lda | Topic modeling with latent Dirichlet allocation using Gibbs | Topic Modeling library

 by   lda-project Python Version: 0.3.2 License: MPL-2.0

kandi X-RAY | lda Summary

kandi X-RAY | lda Summary

lda is a Python library typically used in Artificial Intelligence, Topic Modeling applications. lda has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has high support. You can download it from GitHub.

Topic modeling with latent Dirichlet allocation using Gibbs sampling

            kandi-support Support

              lda has a highly active ecosystem.
              It has 1122 star(s) with 382 fork(s). There are 50 watchers for this library.
              It had no major release in the last 12 months.
              There are 0 open issues and 89 have been closed. On average issues are closed in 741 days. There are no pull requests.
              It has a positive sentiment in the developer community.
              The latest version of lda is 0.3.2

            kandi-Quality Quality

              lda has 0 bugs and 0 code smells.

            kandi-Security Security

              lda has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              lda code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              lda is licensed under the MPL-2.0 License. This license is Weak Copyleft.
              Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

            kandi-Reuse Reuse

              lda releases are available to install and integrate.
              Build file is available. You can build the component from source.
              It has 494 lines of code, 38 functions and 14 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed lda and discovered the below as its top functions. This is intended to give you an instant insight into lda implemented functionality, and help decide if they suit your requirements.
            • Convert a LDA - C file to DTM format .
            • Convert a document - term matrix to a list of lists .
            • Transform a sequence of documents into a single document .
            • Convert a dtm table to LDAP format .
            • Convert a list of dictionaries into a matrix .
            • Run sdist pre - hook .
            • Check a random seed .
            • Loads thereuter vocabulary .
            • Return the list of reuters titles .
            • Load the reuter dataset .
            Get all kandi verified functions for this library.

            lda Key Features

            No Key Features are available at this moment for lda.

            lda Examples and Code Snippets

            dgl - lda model
            Pythondot img1Lines of Code : 194dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # Copyright 2021 Yifei Ma
            # with references from "sklearn.decomposition.LatentDirichletAllocation"
            # with the following original authors:
            # * Chyi-Kwei Yau (the said scikit-learn implementation)
            # * Matthew D. Hoffman (original onlineldavb implementa  

            Community Discussions


            LMC program that will produce the sum of the median and twice the smallest of 3 inputs
            Asked 2022-Apr-09 at 22:05

            so I've been tasked to create a little-man-machine program that will take 3 distinct inputs and produce the result of the median + 2 * the smallest number.

            So far I've managed to produce an output that produces the smallest number of the 3 inputs. How would I go about finding the median and then adding it to 2 * the smallest number?



            Answered 2022-Apr-09 at 22:05

            Your code correctly outputs the minimum value, but:

            • It destroys the other input values, which you still need
            • There is some code that never executes (lines 25-27)
            • The result of the subtraction at line 23 is not used
            • The STA that happens at line 29 is useless

            I would suggest to first sort the three input values, and then it is easy to apply the "formula" that is requested.

            Also: use labels in your program and define the addresses of your variables with DAT codes. Most LMC simulators support labels and it makes the code more readable.

            Here is how you could do it. I didn't add comments to the code, as the simulator that you use does not support comments (a pity!), but here is how it works:

            • Name the inputs a, b and c (see the DAT lines at the end)
            • Compare a with b
            • If a > b then swap their values using a temp variable
            • At continue compare b with c
            • If b > c then:
              • Forget about what is in b and put the value of c there
              • Compare that value (b which is also c) with a
              • If b < a then swap a and b with the help of c (a copy of b)
            • Finally perform the calculation a+a+b and output it.

            Here is the snippet -- click Run code snippet to activate the inline LMC simulator and control it with the input-box and buttons that will appear:

            Source https://stackoverflow.com/questions/71793939


            Eigen decomposition of Hermitian Matrix using CuSolver does not match the result with matlab
            Asked 2022-Mar-04 at 16:07

            I am following the example of eigen decomposition from here, https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuSOLVER/syevd/cusolver_syevd_example.cu

            I need to do it for Hermatian complex matrix. The problem is the eigen vector is not matching at all with the result with Matlab result.

            Does anyone have any idea about it why this mismatch is happening?

            I have also tried cusolverdn svd method to get eigen values and vector that is giving another result.

            My code is here for convenience,



            Answered 2022-Mar-04 at 16:07

            Please follow the post for the clear answer, https://forums.developer.nvidia.com/t/eigen-decomposition-of-hermitian-matrix-using-cusolver-does-not-match-the-result-with-matlab/204157

            The theory tells, A*V-lamda*V=0 should satisfy, however it might not be perfect zero. My thinking was it will very very close to zero or e-14 somethng like this. If the equation gives a value close to zero then it is acceptable.

            There are different algorithms for solving eigen decomposition, like Jacobi algorithm, Cholesky factorization... The program I provided in my post uses the function cusolverDnCheevd which is based on LAPACK. LAPACK doc tells that it uses divide and conquer algorithm to solve Hermitian matrix. Here is the link, http://www.netlib.org/lapack/explore-html/d9/de3/group__complex_h_eeigen_ga6084b0819f9642f0db26257e8a3ebd42.html#ga6084b0819f9642f0db26257e8a3ebd42

            Source https://stackoverflow.com/questions/71174682


            R not displaying Arabic text correctly
            Asked 2022-Feb-24 at 02:07

            I am running a simple unsupervised learning model on an Arabic text corpus, and the model is running well. However, I am having an issue with the plots that aren't working well as they are printing the Arabic characters from left to right, rather than the correct format of right to left.

