Python贝叶斯分析
作者:[阿根廷] 奥斯瓦尔多·马丁
出版社:人民邮电出版社
ISBN:9787115476173
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python贝叶斯网络的实现 评分:
《python贝叶斯思维》书中的例子代码,该代码基于python3.X
上传时间:2017-11 大小:43.1MB
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Bayesian Network(贝叶斯网络) Python Program
2009-04-26用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets in an XML format. SampleNets.py: Some code that constructs a sample Bayes net. This is called when SIGMAEditor.py is started up. SIGMAEditor.py: A main program that can be turned into an experimental application by adding menus, more code, etc. It has some facilities already for loading event sequence files and playing them. sample-event-file.txt: A sequence of events that exemplifies the format for these events. gma-mona.igm: A sample Bayes net in the form of an XML file. The SIGMAEditor program can read this type of file. Here are some limitations of the toolkit as of 23 February 2009: 1. Users cannot yet edit Bayes nets directly in the SIGMAEditor. Code has to be written to create new Bayes nets, at this time. 2. If you select the File menu's option to load a new Bayes net file, you get a fixed example: gma-mona.igm. This should be changed in the future to bring up a file dialog box so that the user can select the file. 3. When you "run" an event sequence in the SIGMAEditor, the program will present each event to each input node and find out if the input node's filter matches the evidence. If it does match, that fact is printed to standard output, but nothing else is done. What should then happen is that the node's probability is updated according to its response method, and if the new probability exceeds the node's threshold, then its successor ("children") get their probabilities updated, too. 4. No animation of the Bayes net is performed when an event sequence is run. Ideally, the diagram would be updated dynamically to show the activity, especially when posterior probabilities of nodes change and thresholds are exceeded. To use the BNT, do three kinds of development: A. create your own Bayes net whose input nodes correspond to pieces of evidence that might be presented and that might be relevant to drawing inferences about what's going on in the situation or process that you are analyzing. You do this by writing Python code that calls constructors etc. See the example in SampleNets.py. B. create a sample event stream that represents a plausible sequence of events that your system should be able to analyze. Put this in a file in the same format as used in sample-event-sequence.txt. C. modify the code of BNT or add new modules as necessary to obtain the functionality you want in your system. This could include code to perform actions whenever an output node's threshold is exceeded. It could include code to generate events (rather than read them from a file). And it could include code to describe more clearly what is going on whenever a node's probability is updated (e.g., what the significance of the update is -- more certainty about something, an indication that the weight of evidence is becoming strong, etc.)
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hedgehog:Python Python中的贝叶斯网络
2021-04-14Python中的贝叶斯网络 这是用于的毫不含糊的Python库。 对于严重的使用,您可能应该使用更成熟的项目,例如 , , (基于后者)或什至 。 还有证据充分的包河嘿,你甚至可以去中世纪和使用类似 -我只是开玩笑,他们实际上有一个。 顺便说一句,如果您不熟悉贝叶斯网络,那么我强烈推荐Patrick Winston的MIT关于概率推理的课程(,)。 该项目的主要目标是用于教育目的。 因此,比起表现,更强调简洁和简洁。 我发现像这样的库很棒。 但是,它们实际上包含数千行非显而易见的代码,这不利于简化和易于理解。 我还付出了一些努力来设计一个可以充分利用的漂亮API。 尽管性能不是此库的主要重点,但它相当有效,应该能够及时满足大多数用例。 目录 安装 用法 :writing_hand: 手动结构 :crystal_ball: 概率推断 :red_question_mark: 缺少价值估算 :person_shrugging: 可能性估计 :game_die: 随机抽样 :abacus: 参数估计 :brick: 结构学习 :eyes: 可视化
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贝叶斯网络工具箱-Python
2018-05-15此工具箱只支持Python2版本,在Python3下可以自行修改。允许使用单纯Python语言构建贝叶斯网络。包含构建离散的贝叶斯网以及高斯贝叶斯网,推理算法包含消息树以及MCMC采样等。允许搭建因子图模式下的BN网,同时包括网络实例。有不清楚的地方欢迎一起交流。
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贝叶斯网络代码
2019-06-14包含GaussianNB、马尔科夫模型、文本分类、中文分词的代码且数据和示例。
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一种基于周期的动态贝叶斯网络预测模型
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【数据挖掘】贝叶斯网络理论及Python实现(csdn)————程序.pdf
2021-12-04【数据挖掘】贝叶斯网络理论及Python实现(csdn)————程序
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pbnt:Python 2.7 的 Python 贝叶斯网络
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2022-04-201、资源配合博文《【python代码实现】决策树分类算法》、《【python代码实现】朴素贝叶斯分类算法》、《【python代码实现】人工神经网络分类算法及其实战案例(股票价格波动分析)》实操可掌握: 2、决策树分类算法...
