lapx 0.5.2.post1

Customized Tomas Kazmar's lap, Linear Assignment Problem solver (LAPJV/LAPMOD).

Linear Assignment Problem Solver

• lapx is a customized edition of Tomas Kazmar's gatagat/lap.
• License: BSD-2-Clause, see LICENSE.
• Source credit: Tomas Kazmar @gatagat.
• pypi.org source code: lap05-0.5.1.tar.gz

Windows ✅ | Linux ✅ | macOS ✅

• Build passed on all Windows/Linux/macOS with Python 3.7/3.8/3.9/3.10/3.11 ✅

• Install from PyPI:

  pip install lapx
  

• Or download tar.gz or Wheels from releases

• Or directly install from GitHub:

  python -m pip install --upgrade pip
  pip install "setuptools>=67.2.0"
  pip install wheel build
  pip install git+https://github.com/rathaROG/lapx.git
  

• Or clone and build directly on your machine:

  git clone https://github.com/rathaROG/lapx.git
  cd lapx
  python -m pip install --upgrade pip
  pip install "setuptools>=67.2.0"
  pip install wheel build
  python -m build --wheel
  cd dist
  

Usage 🧪

Note: Use import lap to import since lapx is just the name for package distribution.

import lap
import numpy as np
print(lap.lapjv(np.random.rand(2, 1), extend_cost=True))

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lap: Linear Assignment Problem solver

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] http://www.assignmentproblems.com/LAPJV.htm

Usage

cost, x, y = lap.lapjv(C)

The function lapjv(C) returns the assignment cost (cost) and two arrays, x, y. If cost matrix C has shape N x M, then x is a size-N array specifying to which column is row is assigned, and y is a size-M array specifying to which row each column is assigned. For example, an output of x = [1, 0] indicates that row 0 is assigned to column 1 and row 1 is assigned to column 0. Similarly, an output of x = [2, 1, 0] indicates that row 0 is assigned to column 2, row 1 is assigned to column 1, and row 2 is assigned to column 0.

Note that this function does not return the assignment matrix (as done by scipy's linear_sum_assignment and lapsolver's solve dense). The assignment matrix can be constructed from x as follows:

A = np.zeros((N, M))
for i in range(N):
    A[i, x[i]] = 1

Equivalently, we could construct the assignment matrix from y:

A = np.zeros((N, M))
for j in range(M):
    A[y[j], j] = 1

Finally, note that the outputs are redundant: we can construct x from y, and vise versa:

x = [np.where(y == i)[0][0] for i in range(N)]
y = [np.where(x == j)[0][0] for j in range(M)]