Vector_training
doc2vec训练词向量 python代码
Doc2Vec是一种用于训练文档向量的模型，它可以学习从文本中提取的单词或短语的含义。
下面是一个使用Python和Gensim库实现Doc2Vec模型的简单示例：首先，确保你已经安装了Gensim库。
如果没有，你可以使用pip来安装：bashpip install gensim然后，你可以使用以下代码来训练一个Doc2Vec模型：pythonfrom gensim.models import Doc2Vecfrom nltk.tokenize import word_tokenizefrom nltk.corpus import abcimport nltk# 下载并加载nltk数据nltk.download('abc')corpus = abc.sents()# 对每个文档进行分词并转换为列表的列表格式corpus_tokens = [word_tokenize(doc) for doc in corpus]# 创建Doc2Vec模型，使用dm=1表示使用分布式内存模型，size=100表示向量维度，window=5表示上下文窗口大小，min_count=5表示忽略出现次数少于5次的词，workers=10表示使用10个线程进行训练model = Doc2Vec(corpus_tokens, vector_size=100, window=5, min_count=5, workers=10, dm=1)# 获取第一个文档的向量表示vector = model.infer_vector(corpus_tokens[0])print(vector)这段代码首先下载并加载了abc数据集，这是一个新闻分类数据集。
然后，对每个文档进行分词，并将结果转换为Doc2Vec所需的格式（即一个列表的列表，其中每个内部列表都是一个文档的单词列表）。
然后，创建一个Doc2Vec模型并训练它。
最后，获取第一个文档的向量表示并打印出来。
Support vector machine reference manual
snsv
ascii2bin bin2ascii
The rest of this document will describe these programs. To nd out more about SVMs, see the bibliography. We will not describe how SVMs work here. The rst program we will describe is the paragen program, as it speci es all parameters needed for the SVM.
sv
- the main SVM program - program for generating parameter sets for the SVM - load a saved SVM and classify a new data set
paragen loadsv
rm sv
- special SVM program for image recognition, that implements virtual support vectors BS97]. - program to convert SN format to our format - program to convert our ASCII format to our binary format - program to convert our binary format to our ASCII format
gensim中常用的Word2Vec，Phrases，Phraser，KeyedVectors
gensim中常⽤的Word2Vec，Phrases，Phraser，KeyedVectors gensim中常⽤的Word2Vec，Phrases，Phraser，KeyedVectors 1. Phrases 和Phrasergensim.models.phrases.Phrases和gensim.models.phrases.Phraser的⽤处是从句⼦中⾃动检测常⽤的短语表达，N-gram多元词组。
Phrases模型可以构建和实现bigram，trigram，quadgram等，提取⽂档中经常出现的2个词，3个词，4个词。
具体可以查看官⽹,两者不同：Phrases基于共现频率提取bigram词组。
基于共现的统计：受min_count与threshold的影响，参数设置越⼤，单词组合成⼆元词组的难度越⼤。
class gensim.models.phrases.Phrases(sentences=None, min_count=5, threshold=10.0, max_vocab_size=40000000, delimiter=b'_', progress_per=10000, scoring='default', common_terms=frozenset({}))Phraser的⽬的是通过丢弃⼀些Phasers模型（phrases_model）状态，减少短语的内存消耗。
如果后⾯不需要⽤新⽂档更新bigram统计信息，就可以使⽤Phraser代替Phrases。
⼀次性初始化后，Phraser会⽐使⽤Phrases模型占⽤内存⼩且速度更快。
class gensim.models.phrases.Phraser(phrases_model)Parametersphrases_model () – Trained phrases instance.Example：>>> from gensim.test.utils import datapath>>> from gensim.models.word2vec import Text8Corpus>>> from gensim.models.phrases import Phrases, Phraser>>>>>> # Load training data.>>> sentences = Text8Corpus(datapath('testcorpus.txt'))>>> # The training corpus must be a sequence (stream, generator) of sentences,>>> # with each sentence a list of tokens:>>> print(list(sentences)[0][:10])['computer', 'human', 'interface', 'computer', 'response', 'survey', 'system', 'time', 'user', 'interface']>>>>>> # Train a toy bigram model.>>> phrases = Phrases(sentences, min_count=1, threshold=1)>>> # Apply the trained phrases model to a new, unseen sentence.>>> phrases[['trees', 'graph', 'minors']]['trees_graph', 'minors']>>> # The toy model considered "trees graph" a single phrase => joined the two>>> # tokens into a single token, `trees_graph`.>>>>>> # Update the model with two new sentences on the fly.>>> phrases.add_vocab([["hello", "world"], ["meow"]])>>>>>> # Export the trained model = use less RAM, faster processing. Model updates no longer possible.>>> bigram = Phraser(phrases)>>> bigram[['trees', 'graph', 'minors']] # apply the exported model to a sentence['trees_graph', 'minors']>>>>>> # Apply the exported model to each sentence of a corpus:>>> for sent in bigram[sentences]:... pass>>>>>> # Save / load an exported collocation model.>>> bigram.save("/tmp/my_bigram_model.pkl")>>> bigram_reloaded = Phraser.load("/tmp/my_bigram_model.pkl")>>> bigram_reloaded[['trees', 'graph', 'minors']] # apply the exported model to a sentence['trees_graph', 'minors']2. Word2Vec涵盖常⽤的word2vec算法skip-gram和CBOW，使⽤hierarchical softmax和负采样。