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2023-06-10采用贝叶斯网络的方法为开展城市火灾预测、利用贝叶斯网络结合火灾事故统计数据以及城市火灾风险进行分析。通过贝叶斯网络的概率推理能力,开展不同区域的火灾风险估计、分析各因素指标对火灾后果的影响以及特定场景...
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pgmpy, 概率图形模型的python 库.zip
2019-09-17pgmpy, 概率图形模型的python 库 pgmpy pgmpy是使用概率图形模型的python 库。支持的文档和算法列表在我们的官方网站 http://pgmpy.org/ 。有关使用 pgmpy: https://github.com/pgmpy/p
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贝叶斯网络程序
2019-03-12提供详细的贝叶斯网络python程序,并提供具体实例验证,
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贝叶斯方法预测的demo(Python版本)
2018-10-17贝叶斯方法预测的demo(Python版本),采用python的sklearn包实现
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用Python构建概率图模型
2017-06-18利用Python的pgmpy包构建概率图模型
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基于贝叶斯网络模型的交通状态预测
2009-07-24基于贝叶斯网络模型的交通状态预测 写的很不错的
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python基于贝叶斯网络的城市火灾预测方法(django).zip
2023-07-06python基于贝叶斯网络的城市火灾预测方法(django) python;django;mysql; 采用贝叶斯网络的方法为开展城市火灾预测、利用贝叶斯网络结合火灾事故统计数据以及城市火灾风险进行分析。通过贝叶斯网络的概率推理...
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(基于python的毕业设计)基于贝叶斯网络的城市火灾预测方法(源码+说明+演示视频).zip
2023-06-17(基于python的毕业设计)基于贝叶斯网络的城市火灾预测方法(源码+说明+演示视频),本科毕业设计高分项目。 【项目技术】 python+Django+mysql 【实现功能】 采用贝叶斯网络的方法为开展城市火灾预测、利用贝叶斯网络...
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基于python贝叶斯、神经网络、KNN进行入侵检测源码+项目说明(KDD-CUP99).zip
2024-01-24【资源说明】 1、该资源包括项目的全部源码,下载可以直接使用! 2、本项目适合作为计算机、数学、电子信息等专业的课程设计、期末大...基于python贝叶斯、神经网络、KNN进行入侵检测源码+项目说明(KDD-CUP99).zip
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贝叶斯网络Python
2021-02-17运行命令: $ python3 main.py --model=model.txt --test=... 如果使用“转发”方法,请运行: $ python3 main.py --model=model.txt --test=test.txt --method=fowardmodel.txt:包含贝叶斯图。 test.txt:包含查询。
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机器学习-基与pgmpy库实现的贝叶斯网络
2023-04-13使用python语言,基与pgmpy库实现的贝叶斯网络,可以实现贝叶斯网络的结构学习、参数学习、预测以及可视化。 贝叶斯网络(Bayesian network),又称信念网络(Belief Network),或有向无环图模型(directed acyclic ...
