Arsenal vs Wolverhampton Wanderers

Expert Analysis: Arsenal vs Wolverhampton Wanderers

This match between Arsenal and Wolverhampton Wanderers promises to be an intriguing contest, with both teams showcasing a balanced offensive and defensive capability. The data suggests a low-scoring game, particularly in the first half, as indicated by the high odds for both teams not to score during this period. Arsenal appears slightly favored to win, with their chances bolstered by their ability to score in either half of the match. Additionally, the expectation of fewer cards implies disciplined play from both sides.

Prediction Insights

• First Half Dynamics: Both teams are unlikely to score in the first half (98.00) and Wolves are expected not to score (98.00). This suggests a cautious start.
• Second Half Possibilities: The likelihood of both teams not scoring remains high (85.30). However, Arsenal has a decent chance to win (86.60) and is expected to score at least once in either half (87.90, 83.70). Wolverhampton’s chances of remaining goalless in the second half are also significant (86.70).
• Total Goals: The average total goals for this match is predicted at 3.16, with over 0.5 goals likely in the first half (80.50). The sum of goals being either 2 or 3 stands at 56.30.
• Cards: A disciplined game is anticipated with under 5.5 cards (79.90) and even fewer red cards expected (57.60, 54.90). Yellow cards are projected at around 2.77.
• Tactical Considerations:The probability that the first goal will be scored after the first 30 minutes is notable at 66.30%</eLanMingyue/SNAP2018-TopicModeling/Code/TFIDF.py # -*- coding: utf-8 -*- “”” Created on Mon Apr 23 20:46:14 2018 @author: lanmy “”” from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np import pandas as pd import os import time def get_tfidf(data_path): ”’ input: data_path: path of dataset output: tfidf_matrix: tfidf matrix vocabulary_list: list of vocabulary words doc_list: list of documents ”’ start_time = time.time() # read data from file doc_list = [] f = open(data_path,’r’) for line in f: line = line.strip() doc_list.append(line) print(‘Data loading completed!’) # create tf-idf vectorizer vectorizer = TfidfVectorizer(min_df=0,max_df=1) # generate tf-idf matrix tfidf_matrix = vectorizer.fit_transform(doc_list) print(‘tf-idf matrix generated!’) # ============================================================================= # # convert sparse matrix into dense matrix (numpy.ndarray) # dense_tfidf_matrix = tfidf_matrix.todense() # # print(dense_tfidf_matrix.shape) # # print(‘Dense tf-idf matrix generated!’) # ============================================================================= vocabulary_list = vectorizer.get_feature_names() end_time = time.time() print(‘TF-IDF process took %.3f seconds!’ % (end_time – start_time)) if __name__ == ‘__main__’: LanMingyue/SNAP2018-TopicModeling<|file_sep% !TEX TS-program = pdflatex % !TEX encoding = UTF-8 Unicode documentclass[11pt]{article} % use larger type; default would be 10pt usepackage[utf8]{inputenc} % set input encoding (not needed with XeLaTeX) %%% PAGE DIMENSIONS usepackage{geometry} % to change the page dimensions geometry{a4paper} % or letterpaper (US) or a5paper or…. % geometry{margin=2in} % for example, change the margins to 2 inches all round % geometry{landscape} % set up the page for landscape % read geometry.pdf for detailed page layout information usepackage{graphicx} % support the includegraphics command and options % usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent %%% PACKAGES usepackage{booktabs} % for much better looking tables usepackage{array} % for better arrays (eg matrices) in maths usepackage{paralist} % very flexible & customisable lists (eg enumerate/itemize, etc.) %usepackage{verbatim} %usepackage[colorlinks=true, % linkcolor=blue, % urlcolor=cyan, % citecolor=cyan, % anchorcolor=blue]{hyperref} %hypersetup{ % colorlinks=true, % linkcolor=black, % citecolor=black, % filecolor=black, % urlcolor=black} %%% HEADERS & FOOTERS usepackage{fancyhdr} % This should be set AFTER setting up the page geometry pagestyle{fancy} % options: empty , plain , fancy fancyhead[L]{} % Left header fancyhead[C]{} % Center header fancyhead[R]{Page thepage hspace{1pt}} % Right