LDA | Perform Latent Dirichlet Allocation on scientific articles | Topic Modeling library

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,

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:

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:

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)

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:

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

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.

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

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?

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: