py-sam | An implementation of the SAM topic model | Topic Modeling library

by austinwaters Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | py-sam Summary

py-sam is a Python library typically used in Artificial Intelligence, Topic Modeling, Bert applications. py-sam has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

An implementation of the SAM topic model, described by:.

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py-sam has a low active ecosystem.

It has 8 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

py-sam has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of py-sam is current.

Quality

py-sam has no bugs reported.

Security

py-sam has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

py-sam 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

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

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed py-sam and discovered the below as its top functions. This is intended to give you an instant insight into py-sam implemented functionality, and help decide if they suit your requirements.

Runs the model
Calculate the virtual machine model
L2 norm of x
Run one iteration
Reads the contents of the file
Return the value at i
Return the data from the buffer
Returns a random subset of documents with probability p
Temporarily seed numpy random ndarray
Check the arguments
Computes the logarithm of the covariance matrix
Loads evidence file
Gradient of l_val_l_val
Takes two HDF5 files and compares them
Load an object from a file
Run a single VEM task
Computes the cosine similarity between two vectors
Parse command line arguments
Writes a basic test corpus
Write the image files
Check the gradient of a model parameter
Check the arguments passed to the command line
Build a dataset
Optimizes a model parameter using a lbfgs function
Optimise a function to minimize a fitness function
Write image files

Get all kandi verified functions for this library.

py-sam Key Features

No Key Features are available at this moment for py-sam.

py-sam Examples and Code Snippets

No Code Snippets are available at this moment for py-sam.

Community Discussions

Trending Discussions on py-sam

Why do I get an AttributeError when using pandas apply?

Python Filter multiple row

How do I clean Dataset with textual data and use it for classification

QUESTION

Why do I get an AttributeError when using pandas apply?

Asked 2019-Jan-29 at 23:05

How should I convert NaN value into categorical value based on condition. I am getting error while trying to convert Nan value.

...

ANSWER

Answered 2018-Jan-01 at 18:38

Some things to note here -

If you're using only two columns, calling apply over 4 columns is wasteful
Calling apply is wasteful in general, because it is slow and offers no vectorisation benefits to you
In apply, you're dealing with scalars, so you do not use the .str accessor as you would a pd.Series object. title.contains would be enough. Or more pythonically, "lip" in title.
gender.isnull is completely wrong, gender is a scalar, it has no isnull attribute

Option 1
np.where

Source https://stackoverflow.com/questions/48052125

QUESTION

Python Filter multiple row

Asked 2018-Apr-09 at 18:45

I am using this query script to get data from api rest.

Script

After doing this, I got the following data:

Dataframe

I am new in python, and I have some difficult to understand how do I select columns:

I tried this following code, but it appears:

...

ANSWER

Answered 2018-Apr-09 at 18:45

You have 2 options to filter a MultiIndex dataframe:

1. Elevate index to columns and filter by columns

Source https://stackoverflow.com/questions/49738489

QUESTION

How do I clean Dataset with textual data and use it for classification

Asked 2017-Dec-29 at 05:44

I am working on gender classifier on a dataset with many missing values and more of categorical values.How should I convert categorical value to numeric value and which algorithm should I use to get better accuracy? https://github.com/lakshmipriya04/py-sample/

...

ANSWER

Answered 2017-Dec-29 at 05:23

There are 2 types of categorical variables encoding: create dummy variables and encode via label encoding.

Missing values for dummy variables will be shown as null-vector for each bunch of dummy columns. For label encoding it may be specific class (label).

To solve missing values problem you can impute them via mean (numerical values) or mode (for categorical). Before it could be useful to create additionally missing-values-indication-column that has 1 if the value was missing and 0 otherwise.

With imputation any classifier from ML may be used. Try SVC (because you have binary classification) and start with the simple logistic regression.

Without imputation only XGBoost can help (it allows to have missing values in the dataset).

But you have a slightly other problem. You need to preprocess texts. Please read about NLP.

Source https://stackoverflow.com/questions/48010445

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

Vulnerabilities

No vulnerabilities reported

Install py-sam

You can download it from GitHub.
You can use py-sam 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.

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 .

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