KNN算法
KNN最邻近规则，主要应用领域是对未知事物的识别，即判断未知事物属于哪一类，判断思想是，基于欧几里得定理，判断未知事物的特征和哪一类已知事物的的特征最接近；K最近邻(k-Nearest Neighbor，KNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。
该方法的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。
KNN算法中，所选择的邻居都是已经正确分类的对象。
该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。
KNN方法虽然从原理上也依赖于极限定理，但在类别决策时，只与极少量的相邻样本有关。
由于KNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，KNN方法较其他方法更为适合。
KNN算法不仅可以用于分类，还可以用于回归。
通过找出一个样本的k个最近邻居，将这些邻居的属性的平均值赋给该样本，就可以得到该样本的属性。
更有用的方法是将不同距离的邻居对该样本产生的影响给予不同的权值(weight)，如权值与距离成正比（组合函数）。
该算法在分类时有个主要的不足是，当样本不平衡时，如一个类的样本容量很大，而其他类样本容量很小时，有可能导致当输入一个新样本时，该样本的K个邻居中大容量类的样本占多数。
该算法只计算“最近的”邻居样本，某一类的样本数量很大，那么或者这类样本并不接近目标样本，或者这类样本很靠近目标样本。
无论怎样，数量并不能影响运行结果。
可以采用权值的方法（和该样本距离小的邻居权值大）来改进。
该方法的另一个不足之处是计算量较大，因为对每一个待分类的文本都要计算它到全体已知样本的距离，才能求得它的K个最近邻点。
目前常用的解决方法是事先对已知样本点进行剪辑，事先去除对分类作用不大的样本。
该算法比较适用于样本容量比较大的类域的自动分类，而那些样本容量较小的类域采用这种算法比较容易产生误分。
Training_Certification
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Your staff needs critical skills and knowledge on Check Point products to implement and maintain the strongest security possible with the flexibility and reliability to keep your business growing—securely.Too often there is little or no time dedicated to mastering essential skills and product information, forcing your staff to sort through documentation, search Web sites, or even worse, learn on the job by trial and error—with dangerous consequences. They need focused training, hands-on experience, and access to knowledgeable experts who help them gain the critical skills they need to successfully implement and support your Check Point products.You need a fast, cost-effective way to bridge the gap between purchaseand implementation, reduce potential security risks, ensure reliable, available resources, and maximize the full benefits of your Check Point solution.OUR SOLUTIONCheck Point training courses and certifications give your security administrators and staff the critical skills and in-depth knowledge that they need to implement your Check Point solutions with the strongest security possible.Written by our training experts, all courses, labs, and exams are developed with an emphasis on practical experience with Check Point products,aligning essential skills to your real-world job functions and business needs for immediate benefits. 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初学liblinear的使用方法
初学liblinear的使⽤⽅法1、主要⽤到的函数如下：A、按照libsvm的数据格式读取txt⽂件 [label_vector, instance_matrix] = libsvmread('data.txt');B、将数据写成SVM规定的形式 libsvmwrite('data.txt', label_vector, instance_matrix]（The instance_matrix must be a sparse matrix. (type must be double)）C、训练函数 model = train(training_label_vector, training_instance_matrix [,'liblinear_options', 'col']); -training_label_vector: An m by 1 vector of training labels. (type must be double)-training_instance_matrix: An m by n matrix of m training instances with n features.It must be a sparse matrix. (type must be double)-liblinear_options:A string of training options in the same format as that of LIBLINEAR.options:-s type : set type of solver (default 1)for multi-class classification0 -- L2-regularized logistic regression (primal)1 -- L2-regularized L2-loss support vector classification (dual)2 -- L2-regularized L2-loss support vector classification (primal)3 -- L2-regularized L1-loss support vector classification (dual)4 -- support vector classification by Crammer and Singer5 -- L1-regularized L2-loss support vector classification6 -- L1-regularized logistic regression7 -- L2-regularized logistic regression (dual)for regression11 -- L2-regularized