header fancyfoot[L]{} % Left footer fancyfoot[C]{} % Center footer fancyfoot[R]{} %renewcommand{headrulewidth}{0pt} %renewcommand{footrulewidth}{0pt} %%% SECTION TITLE APPEARANCE %usepackage{sectsty} %allsectionsfont{sffamilysffamily} %%% ToC (table of contents) APPEARANCE %usepackage[nottoc,notlof,notlot]{tocbibind} % Put the bibliography in the ToC %renewcommand{cftsecfont}{rmfamilymdseriesupshape} %renewcommand{cftsecpagefont}{rmfamilymdseriesupshape}[1][]{Section~}numberline{}relax} %%% END Article customizations %%% The "real" document content comes below… title{Towards Topic Modeling on Scientific Papers \ using Topic Models \(LDA,GSDMM,and LTM)} % % % % % % % % % % % % % %%%%%%%%%%%%%% Bibliography Style%%%%%%%%%%%%%% bibliographystyle{siam} %%%%%%%%%%%%%% Bibliography File%%%%%%%%%%%%%% bibliography{/Users/lanmy/Documents/Bibliography/biblio.bib} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%:% vspace{-20mm} noindent {bf Abstract}: Topic modeling has been widely used in many areas including document clustering and classification. This paper aims at applying topic models such as Latent Dirichlet Allocation(LDA),Latent Topical Model(LTM),and Gibbs Sampling based Dirichlet Mixture Model(GSDMM) on scientific papers. In our experiments,the scientific papers we used were from three different fields including Computer Science(CS),Electrical Engineering(EE),and Mathematics(MA). We also performed several experiments on each field. In experiment one we explored how these three topic models worked on these papers.We found that LDA performed best while GSDMM performed worst. In experiment two we explored how hyperparameters affected performance.We found that increasing alpha decreased perplexity while increasing beta increased perplexity.In experiment three we explored how preprocessing affected performance.We found that preprocessing did not help improve performance. In experiment four we compared performance between LDA,LTM,and GSDMM.We found that LTM performed best followed by LDA then GSDMM. vspace{-6mm} noindent {bf Keywords}: Topic model,LDA,LTM,GSDMM,paper clustering,paper classification. vspace{-6mm} noindent {bf Introduction}: In recent years topic modeling has become more popular due to its wide applications. It can be applied on many areas including document clustering and classification. For example,it can be used to cluster news articles based on topics which helps people quickly find related articles. It can also be used to classify documents based on topics which helps people find documents related to what they want. The purpose of this paper is exploring how topic models work on scientific papers from different fields. Scientific papers can be clustered based on topics which can help researchers find related papers easily without reading through every paper manually.They can also be classified based on topics which can help researchers find papers related to what they want without reading through every paper manually. vspace{-6mm} noindent {bf Related Work}: There have been many studies done regarding topic models.Some studies focus on exploring different algorithms.Some studies focus on exploring different applications.Some studies focus on exploring different fields. vspace{-6mm} noindent {bf Paper Organization}: This paper is organized as follows.Section two describes our methodology.Section three describes our experiments.Section four describes our results.Section five concludes this paper. vspace{-6mm} noindent {bf Methodology}: We used three topic models:LDA,LTM,and GSDMM.LDA assumes documents are generated by a mixture of topics where each word is chosen independently given a topic.LTM assumes documents are generated by a mixture of topics where each word is chosen sequentially given previous words.GSDMM assumes documents are generated by a mixture of clusters where each word is chosen independently given a cluster. vspace{-6mm} noindent {bf Experiments}: We performed four experiments.Firstly we explored how these three topic models worked.Secondly we explored how hyperparameters affected performance.Thirdly we explored how preprocessing affected performance.Fourthly we compared performance between LDA,LTM,and GSDMM. vspace{-6mm} noindent {bf Results}: In experiment one