DNA元基催化与肽计算：第5修订版V00058171解读

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"DNA元基催化与肽计算_第5修订版本V00058171" 本文档详细介绍了DNA元基催化与肽计算的相关理论和技术，并结合了自然语言处理（NLP）和图灵系统等概念，旨在探讨在信息技术领域的应用。文档主要分为四个章节，涵盖德塔自然语言图灵系统、Java数据分析算法引擎系统、德塔ETL人工智能可视化数据流分析引擎系统以及德塔Socket流可编程数据库语言引擎系统。在第一章中，讲解了德塔自然语言图灵系统，强调了知识来源的重要性，特别是德塔分词的催化切词优化方式。分词是NLP的基础，用于将连续的文本分割成有意义的词汇单位。本章还涉及了排序算法、神经网络索引的构建，以及分词在线性文本搜索中的应用，如动态POS函数，用于标记词性。此外，讨论了RNN（循环神经网络）、DNN（深度神经网络）及其在NLP中的应用，以及图灵机的原理。第二章主要关注Java数据分析算法引擎系统，提出了微分催化排序的概念，探讨了计算力与算能优化的思想。内容涵盖了线性与非卷积视觉计算、音频卷积处理、非线性算法、维度处理和智能相诊多媒体图片描述。此外，还讨论了仿生听觉技术、排序算法的对比、搜索应用和股市数据分析。第三章介绍了德塔ETL人工智能可视化数据流分析引擎系统，详细描述了系统的用户界面、ETL引擎图的鼠标操作逻辑、计算节点设计，以及神经元皮肤的使用。还涉及流存储、节点、插件、拓扑结构和神经网络，强调了一键执行功能和ETLUnicorn函数分类。第四章探讨了德塔Socket流可编程数据库语言引擎系统，讲解了Socket REST TCP握手协议，文件数据库的管理，以及VPCS服务器的架构和调度。VPCS应用逻辑介绍提供了深入的理解，同时也提到了PLSQL语言的应用。总体而言，这个文档提供了丰富的信息，结合了生物学、计算机科学和人工智能的前沿知识，为读者提供了一个跨学科的学习平台，有助于理解如何将这些理论应用于实际的数据处理和分析系统。

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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 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

剩余313页未读，继续阅读

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DNA元基催化与肽计算：第5修订版V00058171解读

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解释def copy(): dna=Dna.get() complementary_bases = {"A": "T", "T": "A", "C": "G", "G": "C"} new_dna_seq = "" for base in dna: new_dna_seq += complementary_bases[base] result="DNA replication:"+new_dna_seq label2.config(text=result)

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已知六段dna序列，由FASTA格式存储在文件1.txt中。读取文件中的DNA序列，并将各DNA序列的互补链输出到1.txt相应的位置，并求解原DNA序列的G, C数量。

python DNA翻译

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未定义函数或变量 'readcell'。出错 Untitled (line 3) dna_seqs = readcell(dna_file);