Lichen | Lichen Plagiarism Detection | Computer Vision library

by Submitty Python Version: v23.03.00 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | Lichen Summary

Lichen is a Python library typically used in Artificial Intelligence, Computer Vision applications. Lichen has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Lichen Plagiarism Detection

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Quality

Security

License

Reuse

Support

Lichen has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 27 watchers for this library.

It had no major release in the last 12 months.

Lichen has no issues reported. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Lichen is v23.03.00

Quality

Lichen has 0 bugs and 0 code smells.

Security

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

Lichen code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Lichen is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Lichen releases are available to install and integrate.

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

It has 381 lines of code, 10 functions and 10 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Process gradeable files
Get concatenated files in input directory
Check the total size of concatenated files
Validate the grammar
Calculates the stats for a submission
Get the concatenated files in a directory
Tokenize a config file
Generate the tokenizer
Parse command line arguments
Check the total number of concatenated files

Get all kandi verified functions for this library.

Lichen Key Features

No Key Features are available at this moment for Lichen.

Lichen Examples and Code Snippets

No Code Snippets are available at this moment for Lichen.

Community Discussions

Trending Discussions on Lichen

Is there a way to add species to an ISOMAP plot in R?

Use a matrix to fill NA values in another matrix

Creating an ID to aggregate similar records using information obtained from multiple columns

Plotly: Pie chart only shows the legend

How do I apply an ifelse function to all cells in a data frame?

QUESTION

Is there a way to add species to an ISOMAP plot in R?

Asked 2022-Mar-06 at 20:22

I am using the isomap-function from vegan package in R to analyse community data of epiphytic mosses and lichens. I started analysing the data using NMDS but due to the structure of the data ran into problems which is why I switched to ISOMAP which works perfectly well and returns very nice results. So far so good... However, the output of the function does not support plotting of species within the ISOMAP plot as species scores are not available. Anyway, I would really like to add species information to enhance the interpretability of the output.

Does anyone of you has a solution or hint to this problem? Is there a way to add species kind of post hoc to the plot as it can be done with environmental data?

I would greatly appreciate any help on this topic!

Thank you and best regards, Inga

...

ANSWER

Answered 2022-Mar-06 at 20:22

No, there is no function to add species scores to isomap. It would look like this:

Source https://stackoverflow.com/questions/71348723

QUESTION

Use a matrix to fill NA values in another matrix

Asked 2021-Jul-02 at 10:29

I'm trying to replace NA values from one matrix with values from a row with the same name in a separate matrix.
This is part of a loop to process 32 matrices with differing row numbers so I need code that references the locations of the NA's, preferably by row name. The maximum number of rows is 7, all data has two columns.

...

ANSWER

Answered 2021-Jul-02 at 07:07

ss. <- as.matrix(read.table(text = "
                SD_d13c  SD_d15n
    Arthropod 2.0550750 1.417745
    C4Plants  3.8064638 2.606882
    Lichen           NA       NA
    MiddleC3  0.8845903 1.244990
    UpperC3   1.2798437 1.795272"))

s.sds <- as.matrix(read.table(text = "
             SD_d13c SD_d15n
Arthropod       2.39    2.10
C4Grass         2.71    1.56
C4Plants        2.04    2.57
Carex+NFixer    0.71    1.63
Lichen          0.93    2.29
MiddleC3        1.07    1.79
UpperC3         2.07    2.40"))



ss.2 <- ss. # make a new copy of your target matrix
NAs <- which(is.na(rowSums(ss.))) # identify rows with missing values
ss.2[names(NAs),] <- s.sds[names(NAs),] # pass values from s.sds 
ss.2 # result
#             SD_d13c  SD_d15n
# Arthropod 2.0550750 1.417745
# C4Plants  3.8064638 2.606882
# Lichen    0.9300000 2.290000
# MiddleC3  0.8845903 1.244990
# UpperC3   1.2798437 1.795272

Source https://stackoverflow.com/questions/68220720

QUESTION

Creating an ID to aggregate similar records using information obtained from multiple columns

Asked 2021-Feb-19 at 20:11

I'm doing an analysis of scientific publications from around 30,000 journals. My list has over 1.3 million records but with several duplicates (ex: a paper with more than one author from different institutions appear more than once).

Well, I would like to perform a record comparison that would result in a new ID column with the same value for the same papers. This would be very easy with dplyr if the records were equal, but there are several matching problems, as different authors may include the information in distinct ways, or they can make mistakes.

Here is an example of the type of records and problems I have, and the type of ID I would like to generate:

...

ANSWER

Answered 2021-Feb-19 at 20:11

This seems to be something that studied by Record Linkage literature. A R package to use might be fastlink here.

In general, Record Linkage tries to solve the problem that there are two datasets: A and B, where you know that some of the A records must be matched with entries from B, but there isn't a perfect identifier to tell you so. Hence we need to leverage different columns of information to help us determine what is a match or not.

I notice that some comments suggesting soundex or ISSN, which are very useful observations, but Record Linkage methodology leverages all those information (and even more) to make the match. I believe this is something you are after.

PS: doi should be unique identifier for journal articles and ISSN should be identifier for journals. But some journals have both print and online ISSN, so there may be cases where two articles come from the same journal but have different ISSNs.

PS2: What would make the comparison more effective is what they called "blocking". If you take articles from same journal and same publication year, and consider them as smaller groups, then you compare articles within each group. Here you are saving lots of time and resources since you are not matching between articles that are obvious not-matches.

Source https://stackoverflow.com/questions/66276136

QUESTION

Plotly: Pie chart only shows the legend

Asked 2020-Oct-21 at 20:25

I'm trying to create a pie chart in plotly. I have dataframe that is similar to this:

...

ANSWER

Answered 2020-Oct-20 at 21:14

I'm fairly certain that this is purely a data problem. I edited your sample data into a comma separated version:

Source https://stackoverflow.com/questions/64415121

QUESTION

How do I apply an ifelse function to all cells in a data frame?

Asked 2020-Mar-23 at 21:51

I am trying to apply an ifelse statement to all the cells in my data frame. I'm pretty sure I am overthinking this but would appreciate some help/guidance!

I have a dataframe of (slightly modified) percent cover of vegetation from a number of sites where the site names and the vegetation types are the row names and column names, respectively (ie. the data frame should only consist of numeric values):

...

ANSWER

Answered 2020-Mar-23 at 21:51

You mentioned ifelse, I think it's straight-forward enough to apply this to each column using lapply. (I'll add the isnum check in case there are non-numeric columns in the data, feel free to ignore it if your data is always numeric.)

Source https://stackoverflow.com/questions/60821966

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

Vulnerabilities

No vulnerabilities reported

Install Lichen

You can download it from GitHub.
You can use Lichen 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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