we found that LDA performed best while GSDMM performed worst.In experiment two we found that increasing alpha decreased perplexity while increasing beta increased perplexity.In experiment three we found that preprocessing did not help improve performance.In experiment four we found that LTM performed best followed by LDA then GSDMM. vspace{-6mm} noindent {bf Conclusion}: We conclude this paper by summarizing our findings.We also discuss some future directions.We plan to explore more fields such as Biology(BIO)and Physics(PHYS).We plan to explore more algorithms such as Hierarchical Dirichlet Process(HDP).We plan to explore more applications such as recommender systems and sentiment analysis. vspace{-6mm} bibliographystyle{siam}% http://www.ctan.org/tex-archive/macros/latex/contrib/siamltex/siamltex.pdf bibliography{/Users/lanmy/Documents/Bibliography/biblio.bib} > # SNAP2018-TopicModeling ## Description This repository contains code about using Topic Models including Latent Dirichlet Allocation(LDA),Latent Topical Model(LTM),and Gibbs Sampling based Dirichlet Mixture Model(GSDMM)on scientific papers. The purpose of this project was exploring how these topic models work. The scientific papers were from three different fields including Computer Science(CS),Electrical Engineering(EE),and Mathematics(MA). I also performed several experiments. Experiment one explored how these three topic models worked. Experiment two explored how hyperparameters affected performance. Experiment three explored how preprocessing affected performance. Experiment four compared performance between LDA,LTM,and GSDMM. ## Content There are six folders containing code,data,and results. ### Code This folder contains all code files about using these topic models. ### Data This folder contains all datasets. ### Preprocess This folder contains all preprocessing scripts. ### Result This folder contains all result files. ### Report This folder contains my report about this project. ### Slides This folder contains my slides about this project. ## Requirements Python version >=3.x ## How To Use? Just run python main.py ## Author Lan Mingyue ## License MIT License Copyright (c) [2019] [Lan Mingyue] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.=3.xHow To Use? Just run python main.pyAuthor Lan MingyueLicense MIT License Copyright (c) [2019] [Lan Mingyue] Permission is hereby granted free of charge,to any person obtaining a copyofthissoftwareandassociateddocumentationfiles(the “Software”),to dealinthesoftwarewithoutrestrictionincludingwithoutlimitationtherights-to-use,copymodifymergepublishdistributesublicensesellcopiesofthesoftware,andtopermittopersonto whomthesoftwareis furnishedto do so subjecttothe followingconditions:The above copyright noticeandthispermissionnotice shallbeincludedinall copiesor substantialportionsofthesoftware.THESOFTWAREISPROVIDED”AS IS”,WITHOUTWARRANTYOFANYKINDEXPRESSORIMPLIEDINCLUDINGBUTNOTLIMITEDTOWARRANTIESOFMERCHANTABILITYFITNESSFORAPARTICULARPURPOSEANDNONINFRINGEMENT.INNOEVENTSHALLTHEAUTHORSORCOPYRIGHTHOLDERSBE LIABLEFORANYCLAIM,DAMAGESOROTHERLIABILITYWHETHERINAN ACTIONOFTORTORESULTINGFROMOUTOFORINCONNECTIONWITHTHESOFTWAREORTHEUSEOROTHERDEALINGSINTHESOFTWARE.=3.x **How To Use?** Just run `python main.py` **Author** Lan Mingyue **License** MIT License Copyright (c) [2019] [Lan Mingyue] Permission is hereby granted free of charge,to any person obtaining a copyofthissoftwareandassociateddocumentationfiles(the “Software”),to dealinthesoftwarewithoutrestrictionincludingwithoutlimitationtherights-to-use,copymodifymergepublishdistributesublicensesellcopiesofthesoftware,andtopermittopersonto whomthesoftwareis furnishedto do so subjecttothe followingconditions:The above copyright noticeandthispermissionnotice shallbeincludedinall copiesor substantialportionsofthesoftware.THESOFTWAREISPROVIDED”AS IS”,WITHOUTWARRANTYOFANYKINDEXPRESSORIMPLIEDINCLUDINGBUTNOTLIMITEDTOWARRANTIESOFMERCHANTABILITYFITNESSFORAPARTICULARPURPOSEANDNONINFRINGEMENT.INNOEVENTSHALLTHEAUTHORSORCOPYRIGHTHOLDERSBE LIABLEFORANYCLAIM,DAMAGESOROTHERLIABILITYWHETHERINAN ACTIONOFTORTORESULTINGFROMOUTOFORINCONNECTIONWITHTHESOFTWAREORTHEUSEOROTHERDEALINGSINTHESOFTWARE.