R-lda | Latent Dirichlet allocation package for R | Topic Modeling library

by slycoder C Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | R-lda Summary

R-lda is a C library typically used in Artificial Intelligence, Topic Modeling applications. R-lda has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Latent Dirichlet allocation package for R

Support

Quality

Security

License

Reuse

Support

R-lda has a low active ecosystem.

It has 15 star(s) with 22 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 9 have been closed. On average issues are closed in 154 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of R-lda is current.

Quality

R-lda has no bugs reported.

Security

R-lda has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

R-lda does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

R-lda releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of R-lda

Get all kandi verified functions for this library.

R-lda Key Features

No Key Features are available at this moment for R-lda.

R-lda Examples and Code Snippets

No Code Snippets are available at this moment for R-lda.

Community Discussions

Trending Discussions on R-lda

Which hyperparameter optimization technique is used in Mallet for LDA?

JMeter error when opening my Test Plan - Unexpected error - see log for details

read only odd-numbered/numeric columns with read_csv

How to make a pyspark job properly parallelizable on multiple nodes and avoid memory issues?

QUESTION

Which hyperparameter optimization technique is used in Mallet for LDA?

Asked 2021-May-21 at 13:47

I am wondering which technique is used to learn the Dirichlet priors in Mallet's LDA implementation.

Chapter 2 of Hanna Wallach's Ph.D. thesis gives a great overview and a valuable evaluation of existing and new techniques to learn the Dirichlet priors from the data.

Tom Minka initially provided his famous fixed-point iteration approach, however without any evaluation or recommendations.

Furthermore, Jonathan Chuang did some comparisons between previously proposed methods, including the Newton−Raphson method.

LiangJie Hong says the following in his blog:

A typical approach is to utilize Monte-Carlo EM approach where E-step is approximated by Gibbs sampling while M-step is to perform a gradient-based optimization approach to optimize Dirichlet parameters. Such approach is implemented in Mallet package.

Mallet mentions the Minka's fixed-point iterations with and without histograms.

However, the method that is actually used simply states:

Learn Dirichlet parameters using frequency histograms

Could someone provide any reference that describes the used technique?

...

ANSWER

Answered 2021-May-21 at 13:47

It uses the fixed point iteration. The frequency histograms method is just an efficient way to calculate it. They provide an algebraically equivalent way to do the exact same computation. The update function consists of a sum over a large number of Digamma functions. This function by itself is difficult to compute, but the difference between two Digamma functions (where the arguments differ by an integer) is relatively easy to compute, and even better, it "telescopes" so that the answer to Digamma(a + n) - Digamma(a) is one operation away from the answer to Digamma(a + n + 1) - Digamma(a). If you work through the histogram of counts from 1 to the max, adding up the number of times you saw a count of n at each step, the calculation becomes extremely fast. Initially, we were worried that hyperparameter optimization would take so long that no one would do it. With this trick it's so fast it's not really significant compared to the Gibbs sampling.

Source https://stackoverflow.com/questions/67622671

QUESTION

JMeter error when opening my Test Plan - Unexpected error - see log for details

Asked 2019-Apr-03 at 13:01

I cannot longer open my test plan that I worked on yesterday. I get the following error message: "Unexpected error - see log for details".

I've tried to apply the solution proposed here: jmeter error on opening script but I had no luck finding the line that caused the problem.

Do I have to completely redo this test?

Here is the log file:

jmeter.log

...

ANSWER

Answered 2018-Mar-12 at 08:55

HTTPSampler2 was removed as a part of Bug 60727 so you won't be able to use it with JMeter 3.3.

If you really need this plugin you will have to downgrade to JMeter 3.1, it can be downloaded from JMeter Archives page.

Source https://stackoverflow.com/questions/49231138

QUESTION

read only odd-numbered/numeric columns with read_csv

Asked 2018-Dec-10 at 00:26

Following up on Keep csv feature labels for LDA pca I decided to ignore feature names for my PCA reduction. I am using pandas read_csv() function and would like to ignore string/text columns, which happen to be every odd-numbered column. So either a filter to remove string columns or odd-number columns when reading in my csv would be helpful

...

ANSWER

Answered 2018-Dec-10 at 00:26

One way is to read column labels and then take every second column via the usecols parameter of pd.read_csv. This assumes your column labels are unique, but will be efficient as you are not reading expensive object dtype series.

Source https://stackoverflow.com/questions/53696969

QUESTION

How to make a pyspark job properly parallelizable on multiple nodes and avoid memory issues?

Asked 2017-Aug-29 at 09:51

I am currently working on a PySpark job (Spark 2.2.0) which intends to train a Latent Dirichlet Allocation model based on a set of documents. Input documents are provided as a CSV file located on Google Cloud Storage.

The following code successfully ran on a single node Google Cloud Dataproc cluster (4vCPUs / 15GB of memory) with a small subset of documents (~6500), a low number of topics to generate (10) and a low number of iterations (100). However, other attempts with a larger set of documents or higher values for either the number of topics or number of iterations quickly led to memory issues and job failures.

Also, when submitting this job to a 4 nodes cluster, I could see that only one worker node was actually working (30% CPU usage), letting me think that the code is not properly optimized for parallel processing.

Code ...

ANSWER

Answered 2017-Aug-26 at 01:29

If your input data size is small even if your pipeline ends up doing dense computation on the small data, then size-based partitioning will lead to too few partitions for scalability. Since your getNumPartitions() prints 1, this indicates that Spark will use at most 1 executor core to process that data, which is why you're only seeing one worker node working.

You can try changing your initial spark.read.csv line to include a repartition at the end:

Source https://stackoverflow.com/questions/45882826

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

Vulnerabilities

No vulnerabilities reported

Install R-lda

You can download it from GitHub.

Support

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 .

Find more information at: