kandi background
Explore Kits

lap | Linear Assignment Problem solver | Machine Learning library

 by   gatagat Python Version: 0.4.0 License: BSD-2-Clause

 by   gatagat Python Version: 0.4.0 License: BSD-2-Clause

kandi X-RAY | lap Summary

lap is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. lap has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However lap has 1 bugs. You can install using 'pip install lap' or download it from GitHub, PyPI.
lap is a [linear assignment problem] solver using Jonker-Volgenant algorithm for dense (LAPJV [1]) or sparse (LAPMOD [2]) matrices. Both algorithms are implemented from scratch based solely on the papers [1,2] and the public domain Pascal implementation provided by A. Volgenant [3]. In my tests the LAPMOD implementation seems to be faster than the LAPJV implementation for matrices with a side of more than ~5000 and with less than 50% finite coefficients. [1] R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems", Computing 38, 325-340 (1987) [2] A. Volgenant, "Linear and Semi-Assignment Problems: A Core Oriented Approach", Computer Ops Res. 23, 917-932 (1996) [3]
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • lap has a low active ecosystem.
  • It has 96 star(s) with 26 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 10 open issues and 11 have been closed. On average issues are closed in 60 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of lap is 0.4.0
lap Support
Best in #Machine Learning
Average in #Machine Learning
lap Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • lap has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 54 code smells.
lap Quality
Best in #Machine Learning
Average in #Machine Learning
lap Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • lap has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • lap code analysis shows 0 unresolved vulnerabilities.
  • There are 5 security hotspots that need review.
lap Security
Best in #Machine Learning
Average in #Machine Learning
lap Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • lap is licensed under the BSD-2-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
lap License
Best in #Machine Learning
Average in #Machine Learning
lap License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • lap releases are not available. You will need to build from source code and install.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 1430 lines of code, 122 functions and 15 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
lap Reuse
Best in #Machine Learning
Average in #Machine Learning
lap Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Evaluate the C function
    • Calculate the cost function
    • Calculates the cost of the cost function
    • Calculate hard data
    • Binary search for a given key
  • Setup the package
    • Wrapper function for Cythonize
    • Generate cython
    • Return the current status of numpy
  • Return a Configuration object
    • Evaluate a benchmark
      • Test the CSCY algorithm
        • Test the C_C_c_2_2
          • Test for the MODPY algorithm
            • Test for the C_C_c_c_c_c_1
              • Test the C_C
                • Evaluate the marginal assignment function

                  Get all kandi verified functions for this library.

                  Get all kandi verified functions for this library.

                  lap Key Features

                  Linear Assignment Problem solver (LAPJV/LAPMOD).

                  lap Examples and Code Snippets

                  Community Discussions

                  Trending Discussions on lap
                  • Construct non-overlapping datetime record (start, end datetime) dataframe
                  • ReactJS taking data from API
                  • Can I implement this kind of profiling code with a macro?
                  • How to save time value in dynamically created li to input box with javascript
                  • Flutter iOS Error Class AMSupportURLConnectionDelegate is implemented in both /usr/lib/libamsupport.dylib (0x203913130)
                  • How to accelerate my written python code: function containing nested functions for classification of points by polygons
                  • R+Tidyverse: Tibbles don't appear to store milliseconds
                  • Memoize multi-dimensional recursive solutions in haskell
                  • Edit part of a Message Embed (Discord.JS)
                  • Wrong type casting in FOR-IN loop wrecks seemingly unrelated variable
                  Trending Discussions on lap

                  QUESTION

                  Construct non-overlapping datetime record (start, end datetime) dataframe

                  Asked 2022-Mar-22 at 13:56

                  I need to create a dataframe removing over-lapping start and end datetimes for multiple ids. I will use the start and end datetimes to aggregate values in a high frequency pandas dataframe, so I need to remove those overlapping datetimes in mst_df.

                  import pandas as pd
                   
                  #Proxy reference dataframe
                  master = [['site a', '2021-07-08 00:00:00', '2021-07-08 10:56:00'], 
                          ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], #slightly overlapping
                          ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
                          ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], #overlapping
                          ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], #overlapping
                          ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
                          ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], #overlapping
                          ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
                          ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], #overlapping
                          ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']] #slightly overlapping
                  
                   
                  mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])
                  mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
                  mst_df['end'] = pd.to_datetime(mst_df['end'], infer_datetime_format=True)
                  