L2-loss support vector regression (primal)12 -- L2-regularized L2-loss support vector regression (dual)13 -- L2-regularized L1-loss support vector regression (dual)-c cost : set the parameter C (default 1)-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)-e epsilon : set tolerance of termination criterion-s 0 and 2|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,where f is the primal function and pos/neg are # ofpositive/negative data (default 0.01)-s 11|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)-s 1, 3, 4 and 7Dual maximal violation <= eps; similar to libsvm (default 0.1)-s 5 and 6|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,where f is the primal function (default 0.01)-s 12 and 13\n"|f'(alpha)|_1 <= eps |f'(alpha0)|,where f is the dual function (default 0.1)-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)-wi weight: weights adjust the parameter C of different classes (see README for details)-v n: n-fold cross validation mode-C : find parameter C (only for -s 0 and 2)-q : quiet mode (no outputs)-col:if 'col' is set, each column of training_instance_matrix is a data instance. Otherwise each row is a data instance.D、预测函数[predicted_label, accuracy, decision_values/prob_estimates] = predict(testing_label_vector, testing_instance_matrix,model [, 'liblinear_options', 'col']);2、⽰例：⽤liblinear⾃带的heart_scale做⽰例[heart_scale_label, heart_scale_inst] = libsvmread('heart_scale'); %读数据model=train(heart_scale_label, heart_scale_inst, '-c 1');[predict_label, accuracy, dec_values] = predict(heart_scale_label, heart_scale_inst, model);结果：Accuracy = 84.8148% (229/270)寻优函数，对s=0或2best = train(heart_scale_label, heart_scale_inst, '-C -s 0');将最佳的c代⼊3、模型⾥⾯的具体参数：。
svmtrain用法
svmtrain用法svmtrain 是 MATLAB 中用于训练支持向量机（Support Vector Machine，SVM）的函数。
支持向量机是一种监督学习算法，广泛用于分类和回归任务。
以下是 svmtrain 函数的基本用法：svmStruct = svmtrain(training, group)其中：training 是训练数据，是一个大小为m × n 的矩阵，其中 m 是样本数量，n 是特征数量。
group 是训练样本的类别标签，是一个大小为m × 1 的列向量。
返回值 svmStruct 包含了训练后的 SVM 模型。
如果需要更多的控制和定制，可以使用以下形式：svmStruct = svmtrain(training, group, 'PropertyName', PropertyValue, ...)其中，PropertyName 和 PropertyValue 是一对一对的参数名和参数值，用于设置 SVM 训练的不同选项。
以下是一些常用的参数：'kernel_function'：指定核函数的类型，如 'linear'（线性核函数，默认值）或 'rbf'（径向基函数）等。
'boxconstraint'：指定软间隔 SVM 的惩罚参数，控制对误分类样本的容忍度。
'showplot'：设置为 true 时，在训练过程中显示决策边界的可视化图。
以下是一个简单的例子：% 生成示例数据rng(1); % 设置随机数种子以保持结果的一致性data = randn(100, 2);labels = ones(100, 1);labels(51:end) = -1;% 使用线性核函数训练 SVM 模型svmStruct = svmtrain(data, labels, 'kernel_function', 'linear');% 预测新样本newData = randn(10, 2);predictedLabels = svmclassify(svmStruct, newData);% 显示决策边界sv = svmStruct.SupportVectors;figure;gscatter(data(:,1), data(:,2), labels);hold on;plot(sv(:,1),sv(:,2),'ko','MarkerSize',10);legend('Positive Class','Negative Class','Support Vector');上述例子中，首先生成了一个简单的二分类问题的数据集，然后使用线性核函数训练了一个 SVM 模型，并最后对新样本进行了预测。
Text Categorization with Support Vector Machines说明
Features
Thorsten Joachims
Universit£t Dortmund lnformatik LS8, Baroper Str. 301
This representation scheme leads to very high-dimensional feature spaces containing 10000 dimensions and more. Many have noted the need for feature selection to make the use of conventional learning methods possible, to improve generalization accuracy, and to avoid "overfitting". Following the recommendation of [11], the reformation gain criterion will be used in this paper to select a subset of features.
1 Introduction
With the rapid growth of online information, text categorization has become one of the key techniques for handling and organizing text data. Text categorization techniques are used to classify news stories, to find interesting information on the WWW, and to guide a user's search through hypertext. Since building text classifiers by hand is difficult and time-consuming, it is advantageous to learn classifiers from examples.
