和 词汇 map 索引上. 切词的关联索引价值, 主要体现在将
词汇的文字进行链化提取, 这种链化计算方式将词库中本相对独立的海量词汇进行了按人类语言文学中的顶针方法进行
了有效的前后长度关联(NERO), 其价值有利于大文本的文字进行有必要关联链的 小段小段的提取(NLP), 类似挤牙
膏一样, 挤出来就刷牙用掉(POS).
词汇 map 索引价值, 主要体现在 词汇的文字进行链化合理切分, 这种链化切分方式将词库中根据不同属性的分类 map
来组合匹配按人类语言文学中的词汇词性和主谓宾搭配严谨定义来切分. 其价值在这些分类 map 可以自适应设计和多
样化扩展. 增加切词准确度和灵活度, 适应各种不同的场景, 类似牙刷机制, 挤出牙膏根据 匹配不同的牙刷和刷牙方法
(NERO + POS), 匹配适应不同的口腔环境. 描述人 罗瑶光 , 稍后优化下
The accomplishment of the neural network index is mainly reflected in two sections, 1, the relevance index of word segmentation
and 2, the lexical index map. The associated relevance index value of word segmentation is mainly reflected in the chained
extraction of words. This chained calculation method effectively correlates the relatively independent of a large number of words
in the thesaurus, according to the Thimble Theory in human language and Literature (Nero). The value of the big data document
process splits the word chain links list into a small chars token(max 4) sections, and It is similar to squeezing toothpaste, and
brushing the teeth (POS) when squeezed out by the DetaParser marching engine.
DNA 元基催化与肽计算_第 5 修订版本 V00058 17
The index value of the lexical map is mainly reflected in the reasonable chain segmentation of lexical characters. This chain of
word segmentation method combines and matches the classified maps in the thesaurus according to different attributes. And then
separates them according to the rigorous definition of lexical POS and SVO collocation in human literature languages. The
adaptive industrial system design and diversified expansion of this classification, would increase the accuracy and flexibility of
word segmentation and adapt to different segment scenes. Similar to the way of toothbrushes, the extruded toothpaste is matched
to adapt to different oral cavity environments according to different toothbrushes and brushing methods (Nero + POS).
Author: Luo Yaoguang
分词在线性文本搜索中应用,
1 德塔分词的搜索建立在 map 类的权重计算方法上, 不同的权重叠加产生的打分进行排序输出. refer page 下册 64
2 权重的计算方法按词性的主谓宾如代 名动形 , 和 POS 如 动名形谓介分类. refer page 下册 66
3 权重与词长, 词频进行耦合 bit 叠加计算(bit 位计算比乘法要快一个数量级), 生成最终输出结果. refer page 下册 68
4 权重与词长的 比值可以精度调节, 确定搜索的精确性和记录个人搜索偏好. refer page 下册 68
The Deta Parser word segmentation and its applications in the linear text document environments.
1 There has a lot of rights weight by each indexed map, based on those right weights, Deta Parser did a marching score system to
do the computation and calculation for the Chinese word segmentation logic.
2 the search weight of the computing logic, such as Subject Predicate Object(SVO), and part of speech(POS), for instance, noun,
verb, adjective etc.
3 to make a computing acceleration, the author injected a combination factor in the marching logics, such as bit calculation,
frequency statics and word length observations. similars with the theory of Count Down Latch and Cyclic Barrier logic (makes
definitions first then proves, or proves first then did a conclusion) ways etc
4 Above all things and logic once became JAVA transportations, the author set all global and local valuable scales to build the
Foolishman- Self-Controller components to make the algorithms easy and simple.
动态 POS 函数流水阀门细化遍历 内核匹配
1 动态的核分为前序核和后序核两种. 根据词汇分析的位置进行实时变动更新. refer page 97
2 前序核主要缓存存储词汇的位置和词性, 用于 POS 词性搭配的 POS 函数流水阀门细化遍历 计算. refer page 97
3 后序核主要缓存词汇的切词链 后面准备 跟进的词语. 用于 POS 语法的修正计算, 如连词匹配. refer page 97
4 内核采用 StringBuilder 做核载体进行计算加速. refer page 97
Dynamic River Flows Gate Function Marching and Circustantly Loop the POS Kernel Computing.
1 Dynamic kernel contains prefix and postfix two types can read the word token one by one. It does dynamic computing also at
the same time.
2 Prefix kernel stores a POS cache buffer by each current word piece of information such as positions, frequency etc, to accelerate
the word marching.
DNA 元基催化与肽计算_第 5 修订版本 V00058 18
3 Postfix relevant to the optimization of word marching and segmentation. For example, checking the conjunction relationship
and continuing the word token link list.