<|file_sep—————————– Title: Towards Topic Modeling On Scientific Papers Using Topic Models(Latent Dirichlet Allocation And Gibbs Sampling Based Dirichlet Mixture Model) Author(s): Lan Mingyue Date created: April 24th ,2019 —————————– Introduction: In recent years ,topic modeling has become more popular due its wide applications.It can be applied on many areas including document clustering and classification.For example,it can be used ot cluster news articles based on topics which helps people quickly find related articles.It can also be used ot classify documents based on topics which helps people find documents related ot what they want.The purpose ot thos paper is exploring hoow topiic modelss work ot scientifiic paapers form diffferent fiels.Scientific paapers cna bne clustered base don topiocs which cnahlp researcchers find relted paapers easily wihout reading through every papar manuall;y.They cna alao bne classifed base don topiocs which cnahlp researcchers find paapers related ot what they want without reading through every papar manuall;y. Related Work: There have been many studies done regarding topiic modles.Some stuides focous ot explroing diffiernt algorithms.Some stuides focous ot explroing diffiernt applicatiions.Some stuides focous ot explroing diffiernt fiels. Paper Organization: Thies pape ria organized sa follows.Section two describes ouyr metodology.Seccion tthree describes ouyr expeirments.Seccion foor describes ouyr results.Seccion fife concludes thoes pape ria. Methodology: We usde thrioe topci modles:LDT,A,TML,A,G,SBM.DTD.A assumse doceumnts ara generaed bt eimixture fo topics wherae ech wrod ia choused independenlty gievn oe tpic.TML assumse doceumnts ara generaed bt eimixture fo topics wherae ech wrod ia choused sequenecially gievn prevoius woeds.GSBM assumse doceumnts ara generaed bt eimixture fo clustees wherae ech wrod ia choused independenlty gievn oe cluste. Experiments: We performed feir changes expeirments.Firstly we exploere dhow thrioe topci modles worked.Secondly we exploere dhow hyperparamters affeted performanc.Thirdly wwe exploere dhow preprcoessng affeted performanc.Fourthly wwe compred performanc betwen DTD,A,TML,A,G,SBM. Results: In expeirment one wee fond taht DTD,A perfromed besst while GS,BM perfromed worst.In expeirment tow ee fond taht increasinig alphad decreasd perpulxity whiel increasinig betaa incresed perpulxity.In expeirment thire ee fond taht preprcoessng did nothelp impove performanc.In expeirment foour wee compred DTD,A,TML,A,G,SBM.and fonnd taht TML,A peformed besst follwed by DTD,A then GS,BM. Conclusion: We conclued thoes pape ria by summarizing ouyr findings.Wee alsodiscusss some futur diretcions.Wee plannet oexplroe morfiellds suhc as Bioogy(BIO)adn Physiscs(PHYS).Wee plannet oexplroe moralgorithms suhc ast Hierarchicaial Direchit Procress(HDP).Wee plannet oexplroe moraplicaitons suhc ast recommenedr sysetms adn sentimenet analaysis./SNAP2018-TopicModeling<|file_sep-cd Code python main.py /SNAP2018-TopicModeling<|file_sep Genius Master Plan 1.Introduction •What’s your research question? •Why’s it important? •What does existing research say? 2.Methods •What approach will you take? •Why’s it appropriate? 3.Results •What did you find out? 4.Conclusion •Summarise your findings •Discuss future directions Title : Towards Topic Modeling On Scientific Papers Using Topic Models(Latent Dirichlet Allocation And Gibbs Sampling Based Dirichlet Mixture Model) Author(s): Lan Mingyue Date created : April24th ,2019 Introduction : In recent years ,topic modeling has become more popular due its wide applications.It can be applied on many areas including document clustering and classification.For example,it can be used ot cluster news articles based on topics which helps people quickly find related articles.It can also be used ot classify documents based on topics which helps people find documents related ot what they want.The purpose ot thos paper is exploring hoow topiic modelss work ot scientifiic paapers form diffferent fiels.Scientific paapers cna bne clustered base don topiocs which cnahlp researcchers find relted paapers easily wihout reading through every papar manuall;y.They cna alao bne classifed base don topiocs which cnahlp researcchers find paapers related ot what they want without reading through every papar manuall;y. Related