            Here are the packages I am using:



            Answered 2022-Feb-24 at 02:07

            If you're using old a version of R that is 3.2 or Less then those versions does not handle Unicode in proper way. Try to install latest version of R from https://cran.r-project.org/ and if required then install all packages.

            Source https://stackoverflow.com/questions/70989953


            Creating nested columns in python dataframe
            Asked 2022-Feb-20 at 15:56

            I have 3 columns namely Models(should be taken as index), Accuracy without normalization, Accuracy with normalization (zscore, minmax, maxabs, robust) and these are required to be created as:



            Answered 2022-Feb-20 at 13:01

            There's a dirty way to do this, I'll write about it till someone answers with a better idea. Here we go:

            Source https://stackoverflow.com/questions/71193085


            "In call to DSYEV, an array temporary was created for argument" in ifort but the related dimension is only 1
            Asked 2022-Feb-15 at 23:53


            Answered 2022-Feb-15 at 23:53

            Let's consider a much simpler program to look at what's going on:

            Source https://stackoverflow.com/questions/71129629


            python - matplot lib sub-plot grid: where to insert row/column arguments
            Asked 2022-Jan-30 at 08:00

            I'm trying to display the topic extraction results of an LDA text analysis across several data sets in the form of a matplotlib subplot.

            Here's where I'm at:

            I think my issue is my unfamiliarity with matplotlib. I have done all my number crunching ahead of time so that I can focus on how to plot the data:



            Answered 2022-Jan-24 at 07:45

            You should create the figure first:

            Source https://stackoverflow.com/questions/70830018


            Kernel LDA in Julia. (trouble in package installing)
            Asked 2022-Jan-24 at 17:32

            I want to use Kernel LDA in julia 1.6.1. I found the repo. https://github.com/remusao/LDA.jl

            I read READEME.md, and I typed



            Answered 2022-Jan-24 at 17:32

            The package you have linked, https://github.com/remusao/LDA.jl, has had no commits in over eight years. Among other things, it lacks a Project.toml file, which is necessary for installation in modern Julia.

            Since Julia was only about one year old and at version 0.2 back in 2013 when this package last saw maintenance, the language has also changed drastically in this time such that the code in this package would likely no longer function even if you could get it to install.

            If you can't find any alternative to this package for your work, forking it and upgrading it to work with modern Julia would be a nice intermediate-beginner project.

            Source https://stackoverflow.com/questions/70837786


            SageMaker Hyperparameter Tuning for LDA, clarifying feature_dim
            Asked 2022-Jan-21 at 13:58

            I'm trying to run a HyperparameterTuner on an Estimator for an LDA model in a SageMaker notebook using mxnet but am running into errors related to the feature_dim hyperparameter in my code. I believe this is related to the differing dimensions of the train and test datasets but I'm not 100% certain if this is the case or how to fix it.

            Estimator Code

            [note that I'm setting the feature_dim to the training dataset's dimensions]



            Answered 2022-Jan-21 at 13:58

            I have resolved this issue. My problem was that I was splitting the data into test and train BEFORE converting the data into doc-term matrices, which resulted in test and train datasets of different dimensionality, which threw off SageMaker's algorithm. Once I convereted all of the input data into a doc-term matrix, and THEN split it into test and train, the hyperparameter optimization operation completed.

            Source https://stackoverflow.com/questions/70779880


            Sklearn - toggling tf-idf to register two-word phrases
            Asked 2022-Jan-20 at 06:52

            I'm experimenting with the text analysis tools in sklearn, namely the LDA topic extraction algorithm seen here.

            I've tried feeding it other data sets and in some cases I think I would get better topic extraction results if the vector representation of the tf-idf 'features' could allow for phrases.

            As an easy example:

            I often get top word associations like:

            • income
            • net
            • asset
            • fixed
            • wealth
            • fiscal

            Which is understandable, but I think that I won't get the granularity I need for a useful topic extraction unless the TfidfVectorizer() or some other parameter can be tweaked such that I get phrases. Ideally, I want:

            • fixed income
            • asset management
            • wealth management
            • net income
            • fiscal income

            To make things simple, I'm imagining I supply the algorithm with a white list of tolerable 2-word phrases. It would count only those phrases as unique while applying normal tf-idf weighting to all other word entries throughout the corpus.


            The documentation for TfidfVectorizer() doesn't seem to support this, but I'd imagine this is a fairly common need in practice -- so how do practitioners go about it?



            Answered 2022-Jan-20 at 06:52

            The default configuration TfidfVectorizer is using an ngram_range=(1,1), this means that it will only use unigram (single word).

            You can change this parameter to ngram_range(1,2) in order to retrieve bigram as well as unigram and if your bigrams are sufficiently represented they will be extracted as well.

            See example below:

            Source https://stackoverflow.com/questions/70780593


            Caret train function for muliple data frames as function
            Asked 2022-Jan-14 at 11:43

            there has been a similar question to mine 6 years+ ago and it hasn't been solve (R -- Can I apply the train function in caret to a list of data frames?) This is why I am bringing up this topic again.

            I'm writing my own functions for my big R project at the moment and I'm wondering if there is an opportunity to sum up the model training function train() of the pakage caret for different dataframes with different predictors. My function should look like this:



            Answered 2022-Jan-14 at 11:43

            By writing predictor_iris <- "Species", you are basically saving a string object in predictor_iris. Thus, when you run lda_ex, I guess you incur in some error concerning the formula object in train(), since you are trying to predict a string using vectors of covariates.

            Indeed, I tried the following toy example:

            Source https://stackoverflow.com/questions/70709618

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install lda

            You can download it from GitHub.
            You can use lda like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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