README.md

CyDTW

High performance DTW library written in Cython for Python 3.x.

Installation

From the projects root directory, run the rollowing command:

python setup.py build_ext --inplace

This will compile all the relevant files, and produce a shared object file that can be used with Python.

Usage

Once the package is compiled, and everything is installed, using the package is as simple as any other Python package! Each DTW algorithm has a compute method that can be called.

>>> import cydtw.dtw as dtw
>>> import numpy as np

>>> x = np.array([1, 1, 2, 3, 2, 0])
>>> y = np.array([0, 1, 1, 2, 3, 2, 1])

>>> cost, warping_path, accumulated_cost_matrix, distances_matrix = dtw.compute(x, y)
>>> print((cost, warping_path, accumulated_cost_matrix, distances_matrix))
(2.0,
 [[0, 0], [0, 1], [0, 2], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6]],
 array([[1., 1., 1., 2., 4., 5., 5.],
        [2., 1., 1., 2., 4., 5., 5.],
        [4., 2., 2., 1., 2., 2., 3.],
        [7., 4., 4., 2., 1., 2., 4.],
        [9., 5., 5., 2., 2., 1., 2.],
        [9., 6., 6., 4., 5., 3., 2.]]),
 array([[1., 0., 0., 1., 2., 1., 0.],
        [1., 0., 0., 1., 2., 1., 0.],
        [2., 1., 1., 0., 1., 0., 1.],
        [3., 2., 2., 1., 0., 1., 2.],
        [2., 1., 1., 0., 1., 0., 1.],
        [0., 1., 1., 2., 3., 2., 1.]]))

Algorithms

The following algorithms have been implemented:

• Basic DTW
• Band DTW
• Weighted DTW (In progress)