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

## kandi X-RAY | lda Summary

## kandi X-RAY | lda Summary

Topic modeling with latent Dirichlet allocation using Gibbs sampling

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### Quality

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### Top functions reviewed by kandi - BETA

- 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 .

## lda Key Features

## lda Examples and Code Snippets

```
# 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

Trending Discussions on lda

QUESTION

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?

...ANSWER

Answered 2022-Apr-09 at 22:05Your 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`

)

- Forget about what is in
- 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:

QUESTION

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,

...ANSWER

Answered 2022-Mar-04 at 16:07Please 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

QUESTION

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:

...ANSWER

Answered 2022-Feb-24 at 02:07If 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.

QUESTION

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:

...ANSWER

Answered 2022-Feb-20 at 13:01There's a dirty way to do this, I'll write about it till someone answers with a better idea. Here we go:

QUESTION

Related questions Temporary array creation and routine GEMM Warning message (402) : An array temporary created for argument

For the following Fortran code (modified from dsyev in fortran 90)

...ANSWER

Answered 2022-Feb-15 at 23:53Let's consider a much simpler program to look at what's going on:

QUESTION

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:

...ANSWER

Answered 2022-Jan-24 at 07:45You should create the figure first:

QUESTION

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

...ANSWER

Answered 2022-Jan-24 at 17:32The 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.

QUESTION

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]

...ANSWER

Answered 2022-Jan-21 at 13:58I 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.

QUESTION

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.

QuestionThe 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?

ANSWER

Answered 2022-Jan-20 at 06:52The 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:

QUESTION

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:

ANSWER

Answered 2022-Jan-14 at 11:43By 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:

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

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## Install lda

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.

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