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动态贝叶斯网络
2018-04-13动态贝叶斯网络(Dynamic Bayesian Network, DBN),是一个随着毗邻时间步骤把不同变量联系起来的贝叶斯网络。这通常被叫做“两个时间片”的贝叶斯网络,因为DBN在任意时间点T,变量的值可以从内在的回归量和直接先验值(time T-1)计算。DBN是BN(Baysian Network)的扩展,BN也称作概率网络(Probabilistic Network)或信念网络(Belief Network)。
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动态贝叶斯网络算法的计算和改进_FullFlexBayesNets_matlab源码
2022-03-02【达摩老生出品,必属精品,亲测校正,质量保证】 资源名:动态贝叶斯网络算法的计算和改进_FullFlexBayesNets_matlab源码 资源类型:matlab项目全套源码 源码说明: 全部项目源码都是经过测试校正后百分百成功运行的,如果您下载后不能运行可联系我进行指导或者更换。 适合人群:新手及有一定经验的开发人员
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论文研究-基于密度核估计的贝叶斯网络结构学习算法.pdf
2019-09-13贝叶斯网络结构学习算法主要包括爬山法和K2算法等,但这些方法均要求面向大样本数据集。针对实际问题中样本集规模小的特点,通过引入概率密度核估计方法以实现对原始样本集的拓展,利用K2算法进行贝叶斯网络结构学习。通过优化选择核函数和窗宽,基于密度核估计方法实现了样本集的有效扩展;同时基于互信息度进行变量顺序的确认,进而建立了小规模样本集的贝叶斯结构学习算法。仿真结果验证了新学习算法的有效性和实用性。
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贝叶斯网络构造代码
2018-12-03整数规划求解贝叶斯网络构造算法,算是当前最好的一类算法
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贝叶斯网络的K2结构学习
2015-01-06贝叶斯网络的K2结构学习 clear N = 4; dag = zeros(N,N); %C = 1; S = 2; R = 3; W = 4; % topological order C = 4; S = 2; R = 3; W = 1; % arbitrary order dag(C,[R S]) = 1; dag(R,W) = 1; dag(S,W)=1; false = 1; true = 2; ns = 2*ones(1,N); % binary nodes bnet = mk_bnet(dag, ns); bnet.CPD{C} = tabular_CPD(bnet, C, 'CPT', [0.5 0.5]); bnet.CPD{R} = tabular_CPD(bnet, R, 'CPT', [0.8 0.2 0.2 0.8]); bnet.CPD{S} = tabular_CPD(bnet, S, 'CPT', [0.5 0.9 0.5 0.1]); bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); [n ncases] = size(data); % set default params type = cell(1,n); params = cell(1,n); for i=1:n type{i} = 'tabular'; %params{i} = { 'prior', 1 }; params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; end scoring_fn = 'bayesian'; discrete = 1:n; clamped = zeros(n, ncases); max_fan_in = n; verbose = 0; dag = zeros(n,n); for i=1:n ps = []; j = order(i); u = find(clamped(j,:)==0); score = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); if verbose, fprintf('\nnode %d, empty score %6.4f\n', j, score), end done = 0; while ~done & (length(ps) score score = best_pscore; ps = [ps best_p]; if verbose, fprintf('* adding %d to %d, score %6.4f\n', best_p, j, best_pscore),end else done = 1; end end if ~isempty(ps) % need this check for matlab 5.2 dag(ps, j) = 1; end end
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基于python+django的贝叶斯网络的城市火灾预测方法的实现.zip
2023-06-09基于python+django的贝叶斯网络的城市火灾预测方法的实现.zip 运行步骤 需要先安装Python的相关依赖:pymysql,Django==3.2.8 ,opencv-python==4.5.5.64,numpy,pillow,pgmpy使用pip install 安装 第一步:创建...
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朴素贝叶斯算法实现的文本分类_Python
2014-01-10这个是Python编写的一个情感文本分析程序,定义两种term weight实现,分别为TF 和BOOL,实现了特征选择算法。文件夹中附带数据集
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2023-06-26【基于Python+Django的毕业设计】基于贝叶斯网络的城市火灾预测方法(源码+录像演示+说明).zip 【项目技术】 python+Django+mysql 【实现功能】 采用贝叶斯网络的方法为开展城市火灾预测、利用贝叶斯网络结合火灾事故...
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pybn:一个用于贝叶斯网络建模和推理的简单 python 库
2021-06-10pybn(下架):请尝试一个用于贝叶斯网络建模和推理的简单 python 库特点: 具有以下功能的有向无环图 (DAG) 类:父母、孩子、祖先、后代、所有 v 结构、道德化。 无向图实现。 用于测试独立性的 a-Separation 类。 ...