                  Desired DataFrame:

                      id      start               end
                      site a  2021-07-08 00:00:00 2021-07-08 12:00:00
                      site a  2021-07-08 17:36:00 2021-07-09 11:40:00
                      site b  2021-07-08 00:00:00 2021-07-08 10:24:00
                      site b  2021-07-08 17:32:00 2021-07-09 13:00:00
                  

                  ANSWER

                  Answered 2022-Mar-22 at 13:56

                  I don't know if pandas has special function for this. It has Interval.overlaping() to check if two ranges overlaps (and it works even with datetime) but I don't see function to merge these two ranges so it still needs own code for merging. Fortunately it is easy.


                  Rows are sorted by start so rows are not overlaping when previous_end < next_start and I use it in for-loop.

                  But first I group by site to work with every site separatelly.

                  Next I get first row (as previous) and run loop with other rows (as next) and check previous_end < next_start.

                  If it is True then I can put previous on list of results and get next as previous to work with rest of rows.

                  If it is False then I create new range from both rows and use it to work with rest of rows.

                  Finally I add previous to list.

                  After processing all groups I convert all to DataFrame.

                  import pandas as pd
                   
                  #Proxy reference dataframe
                  master = [
                      ['site a', '2021-07-08 00:00:00',   '2021-07-08 10:56:00'], 
                      ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], # slightly overlapping
                      ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
                      ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], # overlapping
                      ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], # overlapping
                      ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
                      ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], # overlapping
                      ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
                      ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], # overlapping
                      ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']  # slightly overlapping
                  ]
                  
                  mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])
                  
                  mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
                  mst_df['end']   = pd.to_datetime(mst_df['end'], infer_datetime_format=True)
                  
                  result = []
                  
                  for val, group in mst_df.groupby('id'):
                      
                      # get first
                      prev = group.iloc[0]
                      
                      for idx, item in group[1:].iterrows():
                          if prev['end'] < item['start']:
                              # not overlapping - put previous to results and use next as previous
                              result.append(prev)
                              prev = item
                          else:
                              # overlappig - create on range start, end
                              prev['start'] = min(prev['start'], item['start'])
                              prev['end']   = max(prev['end'], item['end'])
                      
                      # add when there is no next item
                      result.append(prev)
                  
                  print(pd.DataFrame(result))
                  

                  Result:

                         id               start                 end
                  0  site a 2021-07-08 00:00:00 2021-07-08 12:00:00
                  2  site a 2021-07-08 17:36:00 2021-07-09 11:40:00
                  5  site b 2021-07-08 00:00:00 2021-07-08 10:24:00
                  7  site b 2021-07-08 17:32:00 2021-07-09 13:00:00
                  

                  Source https://stackoverflow.com/questions/71564490

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

                  Vulnerabilities

                  No vulnerabilities reported

                  Install lap

                  In addition to above, running the tests requires:.
                  Python (2.7, 3.7, 3.8, 3.9)
                  NumPy (>=1.10.1)
                  SciPy, pytest, pytest-timeout
                  Install a C compiler (e.g., g)
                  Python headers (e.g., python-dev package on Debian/Ubuntu)
                  Install Cython (>=0.21)
                  Clone git clone https://github.com/gatagat/lap.git
                  Under the root of the repo python setup.py build python setup.py install

                  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:

                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                  over 650 million Knowledge Items
                  Find more libraries
                  Reuse Solution Kits and Libraries Curated by Popular Use Cases
                  Explore Kits

                  Save this library and start creating your kit

                  Install
                  • pip install lap

                  Clone
                  • https://github.com/gatagat/lap.git

                  • gh repo clone gatagat/lap

                  • git@github.com:gatagat/lap.git

                  Share this Page

                  share link
                  Consider Popular Machine Learning Libraries
                  Try Top Libraries by gatagat
                  Compare Machine Learning Libraries with Highest Support
                  Compare Machine Learning Libraries with Highest Quality
                  Compare Machine Learning Libraries with Highest Security
                  Compare Machine Learning Libraries with Permissive License
                  Compare Machine Learning Libraries with Highest Reuse
                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                  over 650 million Knowledge Items
                  Find more libraries
                  Reuse Solution Kits and Libraries Curated by Popular Use Cases
                  Explore Kits

                  Save this library and start creating your kit