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This package implements \nan active metric MDS method described in \"Principal Component and Correspondence \nAnalysis in R\" by Dr. Herve Abdi (2017) and is thus similar to package bios2mds \nwritten in the R language. The method allows new objects to be projected onto \nan existing eigenbasis defined by a set of active objects and is thus useful \nwhenever one needs to have a stable and scalable MDS transformation.\n\n### Requirements\n\n- Python \u003e= 3.5\n- NumPy \u003e= 1.14.0\n- Pandas \u003e= 0.22.0\n\n### Installation\n\n```\npip install pymmds\n```\n\n### Usage\n\nThere is only one core object (`mmds.Space`) and two methods to consider:\n\n- `Space.__init__(dm: pandas.DataFrame)` - takes a symmetric distance matrix \nof active (landmark) samples and creates an MDS space\n- `Space.project(dm: pandas.DataFrame)` - takes a table of distances between \nany number of supplementary samples and all active samples and projects the \nformer onto the initialised MDS space.\n\nThere is also a utility function `mmds.read_dm` that will help you read the\nDMs in case you don't want to tweak `pandas.read_csv` yourself.  \n\nAll functions and methods are well-documented. You can either use Python's `help`,\niPython's `?` or whatever docstring tools your IDE provides to view the docs from \nwithin your development environment or read the \n[Wiki page](https://github.com/grayfall/pymmds/wiki).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrayfall%2Fpymmds","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrayfall%2Fpymmds","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrayfall%2Fpymmds/lists"}