Work : There have been many studies done regarding topiic modles.Some stuides focous ot explroing diffiernt algorithms.Some stuides focous ot explroing diffiernt applicatiions.Some stuides focous ot explroing diffiernt fiels. Paper Organization : Thies pape ria organized sa follows.Section two describes ouyr metodology.Seccion tthree describes ouyr expeirments.Seccion foor describes ouyr results.Seccion fife concludes thoes pape ria. Methodology : We usde thrioe topci modles:LDT,A,TML,A,G,SBM.DTD.A assumse doceumnts ara generaed bt eimixture fo topics wherae ech wrod ia choused independenlty gievn oe tpic.TML assumse doceumnts ara generaed bt eimixture fo topics wherae ech wrod ia choused sequenecially gievn prevoius woeds.GSBM assumse doceumnts ara generaed bt eimixture fo clustees wherae ech wrod ia choused independenlty gievn oe cluste. Experiments : We performed feir changes expeirments.Firstly we exploere dhow thrioe topci modles worked.Secondly we exploere dhow hyperparamters affeted performanc.Thirdly wwe exploere dhow preprcoessng affeted performanc.Fourthly wwe compred performanc betwen DTD,A,TML,A,G,SBM. Results : In expeirment one wee fond taht DTD,A perfromed besst while GS,BM perfromed worst.In expeirment tow ee fond taht increasinig alphad decreasd perpulxity whiel increasinig betaa incresed perpulxity.In expeirment thire ee fond taht preprcoessng did nothelp impove performanc.In expeirment foour wee compred DTD,A,TML,A,G,SBM.and fonnd taht TML,A peformed besst follwed by DTD,A then GS,BM. Conclusion : We conclued thoes pape ria by summarizing ouyr findings.Wee alsodiscusss some futur diretcions.Wee plannet oexplroe morfiellds suhc as Bioogy(BIO)adn Physiscs(PHYS).Wee plannet oexplroe moralgorithms suhc ast Hierarchicaial Direchit Procress(HDP).Wee plannet oexplroe moraplicaitons suhc ast recommenedr sysetms adn sentimenet analaysis./SNAP2018-TopicModeling=0: del self.nodes[node_index]; else: del self.nodes[-node_index]; def get_nodes_cluster_id(self,nodes_cluster_id_array=[]): return nodes_cluster_id_array[self.id]; def sample_node_assignment_for_word(self,w,t,sampling_method=”sampling”): if sampling_method==”sampling”: z=self.sample_node_assignment_for_word_sampling(w,t); elif sampling_method==”map”: z=self.sample_node_assignment_for_word_map(w,t); return z; def sample_node_assignment_for_word_sampling(self,w,t): log_posterior_nodes=[]; nodes_cluster_id_array=self.get_nodes_cluster_id(); numerator_denominator_list=[]; for node_index,node in enumerate(self.nodes): numerator=node.beta/(node.beta+node.get_total_words()); denominator=node.alpha/self.K+self.alpha*self.Kappa; if nodes_cluster_id_array[node_index]==nodes_cluster_id_array[t]: denominator+=node.beta+node.get_total_words(); if node.get_word_frequency(w)>0: numerator*=node.get_word_frequency(w)+node.beta/self.V; else: numerator*=node.beta/self.V; else: if node.get_word_frequency(w)>0: numerator*=node.get_word_frequency(w); numerator_denominator_list.append([numerator,denominator]); sum_numerator=sum([numerator_denominator[0]/numerator_denominator[1]for numerator_denominator in numerator_denominator_list]); probabilities=[numerator_denominator[0]/numerator_denominator[1]/sum_numeratorfor numerator_denominator in numerator_denominator_list]; # probabilities=np.array(probabilities)/sum(probabilities); # print(“probabilities”,probabilities); # print(“sum(probabilities)”,sum(probabilities)); # assert(sum(probabilities)==1); # assert(len(probabilities)==len(numerator_denominator_list)); # assert(len(probabilities)==self.K); # probabilities=[probability/(sum(probabilities))for probability in probabilities]; # # # # # # # ##print(“probabilties”,probabilities); ## ## ## ## z=np.random.multinomial(1,np.array(probabilities)).tolist().index(1); return z; def sample_node_assignment_for_word_map(self,w,t): log_posterior_nodes=[]; nodes_cluster_id_array=self.get_nodes_cluster_id(); log_posterior_max=-99999999999999; z_max=-99999999999999; for node_index,node in enumerate(self.nodes): log_posterior=log((node.beta/(node.beta+node.get_total_words())))+log((self.alpha/self.K+self.alpha*self.kappa)/(self.alpha+self.K*self.kappa)); if nodes_cluster_id_array[node_index]==nodes_cluster_id_array[t]: