This is the Julia Tutorial series



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This is a Julia based tutorial covering the following topics:

  • Introduction to Linear Algebra
  • Applications of Matrix Factorizations
  • Introduction to Text Mining
  • Introduction to Recommender Systems

This package has each of the above mentioned topics as sub-modules. However as of now only Text Mining tutorial is available, and the rest are under construction.


This is an unregistered package, and can be installed in either of the following two ways:


alternatively, this also could be directly cloned from github as follows,

git clone https://github.com/abhijithch/JuliaTutorial.jl.git

in which case the dependent packages will have to be installed. If installed through the package manager, Pkg.clone() the dependent packages would be automatically installed.


To start using the package, first do using JuliaTutorial. Then according to the options given, include the sub-modules by using JuliaTutorial.TextMining to enable all the functions of Text Mining tutorial.

Text Mining

Please refer to docs/Julia_TextMining.pdf for the theoretical concepts. This Text Mining module depends on TextAnalysis.jl, for most of the preprocessing and preparation of the Term Document Matrix.


The first thing to do is generate a corpus from collection of textual data. In this module we work with documents as the source of textual data. These documents could be collection of research articles, HTML files etc, and the function PrepDocCorpus(dirname::String,DocType::Type) prepares a corpus, i.e., collection of all the documents under one entity. It also standardizes all the documents to a singly type, specified by DocType. The types could be any of StringDocument, TokenDocument or NGramDocument.

The query corpus are to be obtained using the function, PrepQueriesCorpus(NoQueries::Int,QueryFile::String). The NoQueries number of queries are stored in a single text file, QueryFile. Each queries are delimited by 2 blank lines.

The PreProcess!(crps::Corpus) function does all the preprocessing like removal of articles, pronouns, prepositions and stop words.

The functions dtm or the tdm from the TextAnalysis package are used to generate the TDM(Term Document Matrix). All the models end up factoring this TDM.

Query Matching

The proximity measure used is the cosine measure, the function CosTheta(q::Array{Float64,1},d::Array{Float64,1}), returns the cosine of the angle between the query vector q and the document vector d.

Performance Modeling

Like in any information retrieval tasks, Recall, R and Precision, P model the performance. R=Dr/Nr, where Dr is the number of relevant documents retrieved and Nr is the total number of relevant documents in the database, P=Dr/Dt where Dt is the total number of documents retrieved. The function PrepTest() prepares the test matrix, which is human verified list of the relevant documents for the correspoding queries.

VSM - Vector Space Model

This is the basic model in which the column vecotrs of the TDM constitute the Document space, of dimension equal to number of terms(keywords). A new query will also be another vector in the same space, and in the VSM model we just find the cosine similarity between the query and all the documents. A tolerance value decides the number of documents which will be returned. The performance analysis is done for various tolerance levels.

The VSM can be tested using the function VSMModel() with constrained parmeters types. The method VSMModel(A::Array{Float64,2},nq::Int64) gives the Precision and Recall for nq queries which form the first nq columns of the A matrix. The Documents are the remaining column vectors of A.

The method VSMModel(Q_C::Corpus,D_C::Corpus) forms the TDM from the Query and Document corpus, and gives the average Recall and Precision.

The Method VSMModel(QueryNum::Int64,A::Array{Float64,2},nq::Int64) give the Recall and Precision for a single query identified by QueryNum.

Latent Semantic Indexing Model

The LSI model finds the SVD of the Term Document Matrix, and decomposes the same into Document Space and Query Space. The method SVDModel(A::Array{Float64,2},nq::Int64,rank::Int64) uses the reduced rank approximation, and returns the average Recall and Precision. The methods SVDModel(Q_C::Corpus,D_C::Corpus,rank::Int64) does the same for the Query and Document corpus. The methods SVDModel(QueryNum::Int64,A::Array{Float64,2},NumQueries::Int64,rank::Int64) gives the Recall and Precision for the single query QueryNum.

K-Means Model

Considering the Documents to be points in m dimensional space, documents with similar content tend to be closer to each other. Hence by clustering the documents into K clusters, with the centroid of each each clusters representing them. Hence all these k centroid vectors as a mtrix C represent the entire Document Space. But to obtain an orthonormal basis of this space, we do QR-Factorization of C, represented by G. Then by projecting the Document vectors and query vectors onto this space G, we find the cosine measure between the query and all of the douments.

The method KMeansModel(A::Array{Float64,2},NumQueries::Int64,NumClusters::Int64) gives the average Recall and Precision by using NumClusters. The method KMeansModel(QueryNum::Int64,A::Array{Float64,2},NumQueries::Int64,Clusters::Int64) does the same for single query QueryNum. The method KMeansModel(Q_C::Corpus,D_C::Corpus,Clusters::Int64) finds the Recall and Precision for the Query and Document Corpus Q_C and D_C.

Plotting Results

The function plot_DrDtNr(Dr::Array{Float64,1},Dt::Array{Float64,1},Nr::Array{Float64,1},qNum::Int64,tol::Array{Float64,1}) can be used to plot the Dr, Dt and Nr for a single query qNum against the tolerance levels specified by tol.

The function plotNew_RecPrec(Rec::Array{Float64,1},Prec::Array{Float64,1},strMethod::String) must be used to plot the recall and precision. The strMethod specifies the model used, e.x, VSM or LSI etc. This function generates a new figure().

By using the function plotAdd_RecPrec(Rec::Array{Float64,1},Prec::Array{Float64,1},strMethod::String) a plot can be added to an already existing figure object. The plots automatically chooses different colors and corresponding legends are created. It supports upto 7 plots of the following colors, Colors=["red","blue","green","black","cyan","magenta","yellow"]. In the PlotResults.jl, new colors can be added to enable more plots.

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