Metadata-Version: 2.1
Name: mass-ts
Version: 0.1.1
Summary: MASS (Mueen's Algorithm for Similarity Search)
Home-page: https://github.com/tylerwmarrs/mass_ts
Author: Tyler Marrs
Author-email: tylerwmarrs@gmail.com
License: Apache Software License 2.0
Keywords: mass_ts
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Requires-Dist: numpy

MASS (Mueen's Algorithm for Similarity Search)
----------------------------------------------

[<img src="https://img.shields.io/pypi/v/mass_ts.svg">](https://pypi.python.org/pypi/mass_ts)
[<img src="https://img.shields.io/travis/tylerwmarrs/mass-ts.svg">](https://travis-ci.org/tylerwmarrs/mass-ts)
[<img src="https://readthedocs.org/projects/mass-ts/badge/?version=latest">](https://mass-ts.readthedocs.io/en/latest/?badge=latest)

MASS is the fundamental algorithm that the matrix profile algorithm is built on top of. It allows you to search a time series for a smaller series. The result is an array of distances. To find the "closest" section of a time series to yours, simply find the minimum distance(s).

mass-ts is a python 2 and 3 compatible library.

* Free software: Apache Software License 2.0


Features
--------

* MASS - the first implementation of MASS
* MASS2 - the second implementation of MASS that is significantly faster. Typically this is the one you will use.
* MASS3 - a piecewise version of MASS2 that can be tuned to your hardware. Generally this is used to search very large time series.

Installation
------------
```
pip install mass-ts
```

Example Usage
-------------
A dedicated repository for practical examples can be found at the [mass-ts-examples repository](https://github.com/tylerwmarrs/mass-ts-examples).

```python

import numpy as np
import mass_ts as mts

ts = np.loadtxt('ts.txt')
query = np.loadtxt('query.txt')

# mass
distances = mts.mass(ts, query)

# mass2
distances = mts.mass2(ts, query)

# mass3
distances = mts.mass3(ts, query, 256)

# find minimum distance
min_idx = np.argmin(distances)
```

Citations
---------
Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance, URL: http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html


=======
History
=======

0.1.0 (2019-05-16)
------------------

* First release on PyPI.