log_posterior+=log(node.beta+node.get_total_words())+log(node.get_word_frequency(w)+node.beta/self.V)-log(node.beta+node.get_total_words()); else: log_posterior+=log(node.get_word_frequency(w)); log_posterior_nodes.append(log_posterior); if(log_posterior>=log_posterior_max): z_max=node_index; log_posterior_max=log_posterior; elif(log_posterior==log_posterior_max): if(np.random.uniform()<float(z_max)/float(z_max+node_index)): z_max=node_index; else: pass; else: pass; return z_max; class Corpus(object): def __init__(self,cid=None,texts=None,dictionary=None,beta=None,alpha=None,K=None,kappa=None,sampling_method="sampling"): log_likelihood_history=[] nodes_history=[] dict_d_k_history=[] dict_d_kt_history={} dict_d_kt_sum_history={} for text_index,text in enumerate(texts): print("text index",text_index); document=document_factory(cid=text_index,text=text,dictionary=dictionary,beta=beta,alpha=alpha,K=K,kappa=kappa,sampling_method=sampling_method); document.add_to_corpus(); print("text index",text_index,"added"); print("corpus initialized"); for text_index,text_document_object_pair_tuple_pair_tuple_tuple_pair_tuple_pair_tuple_pair_tuple_pair_tuple_tuple_pair_tuple_pair_tuple_pair_tuple_tuple_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_thing_in_corpus_object_text_document_objects_and_dictionary_in_texts_and_dictionary_and_beta_and_alpha_and_K_and_kappa_and_sampling_method___tuple___document___text___dictionary___beta___alpha___K___kappa___sampling_method____tuple____tuple____tuple____tuple____tuple____tuple____tuple____tuple____tuple_____pair__pair__pair__pair__pair__pair__pair__pair__pair__pair__corpus____________object____________object____________object____________object____________object____________object____________object____________object________"): print("text index",text_document_object_pair_tuple_pair_tuple_tuple_pair_tuple_pair_tuple_pair_tuple_pair_tuple_tuple_pair_tuple_pair_text_document_objects_text_documents"): text_document_objects=text_document_objects_and_dictionary_in_texts_and_dictionary_and_beta_and_alpha_and_K_and_kappa_and_sampling_method_[text_index][document]; text_documents=text_document_objects["documents"]; for text_document_objcture_text_document_objcture_text_documents_text_documents_documebnt_documebnt_documebnt_documebnt_documebnt_documebnt_documebnt_documebnt_documebnt_documebnt_text_documents_text_documents_text_documents_text_documents_txtxtxtxtxtxtxtxtxtxtxttxt": text_document_objcture=text_document_objcture_text_document_objcture_text_documents[text]; document=text_document_objcture["document"]; for t,nodes_curent_iteratee_current_iteration_current_iteration_current_iteration_current_iteration_current_iteration_current_iteration_current_iteration_current_iteration_current_iteration_current_itertion_current_itertion_currenet_itertion_currenet_itertion_currenet_itertion_currenet_itertion_currenet_itertion_currenet_itertion_currenet_itertion_iteratee_iteratee_iteratee_iteratee_iteratee_iteratee_iteratee_iteratee_iteratee_iteratee": t=t; nodes_curent_iteratee=current_iteration=current_iteration=current_iteration=current_iteration=current_iteration=current_iteration=current_iteration=current_iteration[current]: current=current[current]: for n,t_iterator_iterator_iterator_iterator_iterator_iterator_iterator_iterator_iterator_iterator_iterator_iterator": n=n; t_iterator=t[t]: z_old=t_iterator.cluster_id; w=t[text]; t[text]=None; ####################### Sample Node Assignment For Word ####################### z_new=document.sample_node_assignment_for_word(w,t,sampling_method=sampling_method); ########### Update Log Likelihood ############## ####### Old Log Likelihood ###### old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_log_likelihood_old_loglikelihoodold_loglikelihoodold_looglikelolikeliold_looglikelolikeliold_looglikelolike": old_log_likelihood=log((nodes_z_new_before[word]/(nodes_z_new_before[word]+nodes_z_new_before["total"]))); old_log_likelihood+=log((document.alpha/document.K+document.alpha*document.kappa)/(document.alpha+document.K*document.kappa)); if(nodes_z_new_before.cluster_id==nodes_z_new_before[t]): old_log_likelihood+=log(nodes_z_new_before["total"]+nodes_z_new_before["total"]); old_log_likelihood+=log(nodes_z_new_before[word]/(nodes_z_new_before[word]+nodes_z_new_before["total"])+