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MOST History and Future
1998 MOST Cooperation founded (DaimlerChrysler, BMW, Becker, OASIS)
2004 Used is serial cars of DaimlerChrysler, BMW, Audi, Porsche, Volvo, Saab, Peugeot, Citroen, Fiat
短的消息内容 （最多8字节）
原始数据
电平信号
01序列
如何解释数据？
消息与信号 消息，数据容器
{ 数据标识 { 传输的数据块（最多8字节） { 用符号描述
信号，实际使用的信息
{ 信号长度可能从1位到多字节 { 需要物理单位 { 需要转换单位 { 对错误的描述 { 用符号描述
数据交换的一个例子
Vector – The Art of Engineering
总部在德国Stuttgart
成立与 1988
员工300余人
世界领先的汽车工程和工 业自动化领域总线分析开 发工具的供应商
网络介绍
什么是网络 网络互连的需求
{ 物理连接 { 共同的语言（协议）
网络的优、缺点
ISO/OSI 七层协议
强大的CAN网络系统 分析和开发工具
——Vector
主要内容
公司简介 汽车总线介绍 汽车总线开发流程 使用CANalyzer CAPL 编程
九州恒润科技
总部在北京 成立于1998年,私营公司 公司员工近70人 上海,成都设有办事处 MathWorks & dSPACE&Vector公司中国唯一代理
LIN History and Future
1999 introduced by LIN consortium
2001 Mercedes SL as first serial car with LIN
2002 Audi, VW, Volvo, Toyota
2003
LIN specification 2.0 introduced (new Diagnostic and Configuration Functions, automatic detection of bus speed, etc.)
Companies with 95% of global automobile production belong to MOST Cooperation 2006 First cars with MOST in USA and Japan (?) Future MOST2 with >150 Mbit/s
(0xFF 代表错误)
发动机温度： ºC =2* Bit value –50 (0x7F 代表错误)
信号
发动机温度（第二字节，0-6位） 未用(第二字节，第七位) 发动机转速（第一，二字节）
位索引计算方法
7 0 7 07 0
ID
B0 B0 B0
CRC
7 0 15 8 23 16
Intel
23 16 15 8 7 0
LIN总线的典型应用
LIN总线的典型应用（主从结构）
MOST
MOST = Media Oriented System Transport（面向媒体系统传输）
多媒体需要很高的数据传输率和总线 速度
高达 22,5 MBit/s的数据传输率 基于光纤的环状总线拓扑结构 支持多达64个多媒体设备
. . .
ECU #10
传动
门
. . .
空调
. . .
安全系统
CAN C 500 kBd CAN B 83 kBd
ECU #1
声音 / 视频 / 通信
诊断 (K-line)
ECU # 24
CAN 主要特性
多主站架构
CSMA/CA 总线获得方式
对消息进行编址
网络范围内的数 据一致性
高的传输速度 （最高1 Mbit/s）
2004 used in (nearly) all new developed cars in Europe
Future LIN will be used in the future for nearly all sensor-actor applications in cars.
Sensors and actors will have LIN integrated in future.
Motorola
K-Matrix
节点 ID 名
消息名 位
信号 信号 接收 转换 错误 物理单位 发送
名
描述 节点 规则 表示
周期
ABS 300 ABS_1 1-4 ST1
h
4-8 ST2
301 ABS_2 1
HR
h
2-8 LZ
CAN 规范
数据帧
帧
仲
开
裁
始
位
位
控
数
制
据
位
位
标准 11位 扩展 29位
校
应帧
检
答结
位
位束
位
LIN总线
LIN = Local Interconnect Network 单主机多从机概念 基于普通UART/SCI 接口的低成本硬件实
现低成本软件或作为纯状态机 从机节点不需要石英或陶瓷谐振器可以实
现自同步 保证信号传输的延迟时间 低成本的单线设备 速度高达20kbit/s
等公司的支持
通讯速率高，可靠性高，连接方便和性能价格比 高
总线式串行通信网络
突出的可靠性、实时性和灵活性 在汽车，工业自动化等方面大量应用
CAN在汽车上的应用
Mercedes S 系列
点火开关
CAN B 动机 ECU
. . .
车辆动力学控制
ABS
GS
MS
201 前面左右轮转速 208 后面左右轮转速
喷射角度，踏板位置 200 发动机速度，温度 100
300 正常发动机力矩
消息与信号的关系 消息：‘engine data’ (ID 100)
ID 100
7 0 7 07 0
B0 B0 B0
CRC
转换规则
发动机转速 ：rpm=1*Bit value
DeviceNet CANopen
应用层 表示层
会话层
CAN
传输层 网络层
数据链路层
物理层
应用层 表示层 会话层
传输层 网络层 数据链路层
物理层
CAN的简介
CAN是ControlAreaNetwork的简称，最早由德国 BOSCH公司推出
总线规范现已被ISO国际标准组织制订为国际标准 得到了Motorola、Intel、Philips、Siemence、NEC