proxyC | R package for large-scale similarity/distance computation | Machine Learning library

by koheiw R Version: 0.2.0 License: GPL-3.0

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | proxyC Summary

proxyC is a R library typically used in Artificial Intelligence, Machine Learning, Numpy applications. proxyC has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

proxyC computes proximity between rows or columns of large matrices efficiently in C++. It is optimized for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast. This code was originally written for quanteda to compute similarity/distance between documents or features in large corpora, but separated as a stand-alone package to make it available for broader data scientific purposes.

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Support

proxyC has a low active ecosystem.

It has 13 star(s) with 2 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 7 have been closed. On average issues are closed in 123 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of proxyC is 0.2.0

Quality

proxyC has no bugs reported.

Security

proxyC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

proxyC is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

proxyC releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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proxyC Key Features

No Key Features are available at this moment for proxyC.

proxyC Examples and Code Snippets

proxyC: R package for large-scale similarity/distance computation

Lines of Code : 22

License : Strong Copyleft (GPL-3.0)

Copy

install.packages("proxyC")

require(Matrix)
## Loading required package: Matrix
require(microbenchmark)
## Loading required package: microbenchmark
require(RcppParallel)
## Loading required package: RcppParallel
require(ggplot2)
## Loading required p

proxyC: R package for large-scale similarity/distance computation,Cosine similarity greater than 0.9

Lines of Code : 15

License : Strong Copyleft (GPL-3.0)

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bm2 <- microbenchmark(
    "proxyC all" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
    "proxyC min_simil" = proxyC::simil(sm1k, margin = 2, method = "cosine", min_simil = 0.9),
    times = 10
)
autoplot(bm2)
## Coordinate system already

proxyC: R package for large-scale similarity/distance computation,Cosine similarity between columns

Lines of Code : 9

License : Strong Copyleft (GPL-3.0)

Copy

bm1 <- microbenchmark(
    "proxy 1k" = proxy::simil(dm1k, method = "cosine"),
    "proxyC 1k" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
    "proxy 10k" = proxy::simil(dm10k, method = "cosine"),
    "proxyC 10k" = proxyC::simil(sm10k,

Community Discussions

Trending Discussions on proxyC

How to go from a tsv with feature list strings to a csr matrix in python?

UInt8 Array Swift to const char* C

QUESTION

How to go from a tsv with feature list strings to a csr matrix in python?

Asked 2021-Apr-19 at 15:21

I have been working with some R packages that calculate (cosine) (sparse) similarity matrices from sparse binary matrices, e.g. proxyC.

As I am now starting (and learning) to use python as well, and I was told it might even be faster, I would like to try and run the same calculations there.

I found this interesting post:

What's the fastest way in Python to calculate cosine similarity given sparse matrix data?

which describes a few methods.

I did try some of them out after writing out a small test matrix myself by hand.
Now I would like to try on 'real' data.
And that's where I encounter a problem I currently cannot solve.

My data come in tsv files that associate objects (ID's) to comma-separated lists of features (FP's). E.g.:

...

ANSWER

Answered 2021-Apr-19 at 15:21

import pandas as pd
df = pd.DataFrame({'ID':[1,2,3], 'FP':["A,B,C","A,D","C,D,F"]})

>>> df
   ID     FP
0   1  A,B,C
1   2    A,D
2   3  C,D,F

Source https://stackoverflow.com/questions/67158157

QUESTION

UInt8 Array Swift to const char* C

Asked 2017-Nov-16 at 09:06

I have the following function

...

ANSWER

Answered 2017-Nov-12 at 19:49

A C function

Source https://stackoverflow.com/questions/47253095

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

Vulnerabilities

No vulnerabilities reported

Install proxyC

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 .

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