4 The algorithms kernel uses StringBuilder to do higher computing affections according to computer language grammar.
POS函数流水阀门细化遍历前序内核关系图, 图中举例 如果是非常理想来进行分词. 首先通过索引字典森林长度匹配可
以切分出 ‘如果’, ‘是非常’, ‘理想’, 3个索引关联词句, 作者词库无‘常理’词汇, 如果有, 可另行讨论. ‘如果’
和 ‘理想’是比较稳定的词汇. ‘是非常’属于三字词, 于是开始流水阀门切分, 3字词索引没有 ‘是非常’ 这个词汇,
于是开始流水阀门自然语言计算处理(如果三字词有这个词汇, 就流水阀门计算三字词的词性词汇搭配, 如果有就return,
没有同样要更进细化成2字词来做流水法门. 这是该算法的强大之处). 首先拆分为‘是非-常’ 和 ‘是-非常’ 这两种
词汇, 于是开始分析两种搭配词汇的POS词性, 通过分析每个词汇的前后链接词汇的词性(如 ‘是非’的前链词汇是
‘如果’, ‘非常’的前链是‘是’,‘常’的前链是‘是非’和‘非’,‘理想’的前链包含‘常’和‘非常’)来确定
切词, (这个词汇搭配是严谨固定的语法, 不含概率计算事件. )如果2字词搭配出现语法错误和无索引搜索关联, 则更进
流水阀门至单字切词, 图中计算比较幸运得到2字切词计算结果, 按照流水阀门NERO-NLP-POS的水流计算, 在连副副
‘如果-是-非常’ 计算时便return了结果, 没有在计算到连名副‘如果-是非-常’是因为连副副的语法计算的流水阀门
DNA 元基催化与肽计算_第 5 修订版本 V00058 19
高, 优先计算并输出了. 描述人 罗瑶光
POS function gate river flows and their relationships. For example, the author did the word segmentation by using '如果是
非常理想' in this sentence. At the first through the indexed forest map dictionary, Deta Parser could cut '如果是非常理想'
into ‘如果’, ‘是非常’, ‘理想’those three associated chars word sets token list. And in this result list, ‘如果’and‘理想’ these
two lexical words seems to be immutably boned. ‘是非常’was a three chars word token then did an inner marching
computing by using POS function gate river flows theory. and at this time, the orthos corpus map base of the author's Deta
Parser system which could not find any verbals such as‘是非常’, then continued do the two chars marched for the next
step. About more powerful of these algorithms, was the Chinese chars literacy grammar marching system, for the chars
segment section, ‘是非常’did a separation into two types such as ‘是非-常’ and ‘是-非常’, then analyzed contrast and
distinguishment by these two segments. after analysis of each word and its prefix and postfix, POS combined relationships,
(the prefix token of‘是非’was‘如果’, the prefix token of‘非常’was‘是’, the prefix tokens of‘常’were‘是非' and‘非’, and the
prefix tokens of '理想’were‘常’and‘非常’). This POS word segmentations theory was fixedly and immutably, which means
it should not contain any probability events here. if at this time the DetaPaser does not find any associated chars
relationships, then promoted to the next steps as reading cutting sequence list chars single one by one. Above all, the
result of the sample graph did a good show that DetaParser did a ‘如果-是-非常’response because the priority of
Conjunction- Adj,v- Adj,v is higher than conjunction- noun- adj,v.
Author: Yaoguang Luo
2019 年 3 月 18 日之前作者 Github 的 该算法函数编码框架已经出现
https://github. com/yaoguangluo/Deta_Parser/commit/25b90c9847d15df85c5c991448f2c271e0ad8106
注意:链接的 CNN 关键词的 历史记录 属于作者用词错误, 作者当年基础学术累积不够, 关于卷积的知识仅仅学了计算
机视觉的理论课, 以为带内核计算的都叫 CNN 卷积
DNA 元基催化与肽计算_第 5 修订版本 V00058 20
另外作者发现自己还有一个错误, 就是以为序列链表方式计算就叫隐马科夫链计算. 所以 CNN+隐马可夫这两个技术词
汇, 伴随作者 10 年之久. 今天进行 ppt 严谨定义, 翻阅大量定义文献资料, 才发现这些错误. 予以纠正. 作者的 ANN 和
RNN 出现的文本分析内核计算才是真正的 CNN 卷积计算.
POS
Deta Parser 的分词词性基于自身的词性语料库, 格式为 词汇/词性, 举例如 香蕉/名词, deta 的语料库录入系统函数作
者的写法是用 string 的 contains 字符串来进行 map 索引登记, 于是这种格式有一个巨大的好处, 可以进行复合标注.
如果香蕉/水果名词, 浏阳/地理名词城市名词, 基于这种格式, 形容词谓词特指等复杂复合词性可以很好的被计算机理解.
德塔分词的词性基于每两个词汇的固定搭配, 如主语后面必为谓语, 名词 + 连词+ 后面必为名词, 形容词 + 连词+ 后面
剩余312页未读,继续阅读
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功